Patent ID: 12229927

DETAILED DESCRIPTION

In the following detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, similar symbols typically identify similar components, unless context dictates otherwise. The illustrative embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter presented here. It will be readily understood that the aspects of the disclosure, as generally described herein, and illustrated in the figures, can be arranged, substituted, combined, and designed in a wide variety of different configurations, all of which are explicitly contemplated and make part of this disclosure.

Introduction

Image guided radiation therapy (IGRT) is used to treat tumors in areas of the body that are subject to voluntary movement, such as the lungs, or involuntary movement, such as organs affected by peristalsis, gas motion, muscle contraction and the like. IGRT involves the use of an imaging system to view target tissues (also referred to as the “target volume”) immediately before or while radiation treatment is delivered thereto. In IGRT, image-based coordinates of the target volume from a previously determined treatment plan are compared to image-based coordinates of the target volume determined immediately before or during the application of the treatment beam. In this way, changes in the surrounding organs at risk and/or motion or deformation of the target volume relative to the radiation therapy system can be detected. Consequently, dose limits to organs at risk are accurately enforced based on the daily position and shape, and the patient's position and/or the treatment beam can be adjusted to more precisely target the radiation dose to the tumor. For example, in pancreatic tumor treatments, organs at risk include the duodenum and stomach. The shape and relative position of these organs at risk with respect to the target volume can vary significantly from day-to-day. Thus, accurate adaption to the shape and relative position of such organs at risk enables escalation of the dose to the target volume and better therapeutic results.

For reconstructions of patient anatomy surrounding a target volume, computed tomography (CT) or cone-beam computed tomography (CBCT) is often employed for generating the two-dimensional (2D) projections images from which the patient anatomy is reconstructed. In such image reconstruction, metal objects inside a scanned anatomical region are a significant source of visual artifacts that negatively impact the quality of images generated from the reconstructed anatomical region. These visual artifacts can be caused by one or more phenomena related to the presence of metals in the scanned anatomical region, including beam hardening in polychromatic CT and CBCT beams, photon starvation by larger metal components, scatter of imaging X-rays that impact metal components, and motion of metal objects during CT or CBCT acquisition. Such visual artifacts typically degrade the quality of the reconstruction and the ability to accurately detect the current location of a target volume and/or critical structures adjacent to the target volume.

According to various embodiments, a reconstructed volume of a region of patient anatomy is processed so that metal-related visual artifacts in the reconstructed volume are reduced. Specifically, in some embodiments, one or more of an automated metal extraction process, a novel method for inpainting portions of 2D projections, a novel blending method for blending image information from multiple types of 2D projections, and/or a novel blending method for restoring metal objects within a reconstructed volume are employed to reduce visual artifacts in a reconstructed volume.

System Overview

FIG.1is a perspective view of a radiation therapy system100that can beneficially implement various aspects of the present disclosure. Radiation therapy (RT) system100is a radiation system configured to detect intra-fraction motion in near-real time using X-ray imaging techniques. Thus, RT system100is configured to provide stereotactic radiosurgery and precision radiotherapy for lesions, tumors, and conditions anywhere in the body where radiation treatment is indicated. As such, RT system100can include one or more of a linear accelerator (LINAC) that generates a megavolt (MV) treatment beam of high energy X-rays, one or more kilovolt (kV) X-ray sources, one or more X-ray imagers, and, in some embodiments, an MV electronic portal imaging device (EPID). By way of example, radiation therapy system100is described herein configured with a circular gantry. In other embodiments, radiation therapy system100can be configured with a C-gantry capable of infinite rotation via a slip ring connection.

Generally, RT system100is capable of kV imaging of a target volume immediately prior to or during application of an MV treatment beam, so that an IGRT and/or an intensity-modulated radiation therapy (IMRT) process can be performed using X-ray imaging. RT system100may include one or more touchscreens101, couch motion controls102, a bore103, a base positioning assembly105, a couch107disposed on base positioning assembly105, and an image acquisition and treatment control computer106, all of which are disposed within a treatment room. RT system100further includes a remote control console110, which is disposed outside the treatment room and enables treatment delivery and patient monitoring from a remote location. Base positioning assembly105is configured to precisely position couch107with respect to bore103, and motion controls102include input devices, such as button and/or switches, that enable a user to operate base positioning assembly105to automatically and precisely position couch107to a predetermined location with respect to bore103. Motion controls102also enable a user to manually position couch107to a predetermined location.

FIG.2schematically illustrates a drive stand200and gantry210of RT system100, according to various embodiments. Covers, base positioning assembly105, couch107, and other components of RT system100are omitted inFIG.2for clarity. Drive stand200is a fixed support structure for components of RT treatment system110, including gantry210and a drive system201for rotatably moving gantry210. Drive stand200rests on and/or is fixed to a support surface that is external to RT treatment system110, such as a floor of an RT treatment facility. Gantry210is rotationally coupled to drive stand200and is a support structure on which various components of RT system100are mounted, including a linear accelerator (LINAC)204, an MV electronic portal imaging device (EPID)205, an imaging X-ray source206, and an X-ray imager207. During operation of RT treatment system110, gantry220rotates about bore103when actuated by drive system201.

Drive system201rotationally actuates gantry210. In some embodiments, drive system201includes a linear motor that can be fixed to drive stand200and interacts with a magnetic track (not shown) mounted on gantry210. In other embodiments, drive system201includes another suitable drive mechanism for precisely rotating gantry210about bore201. LINAC204generates an MV treatment beam230of high energy X-rays (or in some embodiments electrons, protons, and/or other heavy charged particles, ultra-high dose rate X-rays (e.g., for FLASH radiotherapy) or microbeams for microbeam radiation therapy) and EPID205is configured to acquire X-ray images with treatment beam230. Imaging X-ray source206is configured to direct a conical beam of X-rays, referred to herein as imaging X-rays231, through an isocenter203of RT system100to X-ray imager207, and isocenter203typically corresponds to the location of a target volume209to be treated. In the embodiment illustrated inFIG.2, X-ray imager207is depicted as a planar device, whereas in other embodiments, X-ray imager207can have a curved configuration.

