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Zhang J, Qing C, Li Y, Wang Y. BCSwinReg: A cross-modal attention network for CBCT-to-CT multimodal image registration. Comput Biol Med 2024; 171:107990. [PMID: 38377717 DOI: 10.1016/j.compbiomed.2024.107990] [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: 08/29/2023] [Revised: 12/26/2023] [Accepted: 01/13/2024] [Indexed: 02/22/2024]
Abstract
Computed tomography (CT) and cone beam computed tomography (CBCT) registration plays an important role in radiotherapy. However, the poor quality of CBCT makes CBCT-CT multimodal registration challenging. Effective feature fusion and mapping often lead to better registration results for multimodal registration. Therefore, we proposed a new backbone network BCSwinReg and a cross-modal attention module CrossSwin. Specifically, a cross-modal attention CrossSwin is designed to promote multi-modal feature fusion, map the multi-modal domain to the common domain, and thus helping the network learn the correspondence between images better. Furthermore, a new network, BCSwinReg, is proposed to discover correspondence through cross-attention exchange information, obtain multi-level semantic information through a multi-resolution strategy, and finally integrate the deformation of multi-resolutions by the divide-conquer cascade method. We performed experiments on the publicly available 4D-Lung dataset to demonstrate the effectiveness of CrossSwin and BCSwinReg. Compared with VoxelMorph, the BCSwinReg has obtained performance improvements of 3.3% in Dice Similarity Coefficient (DSC) and 0.19 in the average 95% Hausdorff distance (HD95).
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Affiliation(s)
- Jieming Zhang
- The East China University of Science and Technology, Shanghai, 200237, China
| | - Chang Qing
- The East China University of Science and Technology, Shanghai, 200237, China.
| | - Yu Li
- The East China University of Science and Technology, Shanghai, 200237, China
| | - Yaqi Wang
- The East China University of Science and Technology, Shanghai, 200237, China
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Harris TC, Jacobson M, Myronakis M, Lehmann M, Huber P, Morf D, Ozoemelam I, Hu YH, Ferguson D, Fueglistaller R, Corral Arroyo P, Berbeco RI. Impact of a novel multilayer imager on metal artifacts in MV-CBCT. Phys Med Biol 2023; 68:10.1088/1361-6560/ace09a. [PMID: 37343590 PMCID: PMC10382207 DOI: 10.1088/1361-6560/ace09a] [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: 03/09/2023] [Accepted: 06/21/2023] [Indexed: 06/23/2023]
Abstract
Objective. Megavoltage cone-beam computed tomography (MV-CBCT) imaging offers several advantages including reduced metal artifacts and accurate electron density mapping for adaptive or emergent situations. However, MV-CBCT imaging is limited by the poor efficiency of current detectors. Here we examine a new MV imager and compare CBCT reconstructions under clinically relevant scenarios.Approach. A multilayer imager (MLI), consisting of four vertically stacked standard flat-panel imagers, was mounted to a clinical linear accelerator. A custom anthropomorphic pelvis phantom with replaceable femoral heads was imaged using MV-CBCT and kilovoltage CBCT (kV-CBCT). Bone, aluminum, and titanium were used as femoral head inserts. 8 MU 2.5 MV scans were acquired for all four layers and (as reference) the top layer. Prostate and bladder were contoured on a reference CT and transferred to the other scans after rigid registration, from which the structural similarity index measure (SSIM) was calculated. Prostate and bladder were also contoured on CBCT scans without guidance, and Dice coefficients were compared to CT contours.Main results. kV-CBCT demonstrated the highest SSIMs with bone inserts (prostate: 0.86, bladder: 0.94) and lowest with titanium inserts (0.32, 0.37). Four-layer MV-CBCT SSIMs were preserved with bone (0.75, 0.80) as compared to titanium (0.67, 0.74), outperforming kV-CBCT when metal is present. One-layer MV-CBCT consistently underperformed four-layer results across all phantom configurations. Unilateral titanium inserts and bilateral aluminum insert results fell between the bone and bilateral titanium results. Dice coefficients trended similarly, with four-layer MV-CBCT reducing metal artifact impact relative to KV-CBCT to provide better soft-tissue identification.Significance. MV-CBCT with a four-layer MLI showed improvement over single-layer MV scans, approaching kV-CBCT quality for soft-tissue contrast. In the presence of artifact-producing metal implants, four-layer MV-CBCT scans outperformed kV-CBCT by eliminating artifacts and single-layer MV-CBCT by reducing noise. MV-CBCT with a novel multi-layer imager may be a valuable alternative to kV-CBCT, particularly in the presence of metal.
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Affiliation(s)
- T C Harris
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana Farber Cancer Institute and Harvard Medical School, Boston, MA, United States of America
| | - M Jacobson
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana Farber Cancer Institute and Harvard Medical School, Boston, MA, United States of America
| | - M Myronakis
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana Farber Cancer Institute and Harvard Medical School, Boston, MA, United States of America
| | - M Lehmann
- Varian Medical Systems, Baden-Dattwil, Switzerland
| | - P Huber
- Varian Medical Systems, Baden-Dattwil, Switzerland
| | - D Morf
- Varian Medical Systems, Baden-Dattwil, Switzerland
| | - I Ozoemelam
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana Farber Cancer Institute and Harvard Medical School, Boston, MA, United States of America
| | - Y H Hu
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana Farber Cancer Institute and Harvard Medical School, Boston, MA, United States of America
| | - D Ferguson
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana Farber Cancer Institute and Harvard Medical School, Boston, MA, United States of America
| | | | | | - R I Berbeco
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana Farber Cancer Institute and Harvard Medical School, Boston, MA, United States of America
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McDonald BA, Zachiu C, Christodouleas J, Naser MA, Ruschin M, Sonke JJ, Thorwarth D, Létourneau D, Tyagi N, Tadic T, Yang J, Li XA, Bernchou U, Hyer DE, Snyder JE, Bubula-Rehm E, Fuller CD, Brock KK. Dose accumulation for MR-guided adaptive radiotherapy: From practical considerations to state-of-the-art clinical implementation. Front Oncol 2023; 12:1086258. [PMID: 36776378 PMCID: PMC9909539 DOI: 10.3389/fonc.2022.1086258] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 12/21/2022] [Indexed: 01/27/2023] Open
Abstract
MRI-linear accelerator (MR-linac) devices have been introduced into clinical practice in recent years and have enabled MR-guided adaptive radiation therapy (MRgART). However, by accounting for anatomical changes throughout radiation therapy (RT) and delivering different treatment plans at each fraction, adaptive radiation therapy (ART) highlights several challenges in terms of calculating the total delivered dose. Dose accumulation strategies-which typically involve deformable image registration between planning images, deformable dose mapping, and voxel-wise dose summation-can be employed for ART to estimate the delivered dose. In MRgART, plan adaptation on MRI instead of CT necessitates additional considerations in the dose accumulation process because MRI pixel values do not contain the quantitative information used for dose calculation. In this review, we discuss considerations for dose accumulation specific to MRgART and in relation to current MR-linac clinical workflows. We present a general dose accumulation framework for MRgART and discuss relevant quality assurance criteria. Finally, we highlight the clinical importance of dose accumulation in the ART era as well as the possible ways in which dose accumulation can transform clinical practice and improve our ability to deliver personalized RT.
