System and method for flow-resolved, three-dimensional imaging

A system and method are provided for creating an image including quantified flow within vessels of a subject. The method includes providing a single-sweep, three-dimensional (3D) image volume acquired from a subject during a single pass of a computed tomography (CT) imaging system as the subject receives a dose of a contrast agent and determining a phase shift corresponding to pulsatile contrast in vessels within the single-sweep, 3D image volume. The method further includes quantifying a flow through the vessels within the single-sweep, 3D image volume using the phase shift and generating a report including indicating flow through the vessels within the 3D image volume.

BACKGROUND

The present disclosure relates to systems and methods for medical image data acquisition and/or reconstruction. More particularly, systems and methods are provided for producing medical images that are flow-resolved and three-dimensional (3D), thereby, providing four-dimensional (4D) images.

Clinical outcomes for many conditions are directly correlated to the amount of accurate information that is available to a clinician and the speed with which the information or updated information can be provided. The ability to successfully diagnose and treat vascular conditions is highly dependent upon detailed and accurate information being available to clinicians. Furthermore, the dependence upon and availability of accurate information is important in both the diagnostic and treatment phases. Medical imaging is one of the key clinically-available sources of the information necessary to diagnose and treat patients.

Conventional 2D projection x-ray imaging, generally two-dimensional (2D) digital subtraction angiography (DSA) uses an injected contrast agent to visualize the flow of blood, and can be performed in the interventional suite. However, the 2D DSA image does not provide a complete picture of the complex three-dimensional (3D) flow patterns. At best, quantitative 2D DSA does not provide information on true 3D vessel path lengths and cross-sectional parameters which are required for accurate velocity and flow calculation.

More recently, time-resolved or “4D” DSA technology can be coupled with separate flow information to provide time-resolved imaging of contrast agent flow through a 3D vascular tree. However, 4D DSA can be limited in some clinical applications by the requirement for two separate rotational C-arm sweeps, which necessitates acquisition times sufficient to complete both sweeps and the radiation exposure associated with two sweeps. This challenge with using 4D DSA in some clinical application can be particularly acute if multiple 4D DSA acquisitions are required during a single treatment. For example, for treatments in the thorax, the multi-sweep acquisition times required by 4D DSA make its application limited in many clinical instances. This challenge with multi-sweep acquisitions in the thorax is further compounded by the fact that long acquisition times make it harder for the patient to perform a breath hold that extends through the whole acquisition. As a result, respiratory motion can cause artifacts in the images,

Therefore, it would be desirable to have systems and methods for producing 3D images showing flow that are compatible with an interventional setting to provide clinicians with new and valuable information that can guide the intervention.

SUMMARY

The present disclosure overcomes the aforementioned drawbacks by providing systems and methods for generating flow-resolved, three-dimensional (3D) image volumes from computed tomographic (CT) or cone-beam CT (CBCT) data without needing multiple acquisitions, such as to acquire both a non-contrast data set and a contrast-enhanced dataset. That is, present disclosure overcomes the aforementioned drawbacks by providing systems and methods for generating flow-resolved, 3D image volumes from single-sweep, 3D CT or CBCT datasets. Using the systems and methods provided herein, flow dynamics can be identified and even quantified from a single-sweep, 3D CT dataset without subtraction of a “mask” or “non-contrast” dataset.

In accordance with one aspect of the present disclosure, a system is provided for acquiring images of three-dimensional flow within an interior volume of a subject. The system includes an x-ray imaging system configured to acquire images from the subject and a computer system. The computer system is configured to control the x-ray imaging system to perform a single sweep of the subject to acquire a single-sweep, three-dimensional (3D) image dataset and reconstruct the single-sweep, 3D image dataset into a 3D image volume. The computer system is further configured to determine a phase shift corresponding to pulsatile contrast in vessels within the 3D image volume, quantify a flow through the vessels within the 3D image volume using the phase shift, and generate a report including quantified flow through the vessels within the 3D image volume.

In accordance with another aspect of the present disclosure, a method is provided for creating an image including quantified flow within vessels of a subject. The method includes providing a single-sweep, three-dimensional (3D) image volume acquired from a subject during a single pass of a computed tomography (CT) imaging system as the subject receives a dose of a contrast agent and determining a phase shift corresponding to pulsatile contrast in vessels within the single-sweep, 3D image volume. The method further includes quantifying a flow through the vessels within the single-sweep, 3D image volume using the phase shift and generating a report including indicating flow through the vessels within the 3D image volume.