X-ray imager207receives imaging X-rays231and generates suitable projection images therefrom. According to certain embodiments, such projection images can then be employed to construct or update portions of imaging data for a digital volume that corresponds to a three-dimensional (3D) region that includes target volume209. That is, a 3D image of such a 3D region is reconstructed from the projection images. In some embodiments, cone-beam computed tomography (CBCT) and/or digital tomosynthesis (DTS) can be used to process the projection images generated by X-ray imager207. CBCT is typically employed to acquire projection images over a relatively long acquisition arc, for example over a rotation of 180° or more of gantry210. As a result, a high-quality 3D reconstruction of the imaged volume can be generated. CBCT is often employed at the beginning of a radiation therapy session to generate a set-up 3D reconstruction. For example, CBCT may be employed immediately prior to application of treatment beam230to generate a 3D reconstruction confirming that target volume209has not moved or changed shape. Alternatively, or additionally, in some embodiments, partial-data reconstruction is performed by RT system100during portions of an IGRT or IMRT process in which partial image data is employed to generate a 3D reconstruction of target volume209. For example, as treatment beam230is directed to isocenter203while gantry210rotates through a treatment arc, DTS image acquisitions can be performed to generate image data for target volume209. Because DTS image acquisition is performed over a relatively short acquisition arc, for example between about 10° and 60°, near real-time feedback for the shape and position of target volume209can be provided by DTS imaging during the IGRT process.

In the embodiment illustrated inFIG.2, RT system100includes a single X-ray imager and a single corresponding imaging X-ray source. In other embodiments, RT system100can include two or more X-ray imagers, each with a corresponding imaging X-ray source. One such embodiment is illustrated inFIG.3.

FIG.3schematically illustrates a drive stand300and gantry310of RT system100, according to various embodiments. Drive stand300and gantry310are substantially similar in configuration to drive stand200and gantry200inFIG.2, except that the components of RT system100that are mounted on gantry310include a first imaging X-ray source306, a first X-ray imager307, a second imaging X-ray source308, and a second X-ray imager309. In such embodiments, the inclusion of multiple X-ray imagers in RT system100facilitates the generation of projection images (for reconstructing the target volume) over a shorter image acquisition arc. For instance, when RT system100includes two X-ray imagers and corresponding X-ray sources, an image acquisition arc for acquiring projection images of a certain image quality can be approximately half that for acquiring projection images of a similar image quality with a single X-ray imager and X-ray source.

The projection images generated by X-ray imager207(or by first x-ray imager307and second X-ray imager309) are used to construct imaging data for a digital volume of patient anatomy within a 3D region that includes the target volume. Alternatively or additionally, such projection images can be used to update portions of an existing imaging data for the digital volume corresponding to the 3D region. One embodiment of such a digital volume is described below in conjunction withFIG.4.

FIG.4schematically illustrates a digital volume400that is constructed based on projection images generated by one or more X-ray imagers included in RT system100, according to various embodiments. For example, in some embodiments, the projection images can be generated by a single X-ray imager, such as X-ray imager207, and in other embodiments the projection images can be generated by multiple X-ray imagers, such as first x-ray imager307and second X-ray imager309.

Digital volume400includes a plurality of voxels401(dashed lines) of anatomical image data, where each voxel401corresponds to a different location within digital volume400. For clarity, only a single voxel401is shown inFIG.4. Digital volume400corresponds to a 3D region that includes target volume410. InFIG.4, digital volume400is depicted as an 8×8×8 voxel cube, but in practice, digital volume400generally includes many more voxels, for example orders of magnitude more than are shown inFIG.4.

For purposes of discussion, target volume410can refer to the gross tumor volume (GTV), clinical target volume (CTV), or the planning target volume (PTV) for a particular treatment. The GTV depicts the position and extent of the gross tumor, for example what can be seen or imaged; the CTV includes the GTV and an additional margin for sub-clinical disease spread, which is generally not imageable; and the PTV is a geometric concept designed to ensure that a suitable radiotherapy dose is actually delivered to the CTV without adversely affecting nearby organs at risk. Thus, the PTV is generally larger than the CTV, but in some situations can also be reduced in some portions to provide a safety margin around an organ at risk. The PTV is typically determined based on imaging performed prior to the time of treatment, and alignment of the PTV with the current position of patient anatomy at the time of treatment is facilitated by X-ray imaging of digital volume400.

According to various embodiments described below, image information associated with each voxel401of digital volume400is constructed via projection images generated by the single or multiple X-ray imagers via a CBCT process. For example, such a CBCT process can be employed immediately prior to delivering treatment beam230to target volume410, so that the location and shape of target volume410can be confirmed before treatment begins. In addition, in some embodiments, image information associated with some or all of voxels401of digital volume400is updated via projection images generated by the single or multiple X-ray imagers via a DTS process. For example, such a DTS process can be employed after a portion of a planned treatment has begun and before the planned treatment has completed. In this way, the location and shape of target volume410can be confirmed while the treatment is underway.

CBCT Image Acquisition with Metal Object Present

FIG.5schematically illustrates the effect of a metal object501on the imaging of a region502of patient anatomy, according to an embodiment. Region502can be any technically feasible portion of patient anatomy, including the head, chest, abdomen, and the like. In the embodiment illustrated inFIG.5, CBCT image acquisition is performed via imaging X-ray source206and X-ray imager207over a digital volume500that includes target volume209and extends to an edge surface503of region502. In other embodiments, multiple X-ray sources and X-ray imagers can be employed. Alternatively or additionally, in some embodiments, digital volume500does not include all of edge surface503, or does not include any portion of edge surface503.

FIG.5shows a 2D projection being acquired with imaging X-ray source206and X-ray imager207disposed at a one particular image acquisition position551. In practice, CBCT image acquisition is performed at a plurality of image acquisition positions around region502to enable generation of a digital reconstruction of digital volume500. Thus, while disposed image acquisition position551, imaging X-ray source206and X-ray imager207acquire one of a set of multiple CBCT 2D projection images that together are employed to reconstruct region502. Further, inFIG.5, X-ray imager207is viewed edge-on, and therefore is depicted as a one-dimensional imaging structure. In reality, X-ray imager207is typically configured to generate 2D projection images at each of a plurality of image acquisition locations.

Metal object501can be any metallic object that appears in an X-ray image of region502(and/or in a digital reconstruction of region502). For example, in some instances, metal object501is one of a fiducial marker, a surgical staple or other medical device, a dental component, an orthopedic component, and/or the like.

As shown, when X-ray imager207is in first image acquisition position551, metal object501appears in pixels561of X-ray imager207. Thus, in the 2D projection acquired at first image acquisition position551, pixels561are associated with metal object501. It is noted that metal object501can significantly contribute to visual artifacts and/or other inconsistencies when the 2D projection images acquired by X-ray imager207are employed to reconstruct a 3D volume of region502. For example, the presence of high contrast of metal object501modulates the X-ray spectrum in a way that is not modelled by typical reconstruction algorithms (which generally assume that all scanned objects have a radiodensity that is approximately that of water). Consequently, visual artifacts can result. Further, in instances in which high contrast metal object501is relatively large, other information in blocked portions520(cross-hatched) of region502can be obscured, where blocked portions520are the portions of region502that are imaged by the same pixels of a 2D projection of region502as metal object501.

