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Velazco-Garcia JD, Navkar NV, Balakrishnan S, Younes G, Abi-Nahed J, Al-Rumaihi K, Darweesh A, Elakkad MSM, Al-Ansari A, Christoforou EG, Karkoub M, Leiss EL, Tsiamyrtzis P, Tsekos NV. Evaluation of how users interface with holographic augmented reality surgical scenes: Interactive planning MR-Guided prostate biopsies. Int J Med Robot 2021; 17:e2290. [PMID: 34060214 DOI: 10.1002/rcs.2290] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 05/04/2021] [Accepted: 05/27/2021] [Indexed: 12/15/2022]
Abstract
BACKGROUND User interfaces play a vital role in the planning and execution of an interventional procedure. The objective of this study is to investigate the effect of using different user interfaces for planning transrectal robot-assisted MR-guided prostate biopsy (MRgPBx) in an augmented reality (AR) environment. METHOD End-user studies were conducted by simulating an MRgPBx system with end- and side-firing modes. The information from the system to the operator was rendered on HoloLens as an output interface. Joystick, mouse/keyboard, and holographic menus were used as input interfaces to the system. RESULTS The studies indicated that using a joystick improved the interactive capacity and enabled operator to plan MRgPBx in less time. It efficiently captures the operator's commands to manipulate the augmented environment representing the state of MRgPBx system. CONCLUSIONS The study demonstrates an alternative to conventional input interfaces to interact and manipulate an AR environment within the context of MRgPBx planning.
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Affiliation(s)
| | - Nikhil V Navkar
- Department of Surgery, Hamad Medical Corporation, Doha, Qatar
| | | | - Georges Younes
- Department of Surgery, Hamad Medical Corporation, Doha, Qatar
| | | | | | - Adham Darweesh
- Department of Clinical Imaging, Hamad Medical Corporation, Doha, Qatar
| | | | | | | | - Mansour Karkoub
- Department of Mechanical Engineering, Texas A&M University-Qatar, Doha, Qatar
| | - Ernst L Leiss
- Department of Computer Science, University of Houston, Houston, Texas, USA
| | | | - Nikolaos V Tsekos
- Department of Computer Science, University of Houston, Houston, Texas, USA
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Velazco‐Garcia JD, Navkar NV, Balakrishnan S, Abi‐Nahed J, Al‐Rumaihi K, Darweesh A, Al‐Ansari A, Christoforou EG, Karkoub M, Leiss EL, Tsiamyrtzis P, Tsekos NV. End‐user evaluation of software‐generated intervention planning environment for transrectal magnetic resonance‐guided prostate biopsies. Int J Med Robot 2020; 17:1-12. [DOI: 10.1002/rcs.2179] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 09/25/2020] [Accepted: 09/30/2020] [Indexed: 01/20/2023]
Affiliation(s)
| | | | | | | | | | - Adham Darweesh
- Department of Clinical Imaging Hamad Medical Corporation Doha Qatar
| | | | | | - Mansour Karkoub
- Department of Mechanical Engineering Texas A&M University—Qatar Doha Qatar
| | - Ernst L. Leiss
- Department of Computer Science University of Houston Houston Texas USA
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Clinical evaluation of an MRI-to-ultrasound deformable image registration algorithm for prostate brachytherapy. Brachytherapy 2019; 18:95-102. [DOI: 10.1016/j.brachy.2018.08.006] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Revised: 07/16/2018] [Accepted: 08/08/2018] [Indexed: 11/21/2022]
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Lee DH, Lee DW, Henry D, Park HJ, Han BS, Woo DC. Minimisation of Signal Intensity Differences in Distortion Correction Approaches of Brain Magnetic Resonance Diffusion Tensor Imaging. Eur Radiol 2018; 28:4314-4323. [PMID: 29651768 DOI: 10.1007/s00330-018-5382-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Revised: 01/30/2018] [Accepted: 02/12/2018] [Indexed: 11/28/2022]
Abstract
OBJECTIVES To evaluate the effects of signal intensity differences between the b0 image and diffusion tensor imaging (DTI) in the image registration process. METHODS To correct signal intensity differences between the b0 image and DTI data, a simple image intensity compensation (SIMIC) method, which is a b0 image re-calculation process from DTI data, was applied before the image registration. The re-calculated b0 image (b0ext) from each diffusion direction was registered to the b0 image acquired through the MR scanning (b0nd) with two types of cost functions and their transformation matrices were acquired. These transformation matrices were then used to register the DTI data. For quantifications, the dice similarity coefficient (DSC) values, diffusion scalar matrix, and quantified fibre numbers and lengths were calculated. RESULTS The combined SIMIC method with two cost functions showed the highest DSC value (0.802 ± 0.007). Regarding diffusion scalar values and numbers and lengths of fibres from the corpus callosum, superior longitudinal fasciculus, and cortico-spinal tract, only using normalised cross correlation (NCC) showed a specific tendency toward lower values in the brain regions. CONCLUSION Image-based distortion correction with SIMIC for DTI data would help in image analysis by accounting for signal intensity differences as one additional option for DTI analysis. KEY POINTS • We evaluated the effects of signal intensity differences at DTI registration. • The non-diffusion-weighted image re-calculation process from DTI data was applied. • SIMIC can minimise the signal intensity differences at DTI registration.
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Affiliation(s)
- Dong-Hoon Lee
- Faculty of Health Sciences and Brain & Mind Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - Do-Wan Lee
- Center for Bioimaging of New Drug Development, and MR Core Lab., Asan Institute for Life Sciences, Asan Medical Center, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 138-736, Republic of Korea
| | - David Henry
- Faculty of Health Sciences and Brain & Mind Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - Hae-Jin Park
- Department of Radiation Oncology, Ajou University School of Medicine, Suwon, Gyeonggi, Republic of Korea
| | - Bong-Soo Han
- Department of Radiological Science, College of Health Science, Yonsei University, 1 Yonseidae-gil, Wonju, Gangwon, 220-710, Republic of Korea.
| | - Dong-Cheol Woo
- Center for Bioimaging of New Drug Development, and MR Core Lab., Asan Institute for Life Sciences, Asan Medical Center, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 138-736, Republic of Korea. .,Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
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Velez E, Fedorov A, Tuncali K, Olubiyi O, Allard CB, Kibel AS, Tempany CM. Pathologic correlation of transperineal in-bore 3-Tesla magnetic resonance imaging-guided prostate biopsy samples with radical prostatectomy specimen. Abdom Radiol (NY) 2017; 42:2154-2159. [PMID: 28293720 DOI: 10.1007/s00261-017-1102-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
PURPOSE To determine the accuracy of in-bore transperineal 3-Tesla (T) magnetic resonance (MR) imaging-guided prostate biopsies for predicting final Gleason grades in patients who subsequently underwent radical prostatectomy (RP). METHODS A retrospective review of men who underwent transperineal MR imaging-guided prostate biopsy (tpMRGB) with subsequent radical prostatectomy within 1 year was conducted from 2010 to 2015. All patients underwent a baseline 3-T multiparametric MRI (mpMRI) with endorectal coil and were selected for biopsy based on MR findings of a suspicious prostate lesion and high degree of clinical suspicion for cancer. Spearman correlation was performed to assess concordance between tpMRGB and final RP pathology among patients with and without previous transrectal ultrasound (TRUS)-guided biopsies. RESULTS A total of 24 men met all eligibility requirements, with a median age of 65 years (interquartile range [IQR] 11.7). The median time from biopsy to RP was 85 days (IQR 50.5). Final pathology revealed Gleason 3 + 4 = 7 in 12 patients, 4 + 3 = 7 in 10 patients, and 4 + 4 = 8 in 2 patients. A strong correlation (ρ: +0.75, p < 0.001) between tpMRGB and RP results was observed, with Gleason scores concordant in 17 cases (71%). 16 of the 24 patients underwent prior TRUS biopsies. Subsequent tpMRGB revealed Gleason upgrading in 88% of cases, which was concordant with RP Gleason scores in 69% of cases (ρ: +0.75, p < 0.001). CONCLUSION Final Gleason scores diagnosed by tpMRGB at 3-T correlate strongly with final RP surgical pathology. This may facilitate prostate cancer diagnosis, particularly in patients with negative or low-grade TRUS biopsy results in whom clinically significant cancer is suspected or detected on mpMRI.
