Patent Publication Number: US-2023133386-A1

Title: Robotic arm-based clinical micro-ct system and method

Description:
CROSS REFERENCE TO RELATED APPLICATION(S) 
     This application claims the benefit of U.S. Provisional Application No. 63/275,616, filed Nov. 4, 2021, U.S. Provisional Application No. 63/323,751, filed Mar. 25, 2022, and U.S. Provisional Application No. 63/399,346, filed Aug. 19, 2022, which are incorporated by reference as if disclosed herein in their entireties. 
    
    
     GOVERNMENT LICENSE RIGHTS 
     This invention was made with government support under award numbers CA237267, and EB026646, both awarded by the National Institutes of Health. The government has certain rights in the invention. 
    
    
     FIELD 
     The present disclosure relates to a clinical micro-CT system and method, in particular to, a robotic arm-based clinical micro-CT system and method. 
     BACKGROUND 
     Conventional medical CT (computed tomography) is used to provide images of regions a human body. Medical CT may be used to image a variety of middle and inner ear pathologies, but is limited by suboptimal image resolution at the dimensions of, for example, middle and inner ear structures. Micro-CT systems are used in preclinical research but are less so in clinical applications. Clinical micro-CT imaging configured for clinical applications, may provide relatively better image resolution but is limited by challenges associated with interior tomography. 
     SUMMARY 
     In some embodiments, there is provided a micro-CT (computed tomography) apparatus. The micro-CT apparatus includes an x-ray source coupled to a source robotic arm, an x-ray detector coupled to a detector robotic arm, and a computing device. The computing device includes a data acquisition module and a reconstruction module. The data acquisition module is configured to acquire local scan data of a volume of interest (VOI) contained in an imaging object. The reconstruction module is configured to reconstruct an image of the VOI based, at least in part, on the local scan data, and based, at least in part, on background compensation data. 
     In some embodiments of the micro-CT apparatus, the computing device further includes a registration module configured to determine a local scanning geometry. The local scanning geometry is determined based, at least in part, on global scan data of the imaging object. The global scan data includes the VOI and has an associated global scanning geometry. The local scan data is captured for the local scanning geometry. 
     In some embodiments of the micro-CT apparatus, the computing device further includes a background module configured to estimate a background attenuation of the local scan data. The background attenuation corresponds to the background compensation data. 
     In some embodiments of the micro-CT apparatus, the registration module is further configured to determine a relative geometry between the global scanning geometry and the local scanning geometry based, at least in part, on a surface model of at least a portion of the imaging object. The VOI is located relative to the surface. 
     In some embodiments of the micro-CT apparatus, the x-ray source includes a micro-focus tube, and the x-ray detector comprises a photon-counting detector. 
     In some embodiments of the micro-CT apparatus, the computing device further includes a refinement module configured to perform bias correction. 
     In some embodiments of the micro-CT apparatus, the reconstructed image has a resolution of at least 50 micrometers (μm). 
     In some embodiments, there is provided a micro-computed tomography (CT) system. The micro-CT system includes a medical CT scanner, and a micro-CT apparatus. The medical CT scanner is configured to perform a global scan of an imaging object. The micro-CT apparatus includes an x-ray source coupled to a source robotic arm, an x-ray detector coupled to a detector robotic arm, and a computing device. The computing device includes a data acquisition module and a reconstruction module. The data acquisition module is configured to acquire local scan data of a volume of interest (VOI) contained in the imaging object. The reconstruction module is configured to reconstruct an image of the VOI based, at least in part, on the local scan data, and based, at least in part, on background compensation data. 
     In some embodiments of the micro-CT system, the computing device further includes a registration module configured to determine a local scanning geometry. The local scanning geometry is determined based, at least in part, on global scan data of the imaging object. The global scan data includes the VOI and has an associated global scanning geometry. The local scan data is captured for the local scanning geometry. 
     In some embodiments of the micro-CT system, the computing device further includes a background module configured to estimate a background attenuation of the local scan data. The background attenuation corresponds to the background compensation data. 
     In some embodiments of the micro-CT system, the registration module is further configured to determine a relative geometry between the global scanning geometry and the local scanning geometry based, at least in part, on a surface model of at least a portion of the imaging object, the VOI located relative to the surface. 
     In some embodiments of the micro-CT system, the x-ray source includes a micro-focus tube, and the x-ray detector comprises a photon-counting detector. 
     In some embodiments of the micro-CT system, the computing device further includes a refinement module configured to perform bias correction. 
     In some embodiments, the micro-CT system further includes an optical surface scanner. 
     In some embodiments, there is provided a method of clinical micro-computed tomography (CT). The method includes emitting, by an x-ray source, x-ray photons, the x-ray source coupled to a source robotic arm; detecting, by an x-ray detector, at least some of the emitted x-ray photons, the x-ray detector coupled to a detector robotic arm. The method further includes acquiring, by a data acquisition module, local scan data of a volume of interest (VOI) contained in an imaging object. The method further includes reconstructing, by a reconstruction module, an image of the VOI based, at least in part, on the local scan data, and based, at least in part, on background compensation data. 
     In some embodiments, the method further includes determining, by a registration module, a local scanning geometry. The local scanning geometry is determined based, at least in part, on global scan data of the imaging object. The global scan data includes the VOI and has an associated global scanning geometry. The local scan data is captured for the local scanning geometry. 
     In some embodiments, the method further includes estimating, by a background module, a background attenuation of the local scan data. The background attenuation corresponds to the background compensation data. 
     In some embodiments, the method further includes determining, by the registration module, a relative geometry between the global scanning geometry and the local scanning geometry based, at least in part, on a surface model of at least a portion of the imaging object, the VOI located relative to the surface. 
     In some embodiments, the method further includes performing, by a refinement module, bias correction. 
     In some embodiments, the x-ray source includes a micro-focus tube, and the x-ray detector comprises a photon-counting detector. 
     In some embodiments, there is provided a computer readable storage device. The computer readable storage device has stored thereon instructions that when executed by one or more processors result in the following operations including the method. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
       The drawings show embodiments of the disclosed subject matter for the purpose of illustrating features and advantages of the disclosed subject matter. However, it should be understood that the present application is not limited to the precise arrangements and instrumentalities shown in the drawings, wherein: 
         FIG.  1    illustrates a functional block diagram of a clinical micro-computed tomography (CMCT) system, consistent with several embodiments of the present disclosure; 
         FIG.  2    is a sketch illustrating a geometry of a micro-CT imaging system, according to an embodiment of the present disclosure; 
         FIG.  3    is a functional block diagram of an image registration system, according to an embodiment of the present disclosure; 
         FIG.  4    a flowchart of imaging object and region of interest scanning operations according to various embodiments of the present disclosure; and 
         FIG.  5    a flowchart of volume of interest reconstruction operations according to various embodiments of the present disclosure. 
     