Reduction of Visual Artifacts Due to Metal Objects

According to various embodiments described below, visual artifacts (not shown) that occur in a reconstructed volume of region502due to the presence of metal object501are reduced or removed in a computer-implemented process for imaging a subject region, such as a region of patient anatomy. One such embodiment is described below in conjunction withFIG.6.

FIG.6sets forth a flowchart of a computer-implemented process600for imaging a region of patient anatomy, according to one or more embodiments. Computer-implemented process600can be implemented as an imaging-only process, or in conjunction with radiation therapy, such as IGRT, stereotactic radiosurgery (SRS), and the like. Further, computer-implemented process600may be performed over a single rotational arc of a gantry of a radiation therapy or imaging system, over a fraction of a rotational arc, or over multiple rotational arcs. Computer-implemented process600may include one or more operations, functions, or actions as illustrated by one or more of blocks610-697. Although the blocks are illustrated in a sequential order, these blocks may be performed in parallel, and/or in a different order than those described herein. Also, the various blocks may be combined into fewer blocks, divided into additional blocks, and/or eliminated based upon the desired implementation. Although computer-implemented process600is described in conjunction with the X-ray imaging system described herein as part of radiation therapy system100andFIGS.1-5, persons skilled in the art will understand that any suitably configured X-ray imaging system is within the scope of the present embodiments.

In step610, a CT or CBCT scan is performed. For example, in some embodiments, the X-ray imaging system of radiation therapy system100acquires a set of acquired 2D projection images611of region502, which includes target volume209and metal object501. Thus, region502includes at least one metallic object (e.g., metal object501).

In step620, a first-pass reconstruction process is performed. For example, in some embodiments, the X-ray imaging system generates an initial reconstructed volume621(first reconstructed volume) of region502based on acquired 2D projection images611. Initial reconstructed volume621is a 3D volumetric data set of region502. In some embodiments, a Feldkamp, Davis and Kress (FDK) reconstruction algorithm is employed to generate an initial reconstructed volume621. In other embodiments, an algebraic reconstruction technique (ART) or other iterative reconstruction technique is employed to generate initial reconstructed volume621, and in yet other embodiments, any other suitable reconstruction algorithm is employed. It is noted that initial reconstructed volume621generally includes visual artifacts due to the presence of metal object501in region502.

In step630, a novel adaptive autosegmentation process is performed on metal objects disposed within initial reconstructed volume621to generate a 3D representation of the metal objects. For example, in some embodiments, the X-ray imaging system performs an autosegmentation of metal object501to generate a 3D representation631of metal object501disposed within region502. Generally, the adaptive autosegmentation process of step630is performed automatically on initial reconstructed volume621, and user input is not required to perform an adjustment of segmentation parameter values. 3D representation631includes 3D location information of metal object501. In some embodiments, in the novel adaptive autosegmentation process of step630, patient anatomy in region502is identified and classified, and the type of metal present in region502is determined. Based on the patient anatomy and type or volume of metal, suitable segmentation parameter values (such as thresholding values and dilation radii) are determined and employed to generate 3D representation631of metal object501, sometimes referred to as a 3D metal object mask. One embodiment of the novel adaptive autosegmentation process is described below in conjunction withFIG.7. Alternatively, in some embodiments, any suitable segmentation algorithm or software application configured to generate 3D location information of metal object501can be employed in step630, such as an algorithm that depends on user inputs indicating patient anatomy and/or a type of metal associated with metal object(s)501.

In step640, a first-pass forward projection process is performed. For example, in some embodiments, the X-ray imaging system performs a forward projection process on 3D representation631to generate a set of 2D projection metal masks641. Each 2D projection metal mask641in the set of 2D projection metal masks641includes location information indicating pixels that are blocked by metal object501during the forward projection process of step640. In step640, each 2D projection metal mask641generated is selected to correspond to a different acquired 2D projection image611included in the set of acquired 2D projection images611acquired in step610. That is, for each 2D projection metal mask641generated in step640, the forward projection process is performed using the same projection angle used to acquire one of acquired 2D projection images611. Thus, each 2D projection metal mask641matches a corresponding acquired 2D projection image611. For example, in some embodiments, each 2D projection metal mask641can be combined with a corresponding acquired 2D projection image611in the harmonic in-painting process of step650.

In some embodiments, in step640an additional thresholding process is applied to each 2D projection metal mask641. The thresholding process normalizes the pixels of a particular 2D projection metal mask641to be in the interval [0,1], where 1 corresponds to the contrast structure and 0 to the rest of region502(and therefore not a part of 2D projection metal mask641).

In step650, a novel harmonic inpainting process is performed. For example, in some embodiments, the X-ray imaging system performs a harmonic in-painting process on acquired 2D projection images611to generate a set of 2D inpainted projections651of region502. Specifically, each 2D inpainted projection651is generated by modifying a portion of an acquired 2D projection image611. For the acquired 2D projection image611, visual information (e.g., a pixel value) associated with metal object501is removed, based on location information included in the corresponding 2D projection metal mask641. For example, in such an embodiment, the 2D projection metal mask641indicates pixels that are associated with metal object501. Pixel values for these pixels are removed from the particular acquired 2D projection image611and are replaced with lower-contrast pixel values via an inpainting process. One embodiment of the harmonic inpainting process is described below in conjunction withFIG.8. Alternatively, in some embodiments, any suitable inpainting algorithm can be employed in step650to generate the set of 2D inpainted projections651.

In step660, a second-pass reconstruction process is performed. For example, in some embodiments, the X-ray imaging system generates a reconstructed volume661(second reconstructed volume) of region502based on the 2D inpainted projections651generated in step650. In some embodiments, ART is employed to generate reconstructed volume661, and in other embodiments, an FDK reconstruction algorithm or other reconstruction algorithm may be employed to generate reconstructed volume661. Reconstructed volume661is similar to initial reconstructed volume621, except that metal object501has been removed.

In step670, a tissue-flattening process is performed. For example, in some embodiments, the X-ray imaging system performs a flattening process on reconstructed volume661to generate a flattened reconstructed volume671. Generally, flattened reconstructed volume671contains the original information about bones while information related to soft tissue is replaced with a single value. Thus, in a subsequent step, a blending process restores bone information, while for soft tissue the blending process acts as the inpainting performed in the first-pass reconstruction if step620. In some embodiments, a simple thresholding method is employed in step670to generate flattened reconstructed volume671, and in other embodiments, a filtering and/or convolutional neural network may also be employed in step670to generate flattened reconstructed volume671.