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Tani S, Tatli S, Hata N, Garcia-Rojas X, Olubiyi OI, Silverman SG, Tokuda J. Three-dimensional quantitative assessment of ablation margins based on registration of pre- and post-procedural MRI and distance map. Int J Comput Assist Radiol Surg 2016; 11:1133-42. [PMID: 27038962 DOI: 10.1007/s11548-016-1398-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2016] [Accepted: 03/19/2016] [Indexed: 12/14/2022]
Abstract
PURPOSE Contrast-enhanced MR images are widely used to confirm the adequacy of ablation margin after liver ablation for early prediction of local recurrence. However, quantitative assessment of the ablation margin by comparing pre- and post-procedural images remains challenging. We developed and tested a novel method for three-dimensional quantitative assessment of ablation margin based on non-rigid image registration and 3D distance map. METHODS Our method was tested with pre- and post-procedural MR images acquired in 21 patients who underwent image-guided percutaneous liver ablation. The two images were co-registered using non-rigid intensity-based registration. After the tumor and ablation volumes were segmented, target volume coverage, percent of tumor coverage, and Dice similarity coefficient were calculated as metrics representing overall adequacy of ablation. In addition, 3D distance map around the tumor was computed and superimposed on the ablation volume to identify the area with insufficient margins. For patients with local recurrences, the follow-up images were registered to the post-procedural image. Three-dimensional minimum distance between the recurrence and the areas with insufficient margins was quantified. RESULTS The percent tumor coverage for all nonrecurrent cases was 100 %. Five cases had tumor recurrences, and the 3D distance map revealed insufficient tumor coverage or a 0-mm margin. It also showed that two recurrences were remote to the insufficient margin. CONCLUSIONS Non-rigid registration and 3D distance map allow us to quantitatively evaluate the adequacy of the ablation margin after percutaneous liver ablation. The method may be useful to predict local recurrences immediately following ablation procedure.
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Affiliation(s)
- Soichiro Tani
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA. .,Department of Surgery, Biomedical Innovation Center, Shiga University of Medical Science, Seta Tsukinowa-Cho, Otsu, Shiga, 520-2192, Japan.
| | - Servet Tatli
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA
| | - Nobuhiko Hata
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA
| | | | - Olutayo I Olubiyi
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA
| | - Stuart G Silverman
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA
| | - Junichi Tokuda
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA
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Sabater S, Pastor-Juan MDR, Berenguer R, Andres I, Sevillano M, Lozano-Setien E, Jimenez-Jimenez E, Rovirosa A, Sanchez-Prieto R, Arenas M. Analysing the integration of MR images acquired in a non-radiotherapy treatment position into the radiotherapy workflow using deformable and rigid registration. Radiother Oncol 2016; 119:179-84. [DOI: 10.1016/j.radonc.2016.02.032] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2015] [Revised: 02/18/2016] [Accepted: 02/28/2016] [Indexed: 12/22/2022]
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You JY, Lee HJ, Hwang SI, Bae YJ, Kim H, Hong H, Choe G. Value of T1/T2-weighted magnetic resonance imaging registration to reduce the postbiopsy hemorrhage effect for prostate cancer localization. Prostate Int 2015; 3:80-6. [PMID: 26473149 PMCID: PMC4588389 DOI: 10.1016/j.prnil.2015.06.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2015] [Accepted: 06/04/2015] [Indexed: 11/16/2022] Open
Abstract
Background The aim of this study was to evaluate the value of T1/T2-weighted imaging (T1/T2WI) registration to reduce the postbiopsy hemorrhage effect for prostate cancer localization on prostate magnetic resonance imaging (MRI). Methods Twenty-one men with pathology-proven prostate cancer who underwent preoperative MRI in a single institution were selected. The zonal anatomy was divided into 16 sections. T2WI, T1/T2-weighted registered imaging (T1/T2RI), T2WI combined with diffusion-weighted imaging (T2WI + DWI), and T1/T2RI combined with DWI (T1/T2RI + DWI) were scored for the likelihood of cancer by two radiology faculty members and two trainees, and were compared with histology results. Areas under the receiver operating characteristics curve (AUCs) were used to assess diagnostic accuracy. Results For the trainees (Reader 3 and Reader 4), the AUC values were significantly higher (P < 0.05) for T1/T2RI (0.60 and 0.62, respectively) than for T2WI (0.54 and 0.56, respectively) in tumor detection, whereas no significant difference was observed for faculty members. There was no significant difference in AUC values between T1/T2RI and T2WI + DWI for all readers except for Reader 1. There was no additional diagnostic benefit for adding DWI with T1/T2RI for all readers. Conclusions T1/T2WI registration is a feasible technique. For less experienced readers, T1/T2RI is better than T2WI in localization of prostate cancer.
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Affiliation(s)
- Ja Yeon You
- Department of Radiology, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, South Korea
| | - Hak Jong Lee
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, South Korea ; Program in Nano Science and Technology, Department of Transdisciplinary Studies, Seoul National University Graduate School of Convergence Science and Technology, Suwon, South Korea
| | - Sung Il Hwang
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, South Korea
| | - Yun Jung Bae
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, South Korea
| | - Hannah Kim
- Department of Computer Science and Engineering, Seoul Women's University, Seoul, South Korea
| | - Helen Hong
- Department of Multimedia Engineering, Seoul Women's University, Seoul, South Korea
| | - Gheeyoung Choe
- Department of Pathology, Seoul National University College of Medicine and Seoul National University Bundang Hospital, Seongnam, South Korea
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Graphics Processing Unit-Accelerated Nonrigid Registration of MR Images to CT Images During CT-Guided Percutaneous Liver Tumor Ablations. Acad Radiol 2015; 22:722-33. [PMID: 25784325 DOI: 10.1016/j.acra.2015.01.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2014] [Revised: 01/18/2015] [Accepted: 01/20/2015] [Indexed: 11/23/2022]
Abstract
RATIONALE AND OBJECTIVES Accuracy and speed are essential for the intraprocedural nonrigid magnetic resonance (MR) to computed tomography (CT) image registration in the assessment of tumor margins during CT-guided liver tumor ablations. Although both accuracy and speed can be improved by limiting the registration to a region of interest (ROI), manual contouring of the ROI prolongs the registration process substantially. To achieve accurate and fast registration without the use of an ROI, we combined a nonrigid registration technique on the basis of volume subdivision with hardware acceleration using a graphics processing unit (GPU). We compared the registration accuracy and processing time of GPU-accelerated volume subdivision-based nonrigid registration technique to the conventional nonrigid B-spline registration technique. MATERIALS AND METHODS Fourteen image data sets of preprocedural MR and intraprocedural CT images for percutaneous CT-guided liver tumor ablations were obtained. Each set of images was registered using the GPU-accelerated volume subdivision technique and the B-spline technique. Manual contouring of ROI was used only for the B-spline technique. Registration accuracies (Dice similarity coefficient [DSC] and 95% Hausdorff distance [HD]) and total processing time including contouring of ROIs and computation were compared using a paired Student t test. RESULTS Accuracies of the GPU-accelerated registrations and B-spline registrations, respectively, were 88.3 ± 3.7% versus 89.3 ± 4.9% (P = .41) for DSC and 13.1 ± 5.2 versus 11.4 ± 6.3 mm (P = .15) for HD. Total processing time of the GPU-accelerated registration and B-spline registration techniques was 88 ± 14 versus 557 ± 116 seconds (P < .000000002), respectively; there was no significant difference in computation time despite the difference in the complexity of the algorithms (P = .71). CONCLUSIONS The GPU-accelerated volume subdivision technique was as accurate as the B-spline technique and required significantly less processing time. The GPU-accelerated volume subdivision technique may enable the implementation of nonrigid registration into routine clinical practice.