    
    
     Although the following Detailed Description will proceed with reference being made to illustrative embodiments, many alternatives, modifications, and variations thereof will be apparent to those skilled in the art. 
     DETAILED DESCRIPTION 
     Generally, this disclosure relates to a robotic arm-based clinical micro-CT (CMCT) apparatus, method and/or system. In the following, a robotic arm-based clinical micro-CT apparatus, system and method are described using as an example, the clinical practice of otology and neurotology. It should be noted that a robotic arm-based clinical micro-CT apparatus, system and method, according to the present disclosure, may be utilized for other clinical imaging applications, within the scope of the present disclosure. 
     As used herein, medical CT corresponds to CT with a spatial resolution on the order of tenths of millimeters (mm). In one nonlimiting example, a medical CT image may have spatial resolution of 0.3 mm. The terms “medical CT”, “clinical CT” (without micro-) and/or “conventional CT” are used interchangeably. As used herein, “micro-CT” corresponds to CT with spatial resolution of the order of tens of micrometers (μm). As used herein, a “clinical micro-CT” corresponds to a micro-CT configured to image regions of a human body. In one nonlimiting example, a clinical micro-CT image may have a spatial resolution of 50 μm. 
     In the clinical practice of otology and neurotology, medical imaging is used for evaluation and treatment of many diseases. Temporal bone CT may be used for otological imaging. Disorders or therapies where temporal bone CT may be used as an adjunct to surgical management, e.g., for diagnosis or planning, include, but are not limited to chronic otitis media, otosclerosis, temporal bone fracture, congenital aural atresia, cochlear implantation, dehiscent superior semicircular canal, congenital labyrinthine dysplasia, and labyrinthine fistula. In cochlear implantation, an ability to localize electrodes and depict their three-dimensional (3D) anatomical environment in vivo aids understanding variations in threshold, discomfort level, and channel interaction. 
     Medical CT may be used to image a variety of middle and inner ear pathologies but may not provide adequate image resolution. A precise diagnosis may be unavailable until direct assessment during otologic surgery allows visual inspection and palpation of the ossicular chain. Improved image resolution of pre- and post-operative inner ear imaging may allow a relatively more detailed analysis of cochlear morphometry and its relationship to an implanted electrode array. Improved spatial resolution without compromising other image quality indices and at a minimized radiation dose level may thus benefit these as well as other research and clinical applications. 
     A robotic arm-based X-ray imaging system may be configured with an X-ray source coupled to a first (“source”) robotic arm and an X-ray detector coupled to a second (“detector”) robotic arm. The X-ray source may include, but is not limited to, a micro-focus X-ray tube, a dual energy CT source, e.g., a single-source X-ray source configured to emit an X-ray beam in two energy spectra, or a single energy spectrum source. The X-ray detector may include, but is not limited to, an energy-integrating detector (EID), a current integrating detector (CID), a dual-layer detector, and a photon-counting detector (PCD). In one nonlimiting example, each robotic arm may be configured with six degrees of freedom. A robotic-arm-based X-ray imaging system, according to the present disclosure, may provide flexibility in scanning and may facilitate a variety of scanning trajectories. The scanning trajectories may be task-specific and may include relatively diverse tasks targeting various organs and locations. 
     X-ray photon-counting detectors (PCDs) may enable relatively high-resolution (HR) and low-noise imaging, thus providing a spectral dimension to raw data and a corresponding improvement in CT performance. Different from an energy-integrating detector (EID), PCD works in a pulse-counting mode and directly converts individual X-ray photons into corresponding charge signals which are then sorted into different energy bins based on respective pulse heights. Thus, an intensity and wavelength information of incoming photons may be simultaneously obtained. Advantageously, PCDs generally do not suffer from electronic noise effects and may provide a relatively small effective pixel size; e.g., around 0.11 mm×0.11 mm. As used herein, “around” and “approximately” correspond to within ±10 percent (%). PCDs allow applying selected weights to polychromatic photons for improved contrast and dose efficiency. Additionally or alternatively, an energy discrimination ability of PCDs may help reduce beam hardening and metal artifacts, and/or may enable K-edge imaging and material decomposition. 
     Micro-focus X-ray tubes may be used with PCDs in micro-CT applications, according to one embodiment of the present disclosure. In one nonlimiting example, a microfocus X-ray tube may include electron emitting and receiving constructs (i.e., portions). The X-ray tube is configured to emit X-ray photons. The receiving portion may contain an anode with a photoconductor. The emission portion may contain a backplate, a substrate, a cathode, a gate electrode, and an array of field emission electron sources. In another example, a microstructured array anode target (MAAT) X-ray source may be configured to provide a relatively higher flux than an ordinary X-ray source in phase contrast imaging applications. It is contemplated that such technologies could be combined into a micro-focus X-ray tube for temporal bone CT imaging; for example, with a focal spot size of about 0.1 mm or less, corresponding to a PCD element size. 
     Generally, this disclosure relates to a robotic arm-based clinical micro-CT apparatus, method and/or system. The robotic arm-based clinical micro-CT apparatus, method and/or system, according to the present disclosure, may include a clinical micro-CT scanner. In an embodiment, the robotic arm-based clinical micro-CT apparatus may include the clinical micro-CT scanner and a computing device. In one embodiment, the clinical micro-CT scanner may include a micro-focus tube, and a PCD, each mounted on a robotic arm. The clinical micro-CT apparatus, e.g., clinical micro-CT scanner, may be configured to perform interior tomography. In some embodiments, the clinical micro-CT apparatus, e.g., computing device, may be configured to implement deep learning. 
