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Ma YQ, Reynolds T, Ehtiati T, Weiss C, Hong K, Theodore N, Gang GJ, Stayman JW. Fully automatic online geometric calibration for non-circular cone-beam CT orbits using fiducials with unknown placement. Med Phys 2024; 51:3245-3264. [PMID: 38573172 DOI: 10.1002/mp.17041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 02/28/2024] [Accepted: 03/01/2024] [Indexed: 04/05/2024] Open
Abstract
BACKGROUND Cone-beam CT (CBCT) with non-circular scanning orbits can improve image quality for 3D intraoperative image guidance. However, geometric calibration of such scans can be challenging. Existing methods typically require a prior image, specialized phantoms, presumed repeatable orbits, or long computation time. PURPOSE We propose a novel fully automatic online geometric calibration algorithm that does not require prior knowledge of fiducial configuration. The algorithm is fast, accurate, and can accommodate arbitrary scanning orbits and fiducial configurations. METHODS The algorithm uses an automatic initialization process to eliminate human intervention in fiducial localization and an iterative refinement process to ensure robustness and accuracy. We provide a detailed explanation and implementation of the proposed algorithm. Physical experiments on a lab test bench and a clinical robotic C-arm scanner were conducted to evaluate spatial resolution performance and robustness under realistic constraints. RESULTS Qualitative and quantitative results from the physical experiments demonstrate high accuracy, efficiency, and robustness of the proposed method. The spatial resolution performance matched that of our existing benchmark method, which used a 3D-2D registration-based geometric calibration algorithm. CONCLUSIONS We have demonstrated an automatic online geometric calibration method that delivers high spatial resolution and robustness performance. This methodology enables arbitrary scan trajectories and should facilitate translation of such acquisition methods in a clinical setting.
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Affiliation(s)
- Yiqun Q Ma
- Johns Hopkins University, Baltimore, Maryland, USA
| | - Tess Reynolds
- Faculty of Medicine and Health, University of Sydney, Sydney, Australia
| | | | | | - Kelvin Hong
- Johns Hopkins University, Baltimore, Maryland, USA
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2
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Ibad HA, de Cesar Netto C, Shakoor D, Sisniega A, Liu S, Siewerdsen JH, Carrino JA, Zbijewski W, Demehri S. Computed Tomography: State-of-the-Art Advancements in Musculoskeletal Imaging. Invest Radiol 2023; 58:99-110. [PMID: 35976763 PMCID: PMC9742155 DOI: 10.1097/rli.0000000000000908] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
ABSTRACT Although musculoskeletal magnetic resonance imaging (MRI) plays a dominant role in characterizing abnormalities, novel computed tomography (CT) techniques have found an emerging niche in several scenarios such as trauma, gout, and the characterization of pathologic biomechanical states during motion and weight-bearing. Recent developments and advancements in the field of musculoskeletal CT include 4-dimensional, cone-beam (CB), and dual-energy (DE) CT. Four-dimensional CT has the potential to quantify biomechanical derangements of peripheral joints in different joint positions to diagnose and characterize patellofemoral instability, scapholunate ligamentous injuries, and syndesmotic injuries. Cone-beam CT provides an opportunity to image peripheral joints during weight-bearing, augmenting the diagnosis and characterization of disease processes. Emerging CBCT technologies improved spatial resolution for osseous microstructures in the quantitative analysis of osteoarthritis-related subchondral bone changes, trauma, and fracture healing. Dual-energy CT-based material decomposition visualizes and quantifies monosodium urate crystals in gout, bone marrow edema in traumatic and nontraumatic fractures, and neoplastic disease. Recently, DE techniques have been applied to CBCT, contributing to increased image quality in contrast-enhanced arthrography, bone densitometry, and bone marrow imaging. This review describes 4-dimensional CT, CBCT, and DECT advances, current logistical limitations, and prospects for each technique.
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Affiliation(s)
- Hamza Ahmed Ibad
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Cesar de Cesar Netto
- Department of Orthopaedics and Rehabilitation, Carver College of Medicine, University of Iowa, Iowa City, IA, USA
| | - Delaram Shakoor
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Alejandro Sisniega
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Stephen Liu
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jeffrey H Siewerdsen
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - John A. Carrino
- Department of Radiology and Imaging, Hospital for Special Surgery, New York, NY, USA
| | - Wojciech Zbijewski
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Shadpour Demehri
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Hatamikia S, Biguri A, Herl G, Kronreif G, Reynolds T, Kettenbach J, Russ T, Tersol A, Maier A, Figl M, Siewerdsen JH, Birkfellner W. Source-detector trajectory optimization in cone-beam computed tomography: a comprehensive review on today’s state-of-the-art. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac8590] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Accepted: 07/29/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Cone-beam computed tomography (CBCT) imaging is becoming increasingly important for a wide range of applications such as image-guided surgery, image-guided radiation therapy as well as diagnostic imaging such as breast and orthopaedic imaging. The potential benefits of non-circular source-detector trajectories was recognized in early work to improve the completeness of CBCT sampling and extend the field of view (FOV). Another important feature of interventional imaging is that prior knowledge of patient anatomy such as a preoperative CBCT or prior CT is commonly available. This provides the opportunity to integrate such prior information into the image acquisition process by customized CBCT source-detector trajectories. Such customized trajectories can be designed in order to optimize task-specific imaging performance, providing intervention or patient-specific imaging settings. The recently developed robotic CBCT C-arms as well as novel multi-source CBCT imaging systems with additional degrees of freedom provide the possibility to largely expand the scanning geometries beyond the conventional circular source-detector trajectory. This recent development has inspired the research community to innovate enhanced image quality by modifying image geometry, as opposed to hardware or algorithms. The recently proposed techniques in this field facilitate image quality improvement, FOV extension, radiation dose reduction, metal artifact reduction as well as 3D imaging under kinematic constraints. Because of the great practical value and the increasing importance of CBCT imaging in image-guided therapy for clinical and preclinical applications as well as in industry, this paper focuses on the review and discussion of the available literature in the CBCT trajectory optimization field. To the best of our knowledge, this paper is the first study that provides an exhaustive literature review regarding customized CBCT algorithms and tries to update the community with the clarification of in-depth information on the current progress and future trends.