DETAILED DESCRIPTION

Referring now toFIG.1A, a block diagram of an example system10is provided that can be configured to carry out techniques, methods, and processes accordance with the present disclosure. The system may include an imaging system12that is coupled to a computer system14. The coupling of the imaging system12to the computer system14may be a direct or dedicated network connection, or may be through a broad network16, such as an intranet or the Internet.

The computer system14may be a workstation integrated with or separate from the medical imaging systems12or a variety of other medical imaging systems, including, as non-limiting examples, either gantry-based or C-arm computed tomography (CT) systems and the like. The CT system can be configured to utilize a fan or cone beam or other beam geometry. Relatedly, “CT data” or “CT dataset” may refer to 2D data or 3D data, or the like. As used herein “CT” or “CT data” or “CT dataset” can be used in reference to any of these system, acquisition implementations, or data unless specifying otherwise.

The computer system14may be a workstation integrated within the medical imaging system12or may be a separate workstation or mobile device or computing system. To this end, the following description of particular hardware and configurations of the hardware of the example computer system14is for illustrative purposes. Some computer systems may have varied, combined, or different hardware configurations, and may include commercially-available computer systems or specialized computer systems.

Medical imaging data acquired by the medical imaging system12or other imaging system and can be provided to the computer system14, such as over the network16or from a storage device. To this end, the computer system14may include a communications port or other input port18for communication with the network16and system coupled thereto. Also, the computer system14may include memory and storage capacity20to store and access data or images.

In some configuration, computer system14may include one or more processing systems or subsystems. That is, the computer system14may include one or more physical or virtual processors. As an example, the computer system14may include one or more of a digital signal processor (DSP)22, a microprocessor unit (MPU)24, and a graphics processing unit (GPU)26(or other processors, such as field programmable gate arrays (FPGAs)). If the computer system14is integrated into the medical imaging system, a data acquisition unit28may be connected directly to the above-described processor(s)22,24,26over a communications bus30, instead of communicating acquired data or images via the network16. As an example, the communication bus30can be a group of wires, or hardwire used for switching data between the peripherals or between any components, such as the communication buses described above.

The computer system14may also include or be connected to a display32. To this end, the computer system14may include a display controller34. The display32may be a monitor connected to the computer system14or maybe integrated with the computer system14, such as in portable computers or mobile devices.

Referring toFIG.1B, one, non-limiting example of the imaging system12is provided. Specifically, in this example, a so-called “C-arm” x-ray imaging system100is illustrated for use in accordance with some aspects of the present disclosure. However, the systems and methods provided herein are not limited to a particular structure or architecture of x-ray imaging system. That is, in the illustrated configuration, the imaging system12is generally designed for use in connection with interventional procedures. However, non-interventional systems, such as “closed” systems may also be used with the systems and methods described herein.

Thus, in the non-limiting example illustrated inFIG.1B, the imaging system12forms a C-arm CT imaging system100that includes a gantry102to which an x-ray source assembly104is coupled on one end and an x-ray detector array assembly106is coupled at its other end. The gantry102enables the x-ray source assembly104and detector array assembly106to be oriented in different positions and angles around a subject108, such as a medical patient or an object undergoing examination, which is positioned on a table110. When the subject108is a medical patient, this configuration enables a physician access to the subject108.

The x-ray source assembly104includes at least one x-ray source that projects an x-ray beam, which may be a beam, fan-beam, or cone-beam of x-rays, towards the x-ray detector array assembly106on the opposite side of the gantry102. The x-ray detector array assembly106includes at least one x-ray detector, which may include a number of x-ray detector elements. Examples of x-ray detectors that may be included in the x-ray detector array assembly106include flat panel detectors, such as so-called “small flat panel” detectors.

Together, the x-ray detector elements in the one or more x-ray detectors housed in the x-ray detector array assembly106sense the projected x-rays that pass through a subject108. Each x-ray detector element produces an electrical signal that may represent the intensity of an impinging x-ray beam and, thus, the attenuation of the x-ray beam as it passes through the subject108. In some configurations, each x-ray detector element is capable of counting the number of x-ray photons that impinge upon the detector. During a scan to acquire x-ray projection data, the gantry102and the components mounted thereon rotate about an isocenter of the C-arm x-ray imaging system100.