     In some embodiments, a clinical micro-CT (CMCT) system may include the clinical micro-CT apparatus combined with or coupled to a clinical CT scanner. A clinical micro-CT system may thus include the clinical micro-CT scanner, and the computing device and may include the clinical CT scanner. In some embodiments, the clinical micro-CT scanner and/or apparatus may be used separately, i.e., may not be coupled to the clinical CT scanner. 
     In an embodiment, a CMCT workflow may begin with a conventional volumetric scan (i.e., “global scan”) of a patient, and may then proceed to a subsequent image analysis session configured to plan a micro-CT imaging trajectory. The planning may be performed by a trained person and/or an intelligent algorithm. The patient may then be positioned in a robotic scanning space for an interior photon-counting micro-CT scan (i.e., “local scan”). The robotic scanning space may be adjacent to the medical CT scanning space. In some embodiments, the workflow may include registration to the global scan. The interior scan may be configured to target a region of interest (ROI) within the global scan data, using a relatively small detector panel. As used herein, ROI may be two dimensional (2D) or three-dimensional (3D). Volume of interest (VOI) corresponds to a 3D ROI. The scanning trajectory may be configured to enhance image quality. In some embodiments, to facilitate relatively accurate registration between the global scan data/images and local scan data/images, an optical 3D surface scanner may be used at the start of the local scan. As used herein, “global scan” and “global scan data” correspond to a medical CT scan and medical CT scan data, respectively. As used herein, “local scan” and “local scan data” correspond to a clinical micro-CT scan and clinical micro-CT scan data, respectively. “Local scan” and “interior scan” are used interchangeably. “Scan data”, “projection data” and “sinogram” are used interchangeably. In some embodiments, the optical 3D surface scanner may be configured to continue to monitor the head movement of the patient during the local scan to facilitate motion compensation. 
     In some embodiments, a relatively high resolution (HR) local image reconstruction of the ROI (i.e., local or interior scan data) may include background compensation of the HR local/interior scan utilizing corresponding global scan data. Advantageously, a subsequent interior reconstruction may include a relatively small portion of the sinogram, utilizing relatively less memory space and computational time. In some embodiments, for image quality enhancement at relatively reduced X-ray radiation doses, one or more deep learning techniques may be utilized in one or more reconstruction stages, e.g., projection deblurring, image denoising and super-resolution, beam hardening correction and/or material decomposition. 
     In an embodiment, there is provided a micro-CT (computed tomography) apparatus. The micro-CT apparatus includes an x-ray source coupled to a source robotic arm, an x-ray detector coupled to a detector robotic arm, and a computing device. The computing device includes a data acquisition module and a reconstruction module. The data acquisition module is configured to acquire local scan data of a volume of interest (VOI) contained in an imaging object. The reconstruction module is configured to reconstruct an image of the VOI based, at least in part, on the local scan data, and based, at least in part, on background compensation data. In one embodiment, the x-ray source includes a micro-focus tube, and the x-ray detector includes a photon-counting detector. 
       FIG.  1    illustrates a functional block diagram of a clinical micro-CT (CMCT) system  100 , consistent with several embodiments of the present disclosure. CMCT system  100  includes a clinical micro-CT apparatus  102 , and an imaging object  103 . The imaging object  103  includes a region of interest  105  that corresponds to a portion of the imaging object  103 . For example, the imaging object  103  may include a test subject, e.g., a patient, a portion of the patient or a patient internal organ, and the ROI  105  may correspond to a portion of the patient, a patient internal organ or a portion of a patient internal organ. 
     CMCT system  100  may include a medical CT scanner  104  and/or and an optical surface scanner  106 . Micro-CT apparatus  102  includes a computing device  108  and a micro-CT scanner  120  (i.e., a robotic micro-CT scanner). Computing device  108  includes processor circuitry  110 , memory circuitry  112 , input/output (I/O) circuitry  114 , a user interface (UI)  116  and a plurality of functional modules. In some embodiments, processor circuitry  110  may include one or more graphics processing units (GPU(s))  111 . Processor circuitry  110  may be configured to perform operations of one or more modules of the micro-CT apparatus  102 , as described herein. Memory circuitry  112  and/or data store  118  may be configured to store modules and data, as described herein. I/O circuitry  114  may be configured to communicate with medical CT scanner  104 , optical surface scanner  106  and/or micro-CT scanner  120 . UI  116  may include one or more elements configured to capture user input and/or provide output data to the user. UI  116  may thus include one or more of a display (including a touch sensitive display), a loudspeaker, a keyboard, a mouse, a touchpad, a microphone, a camera, etc. 
     Micro-CT apparatus  102 , e.g., micro-CT scanner  120 , includes a micro-CT (μCT) source  124 , a micro-CT (μCT) detector  126 , a source robotic arm  134 , and a detector robotic arm  136 . Micro-CT apparatus  102 , e.g., computing device  108 , includes a micro-CT (μCT) management module  140 , a data acquisition module  142 , a background module  144 , a registration module  146 , and a reconstruction module  148 . In some embodiments, micro-CT apparatus  102 , e.g., computing device  108 , may include a refinement module  150 . In some embodiments, micro-CT scanner  120  may include a robot controller module  122 . In some embodiments, the robot controller module  122  may be included in computing device  108 . 
     In operation, the medical CT scanner  104  may be configured to perform a global scan of imaging object  103 , e.g., a patient head. A known CT reconstruction technique (e.g., filtered back projection (FBP)) can be used to reconstruct head slices corresponding to imaging object  103 . Head slices may include ROI  105 , e.g., an inner ear ROI. The patient corresponding to imaging object  103  may then be positioned relative to the micro-CT apparatus  102 , e.g., relative to the micro-CT scanner  120 , or the robotic micro-CT apparatus  102  may be positioned relative to the patient. As used herein, “micro-CT apparatus” and “robotic micro-CT apparatus” are used interchangeably. As used herein, “micro-CT scanner” and “robotic micro-CT scanner” are used interchangeably. 