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Affiliation(s)
- Brigid A. McDonald
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Cornel Zachiu
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands
| | | | - Mohamed A. Naser
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Mark Ruschin
- Department of Radiation Oncology, University of Toronto, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Jan-Jakob Sonke
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Daniela Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tuebingen, Tuebingen, Germany
| | - Daniel Létourneau
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, ON, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - Neelam Tyagi
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, United States
| | - Tony Tadic
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, ON, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - Jinzhong Yang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - X. Allen Li
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Uffe Bernchou
- Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, Odense, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Daniel E. Hyer
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, IA, United States
| | - Jeffrey E. Snyder
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, IA, United States
| | | | - Clifton D. Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Kristy K. Brock
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
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Cao Y, Fu T, Duan L, Dai Y, Gong L, Cao W, Liu D, Yang X, Ni X, Zheng J. CDFRegNet: A cross-domain fusion registration network for CT-to-CBCT image registration. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 224:107025. [PMID: 35872383 DOI: 10.1016/j.cmpb.2022.107025] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 07/01/2022] [Accepted: 07/13/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Computer tomography (CT) to cone-beam computed tomography (CBCT) image registration plays an important role in radiotherapy treatment placement, dose verification, and anatomic changes monitoring during radiotherapy. However, fast and accurate CT-to-CBCT image registration is still very challenging due to the intensity differences, the poor image quality of CBCT images, and inconsistent structure information. METHODS To address these problems, a novel unsupervised network named cross-domain fusion registration network (CDFRegNet) is proposed. First, a novel edge-guided attention module (EGAM) is designed, aiming at capturing edge information based on the gradient prior images and guiding the network to model the spatial correspondence between two image domains. Moreover, a novel cross-domain attention module (CDAM) is proposed to improve the network's ability to guide the network to effectively map and fuse the domain-specific features. RESULTS Extensive experiments on a real clinical dataset were carried out, and the experimental results verify that the proposed CDFRegNet can register CT to CBCT images effectively and obtain the best performance, while compared with other representative methods, with a mean DSC of 80.01±7.16%, a mean TRE of 2.27±0.62 mm, and a mean MHD of 1.50±0.32 mm. The ablation experiments also proved that our EGAM and CDAM can further improve the accuracy of the registration network and they can generalize well to other registration networks. CONCLUSION This paper proposed a novel CT-to-CBCT registration method based on EGAM and CDAM, which has the potential to improve the accuracy of multi-domain image registration.
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Affiliation(s)
- Yuzhu Cao
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China; Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Tianxiao Fu
- Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou 215006, China
| | - Luwen Duan
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
| | - Yakang Dai
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China; Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Lun Gong
- Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin 300072, China
| | - Weiwei Cao
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China; Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Desen Liu
- Department of Thoracic Surgery, Suzhou Kowloon Hospital, Shanghai Jiao Tong University School of Medicine, Suzhou 215028, China
| | - Xiaodong Yang
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China; Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Xinye Ni
- The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, Changzhou 213003, China.
| | - Jian Zheng
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China; Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China; Jinan Guoke Medical Technology Development Co., Ltd, Jinan, 250101, China.
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Three-Dimensional Analysis of Bone Volume Change at Donor Sites in Mandibular Body Bone Block Grafts by a Computer-Assisted Automatic Registration Method: A Retrospective Study. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12147261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
This study aimed to evaluate the bone volume change at donor sites in patients who received mandibular body bone block grafts using intensity-based automatic image registration. A retrospective study was conducted with 32 patients who received mandibular bone block grafts between 2017 and 2019 at the Pusan National University Dental Hospital. Cone-beam computed tomography (CBCT) images were obtained before surgery (T0), 1 day after surgery (T1), and 4 months after surgery (T2). Scattered artefacts were removed by manual segmentation. The T0 image was used as the reference image for registration of T1 and T2 images using intensity-based registration. A total of 32 donor sites were analyzed three-dimensionally. The volume and pixel value of the bones were measured and analyzed. The mean regenerated bone volume rate on follow-up images (T2) was 34.87% ± 17.11%. However, no statistically significant differences of regenerated bone volume were noted among the four areas of the donor site (upper anterior, upper posterior, lower anterior, and lower posterior). The mean pixel value rate of the follow-up images (T2) was 78.99% ± 16.9% compared with that of T1, which was statistically significant (p < 0.05). Intensity-based registration with histogram matching showed that newly generated bone is generally qualitatively and quantitatively poorer than the original bone, thus revealing the feasibility of pixel value to evaluate bone quality in CBCT images. Considering the bone mass recovered in this study, 4 months may not be sufficient for a second harvesting, and a longer period of follow-up is required.
