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Lu A, Huang H, Hu Y, Zbijewski W, Unberath M, Siewerdsen JH, Weiss CR, Sisniega A. Vessel-targeted compensation of deformable motion in interventional cone-beam CT. Med Image Anal 2024; 97:103254. [PMID: 38968908 DOI: 10.1016/j.media.2024.103254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 06/01/2024] [Accepted: 06/24/2024] [Indexed: 07/07/2024]
Abstract
The present standard of care for unresectable liver cancer is transarterial chemoembolization (TACE), which involves using chemotherapeutic particles to selectively embolize the arteries supplying hepatic tumors. Accurate volumetric identification of intricate fine vascularity is crucial for selective embolization. Three-dimensional imaging, particularly cone-beam CT (CBCT), aids in visualization and targeting of small vessels in such highly variable anatomy, but long image acquisition time results in intra-scan patient motion, which distorts vascular structures and tissue boundaries. To improve clarity of vascular anatomy and intra-procedural utility, this work proposes a targeted motion estimation and compensation framework that removes the need for any prior information or external tracking and for user interaction. Motion estimation is performed in two stages: (i) a target identification stage that segments arteries and catheters in the projection domain using a multi-view convolutional neural network to construct a coarse 3D vascular mask; and (ii) a targeted motion estimation stage that iteratively solves for the time-varying motion field via optimization of a vessel-enhancing objective function computed over the target vascular mask. The vessel-enhancing objective is derived through eigenvalues of the local image Hessian to emphasize bright tubular structures. Motion compensation is achieved via spatial transformer operators that apply time-dependent deformations to partial angle reconstructions, allowing efficient minimization via gradient backpropagation. The framework was trained and evaluated in anatomically realistic simulated motion-corrupted CBCTs mimicking TACE of hepatic tumors, at intermediate (3.0 mm) and large (6.0 mm) motion magnitudes. Motion compensation substantially improved median vascular DICE score (from 0.30 to 0.59 for large motion), image SSIM (from 0.77 to 0.93 for large motion), and vessel sharpness (0.189 mm-1 to 0.233 mm-1 for large motion) in simulated cases. Motion compensation also demonstrated increased vessel sharpness (0.188 mm-1 before to 0.205 mm-1 after) and reconstructed vessel length (median increased from 37.37 to 41.00 mm) on a clinical interventional CBCT. The proposed anatomy-aware motion compensation framework presented a promising approach for improving the utility of CBCT for intra-procedural vascular imaging, facilitating selective embolization procedures.
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Affiliation(s)
- Alexander Lu
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, Traylor Research Building, #622 720 Rutland Avenue Baltimore MD 21205, USA
| | - Heyuan Huang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, Traylor Research Building, #622 720 Rutland Avenue Baltimore MD 21205, USA
| | - Yicheng Hu
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Wojciech Zbijewski
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, Traylor Research Building, #622 720 Rutland Avenue Baltimore MD 21205, USA
| | - Mathias Unberath
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Jeffrey H Siewerdsen
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, Traylor Research Building, #622 720 Rutland Avenue Baltimore MD 21205, USA; Departments of Imaging Physics, Radiation Physics, and Neurosurgery, The University of Texas M.D. Anderson Cancer Center, TX, USA
| | - Clifford R Weiss
- Department of Radiology, Johns Hopkins University, Baltimore, MD, USA
| | - Alejandro Sisniega
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, Traylor Research Building, #622 720 Rutland Avenue Baltimore MD 21205, USA.
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Lin Z, Wang Y, Bian Z, Ma J. [A deep blur learning-based motion artifact reduction algorithm for dental cone-beam computed tomography images]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2024; 44:1198-1208. [PMID: 38977351 PMCID: PMC11237304 DOI: 10.12122/j.issn.1673-4254.2024.06.22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
OBJECTIVE We propose a motion artifact correction algorithm (DMBL) for reducing motion artifacts in reconstructed dental cone-beam computed tomography (CBCT) images based on deep blur learning. METHODS A blur encoder was used to extract motion-related degradation features to model the degradation process caused by motion, and the obtained motion degradation features were imported in the artifact correction module for artifact removal. The artifact correction module adopts a joint learning framework for image blur removal and image blur simulation for treatment of spatially varying and random motion patterns. Comparative experiments were conducted to verify the effectiveness of the proposed method using both simulated motion data sets and clinical data sets. RESULTS The experimental results with the simulated dataset showed that compared with the existing methods, the PSNR of the proposed method increased by 2.88%, the SSIM increased by 0.89%, and the RMSE decreased by 10.58%. The results with the clinical dataset showed that the proposed method achieved the highest expert level with a subjective image quality score of 4.417 (in a 5-point scale), significantly higher than those of the comparison methods. CONCLUSION The proposed DMBL algorithm with a deep blur joint learning network structure can effectively reduce motion artifacts in dental CBCT images and achieve high-quality image restoration.