The gantry102includes a support base112. A support arm114is rotatably fastened to the support base112for rotation about a horizontal pivot axis116. The pivot axis116is aligned with the centerline of the table110and the support arm114extends radially outward from the pivot axis116to support a drive assembly118on its outer end. The gantry102is fastened to the drive assembly118and is coupled to a drive motor (not shown) that slides the gantry102to revolve it about a C-axis, as indicated by arrows120. The pivot axis116and C-axis are orthogonal and intersect each other at the isocenter of the x-ray imaging system100, which is indicated by the black circle and is located above the table110.

The x-ray source assembly104and x-ray detector array assembly106extend radially inward to the pivot axis116such that the center ray of this x-ray beam passes through the system isocenter. The center ray of the x-ray beam can thus be rotated about the system isocenter around either the pivot axis116, the C-axis, or both during the acquisition of x-ray attenuation data from a subject108placed on the table110. During a scan, the x-ray source and detector array are rotated about the system isocenter to acquire x-ray attenuation projection data from different angles. The imaging system12may include or be used with a fluid injection system156. The fluid injection system156may deliver a fluid, such as a contrast agent, to the subject during the imaging acquisition

The x-ray imaging system100also includes an operator workstation122, which typically includes a display124, one or more input devices126, such as a keyboard and mouse; and a computer processor128. The computer processor128may include a commercially available programmable machine running a commercially available operating system. The operator workstation122provides the operator interface that enables scanning control parameters to be entered into the C-arm x-ray imaging system100. In general, the operator workstation122is in communication with a data store server130and an image reconstruction system132. By way of example, the operator workstation122, data store server130, and image reconstruction system132may be connected via a communication system134, which may include any suitable network connection, whether wired, wireless, or a combination of both. As an example, the communication system134may include both proprietary or dedicated networks, as well as open networks, such as the internet.

The operator workstation122is also in communication with a control system136that controls operation of the x-ray imaging system100. The control system136generally includes a C-axis controller138, a pivot axis controller140, an x-ray controller142, a data acquisition system (“DAS”)144, and a table controller146. The x-ray controller142provides power and timing signals to the x-ray source assembly104, and the table controller146is operable to move the table110to different positions and orientations within the x-ray imaging system100.

The rotation of the gantry102to which the x-ray source assembly104and the x-ray detector array assembly106are coupled is controlled by the C-axis controller138and the pivot axis controller140, which respectively control the rotation of the gantry102about the C-axis and the pivot axis116. In response to motion commands from the operator workstation122, the C-axis controller138and the pivot axis controller140provide power to motors in the C-arm x-ray imaging system100that produce the rotations about the C-axis and the pivot axis116, respectively. For example, a program executed by the operator workstation122generates motion commands to the C-axis controller138and pivot axis controller140to move the gantry102, and thereby the x-ray source assembly104and x-ray detector array assembly106, in a prescribed scan path.

The DAS144samples data from the one or more x-ray detectors in the x-ray detector array assembly106and converts the data to digital signals for subsequent processing. For instance, digitized x-ray data is communicated from the DAS144to the data store server130. The image reconstruction system132then retrieves the x-ray data from the data store server130and reconstructs an image therefrom. The image reconstruction system130may include a commercially available computer processor, or may be a highly parallel computer architecture, such as a system that includes multiple-core processors and massively parallel, high-density computing devices. Optionally, image reconstruction can also be performed on the processor128in the operator workstation122. Reconstructed images can then be communicated back to the data store server130for storage or to the operator workstation122to be displayed to the operator or clinician.

The x-ray imaging system100may also include one or more networked workstations148. By way of example, a networked workstation148may include a display150; one or more input devices152, such as a keyboard and mouse; and a processor154. The networked workstation148may be located within the same facility as the operator workstation122, or in a different facility, such as a different healthcare institution or clinic.

The networked workstation148, whether within the same facility or in a different facility as the operator workstation122, may gain remote access to the data store server130, the image reconstruction system132, or both via the communication system134. Accordingly, multiple networked workstations148may have access to the data store server130, the image reconstruction system132, or both. In this manner, x-ray data, reconstructed images, or other data may be exchanged between the data store server130, the image reconstruction system132, and the networked workstations148, such that the data or images may be remotely processed by the networked workstation148. This data may be exchanged in any suitable format, such as in accordance with the transmission control protocol (“TCP”), the Internet protocol (“IP”), or other known or suitable protocols.