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6
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Zhang X, Uneri A, Huang Y, Jones CK, Witham TF, Helm PA, Siewerdsen JH. Deformable 3D-2D image registration and analysis of global spinal alignment in long-length intraoperative spine imaging. Med Phys 2022; 49:5715-5727. [PMID: 35762028 DOI: 10.1002/mp.15819] [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: 02/15/2022] [Revised: 06/03/2022] [Accepted: 06/13/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Spinal deformation during surgical intervention (caused by patient positioning and/or correction of malalignment) confounds conventional navigation due to assumptions of rigid transformation. Moreover, the ability to accurately quantify spinal alignment in the operating room would provide assessment of the surgical product via metrics that correlate with clinical outcome. PURPOSE A method for deformable 3D-2D registration of preoperative CT to intraoperative long-length tomosynthesis images is reported for accurate 3D evaluation of device placement in the presence of spinal deformation and automated evaluation of global spinal alignment (GSA). METHODS Long-length tomosynthesis ("Long Film", LF) images were acquired using an O-arm™ imaging system (Medtronic, Minneapolis USA). A deformable 3D-2D patient registration was developed using multi-scale masking (proceeding from the full-length image to local subvolumes about each vertebra) to transform vertebral labels and planning information from preoperative CT to the LF images. Automatic measurement of GSA [Main Thoracic Kyphosis (MThK) and Lumbar Lordosis (LL)] was obtained using a spline fit to registered labels. The "Known-Component Registration" (KC-Reg) method for device registration was adapted to the multi-scale process for 3D device localization from orthogonal LF images. The multi-scale framework was evaluated using a deformable spine phantom in which pedicle screws were inserted, and deformations were induced over a range in LL ∼25-80°. Further validation was carried out in a cadaver study with implanted pedicle screws and a similar range of spinal deformation. The accuracy of patient and device registration was evaluated in terms of 3D translational error and target registration error (TRE), respectively, and the accuracy of automatic GSA measurements were compared to manual annotation. RESULTS Phantom studies demonstrated accurate registration via the multi-scale framework for all vertebral levels in both the neutral and deformed spine: median (interquartile range, IQR) patient registration error was 1.1 mm (0.7-1.9 mm IQR). Automatic measures of MThK and LL agreed with manual delineation within -1.1° ± 2.2° and 0.7° ± 2.0° (mean and standard deviation), respectively. Device registration error was 0.7 mm (0.4-1.0 mm IQR) at the screw tip and 0.9° (1.0°-1.5°) about the screw trajectory. Deformable 3D-2D registration significantly outperformed conventional rigid registration (p < 0.05), which exhibited device registration error of 2.1 mm (0.8-4.1 mm) and 4.1° (1.2°-9.5°). Cadaver studies verified performance under realistic conditions, demonstrating patient registration error of 1.6 mm (0.9-2.1 mm); MThK within -4.2° ± 6.8° and LL within 1.7° ± 3.5°; and device registration error of 0.8 mm (0.5-1.9 mm) and 0.7° (0.4°-1.2°) for the multi-scale deformable method, compared to 2.5 mm (1.0-7.9 mm) and 2.3° (1.6°-8.1°) for rigid registration (p < 0.05). CONCLUSION The deformable 3D-2D registration framework leverages long-length intraoperative imaging to achieve accurate patient and device registration over extended lengths of the spine (up to 64 cm) even with strong anatomical deformation. The method offers a new means for quantitative validation of spinal correction (intraoperative GSA measurement) and 3D verification of device placement in comparison to preoperative images and planning data. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Xiaoxuan Zhang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
| | - Ali Uneri
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
| | - Yixuan Huang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
| | - Craig K Jones
- The Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD
| | - Timothy F Witham
- Department of Neurosurgery, Johns Hopkins University, Baltimore, MD
| | | | - Jeffrey H Siewerdsen
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD.,The Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD.,Department of Neurosurgery, Johns Hopkins University, Baltimore, MD
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Han R, Jones CK, Lee J, Zhang X, Wu P, Vagdargi P, Uneri A, Helm PA, Luciano M, Anderson WS, Siewerdsen JH. Joint synthesis and registration network for deformable MR-CBCT image registration for neurosurgical guidance. Phys Med Biol 2022; 67:10.1088/1361-6560/ac72ef. [PMID: 35609586 PMCID: PMC9801422 DOI: 10.1088/1361-6560/ac72ef] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 05/24/2022] [Indexed: 01/03/2023]
Abstract
Objective.The accuracy of navigation in minimally invasive neurosurgery is often challenged by deep brain deformations (up to 10 mm due to egress of cerebrospinal fluid during neuroendoscopic approach). We propose a deep learning-based deformable registration method to address such deformations between preoperative MR and intraoperative CBCT.Approach.The registration method uses a joint image synthesis and registration network (denoted JSR) to simultaneously synthesize MR and CBCT images to the CT domain and perform CT domain registration using a multi-resolution pyramid. JSR was first trained using a simulated dataset (simulated CBCT and simulated deformations) and then refined on real clinical images via transfer learning. The performance of the multi-resolution JSR was compared to a single-resolution architecture as well as a series of alternative registration methods (symmetric normalization (SyN), VoxelMorph, and image synthesis-based registration methods).Main results.JSR achieved median Dice coefficient (DSC) of 0.69 in deep brain structures and median target registration error (TRE) of 1.94 mm in the simulation dataset, with improvement from single-resolution architecture (median DSC = 0.68 and median TRE = 2.14 mm). Additionally, JSR achieved superior registration compared to alternative methods-e.g. SyN (median DSC = 0.54, median TRE = 2.77 mm), VoxelMorph (median DSC = 0.52, median TRE = 2.66 mm) and provided registration runtime of less than 3 s. Similarly in the clinical dataset, JSR achieved median DSC = 0.72 and median TRE = 2.05 mm.Significance.The multi-resolution JSR network resolved deep brain deformations between MR and CBCT images with performance superior to other state-of-the-art methods. The accuracy and runtime support translation of the method to further clinical studies in high-precision neurosurgery.
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Affiliation(s)
- R Han
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - C K Jones
- The Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, United States of America
| | - J Lee
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD, United States of America
| | - X Zhang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - P Wu
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - P Vagdargi
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, United States of America
| | - A Uneri
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - P A Helm
- Medtronic Inc., Littleton, MA, United States of America
| | - M Luciano
- Department of Neurosurgery, Johns Hopkins Hospital, Baltimore, MD, United States of America
| | - W S Anderson
- Department of Neurosurgery, Johns Hopkins Hospital, Baltimore, MD, United States of America
| | - J H Siewerdsen
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America
- The Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, United States of America
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, United States of America
- Department of Neurosurgery, Johns Hopkins Hospital, Baltimore, MD, United States of America
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Jiang J, Veeraraghavan H. One shot PACS: Patient specific Anatomic Context and Shape prior aware recurrent registration-segmentation of longitudinal thoracic cone beam CTs. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; PP:10.1109/TMI.2022.3154934. [PMID: 35213307 PMCID: PMC9642320 DOI: 10.1109/tmi.2022.3154934] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/27/2023]
Abstract
Image-guided adaptive lung radiotherapy requires accurate tumor and organs segmentation from during treatment cone-beam CT (CBCT) images. Thoracic CBCTs are hard to segment because of low soft-tissue contrast, imaging artifacts, respiratory motion, and large treatment induced intra-thoracic anatomic changes. Hence, we developed a novel Patient-specific Anatomic Context and Shape prior or PACS-aware 3D recurrent registration-segmentation network for longitudinal thoracic CBCT segmentation. Segmentation and registration networks were concurrently trained in an end-to-end framework and implemented with convolutional long-short term memory models. The registration network was trained in an unsupervised manner using pairs of planning CT (pCT) and CBCT images and produced a progressively deformed sequence of images. The segmentation network was optimized in a one-shot setting by combining progressively deformed pCT (anatomic context) and pCT delineations (shape context) with CBCT images. Our method, one-shot PACS was significantly more accurate (p <0.001) for tumor (DSC of 0.83 ± 0.08, surface DSC [sDSC] of 0.97 ± 0.06, and Hausdorff distance at 95th percentile [HD95] of 3.97±3.02mm) and the esophagus (DSC of 0.78 ± 0.13, sDSC of 0.90±0.14, HD95 of 3.22±2.02) segmentation than multiple methods. Ablation tests and comparative experiments were also done.