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Affiliation(s)
- Z Lin
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - Y Wang
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - Z Bian
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - J Ma
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
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Huang H, Liu Y, Siewerdsen JH, Lu A, Hu Y, Zbijewski W, Unberath M, Weiss CR, Sisniega A. Deformable motion compensation in interventional cone-beam CT with a context-aware learned autofocus metric. Med Phys 2024; 51:4158-4180. [PMID: 38733602 DOI: 10.1002/mp.17125] [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: 12/25/2023] [Revised: 04/02/2024] [Accepted: 05/03/2024] [Indexed: 05/13/2024] Open
Abstract
PURPOSE Interventional Cone-Beam CT (CBCT) offers 3D visualization of soft-tissue and vascular anatomy, enabling 3D guidance of abdominal interventions. However, its long acquisition time makes CBCT susceptible to patient motion. Image-based autofocus offers a suitable platform for compensation of deformable motion in CBCT, but it relies on handcrafted motion metrics based on first-order image properties and that lack awareness of the underlying anatomy. This work proposes a data-driven approach to motion quantification via a learned, context-aware, deformable metric,VI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ , that quantifies the amount of motion degradation as well as the realism of the structural anatomical content in the image. METHODS The proposedVI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ was modeled as a deep convolutional neural network (CNN) trained to recreate a reference-based structural similarity metric-visual information fidelity (VIF). The deep CNN acted on motion-corrupted images, providing an estimation of the spatial VIF map that would be obtained against a motion-free reference, capturing motion distortion, and anatomic plausibility. The deep CNN featured a multi-branch architecture with a high-resolution branch for estimation of voxel-wise VIF on a small volume of interest. A second contextual, low-resolution branch provided features associated to anatomical context for disentanglement of motion effects and anatomical appearance. The deep CNN was trained on paired motion-free and motion-corrupted data obtained with a high-fidelity forward projection model for a protocol involving 120 kV and 9.90 mGy. The performance ofVI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ was evaluated via metrics of correlation with ground truth VIF ${\bm{VIF}}$ and with the underlying deformable motion field in simulated data with deformable motion fields with amplitude ranging from 5 to 20 mm and frequency from 2.4 up to 4 cycles/scan. Robustness to variation in tissue contrast and noise levels was assessed in simulation studies with varying beam energy (90-120 kV) and dose (1.19-39.59 mGy). Further validation was obtained on experimental studies with a deformable phantom. Final validation was obtained via integration ofVI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ on an autofocus compensation framework, applied to motion compensation on experimental datasets and evaluated via metric of spatial resolution on soft-tissue boundaries and sharpness of contrast-enhanced vascularity. RESULTS The magnitude and spatial map ofVI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ showed consistent and high correlation levels with the ground truth in both simulation and real data, yielding average normalized cross correlation (NCC) values of 0.95 and 0.88, respectively. Similarly,VI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ achieved good correlation values with the underlying motion field, with average NCC of 0.90. In experimental phantom studies,VI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ properly reflects the change in motion amplitudes and frequencies: voxel-wise averaging of the localVI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ across the full reconstructed volume yielded an average value of 0.69 for the case with mild motion (2 mm, 12 cycles/scan) and 0.29 for the case with severe motion (12 mm, 6 cycles/scan). Autofocus motion compensation usingVI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ resulted in noticeable mitigation of motion artifacts and improved spatial resolution of soft tissue and high-contrast structures, resulting in reduction of edge spread function width of 8.78% and 9.20%, respectively. Motion compensation also increased the conspicuity of contrast-enhanced vascularity, reflected in an increase of 9.64% in vessel sharpness. CONCLUSION The proposedVI F D L ${\bm{VI}}{{\bm{F}}}_{DL}$ , featuring a novel context-aware architecture, demonstrated its capacity as a reference-free surrogate of structural similarity to quantify motion-induced degradation of image quality and anatomical plausibility of image content. The validation studies showed robust performance across motion patterns, x-ray techniques, and anatomical instances. The proposed anatomy- and context-aware metric poses a powerful alternative to conventional motion estimation metrics, and a step forward for application of deep autofocus motion compensation for guidance in clinical interventional procedures.