In step680, a second-pass forward projection process is performed. For example, in some embodiments, the X-ray imaging system performs a forward projection process on flattened reconstructed volume671to generate a set of flattened 2D projections681. It is noted that in each flattened 2D projection681, pixels that are visually blocked by metal object501(i.e., pixels that are indicated by location information in a 2D projection metal mask to be associated with metal object501) have pixel values that do not include a contribution from metal object501. Instead, in each flattened 2D projection681, the pixel values for pixels that are visually blocked by metal object501are based on flattened reconstructed volume671, which includes bone information for region502.

In step680, each flattened 2D projection681generated is selected to correspond to a different acquired 2D projection image611included in the set of acquired 2D projection images611acquired in step610. That is, for each flattened 2D projection681generated in step680, the forward projection process is performed using the same projection angle used to acquire one of acquired 2D projection images611. Thus, each flattened 2D projection681matches a corresponding acquired 2D projection image611, and can be combined therewith subsequently, for example in the blending process of step690.

In step690, a novel harmonic blending process is performed. For example, in some embodiments, the X-ray imaging system generates a set of low-artifact 2D projections691of region502by modifying acquired 2D projection images611based on 2D projection metal masks641and flattened 2D projections681. Specifically, location information from 2D projection metal masks641indicates certain pixels of acquired 2D projection images611that are to have image information (e.g., pixel values) replaced with image information from corresponding pixels of flattened 2D projections681. It is noted that in general, the majority of pixels of low-artifact 2D projections691have the same pixel values and/or other image information as the corresponding pixels of acquired 2D projection images611. However, the pixels of low-artifact 2D projections691that are indicated to be blocked by or associated with metal object501include pixel values and/or other image information that are different from the corresponding pixels of acquired 2D projection images611.

In some embodiments, a harmonic blending process is performed to minimize or otherwise reduce visual artifacts caused by replacing image information for groups of pixels in acquired 2D projection images611with pixel information from flattened 2D projections681. For example, in an embodiment, acquired 2D projection images611form a set of projections P1. . . Pn, flattened 2D projections681form a set of projections F1. . . Fn, and 2D projection metal masks641form a set of masks M1. . . Mn. In the embodiment, projections F1. . . Fnare combined with projections P1. . . Pnin the metal regions indicated by masks M1. . . Mn. Specifically, the inpainting of such metal regions of projections P1. . . Pnis performed by element-wise division Pi/Fiand after the inpainting, the new values for the metal regions are post-multiplied by values corresponding pixels from Fi. In the inpainting process, pixel values for mask-bordering pixels (pixels within projections P1. . . Pnthat are adjacent to a mask-edge pixel of a metal region of masks M1. . . Mn) are used as boundary conditions. Further, in the harmonic inpainting process, pixel values for mask-edge pixels are constrained to a certain pixel value based on the pixel value of an adjacent mask-bordering pixel. Specifically, for each mask-edge pixel, the pixel value is constrained so that a change in slope of pixel value associated with the mask-edge pixel is equal to a change in slope of pixel value associated with an adjacent mask-bordering pixel. In some embodiments, a harmonic function is employed to enforce such a constraint on the pixel values of the mask-edge pixel. In some embodiments, the harmonic inpainting process for generating low-artifact 2D projections691is similar to the harmonic inpainting process for generating 2D inpainted projections651, which is described in greater detail below in conjunction withFIG.7.

In step693, a third-pass reconstruction process is performed. For example, in some embodiments, the X-ray imaging system executes a reconstruction algorithm using the set of low-artifact 2D projections691of region502to generate a low-artifact reconstructed volume695(third reconstructed volume) of region502. Thus, the X-ray imaging system generates low-artifact reconstructed volume695of region502based on the low-artifact 2D projections691generated in step690. In some embodiments, an FDK reconstruction algorithm is employed to generate low-artifact reconstructed volume695. In other embodiments, an ART algorithm may be employed to generate low-artifact reconstructed volume695. In other embodiments, a penalized-likelihood (PL) reconstruction algorithm is employed to generate low-artifact reconstructed volume695. Alternatively, any other suitable reconstruction algorithm can be employed in step690. Low-artifact reconstructed volume695is similar to initial reconstructed volume621, except that metal object501has been removed and artifacts caused by the presence of metal object501have been removed and/or reduced in visual prominence.

In step697, a blended metal restoration process is performed. For example, in some embodiments, the X-ray imaging system generates a final reconstructed volume699(fourth reconstructed volume) of region502. In step697, the X-ray imaging system blends low-artifact reconstructed volume695with image information (e.g., pixel values) from initial reconstructed volume621and from 3D representation631of metal object501. Thus, final reconstructed volume699is generated by restoring metal object information to low-artifact reconstructed volume695.

In some embodiments, the blended metal restoration process of step697includes operations that create a smooth transition between metal and non-metal parts within final reconstructed volume699. In such embodiments, edge voxels of metal object501are not represented as voxels that have values equal to 100% of the radiographic density of voxels within metal object501. Instead, each edge voxel of metal object501in final reconstructed volume699has a value that is based on a combination of an image value from the corresponding voxel of initial reconstructed volume621, an image value from the corresponding voxel of low-artifact reconstructed volume695, and information from the corresponding voxel of metal mask 3D representation631. Specifically, the blended metal restoration process of step697includes a blending of three inputs: initial reconstructed volume621, low-artifact reconstructed volume695, and a non-binary mask W that is based on metal mask 3D representation631.

Non-binary mask W is based on the metal mask 3D representation631, but differs in some respects. Metal mask 3D representation631is a binary mask that indicates whether or not a particular voxel location in low-artifact reconstructed volume695should be considered a portion of metal object501. Thus, metal mask 3D representation631applies a binary decision to each voxel of low-artifact reconstructed volume695. For example, Mvoxel i=1 when voxel i of a mask M is considered a portion of metal object501and Wvoxel i=0 when voxel i of a mask M is considered not a portion of metal object501. By contrast, non-binary mask W differs from metal mask 3D representation631in that values for non-binary mask W can vary between 0 and 1 for some or all edge voxels of metal object501.