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4
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Huang H, Siewerdsen JH, Zbijewski W, Weiss CR, Unberath M, Ehtiati T, Sisniega A. Reference-free learning-based similarity metric for motion compensation in cone-beam CT. Phys Med Biol 2022; 67:10.1088/1361-6560/ac749a. [PMID: 35636391 PMCID: PMC9254028 DOI: 10.1088/1361-6560/ac749a] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 05/30/2022] [Indexed: 11/12/2022]
Abstract
Purpose. Patient motion artifacts present a prevalent challenge to image quality in interventional cone-beam CT (CBCT). We propose a novel reference-free similarity metric (DL-VIF) that leverages the capability of deep convolutional neural networks (CNN) to learn features associated with motion artifacts within realistic anatomical features. DL-VIF aims to address shortcomings of conventional metrics of motion-induced image quality degradation that favor characteristics associated with motion-free images, such as sharpness or piecewise constancy, but lack any awareness of the underlying anatomy, potentially promoting images depicting unrealistic image content. DL-VIF was integrated in an autofocus motion compensation framework to test its performance for motion estimation in interventional CBCT.Methods. DL-VIF is a reference-free surrogate for the previously reported visual image fidelity (VIF) metric, computed against a motion-free reference, generated using a CNN trained using simulated motion-corrupted and motion-free CBCT data. Relatively shallow (2-ResBlock) and deep (3-Resblock) CNN architectures were trained and tested to assess sensitivity to motion artifacts and generalizability to unseen anatomy and motion patterns. DL-VIF was integrated into an autofocus framework for rigid motion compensation in head/brain CBCT and assessed in simulation and cadaver studies in comparison to a conventional gradient entropy metric.Results. The 2-ResBlock architecture better reflected motion severity and extrapolated to unseen data, whereas 3-ResBlock was found more susceptible to overfitting, limiting its generalizability to unseen scenarios. DL-VIF outperformed gradient entropy in simulation studies yielding average multi-resolution structural similarity index (SSIM) improvement over uncompensated image of 0.068 and 0.034, respectively, referenced to motion-free images. DL-VIF was also more robust in motion compensation, evidenced by reduced variance in SSIM for various motion patterns (σDL-VIF = 0.008 versusσgradient entropy = 0.019). Similarly, in cadaver studies, DL-VIF demonstrated superior motion compensation compared to gradient entropy (an average SSIM improvement of 0.043 (5%) versus little improvement and even degradation in SSIM, respectively) and visually improved image quality even in severely motion-corrupted images.Conclusion: The studies demonstrated the feasibility of building reference-free similarity metrics for quantification of motion-induced image quality degradation and distortion of anatomical structures in CBCT. DL-VIF provides a reliable surrogate for motion severity, penalizes unrealistic distortions, and presents a valuable new objective function for autofocus motion compensation in CBCT.
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Affiliation(s)
- H Huang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - J H Siewerdsen
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America
- Russell H. Morgan Department of Radiology, Johns Hopkins University, Baltimore, MD, United States of America
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, United States of America
| | - W Zbijewski
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - C R Weiss
- Russell H. Morgan Department of Radiology, Johns Hopkins University, Baltimore, MD, United States of America
| | - M Unberath
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, United States of America
| | - T Ehtiati
- Siemens Medical Solutions USA, Inc., Imaging & Therapy Systems, Hoffman Estates, IL, United States of America
| | - A Sisniega
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America
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Ma YQ, Gang GJ, Reynolds T, Ehtiati T, Li J, Dillon O, Russ T, Wang W, Weiss C, Theodore N, Hong K, O'Brien R, Siewerdsen J, Stayman JW. Practical workflow for arbitrary non-circular orbits for CT with clinical robotic C-arms. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2022; 12304:123042H. [PMID: 38187806 PMCID: PMC10769444 DOI: 10.1117/12.2647158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Non-circular orbits in cone-beam CT (CBCT) imaging are increasingly being studied for potential benefits in field-of-view, dose reduction, improved image quality, minimal interference in guided procedures, metal artifact reduction, and more. While modern imaging systems such as robotic C-arms are enabling more freedom in potential orbit designs, practical implementation on such clinical systems remains challenging due to obstacles in critical stages of the workflow, including orbit realization, geometric calibration, and reconstruction. In this work, we build upon previous successes in clinical implementation and address key challenges in the geometric calibration stage with a novel calibration method. The resulting workflow eliminates the need for prior patient scans or dedicated calibration phantoms, and can be conducted in clinically relevant processing times.
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Affiliation(s)
- Yiqun Q Ma
- Johns Hopkins University, Baltimore, USA
| | | | | | | | - Junyuan Li
- Johns Hopkins University, Baltimore, USA
| | | | - Tom Russ
- Heidelberg University, Mannheim, Germany
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6
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Maier J, Nitschke M, Choi JH, Gold G, Fahrig R, Eskofier BM, Maier A. Rigid and Non-Rigid Motion Compensation in Weight-Bearing CBCT of the Knee Using Simulated Inertial Measurements. IEEE Trans Biomed Eng 2022; 69:1608-1619. [PMID: 34714730 PMCID: PMC9134858 DOI: 10.1109/tbme.2021.3123673] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Involuntary subject motion is the main source of artifacts in weight-bearing cone-beam CT of the knee. To achieve image quality for clinical diagnosis, the motion needs to be compensated. We propose to use inertial measurement units (IMUs) attached to the leg for motion estimation. METHODS We perform a simulation study using real motion recorded with an optical tracking system. Three IMU-based correction approaches are evaluated, namely rigid motion correction, non-rigid 2D projection deformation and non-rigid 3D dynamic reconstruction. We present an initialization process based on the system geometry. With an IMU noise simulation, we investigate the applicability of the proposed methods in real applications. RESULTS All proposed IMU-based approaches correct motion at least as good as a state-of-the-art marker-based approach. The structural similarity index and the root mean squared error between motion-free and motion corrected volumes are improved by 24-35% and 78-85%, respectively, compared with the uncorrected case. The noise analysis shows that the noise levels of commercially available IMUs need to be improved by a factor of 105 which is currently only achieved by specialized hardware not robust enough for the application. CONCLUSION Our simulation study confirms the feasibility of this novel approach and defines improvements necessary for a real application. SIGNIFICANCE The presented work lays the foundation for IMU-based motion compensation in cone-beam CT of the knee and creates valuable insights for future developments.