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Affiliation(s)
- Heyuan Huang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Yixuan Liu
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jeffrey H Siewerdsen
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Alexander Lu
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Yicheng Hu
- Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA
| | - Wojciech Zbijewski
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Mathias Unberath
- Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA
| | - Clifford R Weiss
- Department of Radiology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Alejandro Sisniega
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
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Cancelliere NM, van Nijnatten F, Hummel E, Withagen P, van de Haar P, Nishi H, Agid R, Nicholson P, Hallacoglu B, van Vlimmeren M, Pereira VM. Motion artifact correction for cone beam CT stroke imaging: a prospective series. J Neurointerv Surg 2023; 15:e223-e228. [PMID: 36564201 DOI: 10.1136/jnis-2021-018201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 06/28/2022] [Indexed: 12/25/2022]
Abstract
BACKGROUND Imaging assessment for acute ischemic stroke (AIS) patients in the angiosuite using cone beam CT (CBCT) has created increased interest since endovascular treatment became the first line therapy for proximal vessel occlusions. One of the main challenges of CBCT imaging in AIS patients is degraded image quality due to motion artifacts. This study aims to evaluate the prevalence of motion artifacts in CBCT stroke imaging and the effectiveness of a novel motion artifact correction algorithm for image quality improvement. METHODS Patients presenting with acute stroke symptoms and considered for endovascular treatment were included in the study. CBCT scans were performed using the angiosuite X-ray system. All CBCT scans were post-processed using a motion artifact correction algorithm. Motion artifacts were scored before and after processing using a 4-point scale. RESULTS We prospectively included 310 CBCT scans from acute stroke patients. 51% (n=159/310) of scans had motion artifacts, with 24% being moderate to severe. The post-processing algorithm improved motion artifacts in 91% of scans with motion (n=144/159), restoring clinical diagnostic capability in 34%. Overall, 76% of the scans were sufficient for clinical decision-making before correction, which improved to 93% (n=289/310) after post-processing with our algorithm. CONCLUSIONS Our results demonstrate that CBCT motion artifacts are significantly reduced using a novel post-processing algorithm, which improved brain CBCT image quality and diagnostic assessment for stroke. This is an important step on the road towards a direct-to-angio approach for endovascular thrombectomy (EVT) treatment.
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Affiliation(s)
- Nicole M Cancelliere
- Department of Neurosurgery, St Michael's Hospital, Toronto, Ontario, Canada
- RADIS lab, Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, Ontario, Canada
- Department of Medical Imaging, St Michael's Hospital, Toronto, Ontario, Canada
| | - Fred van Nijnatten
- Image Guided Therapy, Philips Healthcare, Best, Noord-Brabant, The Netherlands
| | - Eric Hummel
- Image Guided Therapy, Philips Healthcare, Best, Noord-Brabant, The Netherlands
| | - Paul Withagen
- Image Guided Therapy, Philips Healthcare, Best, Noord-Brabant, The Netherlands
| | - Peter van de Haar
- Image Guided Therapy, Philips Healthcare, Best, Noord-Brabant, The Netherlands
| | - Hidehisa Nishi
- RADIS lab, Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, Ontario, Canada
| | - Ronit Agid
- Medical Imaging, Toronto Western Hospital, Toronto, Ontario, Canada
| | | | - Bertan Hallacoglu
- Image Guided Therapy, Philips Healthcare, Best, Noord-Brabant, The Netherlands
| | | | - Vitor M Pereira
- Department of Neurosurgery, St Michael's Hospital, Toronto, Ontario, Canada
- RADIS lab, Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, Ontario, Canada
- Department of Medical Imaging, St Michael's Hospital, Toronto, Ontario, Canada
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Thies M, Wagner F, Maul N, Folle L, Meier M, Rohleder M, Schneider LS, Pfaff L, Gu M, Utz J, Denzinger F, Manhart M, Maier A. Gradient-based geometry learning for fan-beam CT reconstruction. Phys Med Biol 2023; 68:205004. [PMID: 37779386 DOI: 10.1088/1361-6560/acf90e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 09/12/2023] [Indexed: 10/03/2023]
Abstract