     The micro-CT source  124  may be coupled (e.g., mechanically) to the source robotic arm  134 . The micro-CT detector  126  may be coupled (e.g., mechanically) to the detector robotic arm  136 . The robot controller module  122  is configured to control movement of the robotic arms  134 ,  136 , and thus, position and/or orientation of the micro-CT source  124  and the micro-CT detector  126 . The micro-CT source  124  corresponds to an X-ray source and the micro-CT detector  126  corresponds to an X-ray detector, respectively. The X-ray source  124  may include, but is not limited to, a micro-focus X-ray tube, a dual energy CT source, or a single energy spectrum source. The X-ray detector  126  may include, but is not limited to, an energy-integrating detector (EID), a current integrating detector (CID), a dual-layer detector, and a photon-counting detector (PCD). In an embodiment, the X-ray source  124  may include a micro-focus tube for relatively high spatial resolution imaging. In an embodiment, the X-ray detector  126  may include a flat-panel PCD (pulse counting detector) configured for material decomposition and tissue characterization. 
     The robotic arms  134 ,  136  may be configured to perform a scan along an arbitrary trajectory including, but not limited to, a circular trajectory and/or spiral trajectory. The computing device  108 , and associated modules are configured to manage operation of the robotic micro-CT apparatus  102  including, but not limited to, motion of the robotic arms  134 ,  136 , positioning and controlling the X-ray source  124 , positioning and capturing data from the X-ray detector  126 , etc. The computing device  108 , e.g., robot controller module  122 , may be configured to provide instructions to the robotic arms, X-ray source and detector. The computing device  108 , e.g., data acquisition module  142 , may be configured to acquire raw data from the micro-CT detector  126 . Thus, each robotic arm  134 ,  136  may include and/or be coupled to a control device, configured to receive commands from the computing device  108  and drive robotic arm  134 ,  136  servo systems. 
     In one nonlimiting example, the robotic arms  134 ,  136  may be 6-axis robotic arms with a position repeatability of on the order of ten micrometers (μm). For example, the position repeatability may be at least 30 μm. However, this disclosure is not limited in this regard. In one nonlimiting example, the micro-CT system may achieve a spatial resolution of at least 50 μm. 
     In one nonlimiting example, the X-ray source  124  may include a micro-focus X-ray tube that is configured with an adjustable focal spot size. For example, the adjustable focal spot sizes may include, but are not limited to, 7, 20, and 50 μm. In one nonlimiting example, the X-ray source  124  anode target material may be Tungsten, the tube window may be made of Beryllium of 0.2 mm thickness, and the effective cone beam angle may be 43 degrees. However, this disclosure is not limited in these regards. A weight of the X-ray source  124  may be on the order of 10 kilograms, e.g., 13.5 kg. However, this disclosure is not limited in this regard. In an embodiment, the X-ray detector  126  may include or correspond to a PCD. In one nonlimiting example, the X-ray detector  126  may have a continuously sensitive surface that include an array of 5×5 detector tiles of 1280×1280 pixels. Each tile may include a single hybrid detector (256×256 pixels) with an edgeless CdTe (Cadmium Tellurium) sensor. Each pixel is configured to count a respective number of X-ray photons, thus allowing a relatively large dynamic range. The X-ray detector  126  is configured to support a plurality of energy thresholds. A spatial resolution may be defined by a detector pitch. In one nonlimiting example, the detector pitch may be 55 μm. However, this disclosure is not limited in these regards. 
     In other words, for the example X-ray detector  126 , the imaging sensor is configured to cover a 7 cm×7 cm area. The detector weighs 3.3 kg and can be carried by an appropriately sized robotic arm. Operation of the detector in a 2×2 binning mode provides an image resolution at or near 50 μm, with diameter of the volume of interest (VOI) of 3.5 cm, assuming a magnification factor of 2. Additionally or alternatively, for another example X-ray detector  126 , the detector tile pixels may include a plurality, e.g., two, integrated 12-bit digital counters and two energy discrimination thresholds. Operating the X-ray detector  126  in a 2×2 binning mode provides 8 spectral bins for data collection in a single scan. However, this disclosure is not limited in these regards. 
     As is known, radiation dose is mainly determined by the tube voltage, current and exposure period. With the use of the PCD, there is no electronic noise when recording projection data but Poisson noise cannot be avoided. In the interior scanning mode, the X-ray source  124  may radiate about 1/10 of the diameter of the field of view. It is contemplated that increasing image resolution by four times (for example, from approximately 200 μm to 50 μm) may increase radiation dose a relatively large amount (e.g., by two orders of magnitude). It is contemplated that the radiation dose may be reduced by an order of magnitude by using a deep learning-based low-dose CT imaging techniques. It is estimated that a conventional head CT dose may be maintained for an interior micro-CT scan while achieving about 50 μm resolution. 
       FIG.  2    is a sketch  200  illustrating a geometry of a micro-CT imaging system, according to an embodiment of the present disclosure. Sketch  200  is configured to illustrate a relationship between system geometry and spatial resolution for a micro-CT system, according to several embodiments of the present disclosure. Sketch  200  includes micro-CT source  202 , a micro-CT detector  204  and an imaging object  203  that contains an ROI  205 . A focal spot size of the micro-CT source  202  corresponds to a PCD element size of the micro-CT detector  204 . A center of the focal spot of the source  202  may be defined as S, a center of the detector  204  as D, and a rotation center as 0 corresponding to a center of a VOI  205 . The focal spot, center of the detector and rotation center may generally be aligned. A distance a is the source  202  to VOI  205  distance, a distance b is the VOI  205  to detector  204  distance, and a distance c is the source  202  to detector  204  distance. Geometric magnification factors M and M′ are c/a and c/b for the focal spot, S, and a detector aperture, D, respectively. A spatial resolution r of corresponding imaging system can be approximated as a convolution of the detector size d and the focal spot size x, respectively, scaled by M and M′ as: 
     