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Marami B, Sirouspour S, Ghoul S, Cepek J, Davidson SRH, Capson DW, Trachtenberg J, Fenster A. Elastic registration of prostate MR images based on estimation of deformation states. Med Image Anal 2015; 21:87-103. [PMID: 25624044 DOI: 10.1016/j.media.2014.12.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2014] [Revised: 12/15/2014] [Accepted: 12/19/2014] [Indexed: 10/24/2022]
Abstract
Magnetic resonance imaging (MRI) is being used increasingly for image-guided targeted biopsy and focal therapy of prostate cancer. In this paper, a combined rigid and deformable registration technique is proposed to register pre-treatment diagnostic 3T magnetic resonance (MR) images of the prostate, with the identified target tumor(s), to intra-treatment 1.5T MR images. The pre-treatment T2-weighted MR images were acquired with patients in a supine position using an endorectal coil in a 3T scanner, while the intra-treatment T2-weighted MR images were acquired in a 1.5T scanner before insertion of the needle with patients in the semi-lithotomy position. Both the rigid and deformable registration algorithms employ an intensity-based distance metric defined based on the modality independent neighborhood descriptors (MIND) between images. The optimization routine for estimating the rigid transformation parameters is initialized using four pairs of manually selected approximate corresponding points on the boundaries of the prostate. In this paper, the problem of deformable image registration is approached from the perspective of state estimation for dynamical systems. The registration algorithm employs a rather generic dynamic linear elastic model of the tissue deformation discretized by the finite element method (FEM). We use the model in a classical state estimation framework to estimate the deformation of the prostate based on the distance metric between pre- and intra-treatment images. Our deformable registration results using 17 sets of prostate MR images showed that the proposed method yielded a target registration error (TRE) of 1.87 ± 0.94 mm,2.03 ± 0.94 mm, and 1.70 ± 0.93 mm for the whole gland (WG), central gland (CG), and peripheral zone (PZ), respectively, using 76 manually-identified fiducial points. This was an improvement over the 2.67 ± 1.31 mm, 2.95 ± 1.43 mm, and 2.34 ± 1.11 mm, respectively for the WG, CG, and PZ after rigid registration alone. Dice similarity coefficients (DSC) in the WG, CG and PZ were 88.2 ± 5.3, 85.6 ± 7.6 and 68.7 ± 6.9 percent, respectively. Furthermore, the mean absolute distances (MAD) between surfaces was 1.26 ± 0.56 mm and 1.27 ± 0.55 mm in the WG and CG, after deformable registration. These results indicate that the proposed registration technique has sufficient accuracy for localizing prostate tumors in MRI-guided targeted biopsy or focal therapy of clinically localized prostate cancer.
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Affiliation(s)
- Bahram Marami
- Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada; Biomedical Engineering Graduate Program, The University of Western Ontario, London, Ontario, Canada.
| | - Shahin Sirouspour
- Department of Electrical and Computer Engineering, McMaster University, Hamilton, Ontario, Canada.
| | - Suha Ghoul
- Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada; Department of Medical Imaging, London Health Sciences Center, London, Ontario, Canada.
| | - Jeremy Cepek
- Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada; Biomedical Engineering Graduate Program, The University of Western Ontario, London, Ontario, Canada.
| | - Sean R H Davidson
- Ontario Cancer Institute, University Health Network, Toronto, Ontario, Canada.
| | - David W Capson
- Department of Electrical and Computer Engineering, University of Victoria, Victoria, British Columbia, Canada.
| | - John Trachtenberg
- Department of Surgical Oncology, University Health Network, Toronto, Ontario, Canada.
| | - Aaron Fenster
- Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada; Biomedical Engineering Graduate Program, The University of Western Ontario, London, Ontario, Canada; Department of Medical Biophysics, The University of Western Ontario, London, Ontario, Canada.
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Penzkofer T, Tuncali K, Fedorov A, Song SE, Tokuda J, Fennessy FM, Vangel MG, Kibel AS, Mulkern RV, Wells WM, Hata N, Tempany CMC. Transperineal in-bore 3-T MR imaging-guided prostate biopsy: a prospective clinical observational study. Radiology 2014; 274:170-80. [PMID: 25222067 DOI: 10.1148/radiol.14140221] [Citation(s) in RCA: 64] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
PURPOSE To determine the detection rate, clinical relevance, Gleason grade, and location of prostate cancer ( PCa prostate cancer ) diagnosed with and the safety of an in-bore transperineal 3-T magnetic resonance (MR) imaging-guided prostate biopsy in a clinically heterogeneous patient population. MATERIALS AND METHODS This prospective retrospectively analyzed study was HIPAA compliant and institutional review board approved, and informed consent was obtained. Eighty-seven men (mean age, 66.2 years ± 6.9) underwent multiparametric endorectal prostate MR imaging at 3 T and transperineal MR imaging-guided biopsy. Three subgroups of patients with at least one lesion suspicious for cancer were included: men with no prior PCa prostate cancer diagnosis, men with PCa prostate cancer who were undergoing active surveillance, and men with treated PCa prostate cancer and suspected recurrence. Exclusion criteria were prior prostatectomy and/or contraindication to 3-T MR imaging. The transperineal MR imaging-guided biopsy was performed in a 70-cm wide-bore 3-T device. Overall patient biopsy outcomes, cancer detection rates, Gleason grade, and location for each subgroup were evaluated and statistically compared by using χ(2) and one-way analysis of variance followed by Tukey honestly significant difference post hoc comparisons. RESULTS Ninety biopsy procedures were performed with no serious adverse events, with a mean of 3.7 targets sampled per gland. Cancer was detected in 51 (56.7%) men: 48.1% (25 of 52) with no prior PCa prostate cancer , 61.5% (eight of 13) under active surveillance, and 72.0% (18 of 25) in whom recurrence was suspected. Gleason pattern 4 or higher was diagnosed in 78.1% (25 of 32) in the no prior PCa prostate cancer and active surveillance groups. Gleason scores were not assigned in the suspected recurrence group. MR targets located in the anterior prostate had the highest cancer yield (40 of 64, 62.5%) compared with those for the other parts of the prostate (P < .001). CONCLUSION In-bore 3-T transperineal MR imaging-guided biopsy, with a mean of 3.7 targets per gland, allowed detection of many clinically relevant cancers, many of which were located anteriorly.