In some embodiments, a value Wvoxfor each voxel of non-binary mask W is determined based on a value Vvoxfor a corresponding voxel of initial reconstructed volume621. In some embodiments, when Vvoxis greater than a metal thresholding value tmetal(such as the metal thresholding value employed to generate metal mask 3D representation631in step630), Wvox=1; when Vvoxis less than a tissue thresholding value ttissue(for example 300 HU), Wvox=0; and when Vvoxis between metal thresholding value tmetaland tissue thresholding value tissue, Wvox=w, where w is a value between 0 and 1. In such embodiments, w may be based on a value for the corresponding voxel of initial reconstructed volume621, metal thresholding value tmetal, and tissue thresholding value ttissue. For example, in one such embodiment, when Vvoxis between metal thresholding value tmetaland tissue thresholding value ttissue, w=(Vvox−ttissue)/tmetal−ttissue). Thus, in such an embodiment, a linear interpolation is applied in determining a value for w. In other embodiments, when Vvoxis between metal thresholding value and tissue thresholding value ttissue, w may be calculated based on any other suitable algorithm that includes metal thresholding value tmetaland tissue thresholding value ttissue, or some other measure of how much of the volume of Vvoxcorresponds to a metal material.

In some embodiments, the blended metal restoration process of step697determines pixel values of final reconstructed volume699using a weighted alpha-blending of the three inputs: initial reconstructed volume621, low-artifact reconstructed volume695, and non-binary mask W. Further, in one such embodiment, a maximum operator is applied to voxel values of initial reconstructed volume621and low-artifact reconstructed volume695. In such an embodiment, a value VFINALfor a voxel of final reconstructed volume699is determined based on Equation 1:
VFINAL=(1−w)*V696+w*max{V695, V621}  (1)
where V695is the value of the corresponding voxel of low-artifact reconstructed volume695and V621is the value of the corresponding voxel of initial reconstructed volume621. In such embodiments, the application of the maximum operator to V695and V621restores a smooth transition between soft tissue and the metal objects in final reconstructed volume695.

Adaptive Autosegmentation of Metal Objects

Appropriate extraction of a projection metal mask (also referred to as a metal trace) is important in CBCT reconstruction to ensure the image quality of a final reconstructed volume of a region of patient anatomy. When the metal trace is underestimated, metal-related artifacts are not completely suppressed. On the other hand, overestimation of a metal trace can lead to excessive inpainting, which results in redundant suppression of important, non-metal image information in the final reconstructed volume. In such situations, images generated from the final reconstructed volume can suffer from an unnecessary loss of detail, which is highly undesirable in multiple applications.

In the adaptive autosegmentation process of step630, an autosegmentation is performed on metal object(s)501disposed within initial reconstructed volume621to generate a metal trace (e.g., 3D representation631) of metal objects(s)501. Generally, autosegmentation of metal objects from a reconstructed volume depends on several segmentation parameters, including a radiographic density threshold (referred to herein as a “thresholding parameter”) and a dilation diameter (for improving mask quality at the mask boundary). Fully automated segmentation processes have had poor performance due to the dependence of such processes on correct values for such parameters. For example, an autosegmentation process tuned to detecting a hip prostheses can fail when applied to the detection of dental metals, due to the much smaller size of dental metal objects and the different structure of the environment surrounding such metal objects. Consequently, for a successful autosegmentation process, user inputs are generally required that indicate the anatomical region associated with scanned image data and the specific type of metal objects disposed within the anatomical region.

According to various embodiments, an adaptive autosegmentation process, such as the adaptive autosegmentation process of step630, is performed automatically on a reconstructed volume, and user inputs are not required to perform an adjustment of segmentation parameter values. In such embodiments, a region of patient anatomy associated with the reconstructed volume is identified and classified, and the type of metal present in the reconstructed volume is determined. Based on the region of patient anatomy and the type of metal, accurate segmentation parameter values are determined and employed to generate a 3D metal object mask for the reconstructed volume, such as 3D representation631of metal object501. One such embodiment is described below in conjunction withFIG.7.

FIG.7sets forth a flowchart of a computer-implemented process700for segmenting a reconstructed volume of a region of patient anatomy, according to one or more embodiments. Computer-implemented process700can be implemented as part of an imaging-only process, or in conjunction with radiation therapy, such as IGRT, stereotactic radiosurgery (SRS), and the like. Computer-implemented process700may include one or more operations, functions, or actions as illustrated by one or more of blocks701-720. Although the blocks are illustrated in a sequential order, these blocks may be performed in parallel, and/or in a different order than those described herein. Also, the various blocks may be combined into fewer blocks, divided into additional blocks, and/or eliminated based upon the desired implementation. Although computer-implemented process700is described in conjunction with the X-ray imaging system described herein as part of radiation therapy system100andFIGS.1-6, persons skilled in the art will understand that any suitably configured X-ray imaging system is within the scope of the present embodiments.

In step701, the X-ray imaging system of radiation therapy system100receives a reconstructed digital volume, such as initial reconstructed volume621.

In step702, the X-ray imaging system generates an anatomical mask for reconstructed volume621. In some embodiments, the anatomical mask is configured to include locations of initial reconstructed volume621based on an anatomical threshold value. For example, in some embodiments, the X-ray imaging system performs a thresholding operation on initial reconstructed volume621using an anatomical threshold value. In such embodiments, the anatomical threshold value is selected so that voxels associated with patent anatomy are included in the anatomical mask and voxels associated with air or other matter external to the patient anatomy are excluded from the anatomical mask. In some embodiments, the anatomical threshold value is associated with a material having a radiodensity that is greater than that of air and/or less than that of water.

In step703, the X-ray imaging system determines the anatomical region associated with initial reconstructed volume621. Examples of anatomical regions that can be determined via the anatomical mask include the head and/or neck, the pelvis, the abdomen, portions of a limb, and/or the like. The anatomical mask provides the shape and size in three dimensions of the anatomical region. Thus, any technically feasible approach can be employed to determine the specific anatomical region that is associated with initial reconstructed volume621. For example, in some embodiments, a suitably trained machine-learning algorithm can be employed for classifying the specific anatomical region that is associated with initial reconstructed volume621. Because the number of different anatomical regions that can possibly be associated with initial reconstructed volume621is limited, in some embodiments, a simplified geometrical analysis is employed to classify the specific anatomical region that is associated with initial reconstructed volume621.

In embodiments in which a geometrical analysis is employed, the geometrical analysis includes determining whether the anatomical region is a head region or a body region, based on the anatomical mask. In such embodiments, a minimum lateral width of the anatomical mask can indicate whether the anatomical region is a head region or a body region. For example, in normal instances, a minimum lateral width of patient anatomy represented by the anatomical mask above 25 cm indicates a body scan and a minimum lateral width of patient anatomy represented by the anatomical mask below 20 cm indicates a head scan. Consequently, in some embodiments, a maximum lateral width for each slice in the anatomical mask that has a non-zero width is measured (for example, in voxels). A minimum lateral width of these maximum lateral width measurements is then determined to be the minimum lateral width of the anatomical mask. Comparison of such a minimum lateral width to a maximum head width threshold value and/or to a minimum body width threshold indicates whether the anatomical region is a head region or a body region. Additionally or alternatively, in some embodiments, one or more additional geometric analyses can be applied to certain dimensions of the anatomical mask to determine other anatomical regions, such as an upper arm, lower arm, thigh, etc.