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7
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Hall ME, Black MS, Gold GE, Levenston ME. Validation of watershed-based segmentation of the cartilage surface from sequential CT arthrography scans. Quant Imaging Med Surg 2022; 12:1-14. [PMID: 34993056 PMCID: PMC8666781 DOI: 10.21037/qims-20-1062] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 07/12/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND This study investigated the utility of a 2-dimensional watershed algorithm for identifying the cartilage surface in computed tomography (CT) arthrograms of the knee up to 33 minutes after an intra-articular iohexol injection as boundary blurring increased. METHODS A 2D watershed algorithm was applied to CT arthrograms of 3 bovine stifle joints taken 3, 8, 18, and 33 minutes after iohexol injection and used to segment tibial cartilage. Thickness measurements were compared to a reference standard thickness measurement and the 3-minute time point scan. RESULTS 77.2% of cartilage thickness measurements were within 0.2 mm (1 voxel) of the thickness calculated in the reference scan at the 3-minute time point. 42% fewer voxels could be segmented from the 33-minute scan than the 3-minute scan due to diffusion of the contrast agent out of the joint space and into the cartilage, leading to blurring of the cartilage boundary. The traced watershed lines were closer to the location of the cartilage surface in areas where tissues were in direct contact with each other (cartilage-cartilage or cartilage-meniscus contact). CONCLUSIONS The use of watershed dam lines to guide cartilage segmentation shows promise for identifying cartilage boundaries from CT arthrograms in areas where soft tissues are in direct contact with each other.
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Affiliation(s)
- Mary E. Hall
- Department of Mechanical Engineering, Stanford University, Stanford, CA, USA
| | - Marianne S. Black
- Department of Mechanical Engineering, Stanford University, Stanford, CA, USA
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Garry E. Gold
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Marc E. Levenston
- Department of Mechanical Engineering, Stanford University, Stanford, CA, USA
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
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8
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Kyme AZ, Fulton RR. Motion estimation and correction in SPECT, PET and CT. Phys Med Biol 2021; 66. [PMID: 34102630 DOI: 10.1088/1361-6560/ac093b] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 06/08/2021] [Indexed: 11/11/2022]
Abstract
Patient motion impacts single photon emission computed tomography (SPECT), positron emission tomography (PET) and X-ray computed tomography (CT) by giving rise to projection data inconsistencies that can manifest as reconstruction artifacts, thereby degrading image quality and compromising accurate image interpretation and quantification. Methods to estimate and correct for patient motion in SPECT, PET and CT have attracted considerable research effort over several decades. The aims of this effort have been two-fold: to estimate relevant motion fields characterizing the various forms of voluntary and involuntary motion; and to apply these motion fields within a modified reconstruction framework to obtain motion-corrected images. The aims of this review are to outline the motion problem in medical imaging and to critically review published methods for estimating and correcting for the relevant motion fields in clinical and preclinical SPECT, PET and CT. Despite many similarities in how motion is handled between these modalities, utility and applications vary based on differences in temporal and spatial resolution. Technical feasibility has been demonstrated in each modality for both rigid and non-rigid motion, but clinical feasibility remains an important target. There is considerable scope for further developments in motion estimation and correction, and particularly in data-driven methods that will aid clinical utility. State-of-the-art machine learning methods may have a unique role to play in this context.
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Affiliation(s)
- Andre Z Kyme
- School of Biomedical Engineering, The University of Sydney, Sydney, New South Wales, AUSTRALIA
| | - Roger R Fulton
- Sydney School of Health Sciences, The University of Sydney, Sydney, New South Wales, AUSTRALIA
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Maier J, Maier A, Eskofier B, Fahrig R, Choi JH. 3D Non-Rigid Alignment of Low-Dose Scans Allows to Correct for Saturation in Lower Extremity Cone-Beam CT. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:71821-71831. [PMID: 34141516 PMCID: PMC8208599 DOI: 10.1109/access.2021.3079368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Detector saturation in cone-beam computed tomography occurs when an object of highly varying shape and material composition is imaged using an automatic exposure control (AEC) system. When imaging a subject's knees, high beam energy ensures the visibility of internal structures but leads to overexposure in less dense border regions. In this work, we propose to use an additional low-dose scan to correct the saturation artifacts of AEC scans. Overexposed pixels are identified in the projection images of the AEC scan using histogram-based thresholding. The saturation-free pixels from the AEC scan are combined with the skin border pixels of the low-dose scan prior to volumetric reconstruction. To compensate for patient motion between the two scans, a 3D non-rigid alignment of the projection images in a backward-forward-projection process based on fiducial marker positions is proposed. On numerical simulations, the projection combination improved the structural similarity index measure from 0.883 to 0.999. Further evaluations were performed on two in vivo subject knee acquisitions, one without and one with motion between the AEC and low-dose scans. Saturation-free reference images were acquired using a beam attenuator. The proposed method could qualitatively restore the information of peripheral tissue structures. Applying the 3D non-rigid alignment made it possible to use the projection images with inter-scan subject motion for projection image combination. The increase in radiation exposure due to the additional low-dose scan was found to be negligibly low. The presented methods allow simple but effective correction of saturation artifacts.