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Liu J, Yan H, Cheng H, Liu J, Sun P, Wang B, Mao R, Du C, Luo S. CBCT-based synthetic CT generation using generative adversarial networks with disentangled representation. Quant Imaging Med Surg 2021; 11:4820-4834. [PMID: 34888192 DOI: 10.21037/qims-20-1056] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 06/02/2021] [Indexed: 11/06/2022]
Abstract
Background Cone-beam computed tomography (CBCT) plays a key role in image-guided radiotherapy (IGRT), however its poor image quality limited its clinical application. In this study, we developed a deep-learning based approach to translate CBCT image to synthetic CT (sCT) image that preserves both CT image quality and CBCT anatomical structures. Methods A novel synthetic CT generative adversarial network (sCTGAN) was proposed for CBCT-to-CT translation via disentangled representation. The approach of disentangled representation was employed to extract the anatomical information shared by CBCT and CT image domains. Both on-board CBCT and planning CT of 40 patients were used for network learning and those of another 12 patients were used for testing. Accuracy of our network was quantitatively evaluated using a series of statistical metrics, including the peak signal-to-noise ratio (PSNR), mean structural similarity index (SSIM), mean absolute error (MAE), and root-mean-square error (RMSE). Effectiveness of our network was compared against three state-of-the-art CycleGAN-based methods. Results The PSNR, SSIM, MAE, and RMSE between sCT generated by sCTGAN and deformed planning CT (dpCT) were 34.12 dB, 0.86, 32.70 HU, and 60.53 HU, while the corresponding values between original CBCT and dpCT were 28.67 dB, 0.64, 70.56 HU, and 112.13 HU. The RMSE (60.53±14.38 HU) of sCT generated by sCTGAN was less than that of sCT generated by all the three comparing methods (72.40±16.03 HU by CycleGAN, 71.60±15.09 HU by CycleGAN-Unet512, 64.93±14.33 HU by CycleGAN-AG). Conclusions The sCT generated by our sCTGAN network was closer to the ground truth (dpCT), in comparison to all the three comparing CycleGAN-based methods. It provides an effective way to generate high-quality sCT which has a wide application in IGRT and adaptive radiotherapy.
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Affiliation(s)
- Jiwei Liu
- School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China
| | - Hui Yan
- Department of Radiation Oncology, National Clinical Research Center for Cancer, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hanlin Cheng
- School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China
| | - Jianfei Liu
- School of Electrical Engineering and Automation, Anhui University, Hefei, China
| | - Pengjian Sun
- School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China
| | - Boyi Wang
- School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China
| | - Ronghu Mao
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Chi Du
- Cancer Center, The Second Peoples Hospital of Neijiang, Neijiang, China
| | - Shengquan Luo
- School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China
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Zhou B, Augenfeld Z, Chapiro J, Zhou SK, Liu C, Duncan JS. Anatomy-guided multimodal registration by learning segmentation without ground truth: Application to intraprocedural CBCT/MR liver segmentation and registration. Med Image Anal 2021; 71:102041. [PMID: 33823397 PMCID: PMC8184611 DOI: 10.1016/j.media.2021.102041] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 03/04/2021] [Accepted: 03/10/2021] [Indexed: 12/24/2022]
Abstract
Multimodal image registration has many applications in diagnostic medical imaging and image-guided interventions, such as Transcatheter Arterial Chemoembolization (TACE) of liver cancer guided by intraprocedural CBCT and pre-operative MR. The ability to register peri-procedurally acquired diagnostic images into the intraprocedural environment can potentially improve the intra-procedural tumor targeting, which will significantly improve therapeutic outcomes. However, the intra-procedural CBCT often suffers from suboptimal image quality due to lack of signal calibration for Hounsfield unit, limited FOV, and motion/metal artifacts. These non-ideal conditions make standard intensity-based multimodal registration methods infeasible to generate correct transformation across modalities. While registration based on anatomic structures, such as segmentation or landmarks, provides an efficient alternative, such anatomic structure information is not always available. One can train a deep learning-based anatomy extractor, but it requires large-scale manual annotations on specific modalities, which are often extremely time-consuming to obtain and require expert radiological readers. To tackle these issues, we leverage annotated datasets already existing in a source modality and propose an anatomy-preserving domain adaptation to segmentation network (APA2Seg-Net) for learning segmentation without target modality ground truth. The segmenters are then integrated into our anatomy-guided multimodal registration based on the robust point matching machine. Our experimental results on in-house TACE patient data demonstrated that our APA2Seg-Net can generate robust CBCT and MR liver segmentation, and the anatomy-guided registration framework with these segmenters can provide high-quality multimodal registrations.
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Affiliation(s)
- Bo Zhou
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
| | - Zachary Augenfeld
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Julius Chapiro
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - S Kevin Zhou
- School of Biomedical Engineering & Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Chi Liu
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - James S Duncan
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA; Department of Electrical Engineering, Yale University, New Haven, CT, USA.