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Affiliation(s)
- Tobias Penzkofer
- From the Division of MRI in the Department of Radiology (T.P., K.T., A.F., S.S., J.T., F.M.F., R.V.M., W.M.W., N.H., C.M.C.T.) and the Division of Urology (A.S.K.), Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115; Department of Diagnostic and Interventional Radiology, RWTH Aachen University Hospital, Aachen, Germany (T.P.). Department of Radiology, Massachusetts General Hospital, Boston, Mass (M.G.V.); Department of Radiology, Dana-Farber Cancer Institute, Boston, Mass (F.M.F.); and Department of Radiology, Children's Hospital, Boston, Mass (R.V.M.)
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Odille F, Escanyé JM, Atkinson D, Bonnemains L, Felblinger J. Nonrigid registration improves MRI T2quantification in heart transplant patient follow-up. J Magn Reson Imaging 2014; 42:168-74. [DOI: 10.1002/jmri.24741] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2014] [Accepted: 08/14/2014] [Indexed: 11/09/2022] Open
Affiliation(s)
- Freddy Odille
- U947, Inserm; Nancy France
- Imagerie Adaptative Diagnostique et Interventionnelle; Université de Lorraine; Nancy France
| | | | - David Atkinson
- Centre for Medical Imaging; University College London; London United Kingdom
| | - Laurent Bonnemains
- U947, Inserm; Nancy France
- Imagerie Adaptative Diagnostique et Interventionnelle; Université de Lorraine; Nancy France
- Department of Cardiology; CHU Strasbourg; Strasbourg France
| | - Jacques Felblinger
- U947, Inserm; Nancy France
- Imagerie Adaptative Diagnostique et Interventionnelle; Université de Lorraine; Nancy France
- CIC-IT 1433; CHU Nancy; Nancy France
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Foley D, Browne JE, Zhuang X, Sheane B, O'Driscoll D, Cannon D, Sheehy N, Meaney JF, Fagan AJ. The utility of deformable image registration for small artery visualisation in contrast-enhanced whole body MR angiography. Phys Med 2014; 30:898-908. [PMID: 25182374 DOI: 10.1016/j.ejmp.2014.08.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2014] [Revised: 07/30/2014] [Accepted: 08/09/2014] [Indexed: 12/13/2022] Open
Abstract
PURPOSE An investigation was carried out into the effect of three image registration techniques on the diagnostic image quality of contrast-enhanced magnetic resonance angiography (CE-MRA) images. METHODS Whole-body CE-MRA data from the lower legs of 27 patients recruited onto a study of asymptomatic atherosclerosis were processed using three deformable image registration algorithms. The resultant diagnostic image quality was evaluated qualitatively in a clinical evaluation by four expert observers, and quantitatively by measuring contrast-to-noise ratios and volumes of blood vessels, and assessing the techniques' ability to correct for varying degrees of motion. RESULTS The first registration algorithm ('AIR') introduced significant stenosis-mimicking artefacts into the blood vessels' appearance, observed both qualitatively (clinical evaluation) and quantitatively (vessel volume measurements). The two other algorithms ('Slicer' and 'SEMI'), based on the normalised mutual information (NMI) concept and designed specifically to deal with variations in signal intensity as found in contrast-enhanced image data, did not suffer from this serious issue but were rather found to significantly improve the diagnostic image quality both qualitatively and quantitatively, and demonstrated a significantly improved ability to deal with the common problem of patient motion. CONCLUSIONS This work highlights both the significant benefits to be gained through the use of suitable registration algorithms and the deleterious effects of an inappropriate choice of algorithm for contrast-enhanced MRI data. The maximum benefit was found in the lower legs, where the small arterial vessel diameters and propensity for leg movement during image acquisitions posed considerable problems in making accurate diagnoses from the un-registered images.
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Affiliation(s)
- Daniel Foley
- National Centre for Advanced Medical Imaging (CAMI), St. James's Hospital/School of Medicine, Trinity College Dublin, Ireland
| | - Jacinta E Browne
- Medical Ultrasound Physics Group, School of Physics/IEO & FOCAS Institutes, Dublin Institute of Technology, Kevin's Street, Dublin 8, Ireland
| | - Xiahai Zhuang
- Centre for Medical Image Computing, Department of Medical Physics and Bioengineering, University College London, UK; Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, China
| | - Barry Sheane
- National Centre for Advanced Medical Imaging (CAMI), St. James's Hospital/School of Medicine, Trinity College Dublin, Ireland
| | - Dearbhail O'Driscoll
- National Centre for Advanced Medical Imaging (CAMI), St. James's Hospital/School of Medicine, Trinity College Dublin, Ireland
| | - Daniel Cannon
- National Centre for Advanced Medical Imaging (CAMI), St. James's Hospital/School of Medicine, Trinity College Dublin, Ireland
| | - Niall Sheehy
- National Centre for Advanced Medical Imaging (CAMI), St. James's Hospital/School of Medicine, Trinity College Dublin, Ireland
| | - James F Meaney
- National Centre for Advanced Medical Imaging (CAMI), St. James's Hospital/School of Medicine, Trinity College Dublin, Ireland
| | - Andrew J Fagan
- National Centre for Advanced Medical Imaging (CAMI), St. James's Hospital/School of Medicine, Trinity College Dublin, Ireland.
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Dose accumulation during vaginal cuff brachytherapy based on rigid/deformable registration vs. single plan addition. Brachytherapy 2014; 13:343-51. [DOI: 10.1016/j.brachy.2013.11.006] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2013] [Revised: 11/10/2013] [Accepted: 11/21/2013] [Indexed: 11/20/2022]
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15
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Tao R, Tavakoli M, Sloboda R, Usmani N. A comparison of US- versus MR-based 3-D Prostate Shapes Using Radial Basis Function Interpolation and Statistical Shape Models. IEEE J Biomed Health Inform 2014; 19:623-34. [PMID: 24860042 DOI: 10.1109/jbhi.2014.2324975] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
This paper presents a comparison of three-dimensional (3-D) segmentations of the prostate, based on two-dimensional (2-D) manually segmented contours, obtained using ultrasound (US) and magnetic resonance (MR) imaging data collected from 40 patients diagnosed with localized prostate cancer and scheduled to receive brachytherapy treatment. The approach we propose here for 3-D prostate segmentation first uses radial basis function interpolation to construct a 3-D point distribution model for each prostate. Next, a modified principal axis transformation is utilized for rigid registration of the US and MR images of the same prostate in preparation for the following shape comparison. Then, statistical shape models are used to capture the segmented 3-D prostate geometries for the subsequent cross-modality comparison. Our study includes not only cross-modality geometric comparisons in terms of prostate volumes and dimensions, but also an investigation of interchangeability of the two imaging modalities in terms of automatic contour segmentation at the pre-implant planning stage of prostate brachytherapy treatment. By developing a new scheme to compare the two imaging modalities in terms of the segmented 3-D shapes, we have taken a first step necessary for building coupled US-MR segmentation strategies for prostate brachytherapy pre-implant planning, which at present is predominantly informed by US images only.