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Affiliation(s)
- Jennifer Maier
- Pattern Recognition Laboratory, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany
- Machine Learning and Data Analytics Laboratory, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91052 Erlangen, Germany
| | - Andreas Maier
- Pattern Recognition Laboratory, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany
| | - Bjoern Eskofier
- Machine Learning and Data Analytics Laboratory, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91052 Erlangen, Germany
| | | | - Jang-Hwan Choi
- Division of Mechanical and Biomedical Engineering, Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul 03760, South Korea
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Grupp RB, Murphy RJ, Hegeman RA, Alexander CP, Unberath M, Otake Y, McArthur BA, Armand M, Taylor RH. Fast and automatic periacetabular osteotomy fragment pose estimation using intraoperatively implanted fiducials and single-view fluoroscopy. Phys Med Biol 2020; 65:245019. [PMID: 32590372 DOI: 10.1088/1361-6560/aba089] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Accurate and consistent mental interpretation of fluoroscopy to determine the position and orientation of acetabular bone fragments in 3D space is difficult. We propose a computer assisted approach that uses a single fluoroscopic view and quickly reports the pose of an acetabular fragment without any user input or initialization. Intraoperatively, but prior to any osteotomies, two constellations of metallic ball-bearings (BBs) are injected into the wing of a patient's ilium and lateral superior pubic ramus. One constellation is located on the expected acetabular fragment, and the other is located on the remaining, larger, pelvis fragment. The 3D locations of each BB are reconstructed using three fluoroscopic views and 2D/3D registrations to a preoperative CT scan of the pelvis. The relative pose of the fragment is established by estimating the movement of the two BB constellations using a single fluoroscopic view taken after osteotomy and fragment relocation. BB detection and inter-view correspondences are automatically computed throughout the processing pipeline. The proposed method was evaluated on a multitude of fluoroscopic images collected from six cadaveric surgeries performed bilaterally on three specimens. Mean fragment rotation error was 2.4 ± 1.0 degrees, mean translation error was 2.1 ± 0.6 mm, and mean 3D lateral center edge angle error was 1.0 ± 0.5 degrees. The average runtime of the single-view pose estimation was 0.7 ± 0.2 s. The proposed method demonstrates accuracy similar to other state of the art systems which require optical tracking systems or multiple-view 2D/3D registrations with manual input. The errors reported on fragment poses and lateral center edge angles are within the margins required for accurate intraoperative evaluation of femoral head coverage.
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Affiliation(s)
- R B Grupp
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, United States of America
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11
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Ko Y, Moon S, Baek J, Shim H. Rigid and non-rigid motion artifact reduction in X-ray CT using attention module. Med Image Anal 2020; 67:101883. [PMID: 33166775 DOI: 10.1016/j.media.2020.101883] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 10/13/2020] [Accepted: 10/14/2020] [Indexed: 12/16/2022]
Abstract
Motion artifacts are a major factor that can degrade the diagnostic performance of computed tomography (CT) images. In particular, the motion artifacts become considerably more severe when an imaging system requires a long scan time such as in dental CT or cone-beam CT (CBCT) applications, where patients generate rigid and non-rigid motions. To address this problem, we proposed a new real-time technique for motion artifacts reduction that utilizes a deep residual network with an attention module. Our attention module was designed to increase the model capacity by amplifying or attenuating the residual features according to their importance. We trained and evaluated the network by creating four benchmark datasets with rigid motions or with both rigid and non-rigid motions under a step-and-shoot fan-beam CT (FBCT) or a CBCT. Each dataset provided a set of motion-corrupted CT images and their ground-truth CT image pairs. The strong modeling power of the proposed network model allowed us to successfully handle motion artifacts from the two CT systems under various motion scenarios in real-time. As a result, the proposed model demonstrated clear performance benefits. In addition, we compared our model with Wasserstein generative adversarial network (WGAN)-based models and a deep residual network (DRN)-based model, which are one of the most powerful techniques for CT denoising and natural RGB image deblurring, respectively. Based on the extensive analysis and comparisons using four benchmark datasets, we confirmed that our model outperformed the aforementioned competitors. Our benchmark datasets and implementation code are available at https://github.com/youngjun-ko/ct_mar_attention.
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Affiliation(s)
- Youngjun Ko
- School of the Integrated Technology, Yonsei University, Songdogwahak-ro 85, Yeonsu-gu, Incheon, South Korea
| | - Seunghyuk Moon
- School of the Integrated Technology, Yonsei University, Songdogwahak-ro 85, Yeonsu-gu, Incheon, South Korea
| | - Jongduk Baek
- School of the Integrated Technology, Yonsei University, Songdogwahak-ro 85, Yeonsu-gu, Incheon, South Korea.
| | - Hyunjung Shim
- School of the Integrated Technology, Yonsei University, Songdogwahak-ro 85, Yeonsu-gu, Incheon, South Korea.
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12
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Deep Learning-based Inaccuracy Compensation in Reconstruction of High Resolution XCT Data. Sci Rep 2020; 10:7682. [PMID: 32376852 PMCID: PMC7203197 DOI: 10.1038/s41598-020-64733-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Accepted: 04/17/2020] [Indexed: 11/08/2022] Open
Abstract
While X-ray computed tomography (XCT) is pushed further into the micro- and nanoscale, the limitations of various tool components and object motion become more apparent. For high-resolution XCT, it is necessary but practically difficult to align these tool components with sub-micron precision. The aim is to develop a novel reconstruction methodology that considers unavoidable misalignment and object motion during the data acquisition in order to obtain high-quality three-dimensional images and that is applicable for data recovery from incomplete datasets. A reconstruction software empowered by sophisticated correction modules that autonomously estimates and compensates artefacts using gradient descent and deep learning algorithms has been developed and applied. For motion estimation, a novel computer vision methodology coupled with a deep convolutional neural network approach provides estimates for the object motion by tracking features throughout the adjacent projections. The model is trained using the forward projections of simulated phantoms that consist of several simple geometrical features such as sphere, triangle and rectangular. The feature maps extracted by a neural network are used to detect and to classify features done by a support vector machine. For missing data recovery, a novel deep convolutional neural network is used to infer high-quality reconstruction data from incomplete sets of projections. The forward and back projections of simulated geometric shapes from a range of angular ranges are used to train the model. The model is able to learn the angular dependency based on a limited angle coverage and to propose a new set of projections to suppress artefacts. High-quality three-dimensional images demonstrate that it is possible to effectively suppress artefacts caused by thermomechanical instability of tool components and objects resulting in motion, by center of rotation misalignment and by inaccuracy in the detector position without additional computational efforts. Data recovery from incomplete sets of projections result in directly corrected projections instead of suppressing artefacts in the final reconstructed images. The proposed methodology has been proven and is demonstrated for a ball bearing sample. The reconstruction results are compared to prior corrections and benchmarked with a commercially available reconstruction software. Compared to conventional approaches in XCT imaging and data analysis, the proposed methodology for the generation of high-quality three-dimensional X-ray images is fully autonomous. The methodology presented here has been proven for high-resolution micro-XCT and nano-XCT, however, is applicable for all length scales.