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11
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Duan L, Ni X, Liu Q, Gong L, Yuan G, Li M, Yang X, Fu T, Zheng J. Unsupervised learning for deformable registration of thoracic CT and cone-beam CT based on multiscale features matching with spatially adaptive weighting. Med Phys 2020; 47:5632-5647. [PMID: 32949051 DOI: 10.1002/mp.14464] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 07/29/2020] [Accepted: 08/27/2020] [Indexed: 12/21/2022] Open
Abstract
PURPOSE Cone-beam computed tomography (CBCT) is a common on-treatment imaging widely used in image-guided radiotherapy. Fast and accurate registration between the on-treatment CBCT and planning CT is significant for and precise adaptive radiotherapy treatment (ART). However, existing CT-CBCT registration methods, which are mostly affine or time-consuming intensity- based deformation registration, still need further study due to the considerable CT-CBCT intensity discrepancy and the artifacts in low-quality CBCT images. In this paper, we propose a deep learning-based CT-CBCT registration model to promote rapid and accurate CT-CBCT registration for radiotherapy. METHODS The proposed CT-CBCT registration model consists of a registration network and an innovative deep similarity metric network. The registration network is a novel fully convolution network adapted specially for patch-wise CT-CBCT registration. The metric network, going beyond intensity, automatically evaluates the high-dimensional attribute-based dissimilarity between the registered CT and CBCT images. In addition, considering the artifacts in low-quality CBCT images, we add spatial weighting (SW) block to adaptively attach more importance to those informative voxels while inhibit the interference of artifact regions. Such SW-based metric network is expected to extract the most meaningful and discriminative deep features, and form a more reliable CT-CBCT similarity measure to train the registration network. RESULTS We evaluate the proposed method on clinical thoracic CBCT and CT dataset, and compare the registration results with some other common image similarity metrics and some state-of-the-art registration algorithms. The proposed method provides the highest Structural Similarity index (86.17 ± 5.09), minimum Target Registration Error of landmarks (2.37 ± 0.32 mm), and the best DSC coefficient (78.71 ± 10.95) of tumor volumes. Moreover, our model also obtains comparable distance error of lung surfaces (1.75 ± 0.35 mm). CONCLUSION The proposed model shows both efficiency and efficacy for reliable thoracic CT-CBCT registration, and can generate the matched CT and CBCT images within few seconds, which is of great significance to clinical radiotherapy.
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Affiliation(s)
- Luwen Duan
- School of Biomedical Engineering, University of Science and Technology of China, Hefei, 230026, China.,Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Xinye Ni
- Radiotherapy Department, Second People's Hospital of Changzhou, Nanjing Medical University, Changzhou, 213003, China.,Center for Medical Physics, Nanjing Medical University, Changzhou, 213003, China
| | - Qi Liu
- School of Biomedical Engineering, University of Science and Technology of China, Hefei, 230026, China.,Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Lun Gong
- The Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, 300072, China
| | - Gang Yuan
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Ming Li
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Xiaodong Yang
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Tianxiao Fu
- Department of Radiation Oncology, The First Affiliated Hospital Of Soochow University, Suzhou, 215006, China
| | - Jian Zheng
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
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12
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Zhang Y, Paulson E, Lim S, Hall WA, Ahunbay E, Mickevicius NJ, Straza MW, Erickson B, Li XA. A Patient-Specific Autosegmentation Strategy Using Multi-Input Deformable Image Registration for Magnetic Resonance Imaging-Guided Online Adaptive Radiation Therapy: A Feasibility Study. Adv Radiat Oncol 2020; 5:1350-1358. [PMID: 33305098 PMCID: PMC7718500 DOI: 10.1016/j.adro.2020.04.027] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Revised: 04/08/2020] [Accepted: 04/16/2020] [Indexed: 11/20/2022] Open
Abstract
Purpose Magnetic resonance-guided online adaptive radiation therapy (MRgOART) requires accurate and efficient segmentation. However, the performance of current autosegmentation tools is generally poor for magnetic resonance imaging (MRI) owing to day-to-day variations in image intensity and patient anatomy. In this study, we propose a patient-specific autosegmentation strategy using multiple-input deformable image registration (DIR; PASSMID) to improve segmentation accuracy and efficiency for MRgOART. Methods and materials Longitudinal MRI scans acquired on a 1.5T MRI-Linac for 10 patients with abdominal cancer were used. The proposed PASSMID includes 2 steps: applying a patient-specific image processing pipeline to longitudinal MRI scans, and populating all contours from previous sessions/fractions to a new fractional MRI using multiple DIRs and combining the resulted contours using simultaneous truth and performance level estimation (STAPLE) to obtain the final consensus segmentation. Five contour propagation strategies were compared: planning computed tomography to fractional MRI scans through rigid body registration (RDR), pretreatment MRI to fractional MRI scans through RDR and DIR, and the proposed multi-input DIR/STAPLE without preprocessing, and the PASSMID. Dice similarity coefficient (DSC) and mean distance to agreement (MDA) with ground truth contours were calculated slice by slice to quantify the contour accuracy. A quantitative index, defined as the ratio of acceptable slices, was introduced using a criterion of DSC > 0.8 and MDA < 2 mm. Results The proposed PASSMID performed well with an average 2-dimensional DSC/MDA of 0.94/1.78 mm, 0.93/1.04 mm, 0.93/1.06 mm, 0.93/1.14 mm, 0.92/0.83 mm, 0.84/1.53 mm, 0.86/2.39 mm, 0.81/2.49 mm, 0.72/5.48 mm, and 0.70/5.03 mm for the liver, left kidney, right kidney, spleen, aorta, pancreas, stomach, duodenum, small bowel, and colon, respectively. Starting from the third fractions, the contour accuracy was significantly improved with PASSMID compared with the single-DIR strategy (P < .05). The mean ratio of acceptable slices were 13.9%, 17.5%, 60.8%, 70.6%, and 71.8% for the 5 strategies, respectively. Conclusions The proposed PASSMID solution, by combining image processing, multi-input DIRs, and STAPLE, can significantly improve the accuracy of autosegmentation for intrapatient MRI scans, reducing the time required for further contour editing, thereby facilitating the routine practice of MRgOART.
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Affiliation(s)
- Ying Zhang
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Eric Paulson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Sara Lim
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - William A Hall
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Ergun Ahunbay
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Nikolai J Mickevicius
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Michael W Straza
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Beth Erickson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - X Allen Li
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
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13
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Loi G, Fusella M, Vecchi C, Menna S, Rosica F, Gino E, Maffei N, Menghi E, Savini A, Roggio A, Radici L, Cagni E, Lucio F, Strigari L, Strolin S, Garibaldi C, Romanò C, Piovesan M, Franco P, Fiandra C. Computed Tomography to Cone Beam Computed Tomography Deformable Image Registration for Contour Propagation Using Head and Neck, Patient-Based Computational Phantoms: A Multicenter Study. Pract Radiat Oncol 2019; 10:125-132. [PMID: 31786233 DOI: 10.1016/j.prro.2019.11.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 10/22/2019] [Accepted: 11/12/2019] [Indexed: 12/14/2022]
Abstract
PURPOSE To investigate the performance of various algorithms for deformable image registration (DIR) for propagating regions of interest (ROIs) using multiple commercial platforms, from computed tomography to cone beam computed tomography (CBCT) and megavoltage computed tomography. METHODS AND MATERIALS Fourteen institutions participated in the study using 5 commercial platforms: RayStation (RaySearch Laboratories, Stockholm, Sweden), MIM (Cleveland, OH), VelocityAI and SmartAdapt (Varian Medical Systems, Palo Alto, CA), and ABAS (Elekta AB, Stockholm, Sweden). Algorithms were tested on synthetic images generated with the ImSimQA (Oncology Systems Limited, Shrewsbury, UK) package by applying 2 specific deformation vector fields (DVF) to real head and neck patient datasets. On-board images from 3 systems were used: megavoltage computed tomography from Tomotherapy and 2 kinds of CBCT from a clinical linear accelerator. Image quality of the system was evaluated. The algorithms' accuracy was assessed by comparing the DIR-mapped ROIs returned by each center with those of the reference, using the Dice similarity coefficient and mean distance to conformity metrics. Statistical inference on the validation results was carried out to identify the prognostic factors of DIR performance. RESULTS Analyzing 840 DIR-mapped ROIs returned by the centers, it was demonstrated that DVF intensity and image quality were significant prognostic factors of DIR performance. The accuracy of the propagated contours was generally high, and acceptable DIR performance can be obtained with lower-dose CBCT image protocols. CONCLUSIONS The performance of the systems proved to be image quality specific, depending on the DVF type and only partially on the platforms. All systems proved to be robust against image artifacts and noise, except the demon-based software.