       
         
           
             
               
                 
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                             d 
                             
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     In one nonlimiting example CMCT system design, x may be less than or equal to 50 μm, and d may be equal to about 110 μm (related to 2×2 binning). The source  202  to VOI  205  distance may in the range 150 to 250 mm, and the VOI  205  to detector  204  distance may be in a same range. The magnification factor can be adjusted from 1.60 to 2.67 with the imaging field of view varying from 26 to 44 mm in diameter. Accordingly, the spatial resolution of a system, according to the present disclosure may be at or near 50 μm. 
       FIG.  3    is a functional block diagram  300  of an image registration system, according to an embodiment of the present disclosure. Image registration system  300  includes a medical CT scanner  302 , an optical surface scanner  314  and a clinical micro-CT (CMCT) scanner  316 . The medical CT scanner  302  may correspond to the medical CT scanner  104 , the optical surface scanner  314  may correspond to the optical surface scanner  106  and the clinical micro-CT (CMCT) scanner  316  may correspond to the micro-CT scanner  120 , of  FIG.  1   . 
     In operation, global projection data and local projection data may be obtained in different scanning geometries. The medical CT scanner  302  may be configured to generate a head CT image  304 . A head surface  310  may be determined based, at least in part, on the head CT image  304 , and a first facial surface  312 - 1  may be identified based, at least in part, on the head surface  310 . A temporal bone CT image  306 - 1  may be generated based, at least in part, on the head CT image  304  and a first inner ear  308 - 1  position may be determined based, at least in part, on the temporal bone CT image  306 - 1 . The optical surface scanner  314  may be configured to determine a second facial surface  312 - 2 . A 3D facial surface registration  320  may be performed based, at least in part, on the facial surfaces  312 - 1 ,  312 - 2 . VOI and trajectory planning  322  may be performed based, at least in part, on the temporal bone CT image  306 - 1  and the 3D facial surface registration  320 . 
     The CMCT scanner  316  may be configured to receive the VOI and trajectory planning  322  information. The CMCT scanner  316  may then generate a temporal bone CMCT image  306 - 2 . A second inner ear  308 - 2  position may be determined based, at least in part, on the temporal bone CMCT image  306 - 2 . An inner ear image registration  324  may then be performed based, at least in part, on the inner ear positions  308 - 1 ,  308 - 2 . 
     Thus, the clinical CT scanner  302  is configured to obtain a 3D image (i.e., head CT image data  304 ) of a patient. The patient may wear one or more landmarks such as, for example, a relatively firmly-attached helmet. A boundary detection technique may be configured to extract a facial/helmet surface (e.g., a head surface  310  and/or a first facial surface  312 - 1 ). Key points for mesh generation may be extracted, as a basis for image registration. It may be appreciated that an inner ear (corresponding to inner ear  308 - 1 ) and the first facial surface  312 - 1  are generally in a rigid relation. One nonlimiting example of 3D surface scanners includes a laser scanner Micro-Epsilon LLT2910-100. However, this disclosure is not limited in this regard. In one nonlimiting example, a height range may be set from 125 to 390 mm, and a width range may be fixed to 143.5 mm, with spatial resolution of 12 μm, and at a profiling frequency of 300 Hz. 
     Continuing with the example laser scanner, its relatively compact size of 96 mm×85 mm×33 mm and low weight of 380 grams are useful for static, dynamic and robotic applications. A wavelength of the semiconductor laser may switchable between 658 nm (red) and 405 nm (blue). In one nonlimiting example, the optical scanner and CMCT scanner robotic arms may be mounted on a same pedestal so that they share the same coordinate system. The optically scanned patient head surface may be registered with the clinical CT originated head surface. Then, the coordinate conversion may performed to delineate a VOI for a robotic micro-CT scan. The temporal bone data and images by medical CT and micro-CT may then be registered and fused to facilitate imaging performance. 
     Thus, image registration may be performed based, at least in part, on medical CT scanner image data, optical 3D surface scanner data and CMCT scanner image data. 
     As is known, the CT interior problem is not uniquely solvable in an unconstrained space. By introducing additional prior knowledge on the image to be reconstructed; e.g., an interior sub-region with known attenuation values or a piece-wise constant model of underlying images, the image reconstruction from local projection profiles that are truncated on both sides may become uniquely solvable. However, those assumptions often may not exactly hold in practical cases, and may result in, for example, shifting and cupping in reconstructed attenuation values. In an embodiment, a low-resolution (LR) global CT scan may be utilized to estimate a background attenuation in a sinogram of an HR local scan that includes a surrounding volume of the VOI, and a relatively accurate HR local reconstruction of the VOI may be obtained. 
     A transformation between an underlying/reconstructed image and its projection data is linear and invertible, and an underlying image may be partitioned into two parts: the region of interest and the background. Given the sinogram of the background P background  and the global sinogram P global , a relatively pure sinogram of a region of the interest (ROI) P ROI  may be obtained as follows. The sinogram of the ROI may be determined based, at least in part, on the global sinogram and the sinogram of the background as: 
         P   ROI   =P   global   −P   background   (2)
 
     Eq. 2 becomes nontrivial with laterally truncated projection data. Let trunc(·)denote the truncation operation. Eq. 2 then may be written as: 
       trunc( P   ROI )=trunc( P   global )trunc( P   background ),  (3)
 
     where trunc(P global ) corresponds to a local scan P local  By intentionally letting the local scan cover the ROI, the truncated parts of P ROI  are all zeros, yielding: 
         P   ROI   =P   local −trunc( P   background )  (4)
 