Objective.Incorporating computed tomography (CT) reconstruction operators into differentiable pipelines has proven beneficial in many applications. Such approaches usually focus on the projection data and keep the acquisition geometry fixed. However, precise knowledge of the acquisition geometry is essential for high quality reconstruction results. In this paper, the differentiable formulation of fan-beam CT reconstruction is extended to the acquisition geometry.Approach.The CT fan-beam reconstruction is analytically derived with respect to the acquisition geometry. This allows to propagate gradient information from a loss function on the reconstructed image into the geometry parameters. As a proof-of-concept experiment, this idea is applied to rigid motion compensation. The cost function is parameterized by a trained neural network which regresses an image quality metric from the motion-affected reconstruction alone.Main results.The algorithm improves the structural similarity index measure (SSIM) from 0.848 for the initial motion-affected reconstruction to 0.946 after compensation. It also generalizes to real fan-beam sinograms which are rebinned from a helical trajectory where the SSIM increases from 0.639 to 0.742.Significance.Using the proposed method, we are the first to optimize an autofocus-inspired algorithm based on analytical gradients. Next to motion compensation, we see further use cases of our differentiable method for scanner calibration or hybrid techniques employing deep models.
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Affiliation(s)
- Mareike Thies
- Pattern Recognition Lab, FAU Erlangen-Nürnberg, Germany
| | - Fabian Wagner
- Pattern Recognition Lab, FAU Erlangen-Nürnberg, Germany
| | - Noah Maul
- Pattern Recognition Lab, FAU Erlangen-Nürnberg, Germany
- Siemens Healthcare GmbH, Erlangen, Germany
| | - Lukas Folle
- Pattern Recognition Lab, FAU Erlangen-Nürnberg, Germany
| | - Manuela Meier
- Pattern Recognition Lab, FAU Erlangen-Nürnberg, Germany
- Siemens Healthcare GmbH, Erlangen, Germany
| | - Maximilian Rohleder
- Pattern Recognition Lab, FAU Erlangen-Nürnberg, Germany
- Siemens Healthcare GmbH, Erlangen, Germany
| | | | - Laura Pfaff
- Pattern Recognition Lab, FAU Erlangen-Nürnberg, Germany
- Siemens Healthcare GmbH, Erlangen, Germany
| | - Mingxuan Gu
- Pattern Recognition Lab, FAU Erlangen-Nürnberg, Germany
| | - Jonas Utz
- Department AIBE, FAU Erlangen-Nürnberg, Germany
| | - Felix Denzinger
- Pattern Recognition Lab, FAU Erlangen-Nürnberg, Germany
- Siemens Healthcare GmbH, Erlangen, Germany
| | | | - Andreas Maier
- Pattern Recognition Lab, FAU Erlangen-Nürnberg, Germany
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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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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. [PMID: 35636391 DOI: 10.1088/1361-6560/ac749a] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [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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Fahrig R, Jaffray DA, Sechopoulos I, Webster Stayman J. Flat-panel conebeam CT in the clinic: history and current state. J Med Imaging (Bellingham) 2021; 8:052115. [PMID: 34722795 DOI: 10.1117/1.jmi.8.5.052115] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 09/27/2021] [Indexed: 11/14/2022] Open
Abstract
Research into conebeam CT concepts began as soon as the first clinical single-slice CT scanner was conceived. Early implementations of conebeam CT in the 1980s focused on high-contrast applications where concurrent high resolution ( < 200 μ m ), for visualization of small contrast-filled vessels, bones, or teeth, was an imaging requirement that could not be met by the contemporaneous CT scanners. However, the use of nonlinear imagers, e.g., x-ray image intensifiers, limited the clinical utility of the earliest diagnostic conebeam CT systems. The development of consumer-electronics large-area displays provided a technical foundation that was leveraged in the 1990s to first produce large-area digital x-ray detectors for use in radiography and then compact flat panels suitable for high-resolution and high-frame-rate conebeam CT. In this review, we show the concurrent evolution of digital flat panel (DFP) technology and clinical conebeam CT. We give a brief summary of conebeam CT reconstruction, followed by a brief review of the correction approaches for DFP-specific artifacts. The historical development and current status of flat-panel conebeam CT in four clinical areas-breast, fixed C-arm, image-guided radiation therapy, and extremity/head-is presented. Advances in DFP technology over the past two decades have led to improved visualization of high-contrast, high-resolution clinical tasks, and image quality now approaches the soft-tissue contrast resolution that is the standard in clinical CT. Future technical developments in DFPs will enable an even broader range of clinical applications; research in the arena of flat-panel CT shows no signs of slowing down.