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Affiliation(s)
- Gianfranco Loi
- Department of Medical Physics, University Hospital "Maggiore della Carità," Novara, Italy
| | - Marco Fusella
- Medical Physics Department, Veneto Institute of Oncology IOV-IRCCS, Padua, Italy.
| | | | - Sebastiano Menna
- Fondazione Policlinico Universitario A. Gemelli IRCCS, UOC di Fisica Sanitaria, Dipartimento di diagnostica per immagini, radioterapia oncologica ed ematologia, Rome, Italy
| | | | - Eva Gino
- SC Fisica Sanitaria, A.O. Ordine Mauriziano di Torino, Italy
| | - Nicola Maffei
- Department of Medical Physics, A.O. U. di Modena, Modena, Italy; University of Turin, Post Graduate School in Medical Physics, Turin, Italy
| | - Enrico Menghi
- Medical Physics Department, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, FC, Italy
| | - Alessandro Savini
- Medical Physics Department, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, FC, Italy
| | - Antonella Roggio
- Medical Physics Department, Veneto Institute of Oncology IOV-IRCCS, Padua, Italy
| | - Lorenzo Radici
- Ospedale regionale "Umberto Parini" Azienda USL VDA, Fisica Sanitaria, Italy
| | - Elisabetta Cagni
- Medical Physics Unit, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy; School of Engineering, Cardiff University, Cardiff, Wales, UK
| | | | - Lidia Strigari
- Department of Medical Physics, St. Orsola-Malpighi Hospital, Bologna, Italy
| | | | - Cristina Garibaldi
- IEO, European Institute of Oncology IRCCS, Unit of Medical Physics, Milan, Italy
| | - Chiara Romanò
- IEO, European Institute of Oncology IRCCS, Unit of Medical Physics, Milan, Italy
| | | | | | - Christian Fiandra
- University of Turin, Department of Oncology, Turin, Italy; School of Bioengineering and Medical-Surgical Sciences, Politecnico di Torino, Turin, Italy
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14
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Sheikh K, Lee SH, Cheng Z, Lakshminarayanan P, Peng L, Han P, McNutt TR, Quon H, Lee J. Predicting acute radiation induced xerostomia in head and neck Cancer using MR and CT Radiomics of parotid and submandibular glands. Radiat Oncol 2019; 14:131. [PMID: 31358029 PMCID: PMC6664784 DOI: 10.1186/s13014-019-1339-4] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Accepted: 07/17/2019] [Indexed: 12/24/2022] Open
Abstract
PURPOSE To analyze baseline CT/MR-based image features of salivary glands to predict radiation-induced xerostomia 3-months after head-and-neck cancer (HNC) radiotherapy. METHODS A retrospective analysis was performed on 266 HNC patients who were treated using radiotherapy at our institution between 2009 and 2018. CT and T1 post-contrast MR images along with NCI-CTCAE xerostomia grade (3-month follow-up) were prospectively collected at our institution. CT and MR images were registered on which parotid/submandibular glands were contoured. Image features were extracted for ipsilateral/contralateral parotid and submandibular glands relative to the location of the primary tumor. Dose-volume-histogram (DVH) parameters were also acquired. Features were pre-selected based on Spearman correlation before modelling by examining the correlation with xerostomia (p < 0.05). A shrinkage regression analysis of the pre-selected features was performed using LASSO. The internal validity of the variable selection was estimated by repeating the entire variable selection procedure using a leave-one-out-cross-validation. The most frequently selected variables were considered in the final model. A generalized linear regression with repeated ten-fold cross-validation was developed to predict radiation-induced xerostomia at 3-months after radiotherapy. This model was tested in an independent dataset (n = 50) of patients who were treated at the same institution in 2017-2018. We compared the prediction performances under eight conditions (DVH-only, CT-only, MR-only, CT + MR, DVH + CT, DVH + CT + MR, Clinical+CT + MR, and Clinical+DVH + CT + MR) using the area under the receiver operating characteristic curve (ROC-AUC). RESULTS Among extracted features, 7 CT, 5 MR, and 2 DVH features were selected. The internal cohort (n = 216) ROC-AUC values for DVH, CT, MR, and Clinical+DVH + CT + MR features were 0.73 ± 0.01, 0.69 ± 0.01, 0.70 ± 0.01, and 0.79 ± 0.01, respectively. The validation cohort (n = 50) ROC-AUC values for DVH, CT, MR, and Clinical+DVH + CT + MR features were 0.63, 0.57, 0.66, and 0.68, respectively. The DVH-ROC was not significantly different than the CT-ROC (p = 0.8) or MR-ROC (p = 0.4). However, the CT + MR-ROC was significantly different than the CT-ROC (p = 0.03), but not the Clinical+DVH + CT + MR model (p = 0.5). CONCLUSION Our results suggest that baseline CT and MR image features may reflect baseline salivary gland function and potential risk for radiation injury. The integration of baseline image features into prediction models has the potential to improve xerostomia risk stratification with the ultimate goal of truly personalized HNC radiotherapy.