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Ikeda K, Ino F, Hagihara K. Efficient Acceleration of Mutual Information Computation for Nonrigid Registration Using CUDA. IEEE J Biomed Health Inform 2014; 18:956-68. [DOI: 10.1109/jbhi.2014.2310745] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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17
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Xue N, Doellinger M, Fripp J, Ho CP, Surowiec RK, Schwarz R. Automatic model-based semantic registration of multimodal MRI knee data. J Magn Reson Imaging 2014; 41:633-44. [PMID: 24591252 DOI: 10.1002/jmri.24609] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2013] [Accepted: 02/11/2014] [Indexed: 11/11/2022] Open
Abstract
PURPOSE To propose a robust and automated model-based semantic registration for the multimodal alignment of the knee bone and cartilage from three-dimensional (3D) MR image data. MATERIALS AND METHODS The movement of the knee joint can be semantically interpreted as a combination of movements of each bone. A semantic registration of the knee joint was implemented by separately reconstructing the rigid movements of the three bones. The proposed method was validated by registering 3D morphological MR datasets of 25 subjects into the corresponding T2 map datasets, and was compared with rigid and elastic methods using two criteria: the spatial overlap of the manually segmented cartilage and the distance between the same landmarks in the reference and target datasets. RESULTS The mean Dice Similarity Coefficient (DSC) of the overlapped cartilage segmentation was increased to 0.68 ± 0.1 (mean ± SD) and the landmark distance was reduced to 1.3 ± 0.3 mm after the proposed registration method. Both metrics were statistically superior to using rigid (DSC: 0.59 ± 0.12; landmark distance: 2.1 ± 0.4 mm) and elastic (DSC: 0.64 ± 0.11; landmark distance: 1.5 ± 0.5 mm) registrations. CONCLUSION The proposed method is an efficient and robust approach for the automated registration between morphological knee datasets and T2 MRI relaxation maps.
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Affiliation(s)
- Ning Xue
- Department of Phoniatrics and Pediatric Audiology, University Hospital Erlangen, Germany; Imaging & Therapy Division, Healthcare Sector, Siemens AG, Erlangen, Germany
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Schulz J, Skrøvseth SO, Tømmerås VK, Marienhagen K, Godtliebsen F. A semiautomatic tool for prostate segmentation in radiotherapy treatment planning. BMC Med Imaging 2014; 14:4. [PMID: 24460666 PMCID: PMC3933010 DOI: 10.1186/1471-2342-14-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2013] [Accepted: 01/15/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Delineation of the target volume is a time-consuming task in radiotherapy treatment planning, yet essential for a successful treatment of cancers such as prostate cancer. To facilitate the delineation procedure, the paper proposes an intuitive approach for 3D modeling of the prostate by slice-wise best fitting ellipses. METHODS The proposed estimate is initialized by the definition of a few control points in a new patient. The method is not restricted to particular image modalities but assumes a smooth shape with elliptic cross sections of the object. A training data set of 23 patients was used to calculate a prior shape model. The mean shape model was evaluated based on the manual contour of 10 test patients. The patient records of training and test data are based on axial T1-weighted 3D fast-field echo (FFE) sequences. The manual contours were considered as the reference model. Volume overlap (Vo), accuracy (Ac) (both ratio, range 0-1, optimal value 1) and Hausdorff distance (HD) (mm, optimal value 0) were calculated as evaluation parameters. RESULTS The median and median absolute deviation (MAD) between manual delineation and deformed mean best fitting ellipses (MBFE) was Vo (0.9 ± 0.02), Ac (0.81 ± 0.03) and HD (4.05 ± 1.3)mm and between manual delineation and best fitting ellipses (BFE) was Vo (0.96 ± 0.01), Ac (0.92 ± 0.01) and HD (1.6 ± 0.27)mm. Additional results show a moderate improvement of the MBFE results after Monte Carlo Markov Chain (MCMC) method. CONCLUSIONS The results emphasize the potential of the proposed method of modeling the prostate by best fitting ellipses. It shows the robustness and reproducibility of the model. A small sample test on 8 patients suggest possible time saving using the model.
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Affiliation(s)
- Jörn Schulz
- Department of Mathematics and Statistics, University of Tromsø, 9037 Tromsø, Norway.
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19
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Organ-focused mutual information for nonrigid multimodal registration of liver CT and Gd–EOB–DTPA-enhanced MRI. Med Image Anal 2014; 18:22-35. [DOI: 10.1016/j.media.2013.09.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2012] [Revised: 08/07/2013] [Accepted: 09/05/2013] [Indexed: 11/23/2022]
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20
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Uji A, Ooto S, Hangai M, Arichika S, Yoshimura N. Image quality improvement in adaptive optics scanning laser ophthalmoscopy assisted capillary visualization using B-spline-based elastic image registration. PLoS One 2013; 8:e80106. [PMID: 24265796 PMCID: PMC3827159 DOI: 10.1371/journal.pone.0080106] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2013] [Accepted: 10/06/2013] [Indexed: 11/19/2022] Open
Abstract
PURPOSE To investigate the effect of B-spline-based elastic image registration on adaptive optics scanning laser ophthalmoscopy (AO-SLO)-assisted capillary visualization. METHODS AO-SLO videos were acquired from parafoveal areas in the eyes of healthy subjects and patients with various diseases. After nonlinear image registration, the image quality of capillary images constructed from AO-SLO videos using motion contrast enhancement was compared before and after B-spline-based elastic (nonlinear) image registration performed using ImageJ. For objective comparison of image quality, contrast-to-noise ratios (CNRS) for vessel images were calculated. For subjective comparison, experienced ophthalmologists ranked images on a 5-point scale. RESULTS All AO-SLO videos were successfully stabilized by elastic image registration. CNR was significantly higher in capillary images stabilized by elastic image registration than in those stabilized without registration. The average ratio of CNR in images with elastic image registration to CNR in images without elastic image registration was 2.10 ± 1.73, with no significant difference in the ratio between patients and healthy subjects. Improvement of image quality was also supported by expert comparison. CONCLUSIONS Use of B-spline-based elastic image registration in AO-SLO-assisted capillary visualization was effective for enhancing image quality both objectively and subjectively.
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Affiliation(s)
- Akihito Uji
- Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
- * E-mail:
| | - Sotaro Ooto
- Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Masanori Hangai
- Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Shigeta Arichika
- Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Nagahisa Yoshimura
- Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
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Xu H, Lasso A, Guion P, Krieger A, Kaushal A, Singh AK, Pinto PA, Coleman J, Grubb RL, Lattouf JB, Menard C, Whitcomb LL, Fichtinger G. Accuracy analysis in MRI-guided robotic prostate biopsy. Int J Comput Assist Radiol Surg 2013; 8:937-44. [PMID: 23532560 DOI: 10.1007/s11548-013-0831-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2013] [Accepted: 03/11/2013] [Indexed: 10/27/2022]
Abstract
PURPOSE To assess retrospectively the clinical accuracy of an magnetic resonance imaging-guided robotic prostate biopsy system that has been used in the US National Cancer Institute for over 6 years. METHODS Series of 2D transverse volumetric MR image slices of the prostate both pre (high-resolution T2-weighted)- and post (low-resolution)- needle insertions were used to evaluate biopsy accuracy. A three-stage registration algorithm consisting of an initial two-step rigid registration followed by a B-spline deformable alignment was developed to capture prostate motion during biopsy. The target displacement (distance between planned and actual biopsy target), needle placement error (distance from planned biopsy target to needle trajectory), and biopsy error (distance from actual biopsy target to needle trajectory) were calculated as accuracy assessment. RESULTS A total of 90 biopsies from 24 patients were studied. The registrations were validated by checking prostate contour alignment using image overlay, and the results were accurate to within 2 mm. The mean target displacement, needle placement error, and clinical biopsy error were 5.2, 2.5, and 4.3 mm, respectively. CONCLUSION The biopsy error reported suggests that quantitative imaging techniques for prostate registration and motion compensation may improve prostate biopsy targeting accuracy.