In step704, the X-ray imaging system generates an initial 3D metal object mask Minitfor initial reconstructed volume621. For example, in some embodiments, the X-ray imaging system generates initial 3D metal object mask Minitby performing an image thresholding operation on initial reconstructed volume621, based on an initial metal threshold value tinit. In some embodiments, initial metal threshold value tinitis selected to ensure that all metal objects disposed within initial reconstructed volume621are detected. For example, in some embodiments, tinitis a value between about 1200 HU and 1800 HU. Further, in some embodiments, to ensure that only metal objects inside patient anatomy are included in initial 3D metal object mask Minit, initial 3D metal object mask Minitis multiplied by the anatomical mask (which can be a binary mask of values 1 or 0). Thus, in such embodiments, metal objects outside patient anatomy are eliminated by pixel values associated with such metal objects being multiplied by 0.

In step705, the X-ray imaging system determines whether metal objects, such as tooth fillings, fiducials, or orthopedic prostheses, are disposed within initial reconstructed volume621. Generally, the X-ray imaging system determines metal objects disposed within initial reconstructed volume621using voxels indicated by initial 3D metal object mask Minit. In some embodiments, the X-ray imaging system further determines metal objects disposed within initial reconstructed volume621by detecting one or more connected components contained within initial 3D metal object mask Minit. In such embodiments, each connected component includes a set of connected, adjacent, and/or contiguous voxels within initial reconstructed volume621. In such embodiments, any suitable algorithm can be employed to determine connected components within initial 3D metal object mask Minit. For example, in some embodiments, a method of determining connected components is presented in: “Sequential Operations in Digital Picture Processing”, A. Rosenfeld and J. Pfaltz. Journal of the ACM. Vol. 13, Issue 4, October 1966, Pg. 471-494.

In some embodiments, the X-ray imaging system can determine a size (or volume) scand a mean radiographic density value (such as a mean HU value) vcfor each connected component. In such embodiments, a vector of pairs ([s1,v1], . . . [sn,vn]) can be generated to facilitate determining whether significant metal objects are disposed within initial reconstructed volume621. In such embodiments, a metal classification of initial reconstructed volume621can be determined based on certain information included in and/or associated with the vector of pairs ([s1,v1], . . . [sn,vn]). Examples of such information include a volume of a largest component of the one or more connected components, a cumulative volume of a set of the one or more connected components that are larger than a predetermined volume, and a radiographic density of at least one of the one or more connected components that are larger than a predetermined volume.

In step710, the X-ray imaging system determines whether significant metal objects, such as metal objects of significant size, are disposed within initial reconstructed volume621. For example, in some embodiments, the X-ray imaging system performs a classification operation in step710to determine whether significant metal objects are disposed within initial reconstructed volume621. Examples of metal classifications that can be applied to initial reconstructed volume621include a metal-free anatomical region, a dental region, a fiducial-containing region (such as the abdomen), an orthopedic region, (such as the pelvic region), and/or the like.

In an example metal classification operation performed in step710, the X-ray imaging system determines initial reconstructed volume621to be a metal-free anatomical region when the vector of pairs ([s1,v1], . . . [sn,vn]) is empty, when a largest connected component in a head anatomical region has a volume of less than a first threshold value (e.g. 50 mm3), and/or when no connected component with a volume of less than a second threshold value (e.g. 200 mm3) has a mean radiographic density value vcabove a third threshold value (e.g., 2000 HU). In addition, in the example metal classification operation, the X-ray imaging system determines initial reconstructed volume621to be a dental region when at least one connected component in a head anatomical region has a volume of at least the first threshold value (e.g. 50 mm3). Further, in the example metal classification operation, the X-ray imaging system determines initial reconstructed volume621to be an orthopedic region when connected components in a body anatomical region have a cumulative volume of at least a fourth threshold value (e.g. 10000 mm3). Further, in the example metal classification operation, the X-ray imaging system determines initial reconstructed volume621to be a fiducial-containing region when at least one small connected component (e.g., a connected component having a volume of less than the second threshold value) in a body anatomical region has a mean value of at least a fifth threshold value (e.g. 20000 HU).

In step710, when the X-ray imaging system determines significant metal objects are disposed within initial reconstructed volume621, method proceeds to step711. When the X-ray imaging system determines significant metal objects are not disposed within initial reconstructed volume621, method proceeds to step720.

In step711, for each metal object or connected component disposed within initial reconstructed volume621, the X-ray imaging system determines a suitable value for a thresholding parameter. For example, in some embodiments, a dental threshold value tdentalis selected for connected components associated with a dental region, such as 4000 HU; a fiducial threshold value tfiducialis selected for connected components associated with a fiducial-containing region, such as 2000 HU; and an orthopedic threshold value torthois selected for connected components associated with an orthopedic region, such as 1600 HU.

In step712, for each metal object or connected component disposed within initial reconstructed volume621, the X-ray imaging system determines a suitable value for a dilation radius. For example, in some embodiments, one or more dental dilation radii are selected for connected components associated with a dental region, such as an in-plane radius of 2 mm and an out-of-plane radius of 2 mm; one or more fiducial dilation radii are selected for connected components associated with a fiducial-containing region, such as an in-plane radius of 2 mm and an out-of-plane radius of 2 mm; and one or more orthopedic dilation radii are selected for connected components associated with an orthopedic region, such as an in-plane radius of 3 mm and an out-of-plane radius of 10 mm.

In step713, the X-ray imaging system generates a final 3D metal object mask (e.g., 3D representation631) for initial reconstructed volume621using the values determined in step711and712for the thresholding parameter and the dilation radii. Thus, in step713, the X-ray imaging system performs an autosegmentation of initial reconstructed volume621using the values for the thresholding parameter and the dilation radii.

In step714, the X-ray imaging system generates a set of projection metal masks (e.g., 2D projection metal masks641) for initial reconstructed volume621. The projection metal masks generated in step714can be used for multiple steps in generating a digital volume that has metal-artifacts reduced, including the harmonic inpainting of step650and the harmonic blending of step690inFIG.6.