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13
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Sisniega A, Thawait GK, Shakoor D, Siewerdsen JH, Demehri S, Zbijewski W. Motion compensation in extremity cone-beam computed tomography. Skeletal Radiol 2019; 48:1999-2007. [PMID: 31172206 PMCID: PMC6814492 DOI: 10.1007/s00256-019-03241-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Revised: 04/29/2019] [Accepted: 05/12/2019] [Indexed: 02/02/2023]
Abstract
OBJECTIVES To evaluate the improvement in extremity cone-beam computed tomography (CBCT) image quality in datasets with motion artifact using a motion compensation method based on maximizing image sharpness. METHODS Following IRB approval, retrospective analysis of 308 CBCT scans of lower extremities was performed by a fellowship-trained musculoskeletal radiologist to identify images with moderate to severe motion artifact. Twenty-four scans of 22 patients (18 male, four female; mean, 32 years old, range, 21-74 years old) were chosen for inclusion. Sharp (bone) and smooth (soft tissue) reconstructions were processed using the motion compensation algorithm. Two experts rated visualization of trabecular bone, cortical bone, joint spaces, and tendon on a nine-level Likert scale with and without motion compensation (a total of 96 datasets). Visual grading characteristics (VGC) was used to quantitatively determine the difference in image quality following motion compensation. Intra-class correlation coefficient (ICC) was obtained to assess inter-observer agreement. RESULTS Motion-compensated images exhibited appreciable reduction in artifacts. The observer study demonstrated the associated improvement in diagnostic quality. The fraction of cases receiving scores better than "Fair" increased from less than 10% without compensation to 40-70% following compensation, depending on the task. The area under the VGC curve was 0.75 (tendon) to 0.85 (cortical bone), confirming preference for motion compensated images. ICC values showed excellent agreement between readers before (ICC range, 0.8-0.91) and after motion compensation (ICC range, 0.92-0.97). CONCLUSIONS The motion compensation algorithm significantly improved the visualization of bone and soft tissue structures in extremity CBCT for cases exhibiting patient motion.
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Affiliation(s)
- Alejandro Sisniega
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Gaurav K Thawait
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21205, USA
- Russel H Morgan Department of Radiology, Johns Hopkins University, Baltimore, MD, 21278, USA
| | - Delaram Shakoor
- Russel H Morgan Department of Radiology, Johns Hopkins University, Baltimore, MD, 21278, USA
| | - Jeffrey H Siewerdsen
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21205, USA
- Russel H Morgan Department of Radiology, Johns Hopkins University, Baltimore, MD, 21278, USA
| | - Shadpour Demehri
- Russel H Morgan Department of Radiology, Johns Hopkins University, Baltimore, MD, 21278, USA
| | - Wojciech Zbijewski
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21205, USA.
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14
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Next-generation imaging of the skeletal system and its blood supply. Nat Rev Rheumatol 2019; 15:533-549. [PMID: 31395974 DOI: 10.1038/s41584-019-0274-y] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/08/2019] [Indexed: 12/16/2022]
Abstract
Bone is organized in a hierarchical 3D architecture. Traditionally, analysis of the skeletal system was based on bone mass assessment by radiographic methods or on the examination of bone structure by 2D histological sections. Advanced imaging technologies and big data analysis now enable the unprecedented examination of bone and provide new insights into its 3D macrostructure and microstructure. These technologies comprise ex vivo and in vivo methods including high-resolution computed tomography (CT), synchrotron-based imaging, X-ray microscopy, ultra-high-field magnetic resonance imaging (MRI), light-sheet fluorescence microscopy, confocal and intravital two-photon imaging. In concert, these techniques have been used to detect and quantify a novel vascular system of trans-cortical vessels in bone. Furthermore, structures such as the lacunar network, which harbours and connects osteocytes, become accessible for 3D imaging and quantification using these methods. Next-generation imaging of the skeletal system and its blood supply are anticipated to contribute to an entirely new understanding of bone tissue composition and function, from macroscale to nanoscale, in health and disease. These insights could provide the basis for early detection and precision-type intervention of bone disorders in the future.
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15
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Eldib ME, Hegazy MA, Cho MH, Cho MH, Lee SY. A motion artifact reduction method for dental CT based on subpixel-resolution image registration of projection data. Comput Biol Med 2018; 103:232-243. [DOI: 10.1016/j.compbiomed.2018.10.028] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Revised: 10/24/2018] [Accepted: 10/24/2018] [Indexed: 10/28/2022]
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16
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Hernandez D, Eldib ME, Hegazy MAA, Cho MH, Cho MH, Lee SY. A head motion estimation algorithm for motion artifact correction in dental CT imaging. ACTA ACUST UNITED AC 2018; 63:065014. [DOI: 10.1088/1361-6560/aab17e] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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17
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Range Imaging for Motion Compensation in C-Arm Cone-Beam CT of Knees under Weight-Bearing Conditions. J Imaging 2018. [DOI: 10.3390/jimaging4010013] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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18
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Jang S, Kim S, Kim M, Ra JB. Head motion correction based on filtered backprojection for x-ray CT imaging. Med Phys 2017; 45:589-604. [DOI: 10.1002/mp.12705] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Revised: 11/07/2017] [Accepted: 11/22/2017] [Indexed: 11/08/2022] Open
Affiliation(s)
- Seokhwan Jang
- School of Electrical Engineering; KAIST; Daejeon Republic of Korea
| | - Seungeon Kim
- School of Electrical Engineering; KAIST; Daejeon Republic of Korea
| | - Mina Kim
- School of Electrical Engineering; KAIST; Daejeon Republic of Korea
| | - Jong Beom Ra
- School of Electrical Engineering; KAIST; Daejeon Republic of Korea
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19
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Berger M, Xia Y, Aichinger W, Mentl K, Unberath M, Aichert A, Riess C, Hornegger J, Fahrig R, Maier A. Motion compensation for cone-beam CT using Fourier consistency conditions. Phys Med Biol 2017; 62:7181-7215. [PMID: 28741597 DOI: 10.1088/1361-6560/aa8129] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
In cone-beam CT, involuntary patient motion and inaccurate or irreproducible scanner motion substantially degrades image quality. To avoid artifacts this motion needs to be estimated and compensated during image reconstruction. In previous work we showed that Fourier consistency conditions (FCC) can be used in fan-beam CT to estimate motion in the sinogram domain. This work extends the FCC to [Formula: see text] cone-beam CT. We derive an efficient cost function to compensate for [Formula: see text] motion using [Formula: see text] detector translations. The extended FCC method have been tested with five translational motion patterns, using a challenging numerical phantom. We evaluated the root-mean-square-error and the structural-similarity-index between motion corrected and motion-free reconstructions. Additionally, we computed the mean-absolute-difference (MAD) between the estimated and the ground-truth motion. The practical applicability of the method is demonstrated by application to respiratory motion estimation in rotational angiography, but also to motion correction for weight-bearing imaging of knees. Where the latter makes use of a specifically modified FCC version which is robust to axial truncation. The results show a great reduction of motion artifacts. Accurate estimation results were achieved with a maximum MAD value of 708 μm and 1184 μm for motion along the vertical and horizontal detector direction, respectively. The image quality of reconstructions obtained with the proposed method is close to that of motion corrected reconstructions based on the ground-truth motion. Simulations using noise-free and noisy data demonstrate that FCC are robust to noise. Even high-frequency motion was accurately estimated leading to a considerable reduction of streaking artifacts. The method is purely image-based and therefore independent of any auxiliary data.