     Thus, the ROI within a local scan can be accurately reconstructed from the laterally truncated scan after background subtraction. It may be appreciated that such compensation is configured to improve the stability of interior tomography. 
     For a CMCT system, according to the present disclosure, a VOI may be relatively accurately reconstructed at relatively high resolution, given the HR local projection dataset P local   HR  and an appropriate background estimation. Given a prior LR CT scan of the object P global   LR , e.g., from medical CT scanner  104 , the LR background estimation P background   LR  can be obtained as described herein. The LR background estimation may then be used to approximate the HR background via interpolation, based, at least in part, on an assumption that residual high-frequency background estimation errors will mostly cancel out during the integration, making an accuracy of the LR estimation adequate. As used herein, “background compensation data” corresponds to background estimation. The background estimation may be low resolution (LR), high resolution (HR), and/or truncated, as described herein. 
     It may be appreciated that, in clinical applications, a standard (relatively low resolution) global CT scan may be first performed. Those regions with possible pathology or of physiologic importance may then be further examined with a local micro-CT scan, which provides a HR local/interior reconstruction of a VOL The prior information obtained through the global CT scan may then be utilized to help the interior image reconstruction (at relatively high resolution). 
     Turning again to  FIG.  1   , the μCT management module  140  may be configured to manage operation of the micro-CT apparatus  102 . The medical CT scanner  104  may be configured to scan the imaging object  103  and to generate global scan data  154 , that corresponds to the imaging object  103 . The global scan data  154  may then be provided to the computing device  108 , e.g., to the μCT management module  140  and/or the background module  144 . The optical surface scanner  106  may be configured to optically scan the imaging object  103 , and to provide 3D surface registration data  156 , e.g., to the μCT management module  140  and/or the registration module  146 . The global scan data  154  and 3D surface registration data  156  may be stored in the data store  118  by, for example, the μCT management module  140 . Thus, global scan data  154  and 3D surface registration data  156  may be acquired, and provided to the micro-CT apparatus  102 . 
     The micro-CT apparatus  102  may then be configured to capture and store local scan data. The μCT management module  140  may be configured to provide one or more commands to the robot controller module  122 , to control positions and orientations of the μCT source  124  and the μCT detector  126  relative to the ROI  105 . The positions and orientations may be determined based, at least in part, on the registration data  156  and predetermined scanning trajectories, as described herein. The position and orientation of the μCT source  124  and the position and orientation of the μCT detector  126  may be controlled by the source robotic arm  134  and the detector robotic arm  136 , respectively, based, at least in part, on control inputs from the robot controller module  122 . The μCT management module  140  may be configured to provide control input  158  to the μCT source  124 , and to capture a corresponding detector output  160  from the μCT detector  126 . The detector output  160  corresponds to local scan data that may be stored in the data store  118 , and may be utilized by the local scan module  142 , as described herein. In one nonlimiting example, the μCT source  124  may correspond to a micro-focus X-ray source and the μCT detector  126  may correspond to a PCD, as described herein. Thus, local scan data  160  may be acquired, and provided to the micro-CT apparatus  102 . 
       FIG.  4    a flowchart  400  of imaging object and region of interest scanning operations according to various embodiments of the present disclosure. In particular, the flowchart  400  illustrates global (i.e., medical CT) scanning of an imaging object and local (i.e., micro-CT) scanning of an ROI (e.g., VOI) within the imaging object. The operations may be performed, for example, by the micro-CT apparatus  102  and/or medical CT scanner  104  of  FIG.  1   . 
     Operations of this embodiment may begin with performing a global imaging object CT scan at operation  402 . A VOI may be determined at operation  404 . A scanning geometry for a local VOI micro-CT scan may be planned at operation  406 . A surface scan of the imaging object may be performed at operation  408 . A surface model may be generated at operation  410 . Image registration may be performed at operation  412 . A micro-CT scan of the VOI may be performed at operation  414 . Program flow may then continue at operation  416 . 
     Thus, in an embodiment, the scanning procedure may include the following operations:
         1) Perform a global head CT scan P global  with a scanning geometry G global ;   2) Determine a VOI and then plan a scanning geometry G local  for a local micro-CT scan;   3) Scan the patient, i.e., the imaging object, optically with a surface scanner to generate the surface model S local  for data/image registration between the global and local scans at the start of the local scan;   4) Perform the micro-CT scan P local  following G local  based, at least in part, on registration operations, as described herein. During the local scan, the optical scanner may be configured to continuously scan the imaging object, e.g. a patient head, for tracing unconscious head movement. In some embodiments, the imaging object may be scanned aided by optical markers.       

       FIG.  5    a flowchart  500  of volume of interest (VOI) reconstruction operations according to various embodiments of the present disclosure. In particular, the flowchart  500  illustrates data/image registration, background compensation, and image reconstruction. The operations may be performed, for example, by the micro-CT apparatus  102  and/or medical CT scanner  104  of  FIG.  1   . 
     Operations of this embodiment may begin with registration at operation  502 . Registration may include finding a relative geometry between a global (i.e., imaging object) geometry and a local (i.e., VOI) geometry. Operation  504  includes scan data compensation. Compensation is configured to correct attenuation offsets in local micro-CT scan data to form a relatively pure sinogram of the VOL Operation  506  includes reconstruction. Reconstruction corresponds to reconstructing the VOI from compensated VOI scan data, P VOI . Program flow may then continue at operation  508 . 
     Thus, VOI reconstruction may generally include data/image registration, background compensation, and image reconstruction. In one nonlimiting example, registration module  146  may be configured to perform registration operations, background module  144  may be configured to perform background compensation operations and reconstruction module  148  may be configured to perform image reconstruction. In some embodiments, reconstruction may include bias compensation. Bias compensation may be performed, for example, by refinement module  150 . It is contemplated that head movement effects, if any, may be compensated for in the local projections P local  using a motion correction technique, e.g., locally linear embedding motion correction, based, at least in part, on data from the optical scanner  106 .
         1) Registration: Find a relative geometry between G global  and G local  in reference to a facial surface model S local      a) Reconstruct a global volume V global  from P global ;   b) Render a surface model S global  from the global reconstruction V global ;   c) Register the two surface models, S global  with S local , to align the orientation and position of S global  with S local . The registration result may then be used to guide the micro-CT scan;   d) From P local , directly reconstruct a volume of interest V local  that contains fine structures but may be subject to distorted attenuation values;   e) Refine the registration parameters (obtained in Operation  1   c ) in reference to the registration between V global  from P local ;   2) Compensation: Correct the attenuation offsets in P local  to form a pure sinogram of the VOIP VOI      a) Set the attenuation values inside VOI to zero in the aligned global reconstruction to form the background volume V background   align ;   b) Digitally reproject the background volume V background   align  following the geometry G local  to form the LR background estimation P background   LR ;   c) Interpolate P background   LR  to the same resolution as the local HR projection P local , and obtain P background   HR ;   d) Truncate P background   HR  to the same size as P local , and obtain trunc (P background   HR ); corresponding to background compensation data;   e) Correct P local  with the estimated attenuation background as P local −trunc(P background   HR ) to form the pure sinogram of the VOIP VOI ;   3) Reconstruction: Reconstruct the VOI from P VOI  with geometry G local  using, for example, a cone-beam reconstruction technique. In some embodiments, the reconstruction technique may include or may be related to a deep learning framework.       