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Affiliation(s)
- Rebecca Fahrig
- Innovation, Advanced Therapies, Siemens Healthcare GmbH, Forchheim, Germany.,Friedrich-Alexander Universitat, Department of Computer Science 5, Erlangen, Germany
| | - David A Jaffray
- MD Anderson Cancer Center, Departments of Radiation Physics and Imaging Physics, Houston, Texas, United States
| | - Ioannis Sechopoulos
- Radboud University Medical Center, Department of Medical Imaging, Nijmegen, The Netherlands.,Dutch Expert Center for Screening (LRCB), Nijmegen, The Netherlands.,University of Twente, Technical Medical Center, Enschede, The Netherlands
| | - J Webster Stayman
- Johns Hopkins University, Department of Biomedical Engineering, Baltimore, Maryland, United States
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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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10
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Maschke S, Werncke T, Becker LS, Renne J, Dewald CLA, Olsson KM, Hoeper MM, Wacker FK, Meyer BC, Hinrichs JB. Motion Reduction for C-Arm Computed Tomography of the Pulmonary Arteries: Image Quality of a Motion Correction Algorithm in Patients with Chronic Thromboembolic Hypertension During Balloon Pulmonary Angioplasty. ROFO-FORTSCHR RONTG 2021; 193:1074-1080. [PMID: 33634459 DOI: 10.1055/a-1354-6736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
PURPOSE To evaluate the feasibility and image quality of a motion correction algorithm for supra-selective C-arm computed tomography (CACT) of the pulmonary arteries in patients with chronic thromboembolic pulmonary hypertension (CTEPH) undergoing balloon pulmonary angioplasty (BPA). MATERIALS & METHODS CACT raw data acquired during 30 consecutive BPAs were used for image reconstruction using either standard (CACTorg) or a motion correction algorithm (CACTmc), using 400 iterations. Two readers independently evaluated 188 segmental and 564 sub-segmental contrast-enhanced pulmonary arteries in each reconstruction. The following categories were assessed: Sharpness of the vessel, motion artifacts, delineation of bronchial structures, vessel geometry, and visibility of treatable lesions. The mentioned criteria were rated from grade 1 to grade 3: grade 1: excellent quality; grade 2: good quality; grade 3: poor/seriously impaired quality. Inter-observer agreement was calculated using Cohen's Kappa. Due to an excellent agreement, the ratings of both readers were merged. Differences in the assessed image quality criteria were evaluated using pairwise Wilcoxon signed-rank test. RESULTS Inter-observer agreement was excellent for all evaluated image quality criteria (κ > 0.81). For all assessed image quality criteria, the ratings on CACTorg were good but improved significantly for CACTmc to excellent for the whole vascular tree (p < 0.01). When considering segmental and sub-segmental levels individually, all image quality criteria improved significantly for CACTmc on both levels (p < 0.01). While ratings of CACTmc were constant for both levels (segmental and sub-segmental) for all criteria, the ratings of CACTorg were slightly impaired for the sub-segmental arteries. CONCLUSION Motion correction for supra-selective contrast-enhanced CACT of the pulmonary arteries is feasible and improves the overall image quality. KEY POINTS · Motion artifacts can severely impair the diagnostic accuracy of CACT.. · A motion correction algorithm can significantly improve image quality in CACT of the pulmonary arteries.. · Especially the overall image quality of sub-segmental branches is significantly improved.. CITATION FORMAT · Maschke S, Werncke T, Becker LS et al. Motion Reduction for C-Arm Computed Tomography of the Pulmonary Arteries: Image Quality of a Motion Correction Algorithm in Patients with Chronic Thromboembolic Hypertension During Balloon Pulmonary Angioplasty. Fortschr Röntgenstr 2021; 193: 1074 - 1080.