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Affiliation(s)
- Khadija Sheikh
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, 401 North Broadway, Suite 1440, Baltimore, MD 21287-5678 USA
| | - Sang Ho Lee
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, 401 North Broadway, Suite 1440, Baltimore, MD 21287-5678 USA
| | - Zhi Cheng
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, 401 North Broadway, Suite 1440, Baltimore, MD 21287-5678 USA
| | - Pranav Lakshminarayanan
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, 401 North Broadway, Suite 1440, Baltimore, MD 21287-5678 USA
| | - Luke Peng
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, 401 North Broadway, Suite 1440, Baltimore, MD 21287-5678 USA
| | - Peijin Han
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, 401 North Broadway, Suite 1440, Baltimore, MD 21287-5678 USA
| | - Todd R. McNutt
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, 401 North Broadway, Suite 1440, Baltimore, MD 21287-5678 USA
| | - Harry Quon
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, 401 North Broadway, Suite 1440, Baltimore, MD 21287-5678 USA
| | - Junghoon Lee
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, 401 North Broadway, Suite 1440, Baltimore, MD 21287-5678 USA
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Yao L, Zeng D, Chen G, Liao Y, Li S, Zhang Y, Wang Y, Tao X, Niu S, Lv Q, Bian Z, Ma J, Huang J. Multi-energy computed tomography reconstruction using a nonlocal spectral similarity model. Phys Med Biol 2019; 64:035018. [PMID: 30577033 DOI: 10.1088/1361-6560/aafa99] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Multi-energy computed tomography (MECT) is able to acquire simultaneous multi-energy measurements from one scan. In addition, it allows material differentiation and quantification effectively. However, due to the limited energy bin width, the number of photons detected in an energy-specific channel is smaller than that in traditional CT, which results in image quality degradation. To address this issue, in this work, we develop a statistical iterative reconstruction algorithm to acquire high-quality MECT images and high-accuracy material-specific images. Specifically, this algorithm fully incorporates redundant self-similarities within nonlocal regions in the MECT image at one bin and rich spectral similarities among MECT images at all bins. For simplicity, the presented algorithm is referred to as 'MECT-NSS'. Moreover, an efficient optimization algorithm is developed to solve the MECT-NSS objective function. Then, a comprehensive evaluation of parameter selection for the MECT-NSS algorithm is conducted. In the experiment, the datasets include images from three phantoms and one patient to validate and evaluate the MECT-NSS reconstruction performance. The qualitative and quantitative results demonstrate that the presented MECT-NSS can successfully yield better MECT image quality and more accurate material estimation than the competing algorithms.
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Affiliation(s)
- Lisha Yao
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China. Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangzhou 510515, People's Republic of China. These authors contributed equally
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Kubota Y, Sakai M, Tashiro M, Saitoh JI, Abe T, Ohno T, Nakano T. Technical Note: Predicting dose distribution with replacing stopping power ratio for inter-fractional motion and intra-fractional motion during carbon ion radiotherapy with passive irradiation method for stage I lung cancer. Med Phys 2018; 45:3435-3441. [PMID: 29757472 DOI: 10.1002/mp.12966] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2017] [Revised: 04/06/2018] [Accepted: 05/02/2018] [Indexed: 11/10/2022] Open
Abstract
PURPOSE We designed and evaluated a simple method for predicting the effects of intra-fractional and/or inter-fractional motion on dose distribution during carbon ion radiotherapy (CIRT) for solitary-lesion stage I lung cancer. METHODS The proposed method uses computed tomography (CT) images from treatment planning and intra-tumoral and/or inter-tumoral displacement. The predicted dose distribution (PDD) was calculated by replacing the current tumor region with the stopping power ratio (SPR) of the lung and replacing the moved tumor region with the SPR of the tumor. The actual dose distribution (ADD) was calculated without the replacement. Ten patients with solitary-lesion stage I lung cancer were retrospectively studied to evaluate the prediction method's accuracy. Four PDDs for intra-fractional motion (gate-in, exhalation, gate-out, inhalation phases during four-dimensional CT) and two PDDs for inter-fractional motion (CT images acquired 1-2 days before treatment) with bone- and tumor-matching methods were compared with each of six ADDs on each CT scan. Percentages of the planning/clinical target volumes (PTV/CTV) receiving >95% of the prescribed dose (V95 ) and of minimum doses covering 95% of the PTV/CTV (D95 ) were compared with dose volume histogram parameters. RESULTS The maximum tumor displacements occurred in the superior-inferior direction, with intra-fractional motion values of 3.75 and 8.97 mm for the superior and inferior directions, respectively, and inter-fractional values of 9.61 and 4.10 mm. The maximum average error for PTV V95 regarding intra-fractional motion was -0.43% for the gate-out phase and -0.63% for the inhalation phase. There were no significant differences for these parameters (P = 0.541, P = 0.571). Average errors for PTV and CTV V95 with inter-fractional motion with bone matching were 2.2% and 2.9%, respectively, with no significant differences (P = 0.387, P = 0.155). CONCLUSIONS The accuracy of the proposed method was good. Hence, it is feasible to use the proposed method during CIRT to predict dose distribution with respect to intra-fractional motion and/or inter-fractional motion of the tumor in patients with solitary-lesion stage I lung cancer.
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Affiliation(s)
| | - Makoto Sakai
- Gunma University Heavy Ion Medical Center, Gunma, Japan
| | | | - Jun-Ichi Saitoh
- Department of Radiology, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama, Japan
| | - Takanori Abe
- Gunma University Heavy Ion Medical Center, Gunma, Japan
| | - Tatsuya Ohno
- Gunma University Heavy Ion Medical Center, Gunma, Japan
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17
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Kubota Y, Hayashi H, Abe S, Souda S, Okada R, Ishii T, Tashiro M, Torikoshi M, Kanai T, Ohno T, Nakano T. Evaluation of the accuracy and clinical practicality of a calculation system for patient positional displacement in carbon ion radiotherapy at five sites. J Appl Clin Med Phys 2018; 19:144-153. [PMID: 29369463 PMCID: PMC5849861 DOI: 10.1002/acm2.12261] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Revised: 10/24/2017] [Accepted: 12/18/2017] [Indexed: 01/01/2023] Open
Abstract
PURPOSE We developed a system for calculating patient positional displacement between digital radiography images (DRs) and digitally reconstructed radiography images (DRRs) to reduce patient radiation exposure, minimize individual differences between radiological technologists in patient positioning, and decrease positioning time. The accuracy of this system at five sites was evaluated with clinical data from cancer patients. The dependence of calculation accuracy on the size of the region of interest (ROI) and initial position was evaluated for clinical use. METHODS For a preliminary verification, treatment planning and positioning data from eight setup patterns using a head and neck phantom were evaluated. Following this, data from 50 patients with prostate, lung, head and neck, liver, or pancreatic cancer (n = 10 each) were evaluated. Root mean square errors (RMSEs) between the results calculated by our system and the reference positions were assessed. The reference positions were manually determined by two radiological technologists to best-matching positions with orthogonal DRs and DRRs in six axial directions. The ROI size dependence was evaluated by comparing RMSEs for three different ROI sizes. Additionally, dependence on initial position parameters was evaluated by comparing RMSEs for four position patterns. RESULTS For the phantom study, the average (± standard deviation) translation error was 0.17 ± 0.05, rotation error was 0.17 ± 0.07, and ΔD was 0.14 ± 0.05. Using the optimal ROI size for each patient site, all cases of prostate, lung, and head and neck cancer with initial position parameters of 10 mm or under were acceptable in our tolerance. However, only four liver cancer cases and three pancreatic cancer cases were acceptable, because of low-reproducibility regions in the ROIs. CONCLUSION Our system has clinical practicality for prostate, lung, and head and neck cancer cases. Additionally, our findings suggest ROI size dependence in some cases.