     Thus, VOI reconstruction may be implemented by micro-CT system  100  and may generally include data/image registration, background compensation, and image reconstruction. 
     It may be appreciated reconstruction that reconstruction accuracy may be affected by robustness to mis-registration. Mis-registration may be a result of, for example, imperfect hardware components and their suboptimal coordination; i.e., with respect to mismatches in position, orientation and/or scale. The direct effects of these mismatches on the reconstruction process may include, but are not limited to, an isocenter offset, a falsely tilted initial view angle, and an incorrect magnification factor detrimentally affecting the interpolation between the local scan and the re-projection through a globally reconstructed image volume for background estimation. Experimental results suggest that the VOI reconstruction technique, according to the present disclosure is relatively robust with respect to, for example, isocenter misalignment. 
     It may be appreciated that an effective ROI of reconstruction may be determined by the applied magnification factor, with the regions outside the effective ROI having opposite attenuation shifting as compared to that inside the effective ROI. Different from the distortions observed in the cases of positional and angular misalignments, which are mainly concentrated around a peripheral region, the magnification error causes a global attenuation shift inside the effective ROI. The attenuation shift may be proportional to the magnification mismatch and may be relatively more sensitive than other types of misalignments. This global shift can be mitigated by a bias correction technique, as will be described in more detail below. 
     A global shift, for example related to magnification errors, may be mitigated with bias correction configured to set an attenuation value of a known region to a target value; e.g., to set an attenuation value corresponding to air to a value at or near zero. Other known values may be used for a similar purpose, for example, an attenuation value obtained from the global reconstruction. Continuing with this example, a relatively flat region may be selected and its corresponding mean value may be determined as a benchmark. 
     A quantitative metric, may be used to quantify a reconstruction with a misalignment in reference to the ROI in the GT (ground truth), with and without additional bias correction. Quantitative metrics may include, but are not limited to, SSIM (Structural Similarity Index Measure), Peak Signal-to-Noise Ratio (PSNR), MSE (Mean Squared Error) and Root Mean-Squared Relative Error (RMSRE). 
     In an experiment, an isocenter position, initial angle, and magnification errors were evaluated, with a radius of the ROI set to 21 mm. Experimental results suggested that reconstructions are relatively robust with respect to the isocenter positional and initial angular errors, and implementing a bias correction technique may improve accuracy. The attenuation deviation from GT in the reconstruction with aligned background compensation was relatively small, with MSE 0.454×10 −5  and RMSRE of 1.07%. Increasing the position error corresponded to a decrease in the SSIM and PSNR metrics and an increase in MSE and RMSRE. The RMSRE value was below 2.0% when the position error reached 0.996 mm (millimeters), and the tolerance was extended to up to 1.992 mm after the bias correction, thus demonstrating the robustness of the technique, according to the present disclosure. Similarly, the RMSRE was below 2.0% for all angular errors within ±4.39°. 
     The bias correction improved the magnification error-affected image reconstruction. It should be noted that, since the effective ROI is scaled with the magnification factor, the intersection of the effective ROIs and the original ROI (radius 21 mm) was used for evaluation, with the radius set to 16.8 mm. The metrics on the reconstruction before the bias correction may change as the magnification error varies, demonstrating a relatively high sensitivity. With the bias correction method, RMSRE, in the case of—20% magnification error, is reduced to 1.67% from 10.43% within the effective ROI, demonstrating an acceptable robustness. 
     If the bias correction is performed on an aligned reconstruction, the metric scores decrease slightly in the inner region, within the radius of 16.8 mm, in contrast to a relatively tiny boost when evaluated on the whole ROI region (radius 21 mm). The decrease in metric scores suggests that the reconstruction is relatively accurate in the inner region when the compensation is well aligned. The attenuation estimation from the global reconstruction may not be perfect due to differences in resolution and existence of artifacts. It may be appreciated that linearity of the model to support the compensation method corresponds to an approximation to the polychromatic X-ray imaging process, which may bring residual errors into the peripheral region of the ROI. Thus, bias correction may not be performed if the system is relatively well calibrated and the accuracy of the registration for background compensation is acceptable. Otherwise, bias correction can be used for better performance. 
     In one embodiment, a CMCT system, according to the present disclosure, includes a micro-CT apparatus that incorporates a micro-focus source, a PCD, robotic arms and advanced imaging algorithms into a synergistic companion of a conventional CT scanner. A HR local scan protocol, according to the present disclosure, is configured to improve the dose efficiency. Additionally or alternatively, the HR local scan protocol may be configured to reduce the area of detectors. The cost of PCDs currently remains relatively high due to the complex manufacturing techniques. Hence, a hybrid system for interior tomography, according to the present disclosure, may reduce the system cost and radiation dose without compromising performance. In the local/interior scan, the advanced robotic arms allow the free selection of a VOL Additionally or alternatively, mobility of the robotic system may enable a surgeon to take projections from any view angle without moving the patient. This flexibility in view angle may be helpful in a number of applications, including, but not limited to, high-quality evaluation in emergencies and real-time feedback in surgeries. Additionally or alternatively to the exemplary application in inner ear imaging, the system may be used for other clinical imaging tasks that utilize high resolution in a VOI/ROI. Clinical imaging tasks that utilize high resolution in a VOI/ROI may include, but are not limited to, tumor examination in breast tissue, nodule characterization in lung, bone quality analysis, and plaque imaging in the heart and the neck. 
     While the hybrid design is one imaging system, additionally or alternatively, the robotic micro-CT scanner can be separately used, i.e., may be used without the medical CT scanner. For example, reconstruction results from a traditional CT scanner may be used as the prior knowledge according to the operations, as described herein. It may be appreciated that this may impose extra work in registration due to different positions of the patient in the local scan and an earlier global scan. Advantageously, a reconstruction method, according to the present disclosure, has a relatively good tolerance to geometric misalignment. Although the reconstruction is relatively sensitive to the magnification mismatch, the resultant attenuation shifting can be addressed with the bias correction method, according to the present disclosure. It may be appreciated that an intensity of an X-ray source with micro-focus may have an insufficient flux to produce an appropriate signal to noise ratio through a human head during a relatively short scan time. To obtain an appropriate contrast, an X-ray source with a slightly larger focal spot may be used to provide enough power. The increased focal spot may generate shadows in the projections and blur structural details. A balance between the X-ray intensity and image resolution may be achieved using a deep learning deblurring method. Although the cone-beam projection with the finite focus spot is no longer a spatially invariant linear system, advanced deep learning techniques have the capability to perform shift-variant deblurring tasks. It may be appreciated that big data of paired blurred-original projections may be difficult to obtain for training a deblurring network. Additionally or alternatively, a forward projection model may be constructed to synthesize the paired data. Then, the network trained with simulated data may be fine-tuned with a small amount of paired real projection data. The trained network may then be applied on blurred projections for inference. 