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Affiliation(s)
- Sabine Maschke
- Department of Diagnostic and Interventional Radiology, MHH, Hannover, Germany
| | - Thomas Werncke
- Department of Diagnostic and Interventional Radiology, MHH, Hannover, Germany
| | - Lena Sophie Becker
- Department of Diagnostic and Interventional Radiology, MHH, Hannover, Germany
| | - Julius Renne
- Department of Diagnostic and Interventional Radiology, MHH, Hannover, Germany
| | | | | | | | - Frank K Wacker
- Department of Diagnostic and Interventional Radiology, MHH, Hannover, Germany
| | - Bernhard C Meyer
- Department of Diagnostic and Interventional Radiology, MHH, Hannover, Germany
| | - Jan B Hinrichs
- Department of Diagnostic and Interventional Radiology, MHH, Hannover, Germany
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Capostagno S, Sisniega A, Stayman JW, Ehtiati T, Weiss CR, Siewerdsen JH. Deformable motion compensation for interventional cone-beam CT. Phys Med Biol 2021; 66:055010. [PMID: 33594993 DOI: 10.1088/1361-6560/abb16e] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Image-guided therapies in the abdomen and pelvis are often hindered by motion artifacts in cone-beam CT (CBCT) arising from complex, non-periodic, deformable organ motion during long scan times (5-30 s). We propose a deformable image-based motion compensation method to address these challenges and improve CBCT guidance. Motion compensation is achieved by selecting a set of small regions of interest in the uncompensated image to minimize a cost function consisting of an autofocus objective and spatiotemporal regularization penalties. Motion trajectories are estimated using an iterative optimization algorithm (CMA-ES) and used to interpolate a 4D spatiotemporal motion vector field. The motion-compensated image is reconstructed using a modified filtered backprojection approach. Being image-based, the method does not require additional input besides the raw CBCT projection data and system geometry that are used for image reconstruction. Experimental studies investigated: (1) various autofocus objective functions, analyzed using a digital phantom with a range of sinusoidal motion magnitude (4, 8, 12, 16, 20 mm); (2) spatiotemporal regularization, studied using a CT dataset from The Cancer Imaging Archive with deformable sinusoidal motion of variable magnitude (10, 15, 20, 25 mm); and (3) performance in complex anatomy, evaluated in cadavers undergoing simple and complex motion imaged on a CBCT-capable mobile C-arm system (Cios Spin 3D, Siemens Healthineers, Forchheim, Germany). Gradient entropy was found to be the best autofocus objective for soft-tissue CBCT, increasing structural similarity (SSIM) by 42%-92% over the range of motion magnitudes investigated. The optimal temporal regularization strength was found to vary widely (0.5-5 mm-2) over the range of motion magnitudes investigated, whereas optimal spatial regularization strength was relatively constant (0.1). In cadaver studies, deformable motion compensation was shown to improve local SSIM by ∼17% for simple motion and ∼21% for complex motion and provided strong visual improvement of motion artifacts (reduction of blurring and streaks and improved visibility of soft-tissue edges). The studies demonstrate the robustness of deformable motion compensation to a range of motion magnitudes, frequencies, and other factors (e.g. truncation and scatter).
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Affiliation(s)
- S Capostagno
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, United States of America
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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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Shieh CC, Barber J, Counter W, Sykes J, Bennett P, Heng SM, White P, Corde S, Jackson M, Ahern V, Feain I, O’Brien R, Keall PJ. Cone-beam CT reconstruction with gravity-induced motion. ACTA ACUST UNITED AC 2018; 63:205007. [DOI: 10.1088/1361-6560/aae1bb] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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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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