As will be described, the systems and methods of the present disclosure are particularly useful in a variety of different clinical settings. For example, liver embolization (e.g., transarterial chemoembolization (TACE)) is an effective, minimally invasive treatment option for patients with intermediate or advanced cancer (e.g., hepatocellular carcinoma (HCC)) and select patients with liver metastases. Angiographic monitoring of residual blood flow to treated tumors is important to the success of the procedure and impacts outcomes. However, outcomes are tied to good visualization of eliminating blood flow to the tumor using angiography. While efforts have been made to standardize the angiographic endpoint for liver embolization, it remains largely subjective and is not reproducible. In particular, perfusion and patency of the tumor microvasculature depend on the degree of stasis achieved on angiography.

To address this need for understanding the tumor microvaculature, some have attempted to use digital subtraction angiography (DSA), both 2D and 4D. For example, quantitative 2D DSA has been proposed as means to produce quantitative endpoints for liver embolization. Quantitative 2D DSA does not provide information on true 3D vessel path lengths and cross-sectional parameters, which are required for accurate velocity and flow calculation. While 4D DSA could provide 3D blood flow information, its usability is limited by the requirement for two separate C-arm sweeps (non-contrast (mask) and contrast enhanced (fill)), which increase radiation exposure to the patient (especially if multiple 4D DSA acquisitions are required during a single treatment). It also increases acquisition time, which is crucial for procedures in the thorax and abdomen where respiratory motion can cause severe reconstruction artifacts.

As will be described, the present disclosure provides systems and methods that can utilize a single imaging sweep (for example, a single C-arm sweep) and produce a 4D (3D+time-resolved flow) image. Thus, the systems and methods provided herein overcome the shortcomings of 2D DSA by providing the needed 4D images. Furthermore, the systems and methods provided herein overcome the shortcomings of prior 4D imaging attempts, such as 4D DSA, by generating the 4D images from the data acquired with only one imaging sweep, thereby doing so with lower radiation dose and more efficiently than prior attempts, such as using 4D DSA. The systems and methods provided herein provide accurate blood flow and velocity measurement where prior systems and methods have not.

Referring now toFIG.2, a process diagram is provided for a process200in accordance with the present disclosure for single-sweep 4D angiography. The process includes acquiring imaging data. In one non limiting example, the imaging data may be acquired using a rotational C-arm CT system, with contrast injection performed. In accordance with the present disclosure, a single-sweep acquisition may be performed with contrast present. Unlike the systems and methods described above, a pre-contrast acquisition or separate mask acquisition is not required.

In one non-limiting example, the CT acquisition may be a cone beam CT (CBCT) acquisition. Also, in a non-limiting example, the acquisition may be performed over a 260-degree gantry rotation providing projection images from, for example, 304 angles.

Once the imaging data is acquired at process step202, images may be reconstructed at process block204. In one non-limiting example, reconstruction may include a conventional filtered back projection (FBP) reconstruction. The resulting images form 3D volume that can then be used at process block206to perform vessel segmentation. In accordance with one non-limiting example, the vessel segmentation may be performed using a deep learning-based vessel segmentation network. In one non-limiting example, the network may use a U-net architecture and be trained using previous 3D acquisitions, whether using the present systems and methods or even DSA or other techniques. The result of the vessel segmentation at process block206is a binary representation of the vasculature, which can then be used at process block208to extract vessel centerlines. Notably, process blocks202-208may be performed as described or may be performed via other steps that likewise deliver the reconstructed image, vessel segmentation, and centerline extractions. As just one example, it is possible that process blocks202-208may be omitted or considered optional, as some other systems or methods may be used to yield the desired results.