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Affiliation(s)
- Helen Xu
- Queen's University, Kingston, ON, Canada,
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22
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Jiang L, Greenwood TR, Amstalden van Hove ER, Chughtai K, Raman V, Winnard PT, Heeren R, Artemov D, Glunde K. Combined MR, fluorescence and histology imaging strategy in a human breast tumor xenograft model. NMR IN BIOMEDICINE 2013; 26:285-298. [PMID: 22945331 PMCID: PMC4162316 DOI: 10.1002/nbm.2846] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2011] [Revised: 07/25/2012] [Accepted: 07/26/2012] [Indexed: 05/29/2023]
Abstract
Applications of molecular imaging in cancer and other diseases frequently require the combination of in vivo imaging modalities, such as MR and optical imaging, with ex vivo optical, fluorescence, histology and immunohistochemical imaging to investigate and relate molecular and biological processes to imaging parameters within the same region of interest. We have developed a multimodal image reconstruction and fusion framework that accurately combines in vivo MRI and MRSI, ex vivo brightfield and fluorescence microscopic imaging and ex vivo histology imaging. Ex vivo brightfield microscopic imaging was used as an intermediate modality to facilitate the ultimate link between ex vivo histology and in vivo MRI/MRSI. Tissue sectioning necessary for optical and histology imaging required the generation of a three-dimensional reconstruction module for two-dimensional ex vivo optical and histology imaging data. We developed an external fiducial marker-based three-dimensional reconstruction method, which was able to fuse optical brightfield and fluorescence with histology imaging data. The registration of the three-dimensional tumor shape was pursued to combine in vivo MRI/MRSI and ex vivo optical brightfield and fluorescence imaging data. This registration strategy was applied to in vivo MRI/MRSI, ex vivo optical brightfield/fluorescence and histology imaging datasets obtained from human breast tumor models. Three-dimensional human breast tumor datasets were successfully reconstructed and fused with this platform.
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Affiliation(s)
- Lu Jiang
- The Johns Hopkins University In Vivo Cellular and Molecular Imaging Center, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Tiffany R. Greenwood
- The Johns Hopkins University In Vivo Cellular and Molecular Imaging Center, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | | | - Kamila Chughtai
- FOM-Institute for Atomic and Molecular Physics, Amsterdam, The Netherlands
| | - Venu Raman
- The Johns Hopkins University In Vivo Cellular and Molecular Imaging Center, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Paul T. Winnard
- The Johns Hopkins University In Vivo Cellular and Molecular Imaging Center, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Ron Heeren
- FOM-Institute for Atomic and Molecular Physics, Amsterdam, The Netherlands
| | - Dmitri Artemov
- The Johns Hopkins University In Vivo Cellular and Molecular Imaging Center, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Kristine Glunde
- The Johns Hopkins University In Vivo Cellular and Molecular Imaging Center, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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Pursley J, Risholm P, Fedorov A, Tuncali K, Fennessy FM, Wells WM, Tempany CM, Cormack RA. A Bayesian nonrigid registration method to enhance intraoperative target definition in image-guided prostate procedures through uncertainty characterization. Med Phys 2012; 39:6858-67. [PMID: 23127078 PMCID: PMC3494726 DOI: 10.1118/1.4760992] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2012] [Revised: 09/28/2012] [Accepted: 10/02/2012] [Indexed: 11/07/2022] Open
Abstract
PURPOSE This study introduces a probabilistic nonrigid registration method for use in image-guided prostate brachytherapy. Intraoperative imaging for prostate procedures, usually transrectal ultrasound (TRUS), is typically inferior to diagnostic-quality imaging of the pelvis such as endorectal magnetic resonance imaging (MRI). MR images contain superior detail of the prostate boundaries and provide substructure features not otherwise visible. Previous efforts to register diagnostic prostate images with the intraoperative coordinate system have been deterministic and did not offer a measure of the registration uncertainty. The authors developed a Bayesian registration method to estimate the posterior distribution on deformations and provide a case-specific measure of the associated registration uncertainty. METHODS The authors adapted a biomechanical-based probabilistic nonrigid method to register diagnostic to intraoperative images by aligning a physician's segmentations of the prostate in the two images. The posterior distribution was characterized with a Markov Chain Monte Carlo method; the maximum a posteriori deformation and the associated uncertainty were estimated from the collection of deformation samples drawn from the posterior distribution. The authors validated the registration method using a dataset created from ten patients with MRI-guided prostate biopsies who had both diagnostic and intraprocedural 3 Tesla MRI scans. The accuracy and precision of the estimated posterior distribution on deformations were evaluated from two predictive distance distributions: between the deformed central zone-peripheral zone (CZ-PZ) interface and the physician-labeled interface, and based on physician-defined landmarks. Geometric margins on the registration of the prostate's peripheral zone were determined from the posterior predictive distance to the CZ-PZ interface separately for the base, mid-gland, and apical regions of the prostate. RESULTS The authors observed variation in the shape and volume of the segmented prostate in diagnostic and intraprocedural images. The probabilistic method allowed us to convey registration results in terms of posterior distributions, with the dispersion providing a patient-specific estimate of the registration uncertainty. The median of the predictive distance distribution between the deformed prostate boundary and the segmented boundary was ≤3 mm (95th percentiles within ±4 mm) for all ten patients. The accuracy and precision of the internal deformation was evaluated by comparing the posterior predictive distance distribution for the CZ-PZ interface for each patient, with the median distance ranging from -0.6 to 2.4 mm. Posterior predictive distances between naturally occurring landmarks showed registration errors of ≤5 mm in any direction. The uncertainty was not a global measure, but instead was local and varied throughout the registration region. Registration uncertainties were largest in the apical region of the prostate. CONCLUSIONS Using a Bayesian nonrigid registration method, the authors determined the posterior distribution on deformations between diagnostic and intraprocedural MR images and quantified the uncertainty in the registration results. The feasibility of this approach was tested and results were positive. The probabilistic framework allows us to evaluate both patient-specific and location-specific estimates of the uncertainty in the registration result. Although the framework was tested on MR-guided procedures, the preliminary results suggest that it may be applied to TRUS-guided procedures as well, where the addition of diagnostic MR information may have a larger impact on target definition and clinical guidance.
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Affiliation(s)
- Jennifer Pursley
- Department of Radiation Oncology, Harvard Medical School, Boston, MA 02115, USA.