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Affiliation(s)
- M Berger
- Pattern Recognition Lab, Friedrich-Alexander-Universtät Erlangen-Nürnberg, 91058 Erlangen, Germany. Graduate School 1773, Heterogeneous Image Systems, 91058 Erlangen, Germany
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20
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Maier J, Black M, Bonaretti S, Bier B, Eskofier B, Choi JH, Levenston M, Gold G, Fahrig R, Maier A. Comparison of Different Approaches for Measuring Tibial Cartilage Thickness. J Integr Bioinform 2017; 14:/j/jib.2017.14.issue-2/jib-2017-0015/jib-2017-0015.xml. [PMID: 28753537 PMCID: PMC6042828 DOI: 10.1515/jib-2017-0015] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Accepted: 04/27/2017] [Indexed: 01/08/2023] Open
Abstract
Osteoarthritis is a degenerative disease affecting bones and cartilage especially in the human knee. In this context, cartilage thickness is an indicator for knee cartilage health. Thickness measurements are performed on medical images acquired in-vivo. Currently, there is no standard method agreed upon that defines a distance measure in articular cartilage. In this work, we present a comparison of different methods commonly used in literature. These methods are based on nearest neighbors, surface normal vectors, local thickness and potential field lines. All approaches were applied to manual segmentations of tibia and lateral and medial tibial cartilage performed by experienced raters. The underlying data were contrast agent-enhanced cone-beam C-arm CT reconstructions of one healthy subject's knee. The subject was scanned three times, once in supine position and two times in a standing weight-bearing position. A comparison of the resulting thickness maps shows similar distributions and high correlation coefficients between the approaches above 0.90. The nearest neighbor method results on average in the lowest cartilage thickness values, while the local thickness approach assigns the highest values. We showed that the different methods agree in their thickness distribution. The results will be used for a future evaluation of cartilage change under weight-bearing conditions.
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21
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Sisniega A, Stayman JW, Yorkston J, Siewerdsen JH, Zbijewski W. Motion compensation in extremity cone-beam CT using a penalized image sharpness criterion. Phys Med Biol 2017; 62:3712-3734. [PMID: 28327471 PMCID: PMC5478238 DOI: 10.1088/1361-6560/aa6869] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Cone-beam CT (CBCT) for musculoskeletal imaging would benefit from a method to reduce the effects of involuntary patient motion. In particular, the continuing improvement in spatial resolution of CBCT may enable tasks such as quantitative assessment of bone microarchitecture (0.1 mm-0.2 mm detail size), where even subtle, sub-mm motion blur might be detrimental. We propose a purely image based motion compensation method that requires no fiducials, tracking hardware or prior images. A statistical optimization algorithm (CMA-ES) is used to estimate a motion trajectory that optimizes an objective function consisting of an image sharpness criterion augmented by a regularization term that encourages smooth motion trajectories. The objective function is evaluated using a volume of interest (VOI, e.g. a single bone and surrounding area) where the motion can be assumed to be rigid. More complex motions can be addressed by using multiple VOIs. Gradient variance was found to be a suitable sharpness metric for this application. The performance of the compensation algorithm was evaluated in simulated and experimental CBCT data, and in a clinical dataset. Motion-induced artifacts and blurring were significantly reduced across a broad range of motion amplitudes, from 0.5 mm to 10 mm. Structure similarity index (SSIM) against a static volume was used in the simulation studies to quantify the performance of the motion compensation. In studies with translational motion, the SSIM improved from 0.86 before compensation to 0.97 after compensation for 0.5 mm motion, from 0.8 to 0.94 for 2 mm motion and from 0.52 to 0.87 for 10 mm motion (~70% increase). Similar reduction of artifacts was observed in a benchtop experiment with controlled translational motion of an anthropomorphic hand phantom, where SSIM (against a reconstruction of a static phantom) improved from 0.3 to 0.8 for 10 mm motion. Application to a clinical dataset of a lower extremity showed dramatic reduction of streaks and improvement in delineation of tissue boundaries and trabecular structures throughout the whole volume. The proposed method will support new applications of extremity CBCT in areas where patient motion may not be sufficiently managed by immobilization, such as imaging under load and quantitative assessment of subchondral bone architecture.