In step720, the X-ray imaging system aborts the current metal artifact reduction process, and reconstruction, filtering, and/or other processes are performed normally on initial reconstructed volume621.

Harmonic Inpainting of Projections

In conventional approaches, inpainting of 2D projection images, such as 2D projection metal masks641shown inFIG.6, is performed with linear one-dimensional interpolation. Such inpainting creates both intra-projection discontinuities (i.e., between neighboring rows inside the same 2D projection) and inter-projection discontinuities (i.e., between corresponding rows of pixels in adjacent projections. Such discontinuities create significant visual artifacts in a final reconstructed volume, even though image information blocked by metal objects has been inpainted. According to various embodiments, a harmonic inpainting process improves intra-projection and inter-projection discontinuities in pixel values, which reduces visual artifacts that normally result from inpainting. One such embodiment is described below in conjunction withFIGS.8-11.

FIG.8sets forth a flowchart of a computer-implemented process800for inpainting 2D projections of a region of patient anatomy, according to one or more embodiments. Computer-implemented process800can be implemented as part of an imaging-only process, or in conjunction with radiation therapy, such as IGRT, stereotactic radiosurgery (SRS), and the like. Computer-implemented process800may include one or more operations, functions, or actions as illustrated by one or more of blocks801-806. Although the blocks are illustrated in a sequential order, these blocks may be performed in parallel, and/or in a different order than those described herein. Also, the various blocks may be combined into fewer blocks, divided into additional blocks, and/or eliminated based upon the desired implementation. Although computer-implemented process800is described in conjunction with the X-ray imaging system described herein as part of radiation therapy system100andFIGS.1-6, persons skilled in the art will understand that any suitably configured X-ray imaging system is within the scope of the present embodiments.

In step801, the X-ray imaging system of radiation therapy system100receives a set of initial 2D projections and a set of 2D projection metal masks for a reconstructed digital volume, such as acquired 2D projection images611and 2D projection metal masks641for initial reconstructed volume621.

In step802, the X-ray imaging system generates a set of combined 2D projections based on acquired 2D projection images611and 2D projection metal masks641. In some embodiments, each combined 2D projection is generated based on a corresponding acquired 2D projection images611and a corresponding 2D projection metal masks641. Generally, for a particular combined 2D projection, the corresponding acquired 2D projection images611and a corresponding 2D projection metal masks641each represent a projection from the same projection angle. One embodiment of a combined 2D projection generated from an acquired 2D projection image611and a 2D projection metal mask641is illustrated inFIG.9.

FIG.9schematically illustrates an acquired 2D projection image611being combined with a 2D projection metal mask641to form a combined 2D projection901, according to various embodiments. As shown, acquired 2D projection image611includes a plurality of pixels902that each have a pixel value associated therewith, and 2D projection metal mask641includes location information903that indicates pixels that are blocked by metal object501during the forward projection process of step640. Combined 2D projection901includes pixels902of acquired 2D projection image611, except in pixel locations that correspond to location information903of 2D projection metal mask641.

Returning toFIG.8, in step803, the X-ray imaging system arranges the set of combined 2D projections901into an array in projection space. For example, in some embodiments, the array of combined 2D projections are arranged in a 3D matrix or stack. In addition, the combined 2D projections901are sequentially ordered in the 3D matrix. Thus, in such embodiments, the combined 2D projection901that is generated from an acquired 2D projection image611and a 2D projection metal mask641that are each associated with a first projection angle is sequenced as the first 2D projection901in the 3D matrix, the combined 2D projection901that is generated from an acquired 2D projection image611and a 2D projection metal mask641that are each associated with a second projection angle is sequenced as the second 2D projection901in the 3D matrix, and so on. One embodiment of a 3D matrix of a combined 2D projections is illustrated inFIG.10.

FIG.10schematically illustrates a 3D matrix1000of combined 2D projections901, according to various embodiments. In the embodiment illustrated inFIG.10, array1000includes five combined 2D projections901. In practice, 3D matrix1000can include many more than five combined 2D projections901. As a series of 2D images arranged in three dimensions, 3D matrix1000forms a 3D array of image data that is analogous to a 2D CT sinogram. That is, while a CT sinogram extends a plurality of one-dimensional sets of image data into two dimensions by arranging the plurality of one-dimensional sets of data in a planar array, 3D matrix1000arranges a plurality of two-dimensional sets of image data associated with each of the different 2D projections in a 3D matrix or “stack”.

As shown, each combined 2D projection901includes mask pixels (black)1010and image pixels1020. Image pixels1020each have a pixel value (not shown) associated therewith. By contrast, each mask pixel1010indicates a location of a pixel that is blocked by a metal object (such as metal object501inFIG.5), and therefore is inpainted during computer-implemented process800. Image pixels1020include mask-bordering pixels1021(cross-hatched), which are pixels that are adjacent to one or more mask pixels1010. Mask pixels1010include mask-edge pixels1011, which are adjacent to one or more image pixels1020. Generally, each mask-bordering pixel1021is adjacent to at least one mask-edge pixel1011in projection space. In the instance illustrated inFIG.10, mask-bordering pixels1021are shown to be adjacent to mask pixels1010within the same combined 2D projection901. In other instances, mask-bordering pixels1021can be adjacent to a mask pixel1010in a different combined 2D projection901within 3D matrix1000. One such embodiment is illustrated inFIG.11.

FIG.11schematically illustrates a portion1100of 3D matrix1000and inter-projection pixels that are adjacent to each other, according to various embodiments. In the embodiment illustrated inFIG.11, portion1100includes a first combined 2D projection1101, a second combined 2D projection1102, a third combined 2D projection1103, and a fourth combined 2D projection1104. As shown, second combined 2D projection1102includes a mask-bordering pixel1021(cross-hatched) that is adjacent (in projection space) to a corresponding mask-edge pixel1011that is included in third combined 2D projection1103. In addition, first combined 2D projection1101includes an image pixel1020that is adjacent in projection space to a corresponding mask-bordering pixel1021in second combined 2D projection1102, and fourth combined 2D projection1104includes a mask pixel1010that is adjacent to a corresponding mask-edge pixel1011that is included in third combined 2D projection1103.