Thus, irrespective of the particular processes used to generate the 3D data, reconstructed images from the 3D data, segmented vessels, and centerline extractions, the following steps are performed to synthesize non-contrast projection images from the 3D reconstruction and the vessel anatomy. At process block210, this process is performed by replacing vessel voxels from the vessel segmentation with an attenuation value representing non-contrast enhanced blood. The attention value may be a constant. Then, at process block212, non-contrast images are synthesized. For example, a digital forward projection can be performed using a Siddon ray tracing approach. As one non-limiting example, such as Siddon ray tracing approach is described, for example, in F. Jacobs, E. Sundermann, B. De Sutter, M. Christiaens, and I. Lemahieu, “A fast algorithm to calculate the exact radiological path through a pixel or voxel space,” CIT. Journal of computing and information technology, vol. 6, no. 1, Art. no. 1, 1998, which is incorporated herein by reference in its entirety.

To the extent that there is truncation in the images, such as caused by the limited field of view of the 3D reconstruction, or patient motion (e.g., respiratory motion, other physiological motion, or bulk motion), artifacts may be seen in the digital projections. As such, truncation and motion correction can be performed. At process block214, a forward projection of the 3D binary vessel mask into the 2D image space may be performed. Then, at process block216, pixels in the original projection images may be replaced using interpolation and subtracting the result from the synthesized projection images. At process block218, a median filter can be applied using the raw imaging data to extract the low frequency components expected due to truncation.

Then, at process block220, the estimated offset can be added to the synthesized images from process block212to create truncation corrected non-contrast projection images. A block-matching based motion-compensation approach can be performed between the original projection images and the synthesized masks to account for small involuntary patient motion and the registered images are subsequently subtracted to extract the contrast (e.g., iodine) signal only.

At process block222, time-attenuation curves (TACs) can then be calculated for each point along the vessel centerlines from process block208, for example, by forward projecting the points into the 2D image space, interpolating the corresponding gray values, and normalizing by the number of voxels that contribute to each pixel. Since each projection is acquired at a different point in time, this generates a time-dependent curve of the iodine enhancement at each point along the centerline. To isolate the pulsatile signal used for flow quantification in each TAC, the TACs can be low-pass filtered at process block224, for example, using a moving average filter. The result is subtracted from the original TAC to yield the desired TACs. Finally, at process block226, a report can be generated that includes flow quantification. For example, flow quantification can be estimating using the phase shift of the pulsatile signal in frequency space compared to the distance along the vessel centerlines.

In one non-limiting example, the above-described process was applied to data where contrast enhanced CBCT acquisitions were acquired in regular intervals before, during, and after embolization. Specifically, acquisitions were performed after 0, 3, 6, and 9 ml of embolic particles were delivered.FIG.3Ashows one example of a quantified flow included in a 3D image of the vasculature with color-coded velocities positioned along vessel centerlines for all major vessel branches. Also,FIG.3Bis a graph showing a comparison of the average blood velocity in the embolized branch after different levels of embolization, showing a consistent decrease of velocity with increased embolization. Since, overall blood flow might not be constant over time,FIG.3Cis a graph showing a comparison of the relative velocities calculated by dividing the average velocity of the embolized branch by the average velocity of the parent branch resulting in a more linear relationship between embolic particles delivered and blood velocity.

The above-described systems and methods may be implemented in a variety of ways. Referring toFIG.4, another process400, alternative to the approach described with respect toFIG.2, will be described. As described above with respect toFIG.2, process block202-208may be performed. That is, the purpose of process blocks202-208is to extract the 3D geometry of the vasculature from a contrast enhanced cone beam CT (CBCT) reconstruction. Though the process400may use the same data and initial steps, the process400uses the single-sweep data to perform a signal extraction in temporal/frequency space, instead of subtraction of a synthesized mask image. As will be described, time-attenuation curves (TACs) in this process400can be calculated directly from unsubtracted contrast-enhanced projection images and contaminating signal (for example, signal from other anatomic structures not related to the contrast in the vasculature) is filtered out in time and frequency space.

At process block402, 3D vessel centerlines are forward projected into the projection image. By doing so, a cross-profile can be calculated for points along the centerlines. In particular, at process block404, a vector perpendicular to the centerline at a given point is identified and contrast values (e.g., gray values) at spaced sampling points, for example, equally spaced points, are interpolated along this line. Values from sample points inside of the vessel can be normalized at process block406. For example, values from sample points inside the vessel can be averaged to reduce noise. Optionally, the average value of points just outside the vessel can be subtracted to remove background signal. This can be readily achieved, for example, using vessel thickness, which can be determined from the 3D segmentations. The result is a time-attenuation curve (one time point for each projection image) for each point along the centerlines, which are still “contaminated” by signal from organs or other anatomical structures.