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Affiliation(s)
- Yoshiki Kubota
- Gunma University Heavy Ion Medical Center, Maebashi, Gunma, Japan
| | - Hayato Hayashi
- Gunma University Graduate School of Medicine, Maebashi, Gunma, Japan
| | - Satoshi Abe
- Department of Radiology, Gunma University Hospital, Maebashi, Gunma, Japan
| | - Saki Souda
- Department of Radiology, Gunma University Hospital, Maebashi, Gunma, Japan
| | - Ryosuke Okada
- Department of Radiology, Gunma University Hospital, Maebashi, Gunma, Japan
| | - Takayoshi Ishii
- Department of Radiology, Gunma University Hospital, Maebashi, Gunma, Japan
| | - Mutsumi Tashiro
- Gunma University Heavy Ion Medical Center, Maebashi, Gunma, Japan
| | - Masami Torikoshi
- Gunma University Heavy Ion Medical Center, Maebashi, Gunma, Japan
| | - Tatsuaki Kanai
- Gunma University Heavy Ion Medical Center, Maebashi, Gunma, Japan
| | - Tatsuya Ohno
- Gunma University Heavy Ion Medical Center, Maebashi, Gunma, Japan
| | - Takashi Nakano
- Gunma University Heavy Ion Medical Center, Maebashi, Gunma, Japan
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Park S, Farah R, Shea SM, Tryggestad E, Hales R, Lee J. Simultaneous tumor and surrogate motion tracking with dynamic MRI for radiation therapy planning. Phys Med Biol 2018; 63:025015. [PMID: 29243669 DOI: 10.1088/1361-6560/aaa20b] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Respiration-induced tumor motion is a major obstacle for achieving high-precision radiotherapy of cancers in the thoracic and abdominal regions. Surrogate-based estimation and tracking methods are commonly used in radiotherapy, but with limited understanding of quantified correlation to tumor motion. In this study, we propose a method to simultaneously track the lung tumor and external surrogates to evaluate their spatial correlation in a quantitative way using dynamic MRI, which allows real-time acquisition without ionizing radiation exposure. To capture the lung and whole tumor, four MRI-compatible fiducials are placed on the patient's chest and upper abdomen. Two different types of acquisitions are performed in the sagittal orientation including multi-slice 2D cine MRIs to reconstruct 4D-MRI and two-slice 2D cine MRIs to simultaneously track the tumor and fiducials. A phase-binned 4D-MRI is first reconstructed from multi-slice MR images using body area as a respiratory surrogate and groupwise registration. The 4D-MRI provides 3D template volumes for different breathing phases. 3D tumor position is calculated by 3D-2D template matching in which 3D tumor templates in the 4D-MRI reconstruction and the 2D cine MRIs from the two-slice tracking dataset are registered. 3D trajectories of the external surrogates are derived via matching a 3D geometrical model of the fiducials to their segmentations on the 2D cine MRIs. We tested our method on ten lung cancer patients. Using a correlation analysis, the 3D tumor trajectory demonstrates a noticeable phase mismatch and significant cycle-to-cycle motion variation, while the external surrogate was not sensitive enough to capture such variations. Additionally, there was significant phase mismatch between surrogate signals obtained from the fiducials at different locations.
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Affiliation(s)
- Seyoun Park
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD, United States of America
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Zachiu C, de Senneville BD, Tijssen RHN, Kotte ANTJ, Houweling AC, Kerkmeijer LGW, Lagendijk JJW, Moonen CTW, Ries M. Non-rigid CT/CBCT to CBCT registration for online external beam radiotherapy guidance. ACTA ACUST UNITED AC 2017; 63:015027. [DOI: 10.1088/1361-6560/aa990e] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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Joshi KD, Marchant TE. Iterative peak combination: a robust technique for identifying relevant features in medical image histograms. Biomed Phys Eng Express 2017. [DOI: 10.1088/2057-1976/aa929d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Hart V, Burrow D, Allen Li X. A graphical approach to optimizing variable-kernel smoothing parameters for improved deformable registration of CT and cone beam CT images. Phys Med Biol 2017; 62:6246-6260. [PMID: 28714458 DOI: 10.1088/1361-6560/aa7ccb] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
A systematic method is presented for determining optimal parameters in variable-kernel deformable image registration of cone beam CT and CT images, in order to improve accuracy and convergence for potential use in online adaptive radiotherapy. Assessed conditions included the noise constant (symmetric force demons), the kernel reduction rate, the kernel reduction percentage, and the kernel adjustment criteria. Four such parameters were tested in conjunction with reductions of 5, 10, 15, 20, 30, and 40%. Noise constants ranged from 1.0 to 1.9 for pelvic images in ten prostate cancer patients. A total of 516 tests were performed and assessed using the structural similarity index. Registration accuracy was plotted as a function of iteration number and a least-squares regression line was calculated, which implied an average improvement of 0.0236% per iteration. This baseline was used to determine if a given set of parameters under- or over-performed. The most accurate parameters within this range were applied to contoured images. The mean Dice similarity coefficient was calculated for bladder, prostate, and rectum with mean values of 98.26%, 97.58%, and 96.73%, respectively; corresponding to improvements of 2.3%, 9.8%, and 1.2% over previously reported values for the same organ contours. This graphical approach to registration analysis could aid in determining optimal parameters for Demons-based algorithms. It also establishes expectation values for convergence rates and could serve as an indicator of non-physical warping, which often occurred in cases >0.6% from the regression line.
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Affiliation(s)
- Vern Hart
- Department of Radiation Oncology, Medical College of Wisconsin, 8701 W Watertown Plank Road, Milwaukee, WI 53226, United States of America. Department of Physics, Utah Valley University, 800 W University Parkway, Orem, UT 84058, United States of America
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