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Affiliation(s)
- A. Sisniega
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore MD USA 21205
| | - J. W. Stayman
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore MD USA 21205
| | | | - J. H. Siewerdsen
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore MD USA 21205
- Russell H. Morgan Department of Radiology, Johns Hopkins University, Baltimore MD USA 21205
| | - W. Zbijewski
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore MD USA 21205
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22
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Berger M, Müller K, Aichert A, Unberath M, Thies J, Choi JH, Fahrig R, Maier A. Marker-free motion correction in weight-bearing cone-beam CT of the knee joint. Med Phys 2016; 43:1235-48. [PMID: 26936708 DOI: 10.1118/1.4941012] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE To allow for a purely image-based motion estimation and compensation in weight-bearing cone-beam computed tomography of the knee joint. METHODS Weight-bearing imaging of the knee joint in a standing position poses additional requirements for the image reconstruction algorithm. In contrast to supine scans, patient motion needs to be estimated and compensated. The authors propose a method that is based on 2D/3D registration of left and right femur and tibia segmented from a prior, motion-free reconstruction acquired in supine position. Each segmented bone is first roughly aligned to the motion-corrupted reconstruction of a scan in standing or squatting position. Subsequently, a rigid 2D/3D registration is performed for each bone to each of K projection images, estimating 6 × 4 × K motion parameters. The motion of individual bones is combined into global motion fields using thin-plate-spline extrapolation. These can be incorporated into a motion-compensated reconstruction in the backprojection step. The authors performed visual and quantitative comparisons between a state-of-the-art marker-based (MB) method and two variants of the proposed method using gradient correlation (GC) and normalized gradient information (NGI) as similarity measure for the 2D/3D registration. RESULTS The authors evaluated their method on four acquisitions under different squatting positions of the same patient. All methods showed substantial improvement in image quality compared to the uncorrected reconstructions. Compared to NGI and MB, the GC method showed increased streaking artifacts due to misregistrations in lateral projection images. NGI and MB showed comparable image quality at the bone regions. Because the markers are attached to the skin, the MB method performed better at the surface of the legs where the authors observed slight streaking of the NGI and GC methods. For a quantitative evaluation, the authors computed the universal quality index (UQI) for all bone regions with respect to the motion-free reconstruction. The authors quantitative evaluation over regions around the bones yielded a mean UQI of 18.4 for no correction, 53.3 and 56.1 for the proposed method using GC and NGI, respectively, and 53.7 for the MB reference approach. In contrast to the authors registration-based corrections, the MB reference method caused slight nonrigid deformations at bone outlines when compared to a motion-free reference scan. CONCLUSIONS The authors showed that their method based on the NGI similarity measure yields reconstruction quality close to the MB reference method. In contrast to the MB method, the proposed method does not require any preparation prior to the examination which will improve the clinical workflow and patient comfort. Further, the authors found that the MB method causes small, nonrigid deformations at the bone outline which indicates that markers may not accurately reflect the internal motion close to the knee joint. Therefore, the authors believe that the proposed method is a promising alternative to MB motion management.
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Affiliation(s)
- M Berger
- Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg, 91058 Erlangen, Germany
| | - K Müller
- Radiological Sciences Laboratory, Stanford University, Stanford, California 94305
| | - A Aichert
- Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg, 91058 Erlangen, Germany
| | - M Unberath
- Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg, 91058 Erlangen, Germany
| | - J Thies
- Computer Graphics Lab, Friedrich-Alexander-University Erlangen-Nuremberg, 91058 Erlangen, Germany
| | - J-H Choi
- Radiological Sciences Laboratory, Stanford University, Stanford, California 94305
| | - R Fahrig
- Radiological Sciences Laboratory, Stanford University, Stanford, California 94305
| | - A Maier
- Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg, 91058 Erlangen, Germany
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23
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Kinect-Based Correction of Overexposure Artifacts in Knee Imaging with C-Arm CT Systems. Int J Biomed Imaging 2016; 2016:2502486. [PMID: 27516772 PMCID: PMC4969567 DOI: 10.1155/2016/2502486] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2016] [Accepted: 06/13/2016] [Indexed: 11/17/2022] Open
Abstract
Objective. To demonstrate a novel approach of compensating overexposure artifacts in CT scans of the knees without attaching any supporting appliances to the patient. C-Arm CT systems offer the opportunity to perform weight-bearing knee scans on standing patients to diagnose diseases like osteoarthritis. However, one serious issue is overexposure of the detector in regions close to the patella, which can not be tackled with common techniques. Methods. A Kinect camera is used to algorithmically remove overexposure artifacts close to the knee surface. Overexposed near-surface knee regions are corrected by extrapolating the absorption values from more reliable projection data. To achieve this, we develop a cross-calibration procedure to transform surface points from the Kinect to CT voxel coordinates. Results. Artifacts at both knee phantoms are reduced significantly in the reconstructed data and a major part of the truncated regions is restored. Conclusion. The results emphasize the feasibility of the proposed approach. The accuracy of the cross-calibration procedure can be increased to further improve correction results. Significance. The correction method can be extended to a multi-Kinect setup for use in real-world scenarios. Using depth cameras does not require prior scans and offers the possibility of a temporally synchronized correction of overexposure artifacts. To achieve this, we develop a cross-calibration procedure to transform surface points from the Kinect to CT voxel coordinates.
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24
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Choi JH, Constantin D, Ganguly A, Girard E, Morin RL, Dixon RL, Fahrig R. Practical dose point-based methods to characterize dose distribution in a stationary elliptical body phantom for a cone-beam C-arm CT system. Med Phys 2016; 42:4920-32. [PMID: 26233218 DOI: 10.1118/1.4927257] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE To propose new dose point measurement-based metrics to characterize the dose distributions and the mean dose from a single partial rotation of an automatic exposure control-enabled, C-arm-based, wide cone angle computed tomography system over a stationary, large, body-shaped phantom. METHODS A small 0.6 cm(3) ion chamber (IC) was used to measure the radiation dose in an elliptical body-shaped phantom made of tissue-equivalent material. The IC was placed at 23 well-distributed holes in the central and peripheral regions of the phantom and dose was recorded for six acquisition protocols with different combinations of minimum kVp (109 and 125 kVp) and z-collimator aperture (full: 22.2 cm; medium: 14.0 cm; small: 8.4 cm). Monte Carlo (MC) simulations were carried out to generate complete 2D dose distributions in the central plane (z = 0). The MC model was validated at the 23 dose points against IC experimental data. The planar dose distributions were then estimated using subsets of the point dose measurements using two proposed methods: (1) the proximity-based weighting method (method 1) and (2) the dose point surface fitting method (method 2). Twenty-eight different dose point distributions with six different point number cases (4, 5, 6, 7, 14, and 23 dose points) were evaluated to determine the optimal number of dose points and their placement in the phantom. The performances of the methods were determined by comparing their results with those of the validated MC simulations. The performances of the methods in the presence of measurement uncertainties were evaluated. RESULTS The 5-, 6-, and 7-point cases had differences below 2%, ranging from 1.0% to 1.7% for both methods, which is a performance comparable to that of the methods with a relatively large number of points, i.e., the 14- and 23-point cases. However, with the 4-point case, the performances of the two methods decreased sharply. Among the 4-, 5-, 6-, and 7-point cases, the 7-point case (1.0% [±0.6%] difference) and the 6-point case (0.7% [±0.6%] difference) performed best for method 1 and method 2, respectively. Moreover, method 2 demonstrated high-fidelity surface reconstruction with as few as 5 points, showing pixelwise absolute differences of 3.80 mGy (±0.32 mGy). Although the performance was shown to be sensitive to the phantom displacement from the isocenter, the performance changed by less than 2% for shifts up to 2 cm in the x- and y-axes in the central phantom plane. CONCLUSIONS With as few as five points, method 1 and method 2 were able to compute the mean dose with reasonable accuracy, demonstrating differences of 1.7% (±1.2%) and 1.3% (±1.0%), respectively. A larger number of points do not necessarily guarantee better performance of the methods; optimal choice of point placement is necessary. The performance of the methods is sensitive to the alignment of the center of the body phantom relative to the isocenter. In body applications where dose distributions are important, method 2 is a better choice than method 1, as it reconstructs the dose surface with high fidelity, using as few as five points.