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Fedorov A, Tuncali K, Fennessy FM, Tokuda J, Hata N, Wells WM, Kikinis R, Tempany CM. Image registration for targeted MRI-guided transperineal prostate biopsy. J Magn Reson Imaging 2012; 36:987-92. [PMID: 22645031 PMCID: PMC3434292 DOI: 10.1002/jmri.23688] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2011] [Accepted: 03/28/2012] [Indexed: 11/09/2022] Open
Abstract
PURPOSE To develop and evaluate image registration methodology for automated re-identification of tumor-suspicious foci from preprocedural MR exams during MR-guided transperineal prostate core biopsy. MATERIALS AND METHODS A hierarchical approach for automated registration between planning and intra-procedural T2-weighted prostate MRI was developed and evaluated on the images acquired during 10 consecutive MR-guided biopsies. Registration accuracy was quantified at image-based landmarks and by evaluating spatial overlap for the manually segmented prostate and sub-structures. Registration reliability was evaluated by simulating initial mis-registration and analyzing the convergence behavior. Registration precision was characterized at the planned biopsy targets. RESULTS The total computation time was compatible with a clinical setting, being at most 2 min. Deformable registration led to a significant improvement in spatial overlap of the prostate and peripheral zone contours compared with both rigid and affine registration. Average in-slice landmark registration error was 1.3 ± 0.5 mm. Experiments simulating initial mis-registration resulted in an estimated average capture range of 6 mm and an average in-slice registration precision of ±0.3 mm. CONCLUSION Our registration approach requires minimum user interaction and is compatible with the time constraints of our interventional clinical workflow. The initial evaluation shows acceptable accuracy, reliability and consistency of the method.
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Affiliation(s)
- Andriy Fedorov
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
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25
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Tokuda J, Tuncali K, Iordachita I, Song SE, Fedorov A, Oguro S, Lasso A, Fennessy FM, Tempany CM, Hata N. In-bore setup and software for 3T MRI-guided transperineal prostate biopsy. Phys Med Biol 2012; 57:5823-40. [PMID: 22951350 DOI: 10.1088/0031-9155/57/18/5823] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
MRI-guided prostate biopsy in conventional closed-bore scanners requires transferring the patient outside the bore during needle insertion due to the constrained in-bore space, causing a safety hazard and limiting image feedback. To address this issue, we present our custom-made in-bore setup and software to support MRI-guided transperineal prostate biopsy in a wide-bore 3 T MRI scanner. The setup consists of a specially designed tabletop and a needle-guiding template with a Z-frame that gives a physician access to the perineum of the patient at the imaging position and allows the physician to perform MRI-guided transperineal biopsy without moving the patient out of the scanner. The software and Z-frame allow registration of the template, target planning and biopsy guidance. Initially, we performed phantom experiments to assess the accuracy of template registration and needle placement in a controlled environment. Subsequently, we embarked on our clinical trial (N = 10). The phantom experiments showed that the translational errors of the template registration along the right-left (RP) and anterior-posterior (AP) axes were 1.1 ± 0.8 and 1.4 ± 1.1 mm, respectively, while the rotational errors around the RL, AP and superior-inferior axes were (0.8 ± 1.0)°, (1.7 ± 1.6)° and (0.0 ± 0.0)°, respectively. The 2D root-mean-square (RMS) needle-placement error was 3 mm. The clinical biopsy procedures were safely carried out in all ten clinical cases with a needle-placement error of 5.4 mm (2D RMS). In conclusion, transperineal prostate biopsy in a wide-bore 3T scanner is feasible using our custom-made tabletop setup and software, which supports manual needle placement without moving the patient out of the magnet.
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Affiliation(s)
- Junichi Tokuda
- Department of Radiology, Brigham and Womens Hospital and Harvard Medical School, Boston, MA, USA.
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26
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A spline-based non-linear diffeomorphism for multimodal prostate registration. Med Image Anal 2012; 16:1259-79. [PMID: 22705289 DOI: 10.1016/j.media.2012.04.006] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2011] [Revised: 04/24/2012] [Accepted: 04/25/2012] [Indexed: 11/24/2022]
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Shi Y, Liao S, Shen D. Learning statistical correlation for fast prostate registration in image-guided radiotherapy. Med Phys 2012; 38:5980-91. [PMID: 22047362 DOI: 10.1118/1.3641645] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE In adaptive radiation therapy of prostate cancer, fast and accurate registration between the planning image and treatment images of the patient is of essential importance. With the authors' recently developed deformable surface model, prostate boundaries in each treatment image can be rapidly segmented and their correspondences (or relative deformations) to the prostate boundaries in the planning image are also established automatically. However, the dense correspondences on the nonboundary regions, which are important especially for transforming the treatment plan designed in the planning image space to each treatment image space, are remained unresolved. This paper presents a novel approach to learn the statistical correlation between deformations of prostate boundary and nonboundary regions, for rapidly estimating deformations of the nonboundary regions when given the deformations of the prostate boundary at a new treatment image. METHODS The main contributions of the proposed method lie in the following aspects. First, the statistical deformation correlation will be learned from both current patient and other training patients, and further updated adaptively during the radiotherapy. Specifically, in the initial treatment stage when the number of treatment images collected from the current patient is small, the statistical deformation correlation is mainly learned from other training patients. As more treatment images are collected from the current patient, the patient-specific information will play a more important role in learning patient-specific statistical deformation correlation to effectively reflect prostate deformation of the current patient during the treatment. Eventually, only the patient-specific statistical deformation correlation is used to estimate dense correspondences when a sufficient number of treatment images have been acquired from the current patient. Second, the statistical deformation correlation will be learned by using a multiple linear regression (MLR) model, i.e., ridge regression (RR) model, which has the best prediction accuracy than other MLR models such as canonical correlation analysis (CCA) and principal component regression (PCR). RESULTS To demonstrate the performance of the proposed method, we first evaluate its registration accuracy by comparing the deformation field predicted by our method with the deformation field estimated by the thin plate spline (TPS) based correspondence interpolation method on 306 serial prostate CT images of 24 patients. The average predictive error on the voxels around 5 mm of prostate boundary is 0.38 mm for our method of RR-based correlation model. Also, the corresponding maximum error is 2.89 mm. We then compare the speed for deformation interpolation by different methods. When considering the larger region of interest (ROI) with the size of 512 × 512 × 61, our method takes 24.41 seconds to interpolate the dense deformation field while TPS method needs 6.7 minutes; when considering a small ROI (surrounding prostate) with size of 112 × 110 × 93, our method takes 1.80 seconds, while TPS method needs 25 seconds. CONCLUSIONS Experimental results show that the proposed method can achieve much faster registration speed yet with comparable registration accuracy, compared to the TPS-based correspondence (or deformation) interpolation approach.
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Affiliation(s)
- Yonghong Shi
- Fudan University, Shanghai Medical College, Shanghai, Taiwan.
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Kapur T, Pieper S, Whitaker R, Aylward S, Jakab M, Schroeder W, Kikinis R. The National Alliance for Medical Image Computing, a roadmap initiative to build a free and open source software infrastructure for translational research in medical image analysis. J Am Med Inform Assoc 2011; 19:176-80. [PMID: 22081219 DOI: 10.1136/amiajnl-2011-000493] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
The National Alliance for Medical Image Computing (NA-MIC), is a multi-institutional, interdisciplinary community of researchers, who share the recognition that modern health care demands improved technologies to ease suffering and prolong productive life. Organized under the National Centers for Biomedical Computing 7 years ago, the mission of NA-MIC is to implement a robust and flexible open-source infrastructure for developing and applying advanced imaging technologies across a range of important biomedical research disciplines. A measure of its success, NA-MIC is now applying this technology to diseases that have immense impact on the duration and quality of life: cancer, heart disease, trauma, and degenerative genetic diseases. The targets of this technology range from group comparisons to subject-specific analysis.