To further reduce the influence of other anatomical structures and extract the pulsatile signal from the contrast injection in the vessels, a filter is applied at process block408, which removes low frequency changes in the contrast. For example, the filter may be a high-pass filter. In one non-limiting example, this can be achieved by subtracting a low pass filtered version of each TAC from the original TAC (e.g. moving average filter: approximately 1 s width). However, many other low-pass filters can also be used instead. The resulting curve is a “cleaned” time-attenuation curve showing the pulsatile contrast changes in the vessels. For example,FIG.5Ais a graph showing an example of a time-attenuation curve that is “contaminated” with background signal from organs and other anatomical structures. On the other hand,FIG.5Bis a graph showing the same time-attenuation curve after being subject to a high-pass filter, clearly showing pulsatile signal of contrast in vessels.

Referring again toFIG.4, at process block410, pulsatile information can be extracted. As one non-limiting example, a frequency-based analysis can be used to extract only the frequency corresponding to the pulsatile contrast changes in the vessels. For example, a 1D Fourier transform can be applied to each TAC and the average power spectrum over all centerline points (of a single centerline) can be calculated, as shown inFIG.5C. In one non-limiting example, the frequency corresponding to the highest power spectral density can be used for further velocity and flow analysis.

Flow velocities, for example, local flow velocities can then be calculated by determining the phase shift of the selected frequency along the centerline, at process block412. In one non-limiting example, the phase corresponding to each point along the centerline can be extracted directly from the Fourier transform using the angular component of the selected frequency. Phase unwrapping can then be applied to remove any jumps in the phases along the centerline. For example, referring toFIG.5D, the phase for the selected peak frequency at each centerline point without correction500is illustrated. Then, the phase for the selected peak frequency at each centerline point after phase unwrapping502is shown. In one non-limiting example, velocities can be calculated by fitting linear functions to sliding windows of the phase unwrapped curve502. Finally, a function (linear function) can be fit to each sliding window along the centerline (e.g.20sample points window) to estimate local velocities (slope of the linear function). A smoothing spline can be fit to the estimated local velocities of a centerline to remove outliers and noise.

As a result, at process block414, a report can be generated with flow quantification, including local flow quantification. The report may be an image with color, graphical, and/or numerical overlays, or a written report or table.

Therefore, the present disclosure provides systems and methods for 3D quantitative flow information for intraprocedural assessment of treatment progress and determination of endpoints. In doing so, the systems and methods provided herein provide consistent, quantitative information needed for procedures like embolization, where the degree of embolization is critical for the success of the procedure (under embolization may lead to tumor survival, while over embolization may cause necrosis of healthy tissue and increase the risk to the patient).

The systems and methods provided herein may be used as an external software package or integrated add-on to scanner or other systems associated with commercial CT systems, such as existing C-arm systems. During embolization procedures, acquisitions may be performed before, during, or after embolization. Data may be transferred to an external workstation where reconstruction is performed, and blood velocities can be displayed within minutes without interrupting the clinical workflow. Based on the resulting flow measurements additional embolization material may be injected until the desired endpoint is reached.

The present disclosure provides systems and methods not contemplated by the prior art. The systems and methods provided herein yield 4D (3D+time) reconstruction of arterial blood flow, and can include quantitative information regarding local blood flow and velocities, from a single contrast-enhanced cone beam CT sweep or scan. The systems and method provided herein do not require non-contrast, mask images to be acquired. Thus, proper visualization of blood flow is provided using a standardizable/repeatable angiographic process that does not subject the patient to the radiation dose of multiple sweeps/acquisitions and does not require the time to perform extended acquisitions. In the case of embolization, endpoints can be readily identified and tracked in an objective and reproducible way.

The systems and methods provided herein provide 3D time resolved contrast information that can be used for accurate blood flow and velocity measurement. Alternative subtraction-based techniques require separate mask runs to (1) reconstruct a subtracted 3D constraining volume showing only vessels and (2) isolate signal from blood (contrast) in the 2D projection images to generate time-concentration curves. In contrast to subtraction-based methods, such as 4D DSA, only a single C-arm sweep is used in the systems and methods provided herein, thus reducing radiation exposure, acquisition time, and risk of motion artifacts.