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Affiliation(s)
- Jang-Hwan Choi
- Department of Radiology, Stanford University, Stanford, California 94305 and Department of Mechanical Engineering, Stanford University, Stanford, California 94305
| | - Dragos Constantin
- Microwave Physics R&E, Varian Medical Systems, Palo Alto, California 94304
| | - Arundhuti Ganguly
- Department of Radiology, Stanford University, Stanford, California 94305
| | - Erin Girard
- Department of Radiology, Stanford University, Stanford, California 94305
| | | | - Robert L Dixon
- Department of Radiology, Wake Forest University, Winston-Salem, North Carolina 27157
| | - Rebecca Fahrig
- Department of Radiology, Stanford University, Stanford, California 94305
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Sisniega A, Stayman JW, Cao Q, Yorkston J, Siewerdsen JH, Zbijewski W. Image-Based Motion Compensation for High-Resolution Extremities Cone-Beam CT. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2016; 9783. [PMID: 27346909 DOI: 10.1117/12.2217243] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
PURPOSE Cone-beam CT (CBCT) of the extremities provides high spatial resolution, but its quantitative accuracy may be challenged by involuntary sub-mm patient motion that cannot be eliminated with simple means of external immobilization. We investigate a two-step iterative motion compensation based on a multi-component metric of image sharpness. METHODS Motion is considered with respect to locally rigid motion within a particular region of interest, and the method supports application to multiple locally rigid regions. Motion is estimated by maximizing a cost function with three components: a gradient metric encouraging image sharpness, an entropy term that favors high contrast and penalizes streaks, and a penalty term encouraging smooth motion. Motion compensation involved initial coarse estimation of gross motion followed by estimation of fine-scale displacements using high resolution reconstructions. The method was evaluated in simulations with synthetic motion (1-4 mm) applied to a wrist volume obtained on a CMOS-based CBCT testbench. Structural similarity index (SSIM) quantified the agreement between motion-compensated and static data. The algorithm was also tested on a motion contaminated patient scan from dedicated extremities CBCT. RESULTS Excellent correction was achieved for the investigated range of displacements, indicated by good visual agreement with the static data. 10-15% improvement in SSIM was attained for 2-4 mm motions. The compensation was robust against increasing motion (4% decrease in SSIM across the investigated range, compared to 14% with no compensation). Consistent performance was achieved across a range of noise levels. Significant mitigation of artifacts was shown in patient data. CONCLUSION The results indicate feasibility of image-based motion correction in extremities CBCT without the need for a priori motion models, external trackers, or fiducials.
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Affiliation(s)
- A Sisniega
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA
| | - J W Stayman
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA
| | - Q Cao
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA
| | | | - J H Siewerdsen
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA; Russell H. Morgan Department of Radiology, Johns Hopkins University, Baltimore, MD USA
| | - W Zbijewski
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA
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Herbst M, Schebesch F, Berger M, Choi JH, Fahrig R, Hornegger J, Maier A. Dynamic detector offsets for field of view extension in C-arm computed tomography with application to weight-bearing imaging. Med Phys 2015; 42:2718-29. [PMID: 25979070 DOI: 10.1118/1.4915542] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE In C-arm computed tomography (CT), the field of view (FOV) is often not sufficient to acquire certain anatomical structures, e.g., a full hip or thorax. Proposed methods to extend the FOV use a fixed detector displacement and a 360° scan range to double the radius of the FOV. These trajectories are designed for circular FOVs. However, there are cases in which the required FOV is not circular but rather an ellipsoid. METHODS In this work, the authors show that in fan-beam CT, the use of a dynamically adjusting detector offset can reduce the required scan range when using a noncircular FOV. Furthermore, the authors present an analytic solution to determine the minimal required scan ranges for elliptic FOVs given a certain detector size and an algorithmic approach for arbitrary FOVs. RESULTS The authors show that the proposed method can result in a substantial reduction of the required scan range. Initial reconstructions of data sets acquired with our new minimal trajectory yielded image quality comparable to reconstructions of data acquired using a fixed detector offset and a full 360° rotation. CONCLUSIONS Our results show a promising reduction of the necessary scan range especially for ellipsoidal objects that extend the FOV. In noncircular FOVs, there exists a set of solutions that allow a trade-off between detector size and scan range.
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Affiliation(s)
- Magdalena Herbst
- Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen 91058, Germany
| | - Frank Schebesch
- Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen 91058, Germany
| | - Martin Berger
- Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen 91058, Germany
| | - Jang-Hwan Choi
- Department of Radiology, Stanford University, Stanford, California 94305
| | - Rebecca Fahrig
- Department of Radiology, Stanford University, Stanford, California 94305
| | - Joachim Hornegger
- Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen 91058, Germany Erlangen Graduate School in Advanced Optical Technologies (SAOT), Erlangen 91058, Germany
| | - Andreas Maier
- Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen 91058, Germany Erlangen Graduate School in Advanced Optical Technologies (SAOT), Erlangen 91058, Germany
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