     Deep image denoising techniques may be used to reduce radiation dose and improve image quality. According to a level of supervision during training, three types of deep denoising methods have been developed, i.e., supervised learning, weakly-supervised learning, and unsupervised learning. Supervised learning methods were designed for image denoising and achieved the best performance, such as deep CNNs (convolutional neural networks) with residual learning or with recurrent persistent memory units. Weakly-supervised learning methods relax the requirement of paired noisy-clean data to unpaired noisy-clean data or paired noise-to-noise data. Using the unpaired noisy-clean data, a GAN (Generative Adversarial Network)-based learning technique may be configured to create pairs of corresponding noisy-clean images as the training data. It is contemplated that paired noise-to-noise images may be equivalent to the paired noisy-clean images in training a model, achieving a denoising performance competitive with supervised learning methods. For the applications where unpaired noisy-clean or paired noise-noise images are unavailable, unsupervised learning methods may be configured to use single noisy images for training. Deep image prior is a generation process that maps the random noise to a single noisy image, and when the training process is terminated at a selected moment the network is configured to produce a denoised image. A Noise2Void network and its variants may achieve promising results using individual noisy images in training a network. It may be appreciated that a Noise2Void network is configured to estimate a blind-spot in an image so that the network learns to map the surrounding pixels to the blind-spot, achieving relatively good denoising results. 
     In an inner ear imaging application, paired noise-clean images may be synthesized via a Monte-Carlo simulation, for example, and single actual noisy images may be acquired with the CMCT system, according to the present disclosure. The former data type (i.e., synthesized paired noise-clean images) may support supervised training although the noise may not perfectly match the real counterpart, while the latter type of data (i.e., single actual noisy images) contains realistic noise and texture. Combining these two types of datasets, a semi-supervised leaning method may be configured to learn from the data with and without ground truth labels simultaneously. For example, the model can be trained in the Noise2Void mode first and then fine-tuned with the paired noisy-clean data or vice versa. Additionally or alternatively, the model may be trained in the Noise2Void and supervised modes simultaneously. 
     It may be appreciated that performing a local/interior scan in a multi-channel photon-counting mode, yields energy information that may then be used for spectral analysis. Spectral analysis may then allow or facilitate K-edge imaging, material decomposition, beam hardening correction, and/or metal artifact reduction. Compared with dual-energy CT, the PCD provides relatively more energy channels and may be relatively more informative. It is contemplated that spectral distortion issues at high imaging speed, if present, may be overcome with a deep learning based correction method. It is contemplated that the direct spectral measurement with the PCD may allow better spectral separation than dual-source, fast kVp-switching, and dual-layer detector techniques. Most relevant to the example inner ear imaging application is to utilize the X-ray energy dependent attenuation information for beam hardening correction, metal artifact reduction and material decomposition so that the effects of the implanted electrodes and micro-environments can be relatively accurately modeled. 
     Thus, this disclosure relates to a robotic arm-based clinical micro-CT apparatus, method and/or system. The robotic arm-based clinical micro-CT apparatus, method and/or system, according to the present disclosure, may include a clinical micro-CT scanner. In an embodiment, the robotic arm-based clinical micro-CT apparatus may include the clinical micro-CT scanner and a computing device. In an embodiment, the clinical micro-CT scanner may include a micro-focus tube, and a PCD, each mounted on a robotic arm. The clinical micro-CT apparatus may be configured to perform interior tomography. In some embodiments, the clinical micro-CT apparatus may be configured to implement deep learning. 
     In some embodiments, a clinical micro-CT (CMCT) system may include the clinical micro-CT apparatus combined with or coupled to a clinical CT scanner. A clinical micro-CT system may thus include the clinical micro-CT scanner, and the computing device and may include the clinical CT scanner. In some embodiments, the clinical micro-CT scanner and/or apparatus may be used separately, i.e., may not be coupled to the clinical CT scanner. 
     As used in any embodiment herein, the term “module” may refer to an app, software, firmware and/or circuitry configured to perform any of the aforementioned operations. Software may be embodied as a software package, code, instructions, instruction sets and/or data recorded on non-transitory computer readable storage medium. Firmware may be embodied as code, instructions or instruction sets and/or data that are hard-coded (e.g., nonvolatile) in memory devices. 
     “Circuitry”, as used in any embodiment herein, may include, for example, singly or in any combination, hardwired circuitry, programmable circuitry such as computer processors comprising one or more individual instruction processing cores, state machine circuitry, and/or firmware that stores instructions executed by programmable circuitry. Each module may, collectively or individually, be embodied as circuitry that forms part of a larger system, for example, an integrated circuit (IC), an application-specific integrated circuit (ASIC), a system on-chip (SoC), desktop computers, laptop computers, tablet computers, servers, smart phones, etc. 
     Memory circuitry  112  may include one or more of the following types of memory: semiconductor firmware memory, programmable memory, non-volatile memory, read only memory, electrically programmable memory, random access memory, flash memory, magnetic disk memory, and/or optical disk memory. Either additionally or alternatively memory circuitry may include other and/or later-developed types of computer-readable memory. 
     Embodiments of the operations described herein may be implemented in a computer-readable storage device having stored thereon instructions that when executed by one or more processors perform the methods. The processor may include, for example, a processing unit and/or programmable circuitry. The storage device may include a machine readable storage device including any type of tangible, non-transitory storage device, for example, any type of disk including floppy disks, optical disks, compact disk read-only memories (CD-ROMs), compact disk rewritables (CD-RWs), and magneto-optical disks, semiconductor devices such as read-only memories (ROMs), random access memories (RAMs) such as dynamic and static RAMs, erasable programmable read-only memories (EPROMs), electrically erasable programmable read-only memories (EEPROMs), flash memories, magnetic or optical cards, or any type of storage devices suitable for storing electronic instructions. 
     The terms and expressions which have been employed herein are used as terms of description and not of limitation, and there is no intention, in the use of such terms and expressions, of excluding any equivalents of the features shown and described (or portions thereof), and it is recognized that various modifications are possible within the scope of the claims. Accordingly, the claims are intended to cover all such equivalents. 
     Various features, aspects, and embodiments have been described herein. The features, aspects, and embodiments are susceptible to combination with one another as well as to variation and modification, as will be understood by those having skill in the art. The present disclosure should, therefore, be considered to encompass such combinations, variations, and modifications.