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Affiliation(s)
- Tina Kapur
- Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA.
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Tadayyon H, Lasso A, Kaushal A, Guion P, Fichtinger G. Target Motion Tracking in MRI-guided Transrectal Robotic Prostate Biopsy. IEEE Trans Biomed Eng 2011; 58:3135-42. [DOI: 10.1109/tbme.2011.2163633] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Hu Y, Carter TJ, Ahmed HU, Emberton M, Allen C, Hawkes DJ, Barratt DC. Modelling prostate motion for data fusion during image-guided interventions. IEEE TRANSACTIONS ON MEDICAL IMAGING 2011; 30:1887-1900. [PMID: 21632296 DOI: 10.1109/tmi.2011.2158235] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
There is growing clinical demand for image registration techniques that allow multimodal data fusion for accurate targeting of needle biopsy and ablative prostate cancer treatments. However, during procedures where transrectal ultrasound (TRUS) guidance is used, substantial gland deformation can occur due to TRUS probe pressure. In this paper, the ability of a statistical shape/motion model, trained using finite element simulations, to predict and compensate for this source of motion is investigated. Three-dimensional ultrasound images acquired on five patient prostates, before and after TRUS-probe-induced deformation, were registered using a nonrigid, surface-based method, and the accuracy of different deformation models compared. Registration using a statistical motion model was found to outperform alternative elastic deformation methods in terms of accuracy and robustness, and required substantially fewer target surface points to achieve a successful registration. The mean final target registration error (based on anatomical landmarks) using this method was 1.8 mm. We conclude that a statistical model of prostate deformation provides an accurate, rapid and robust means of predicting prostate deformation from sparse surface data, and is therefore well-suited to a number of interventional applications where there is a need for deformation compensation.
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Affiliation(s)
- Yipeng Hu
- UCL Centre for Medical Image Computing, the Departmentof Medical Physics and Bioengineering, and the Department of ComputerScience, University College London, UK.
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31
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Mitra J, Martí R, Oliver A, Lladó X, Ghose S, Vilanova JC, Meriaudeau F. Prostate multimodality image registration based on B-splines and quadrature local energy. Int J Comput Assist Radiol Surg 2011; 7:445-54. [PMID: 21706302 DOI: 10.1007/s11548-011-0635-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2011] [Accepted: 06/08/2011] [Indexed: 10/18/2022]
Abstract
PURPOSE Needle biopsy of the prostate is guided by Transrectal Ultrasound (TRUS) imaging. The TRUS images do not provide proper spatial localization of malignant tissues due to the poor sensitivity of TRUS to visualize early malignancy. Magnetic Resonance Imaging (MRI) has been shown to be sensitive for the detection of early stage malignancy, and therefore, a novel 2D deformable registration method that overlays pre-biopsy MRI onto TRUS images has been proposed. METHOD The registration method involves B-spline deformations with Normalized Mutual Information (NMI) as the similarity measure computed from the texture images obtained from the amplitude responses of the directional quadrature filter pairs. Registration accuracy of the proposed method is evaluated by computing the Dice Similarity coefficient (DSC) and 95% Hausdorff Distance (HD) values for 20 patients prostate mid-gland slices and Target Registration Error (TRE) for 18 patients only where homologous structures are visible in both the TRUS and transformed MR images. RESULTS The proposed method and B-splines using NMI computed from intensities provide average TRE values of 2.64 ± 1.37 and 4.43 ± 2.77 mm respectively. Our method shows statistically significant improvement in TRE when compared with B-spline using NMI computed from intensities with Student's t test p = 0.02. The proposed method shows 1.18 times improvement over thin-plate splines registration with average TRE of 3.11 ± 2.18 mm. The mean DSC and the mean 95% HD values obtained with the proposed method of B-spline with NMI computed from texture are 0.943 ± 0.039 and 4.75 ± 2.40 mm respectively. CONCLUSIONS The texture energy computed from the quadrature filter pairs provides better registration accuracy for multimodal images than raw intensities. Low TRE values of the proposed registration method add to the feasibility of it being used during TRUS-guided biopsy.
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Affiliation(s)
- Jhimli Mitra
- Computer Vision and Robotics Group, Universitat de Girona, Girona, Spain.
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Risholm P, Fedorov A, Pursley J, Tuncali K, Cormack R, Wells WM. PROBABILISTIC NON-RIGID REGISTRATION OF PROSTATE IMAGES: MODELING AND QUANTIFYING UNCERTAINTY. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2011; 2011:553-556. [PMID: 22288004 DOI: 10.1109/isbi.2011.5872467] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Registration of pre- to intra-procedural prostate images needs to handle the large changes in position and shape of the prostate caused by varying rectal filling and patient positioning. We describe a probabilistic method for non-rigid registration of prostate images which can quantify the most probable deformation as well as the uncertainty of the estimated deformation. The method is based on a biomechanical Finite Element model which treats the prostate as an elastic material. We use a Markov Chain Monte Carlo sampler to draw deformation configurations from the posterior distribution. In practice, we simultaneously estimate the boundary conditions (surface displacements) and the internal deformations of our biomechanical model. The proposed method was validated on a clinical MRI dataset with registration results comparable to previously published methods, but with the added benefit of also providing uncertainty estimates which may be important to take into account during prostate biopsy and brachytherapy procedures.
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Affiliation(s)
- Petter Risholm
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA
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33
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Effectiveness of temporal and dynamic subtraction images of the liver for detection of small HCC on abdominal CT images: comparison of 3D nonlinear image-warping and 3D global-matching techniques. Radiol Phys Technol 2011; 4:109-20. [DOI: 10.1007/s12194-010-0110-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2010] [Revised: 12/04/2010] [Accepted: 12/06/2010] [Indexed: 12/22/2022]
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Hu Y, Ahmed HU, Taylor Z, Allen C, Emberton M, Hawkes D, Barratt D. MR to ultrasound registration for image-guided prostate interventions. Med Image Anal 2010; 16:687-703. [PMID: 21216180 DOI: 10.1016/j.media.2010.11.003] [Citation(s) in RCA: 108] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2009] [Revised: 09/14/2010] [Accepted: 11/04/2010] [Indexed: 12/31/2022]
Abstract
A deformable registration method is described that enables automatic alignment of magnetic resonance (MR) and 3D transrectal ultrasound (TRUS) images of the prostate gland. The method employs a novel "model-to-image" registration approach in which a deformable model of the gland surface, derived from an MR image, is registered automatically to a TRUS volume by maximising the likelihood of a particular model shape given a voxel-intensity-based feature that represents an estimate of surface normal vectors at the boundary of the gland. The deformation of the surface model is constrained by a patient-specific statistical model of gland deformation, which is trained using data provided by biomechanical simulations. Each simulation predicts the motion of a volumetric finite element mesh due to the random placement of a TRUS probe in the rectum. The use of biomechanical modelling in this way also allows a dense displacement field to be calculated within the prostate, which is then used to non-rigidly warp the MR image to match the TRUS image. Using data acquired from eight patients, and anatomical landmarks to quantify the registration accuracy, the median final RMS target registration error after performing 100 MR-TRUS registrations for each patient was 2.40 mm.
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Affiliation(s)
- Yipeng Hu
- Centre for Medical Image Computing, University College London, London, UK.
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