Returning toFIG.8, in step804, the X-ray imaging system generates a linear algebraic system based on array1000. In some embodiments, the X-ray imaging system generates the linear algebraic system by applying a harmonic function to a domain represented by pixels included in array1000, which is an array in projection space of a set of combined 2D projections901. Specifically, in such embodiments, array1000is a 3D stack of combined 2D projections901that represents an image f. Thus, in such embodiments, Equation 2 is applied to the pixels included in array1000:
∇2f=0  (2)

In some embodiments, the X-ray imaging system generates the linear algebraic system by solving a discretized form of Equation 2 over the domain represented by the pixels of the image, for example via the method of finite differences. In such embodiments, Equation 2 is solved over the unknown part of the image, i.e., mask pixels1010inFIGS.10and11, using the values from known regions as the boundary condition, i.e., mask-bordering pixels1021inFIGS.10and11. In such embodiments, Equation 2 ensures continuity and smoothness of pixel values at the edges of the inpainted portions of each combined 2D projection. Further, because Equation 2 is solved for the domain represented by the mask pixels1010of all combined 2D projections901simultaneously, continuity and smoothness of pixel values between combined 2D projections901is also ensured. Thus, intra-projection and inter-projection discontinuities in pixel values are reduced. Specifically, in the harmonic inpainting process of computer-implemented process800, when mask pixels of combined 2D projections901are inpainted with new pixel values, a change in slope of a pixel value associated with a mask-edge pixel is constrained to equal a change in slope of pixel value associated with an adjacent mask-bordering pixel. It is noted that the mask-edge pixel and the mask-bordering pixel are adjacent in projection space, and therefore can be included in the same combined 2D projection901(as shown inFIG.10) or each can be included in different (but adjacent) combined 2D projections901(as shown inFIG.11).

In some embodiments, in step804, a linear algebraic system (a system of linear equations) is employed having the form [A][x]=[b], where [A] is a coefficient matrix of the linear algebraic system, [x] is a variable vector of the linear algebraic system, and [b] is a constant vector of the linear algebraic system. In such embodiments, the size of [A] and [b] is based on the number of mask pixels1010to be inpainted in array1000. In such embodiments, values for [A] and [b] can be assembled using a 7-point finite difference method. For example, in one such embodiment, a 3D Laplacian kernel of size 3×3×3 kernels having 7 non-zero values is employed to generate values for [A] and [b]. A method of generating a linear algebraic system by applying a harmonic function to a domain of pixels is described in detail in: “On surface completion and image inpainting by biharmonic functions: Numerical aspects,” S. B. Damelin, N. S. Hoang (2018), International Journal of Mathematics and Mathematical Sciences, 2018.

In step805, the X-ray imaging system determines values for variable vector [x] of the linear algebraic system by solving the linear algebraic system. In some embodiments, values for variable vector [x] of the linear algebraic system are computed via a conjugate gradient method.

In step806, the X-ray imaging system generates a set of inpainted 2D projections (e.g., 2D inpainted projections651) by modifying the set of acquired 2D projection images611with the values for the variable vector determined in step805. In step806, pixels modified with the values for the variable vector are indicated by location information for mask pixels1010, which may be included in 2D projection metal masks641.

Example Computing Device

FIG.12is an illustration of computing device1200configured to perform various embodiments of the present disclosure. For example, in some embodiments, computing device1200can be implemented as image acquisition and treatment control computer106and/or remote control console110inFIG.1. Computing device1200may be a desktop computer, a laptop computer, a smart phone, or any other type of computing device suitable for practicing one or more embodiments of the present disclosure. In operation, computing device1200is configured to execute instructions associated with computer-implemented process600, computer-implemented process700, and/or computer-implemented process800as described herein. It is noted that the computing device described herein is illustrative and that any other technically feasible configurations fall within the scope of the present disclosure.

As shown, computing device1200includes, without limitation, an interconnect (bus)1240that connects a processing unit1250, an input/output (I/O) device interface1260coupled to input/output (I/O) devices1280, memory1210, a storage1230, and a network interface1270. Processing unit1250may be any suitable processor implemented as a central processing unit (CPU), a graphics processing unit (GPU), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), any other type of processing unit, or a combination of different processing units, such as a CPU configured to operate in conjunction with a GPU or digital signal processor (DSP). In general, processing unit1250may be any technically feasible hardware unit capable of processing data and/or executing software applications, including computer-implemented process600, computer-implemented process700, and/or computer-implemented process800.

I/O devices1280may include devices capable of providing input, such as a keyboard, a mouse, a touch-sensitive screen, and so forth, as well as devices capable of providing output, such as a display device and the like. Additionally, I/O devices1280may include devices capable of both receiving input and providing output, such as a touchscreen, a universal serial bus (USB) port, and so forth. I/O devices1280may be configured to receive various types of input from an end-user of computing device1200, and to also provide various types of output to the end-user of computing device1200, such as displayed digital images or digital videos. In some embodiments, one or more of I/O devices1280are configured to couple computing device1200to a network.

Memory1210may include a random access memory (RAM) module, a flash memory unit, or any other type of memory unit or combination thereof. Processing unit1250, I/O device interface1260, and network interface1270are configured to read data from and write data to memory1210. Memory1210includes various software programs that can be executed by processor1250and application data associated with said software programs, including computer-implemented process600, computer-implemented process700, and/or computer-implemented process800.

Example Computer Program Product

FIG.13is a block diagram of an illustrative embodiment of a computer program product1300for implementing a method for segmenting an image, according to one or more embodiments of the present disclosure. Computer program product1300may include a signal bearing medium1304. Signal bearing medium1304may include one or more sets of executable instructions1302that, when executed by, for example, a processor of a computing device, may provide at least the functionality described above with respect toFIGS.1-11.

In some implementations, signal bearing medium1304may encompass a non-transitory computer readable medium1308, such as, but not limited to, a hard disk drive, a Compact Disc (CD), a Digital Video Disk (DVD), a digital tape, memory, etc. In some implementations, signal bearing medium1304may encompass a recordable medium1310, such as, but not limited to, memory, read/write (R/W) CDs, R/W DVDs, etc. In some implementations, signal bearing medium1304may encompass a communications medium1306, such as, but not limited to, a digital and/or an analog communication medium (e.g., a fiber optic cable, a waveguide, a wired communications link, a wireless communication link, etc.). Computer program product1300may be recorded on non-transitory computer readable medium1308or another similar recordable medium1310.

In sum, embodiments described herein reduce and/or eliminate visual artifacts that occur in the reconstruction of a volume that includes one or more significant metal objects. Further, in some instances, the embodiments reveal structures previously obscured by such visual artifacts. Thus, the embodiments improve the perceived image quality of CBCT-based reconstructions and, in some instances improve accuracy in differentiating tissue types in a reconstructed CBCT image. Such improvements over prior art techniques may be employed in adaptive planning and/or during radiation therapy.

The descriptions of the various embodiments have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments.

Aspects of the present embodiments may be embodied as a system, method or computer program product. Accordingly, aspects of the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, aspects of the present disclosure may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims.