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Ahmadi SA, Bötzel K, Levin J, Maiostre J, Klein T, Wein W, Rozanski V, Dietrich O, Ertl-Wagner B, Navab N, Plate A. Analyzing the co-localization of substantia nigra hyper-echogenicities and iron accumulation in Parkinson's disease: A multi-modal atlas study with transcranial ultrasound and MRI. NEUROIMAGE-CLINICAL 2020; 26:102185. [PMID: 32050136 PMCID: PMC7013333 DOI: 10.1016/j.nicl.2020.102185] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2019] [Revised: 01/12/2020] [Accepted: 01/14/2020] [Indexed: 12/23/2022]
Abstract
Volumetric 3D analysis of hyper-echogenicities from transcranial ultrasound (TCS). First multi-modal analysis of TCS and QSM-MRI in Parkinson's disease. Computations of TCS-MRI registration and a novel multi-modal anatomical template. TCS hyper-echogenicities are co-localized with QSM iron accumulations. Co-localizations occur in the SNc and VTA, but nowhere else in the midbrain.
Background Transcranial B-mode sonography (TCS) can detect hyperechogenic speckles in the area of the substantia nigra (SN) in Parkinson's disease (PD). These speckles correlate with iron accumulation in the SN tissue, but an exact volumetric localization in and around the SN is still unknown. Areas of increased iron content in brain tissue can be detected in vivo with magnetic resonance imaging, using quantitative susceptibility mapping (QSM). Methods In this work, we i) acquire, co-register and transform TCS and QSM imaging from a cohort of 23 PD patients and 27 healthy control subjects into a normalized atlas template space and ii) analyze and compare the 3D spatial distributions of iron accumulation in the midbrain, as detected by a signal increase (TCS+ and QSM+) in both modalities. Results We achieved sufficiently accurate intra-modal target registration errors (TRE<1 mm) for all MRI volumes and multi-modal TCS-MRI co-localization (TRE<4 mm) for 66.7% of TCS scans. In the caudal part of the midbrain, enlarged TCS+ and QSM+ areas were located within the SN pars compacta in PD patients in comparison to healthy controls. More cranially, overlapping TCS+ and QSM+ areas in PD subjects were found in the area of the ventral tegmental area (VTA). Conclusion Our findings are concordant with several QSM-based studies on iron-related alterations in the area SN pars compacta. They substantiate that TCS+ is an indicator of iron accumulation in Parkinson's disease within and in the vicinity of the SN. Furthermore, they are in favor of an involvement of the VTA and thereby the mesolimbic system in Parkinson's disease.
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Affiliation(s)
- Seyed-Ahmad Ahmadi
- Department of Neurology, Ludwig-Maximilians University, Marchioninistraße 15, Munich 81377, Germany; German Center for Vertigo and Balance Disorders (DSGZ), Ludwig-Maximilians University, Marchioninistraße 15, Munich 81377, Germany; Chair for Computer Aided Medical Procedures (CAMP), Technical University of Munich, Boltzmannstr. 3, Garching 85748, Germany
| | - Kai Bötzel
- Department of Neurology, Ludwig-Maximilians University, Marchioninistraße 15, Munich 81377, Germany
| | - Johannes Levin
- Department of Neurology, Ludwig-Maximilians University, Marchioninistraße 15, Munich 81377, Germany
| | - Juliana Maiostre
- Department of Neurology, Ludwig-Maximilians University, Marchioninistraße 15, Munich 81377, Germany
| | | | - Wolfgang Wein
- ImFusion GmbH, Agnes-Pockels-Bogen 1, München 80992, Germany
| | | | - Olaf Dietrich
- Department of Radiology, Ludwig-Maximilians University, Marchioninistr. 15, Munich 81377, Germany
| | - Birgit Ertl-Wagner
- Department of Radiology, Ludwig-Maximilians University, Marchioninistr. 15, Munich 81377, Germany; The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1 × 8, Canada
| | - Nassir Navab
- Chair for Computer Aided Medical Procedures (CAMP), Technical University of Munich, Boltzmannstr. 3, Garching 85748, Germany
| | - Annika Plate
- Department of Neurology, Ludwig-Maximilians University, Marchioninistraße 15, Munich 81377, Germany.
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Gonet M, Epel B, Elas M. Data processing of 3D and 4D in-vivo electron paramagnetic resonance imaging co-registered with ultrasound. 3D printing as a registration tool. COMPUTERS & ELECTRICAL ENGINEERING : AN INTERNATIONAL JOURNAL 2019; 74:130-137. [PMID: 30820068 PMCID: PMC6388699 DOI: 10.1016/j.compeleceng.2019.01.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
We present the concept of image registration using ultrasound (US) and electron paramagnetic resonance (EPR) imaging and discuss the benefits of this solution, as well as its limitations. Both phantoms and murine tumors were used to test US and EPR image co-registration. Comparison of dental molding cast immobilization and predesigned cradle revealed that the latter approach is more effective in stabilizing the fiducial position. In vivo imaging of mouse tumors, image registration and comparison of fiducials system for 3D spatial as well as 4D spatial-spectral EPR imaging supported by 3D US were demonstrated. Ultrasound may provide a convenient alternative to other anatomical imaging methods for image registration in preclinical research. Of particular interest is a fusion of US tissue structure, doppler vascular function and EPR oxygen or redox imaging.
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Affiliation(s)
- M Gonet
- 1. Department of Biophysics, Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, Krakow, Poland
- 3. Novilet, Poznan, Poland
| | - B Epel
- 2. Department of Radiation and Cellular Oncology, and Center for EPR Imaging In Vivo Physiology, University of Chicago, Chicago, IL 60637, United States
| | - M Elas
- 1. Department of Biophysics, Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, Krakow, Poland
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Onofrey JA, Staib LH, Sarkar S, Venkataraman R, Nawaf CB, Sprenkle PC, Papademetris X. Learning Non-rigid Deformations for Robust, Constrained Point-based Registration in Image-Guided MR-TRUS Prostate Intervention. Med Image Anal 2017; 39:29-43. [PMID: 28431275 DOI: 10.1016/j.media.2017.04.001] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2016] [Revised: 02/28/2017] [Accepted: 04/03/2017] [Indexed: 01/13/2023]
Abstract
Accurate and robust non-rigid registration of pre-procedure magnetic resonance (MR) imaging to intra-procedure trans-rectal ultrasound (TRUS) is critical for image-guided biopsies of prostate cancer. Prostate cancer is one of the most prevalent forms of cancer and the second leading cause of cancer-related death in men in the United States. TRUS-guided biopsy is the current clinical standard for prostate cancer diagnosis and assessment. State-of-the-art, clinical MR-TRUS image fusion relies upon semi-automated segmentations of the prostate in both the MR and the TRUS images to perform non-rigid surface-based registration of the gland. Segmentation of the prostate in TRUS imaging is itself a challenging task and prone to high variability. These segmentation errors can lead to poor registration and subsequently poor localization of biopsy targets, which may result in false-negative cancer detection. In this paper, we present a non-rigid surface registration approach to MR-TRUS fusion based on a statistical deformation model (SDM) of intra-procedural deformations derived from clinical training data. Synthetic validation experiments quantifying registration volume of interest overlaps of the PI-RADS parcellation standard and tests using clinical landmark data demonstrate that our use of an SDM for registration, with median target registration error of 2.98 mm, is significantly more accurate than the current clinical method. Furthermore, we show that the low-dimensional SDM registration results are robust to segmentation errors that are not uncommon in clinical TRUS data.
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Affiliation(s)
| | - Lawrence H Staib
- Department of Radiology & Biomedical Imaging, USA; Department of Electrical Engineering, USA; Department of Biomedical Engineering, USA.
| | | | | | - Cayce B Nawaf
- Department of Urology, Yale University, New Haven, Connecticut, USA.
| | | | - Xenophon Papademetris
- Department of Radiology & Biomedical Imaging, USA; Department of Biomedical Engineering, USA.
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4
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Gong L, Wang H, Peng C, Dai Y, Ding M, Sun Y, Yang X, Zheng J. Non-rigid MR-TRUS image registration for image-guided prostate biopsy using correlation ratio-based mutual information. Biomed Eng Online 2017; 16:8. [PMID: 28086888 PMCID: PMC5234261 DOI: 10.1186/s12938-016-0308-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2016] [Accepted: 12/27/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND To improve the accuracy of ultrasound-guided biopsy of the prostate, the non-rigid registration of magnetic resonance (MR) images onto transrectal ultrasound (TRUS) images has gained increasing attention. Mutual information (MI) is a widely used similarity criterion in MR-TRUS image registration. However, the use of MI has been challenged because of intensity distortion, noise and down-sampling. Hence, we need to improve the MI measure to get better registration effect. METHODS We present a novel two-dimensional non-rigid MR-TRUS registration algorithm that uses correlation ratio-based mutual information (CRMI) as the similarity criterion. CRMI includes a functional mapping of intensity values on the basis of a generalized version of intensity class correspondence. We also analytically acquire the derivative of CRMI with respect to deformation parameters. Furthermore, we propose an improved stochastic gradient descent (ISGD) optimization method based on the Metropolis acceptance criteria to improve the global optimization ability and decrease the registration time. RESULTS The performance of the proposed method is tested on synthetic images and 12 pairs of clinical prostate TRUS and MR images. By comparing label map registration frame (LMRF) and conditional mutual information (CMI), the proposed algorithm has a significant improvement in the average values of Hausdorff distance and target registration error. Although the average Dice Similarity coefficient is not significantly better than CMI, it still has a crucial increase over LMRF. The average computation time consumed by the proposed method is similar to LMRF, which is 16 times less than CMI. CONCLUSION With more accurate matching performance and lower sensitivity to noise and down-sampling, the proposed algorithm of minimizing CRMI by ISGD is more robust and has the potential for use in aligning TRUS and MR images for needle biopsy.
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Affiliation(s)
- Lun Gong
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Haifeng Wang
- Department of Urology, Shanghai Changhai Hospital, Shanghai, 200433, China
| | - Chengtao Peng
- Department of Electronic Science and Technology, University of Science and Technology of China, Hefei, 230061, China
| | - Yakang Dai
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Min Ding
- School of Science, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Yinghao Sun
- Department of Urology, Shanghai Changhai Hospital, Shanghai, 200433, China
| | - Xiaodong Yang
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Jian Zheng
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China.
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Suárez-Mejías C, Pérez-Carrasco JA, Serrano C, López-Guerra JL, Parra-Calderón C, Gómez-Cía T, Acha B. Three-dimensional segmentation of retroperitoneal masses using continuous convex relaxation and accumulated gradient distance for radiotherapy planning. Med Biol Eng Comput 2016; 55:1-15. [PMID: 27099157 DOI: 10.1007/s11517-016-1505-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2015] [Accepted: 03/28/2016] [Indexed: 11/25/2022]
Abstract
An innovative algorithm has been developed for the segmentation of retroperitoneal tumors in 3D radiological images. This algorithm makes it possible for radiation oncologists and surgeons semiautomatically to select tumors for possible future radiation treatment and surgery. It is based on continuous convex relaxation methodology, the main novelty being the introduction of accumulated gradient distance, with intensity and gradient information being incorporated into the segmentation process. The algorithm was used to segment 26 CT image volumes. The results were compared with manual contouring of the same tumors. The proposed algorithm achieved 90 % sensitivity, 100 % specificity and 84 % positive predictive value, obtaining a mean distance to the closest point of 3.20 pixels. The algorithm's dependence on the initial manual contour was also analyzed, with results showing that the algorithm substantially reduced the variability of the manual segmentation carried out by different specialists. The algorithm was also compared with four benchmark algorithms (thresholding, edge-based level-set, region-based level-set and continuous max-flow with two labels). To the best of our knowledge, this is the first time the segmentation of retroperitoneal tumors for radiotherapy planning has been addressed.
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Affiliation(s)
- Cristina Suárez-Mejías
- Technological Innovation Group, Virgen del Rocío University Hospital, Seville, Spain.
- Signal Theory and Communications Department, University of Seville, Seville, Spain.
| | | | - Carmen Serrano
- Signal Theory and Communications Department, University of Seville, Seville, Spain
| | | | - Carlos Parra-Calderón
- Technological Innovation Group, Virgen del Rocío University Hospital, Seville, Spain
| | - Tomás Gómez-Cía
- Surgery Unit, Virgen del Rocío University Hospital, Seville, Spain
| | - Begoña Acha
- Signal Theory and Communications Department, University of Seville, Seville, Spain
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Khallaghi S, Sánchez CA, Rasoulian A, Nouranian S, Romagnoli C, Abdi H, Chang SD, Black PC, Goldenberg L, Morris WJ, Spadinger I, Fenster A, Ward A, Fels S, Abolmaesumi P. Statistical Biomechanical Surface Registration: Application to MR-TRUS Fusion for Prostate Interventions. IEEE TRANSACTIONS ON MEDICAL IMAGING 2015; 34:2535-2549. [PMID: 26080380 DOI: 10.1109/tmi.2015.2443978] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
A common challenge when performing surface-based registration of images is ensuring that the surfaces accurately represent consistent anatomical boundaries. Image segmentation may be difficult in some regions due to either poor contrast, low slice resolution, or tissue ambiguities. To address this, we present a novel non-rigid surface registration method designed to register two partial surfaces, capable of ignoring regions where the anatomical boundary is unclear. Our probabilistic approach incorporates prior geometric information in the form of a statistical shape model (SSM), and physical knowledge in the form of a finite element model (FEM). We validate results in the context of prostate interventions by registering pre-operative magnetic resonance imaging (MRI) to 3D transrectal ultrasound (TRUS). We show that both the geometric and physical priors significantly decrease net target registration error (TRE), leading to TREs of 2.35 ± 0.81 mm and 2.81 ± 0.66 mm when applied to full and partial surfaces, respectively. We investigate robustness in response to errors in segmentation, varying levels of missing data, and adjusting the tunable parameters. Results demonstrate that the proposed surface registration method is an efficient, robust, and effective solution for fusing data from multiple modalities.
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Khallaghi S, Sánchez CA, Rasoulian A, Sun Y, Imani F, Khojaste A, Goksel O, Romagnoli C, Abdi H, Chang S, Mousavi P, Fenster A, Ward A, Fels S, Abolmaesumi P. Biomechanically Constrained Surface Registration: Application to MR-TRUS Fusion for Prostate Interventions. IEEE TRANSACTIONS ON MEDICAL IMAGING 2015; 34:2404-2414. [PMID: 26054062 DOI: 10.1109/tmi.2015.2440253] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
In surface-based registration for image-guided interventions, the presence of missing data can be a significant issue. This often arises with real-time imaging modalities such as ultrasound, where poor contrast can make tissue boundaries difficult to distinguish from surrounding tissue. Missing data poses two challenges: ambiguity in establishing correspondences; and extrapolation of the deformation field to those missing regions. To address these, we present a novel non-rigid registration method. For establishing correspondences, we use a probabilistic framework based on a Gaussian mixture model (GMM) that treats one surface as a potentially partial observation. To extrapolate and constrain the deformation field, we incorporate biomechanical prior knowledge in the form of a finite element model (FEM). We validate the algorithm, referred to as GMM-FEM, in the context of prostate interventions. Our method leads to a significant reduction in target registration error (TRE) compared to similar state-of-the-art registration algorithms in the case of missing data up to 30%, with a mean TRE of 2.6 mm. The method also performs well when full segmentations are available, leading to TREs that are comparable to or better than other surface-based techniques. We also analyze robustness of our approach, showing that GMM-FEM is a practical and reliable solution for surface-based registration.
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Onofrey JA, Staib LH, Sarkar S, Venkataraman R, Papademetris X. LEARNING NONRIGID DEFORMATIONS FOR CONSTRAINED POINT-BASED REGISTRATION FOR IMAGE-GUIDED MR-TRUS PROSTATE INTERVENTION. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2015; 2015:1592-1595. [PMID: 26405508 DOI: 10.1109/isbi.2015.7164184] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
This paper presents and validates a low-dimensional nonrigid registration method for fusing magnetic resonance imaging (MRI) and trans-rectal ultrasound (TRUS) in image-guided prostate biopsy. Prostate cancer is one of the most prevalent forms of cancer and the second leading cause of cancer-related death in men in the United States. Conventional clinical practice uses TRUS to guide prostate biopsies when there is a suspicion of cancer. Pre-procedural MRI information can reveal lesions and may be fused with intra-procedure TRUS imaging to provide patient-specific, localization of lesions for targeting. The state-of-the-art MRI-TRUS nonrigid image fusion process relies upon semi-automated segmentation of the prostate in both the MRI and TRUS images. In this paper, we develop a fast, automated nonrigid registration approach to MRI-TRUS fusion based on a statistical deformation model of intra-procedural deformations derived from a clinical sample.
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Affiliation(s)
- John A Onofrey
- Department of Diagnostic Radiology, Yale University, New Haven, CT, USA
| | - Lawrence H Staib
- Department of Diagnostic Radiology, Yale University, New Haven, CT, USA ; Department of Biomedical Engineering, Yale University, New Haven, CT, USA ; Department of Electrical Engineering, Yale University, New Haven, CT, USA
| | | | | | - Xenophon Papademetris
- Department of Diagnostic Radiology, Yale University, New Haven, CT, USA ; Department of Biomedical Engineering, Yale University, New Haven, CT, USA
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Sun Y, Qiu W, Yuan J, Romagnoli C, Fenster A. Three-dimensional nonrigid landmark-based magnetic resonance to transrectal ultrasound registration for image-guided prostate biopsy. J Med Imaging (Bellingham) 2015; 2:025002. [PMID: 26158111 DOI: 10.1117/1.jmi.2.2.025002] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2015] [Accepted: 05/27/2015] [Indexed: 12/13/2022] Open
Abstract
Registration of three-dimensional (3-D) magnetic resonance (MR) to 3-D transrectal ultrasound (TRUS) prostate images is an important step in the planning and guidance of 3-D TRUS guided prostate biopsy. In order to accurately and efficiently perform the registration, a nonrigid landmark-based registration method is required to account for the different deformations of the prostate when using these two modalities. We describe a nonrigid landmark-based method for registration of 3-D TRUS to MR prostate images. The landmark-based registration method first makes use of an initial rigid registration of 3-D MR to 3-D TRUS images using six manually placed approximately corresponding landmarks in each image. Following manual initialization, the two prostate surfaces are segmented from 3-D MR and TRUS images and then nonrigidly registered using the following steps: (1) rotationally reslicing corresponding segmented prostate surfaces from both 3-D MR and TRUS images around a specified axis, (2) an approach to find point correspondences on the surfaces of the segmented surfaces, and (3) deformation of the surface of the prostate in the MR image to match the surface of the prostate in the 3-D TRUS image and the interior using a thin-plate spline algorithm. The registration accuracy was evaluated using 17 patient prostate MR and 3-D TRUS images by measuring the target registration error (TRE). Experimental results showed that the proposed method yielded an overall mean TRE of [Formula: see text] for the rigid registration and [Formula: see text] for the nonrigid registration, which is favorably comparable to a clinical requirement for an error of less than 2.5 mm. A landmark-based nonrigid 3-D MR-TRUS registration approach is proposed, which takes into account the correspondences on the prostate surface, inside the prostate, as well as the centroid of the prostate. Experimental results indicate that the proposed method yields clinically sufficient accuracy.
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Affiliation(s)
- Yue Sun
- University of Western Ontario , Imaging Research Laboratories, Robarts Research Institute, London, Ontario N6A 5K8, Canada
| | - Wu Qiu
- University of Western Ontario , Imaging Research Laboratories, Robarts Research Institute, London, Ontario N6A 5K8, Canada
| | - Jing Yuan
- University of Western Ontario , Imaging Research Laboratories, Robarts Research Institute, London, Ontario N6A 5K8, Canada
| | - Cesare Romagnoli
- University of Western Ontario , Department of Medical Imaging, London, Ontario N6A 5K8, Canada
| | - Aaron Fenster
- University of Western Ontario , Imaging Research Laboratories, Robarts Research Institute, London, Ontario N6A 5K8, Canada ; University of Western Ontario , Department of Medical Imaging, London, Ontario N6A 5K8, Canada ; University of Western Ontario , Department of Medical Biophysics, London, Ontario N6A 5K8, Canada
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Fedorov A, Khallaghi S, Sánchez CA, Lasso A, Fels S, Tuncali K, Sugar EN, Kapur T, Zhang C, Wells W, Nguyen PL, Abolmaesumi P, Tempany C. Open-source image registration for MRI-TRUS fusion-guided prostate interventions. Int J Comput Assist Radiol Surg 2015; 10:925-34. [PMID: 25847666 DOI: 10.1007/s11548-015-1180-7] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2015] [Accepted: 03/17/2015] [Indexed: 11/30/2022]
Abstract
PURPOSE We propose two software tools for non-rigid registration of MRI and transrectal ultrasound (TRUS) images of the prostate. Our ultimate goal is to develop an open-source solution to support MRI-TRUS fusion image guidance of prostate interventions, such as targeted biopsy for prostate cancer detection and focal therapy. It is widely hypothesized that image registration is an essential component in such systems. METHODS The two non-rigid registration methods are: (1) a deformable registration of the prostate segmentation distance maps with B-spline regularization and (2) a finite element-based deformable registration of the segmentation surfaces in the presence of partial data. We evaluate the methods retrospectively using clinical patient image data collected during standard clinical procedures. Computation time and Target Registration Error (TRE) calculated at the expert-identified anatomical landmarks were used as quantitative measures for the evaluation. RESULTS The presented image registration tools were capable of completing deformable registration computation within 5 min. Average TRE was approximately 3 mm for both methods, which is comparable with the slice thickness in our MRI data. Both tools are available under nonrestrictive open-source license. CONCLUSIONS We release open-source tools that may be used for registration during MRI-TRUS-guided prostate interventions. Our tools implement novel registration approaches and produce acceptable registration results. We believe these tools will lower the barriers in development and deployment of interventional research solutions and facilitate comparison with similar tools.
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Moradi M, Mahdavi SS, Nir G, Mohareri O, Koupparis A, Gagnon LO, Fazli L, Casey RG, Ischia J, Jones EC, Goldenberg SL, Salcudean SE. Multiparametric 3D in vivo ultrasound vibroelastography imaging of prostate cancer: Preliminary results. Med Phys 2015; 41:073505. [PMID: 24989419 DOI: 10.1118/1.4884226] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
PURPOSE Ultrasound-based solutions for diagnosis and prognosis of prostate cancer are highly desirable. The authors have devised a method for detecting prostate cancer using a vibroelastography (VE) system developed in our group and a tissue classification approach based on texture analysis of VE images. METHODS The VE method applies wide-band mechanical vibrations to the tissue. Here, the authors report on the use of this system for cancer detection and show that the texture of VE images characterized by the first and the second order statistics of the pixel intensities form a promising set of features for tissue typing to detect prostate cancer. The system was used to image patients prior to radical surgery. The removed specimens were sectioned and studied by an experienced histopathologist. The authors registered the whole-mount histology sections to the ultrasound images using an automatic registration algorithm. This enabled the quantitative evaluation of the performance of the authors' imaging method in cancer detection in an unbiased manner. The authors used support vector machine (SVM) classification to measure the cancer detection performance of the VE method. Regions of tissue of size 5 × 5 mm, labeled as cancer and noncancer based on automatic registration to histology slides, were classified using SVM. RESULTS The authors report an area under ROC of 0.81 ± 0.10 in cancer detection on 1066 tissue regions from 203 images. All cancer tumors in all zones were included in this analysis and were classified versus the noncancer tissue in the peripheral zone. This outcome was obtained in leave-one-patient-out validation. CONCLUSIONS The developed 3D prostate vibroelastography system and the proposed multiparametric approach based on statistical texture parameters from the VE images result in a promising cancer detection method.
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Affiliation(s)
- Mehdi Moradi
- University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
| | - S Sara Mahdavi
- British Columbia Cancer Agency, Vancouver, British Columbia V5Z 4E6, Canada
| | - Guy Nir
- University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
| | - Omid Mohareri
- University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
| | - Anthony Koupparis
- Bristol Urological Institute, Brunel Building, Southmead Hospital, Bristol BS10 5NB, UK
| | | | - Ladan Fazli
- Vancouver Prostate Center, Vancouver, British Columbia V6H 3Z6, Canada
| | - Rowan G Casey
- Consultant Urologist, Essex Cancer Centre, Colchester University NHS Foundation Trust, Essex, CO62QL, UK
| | - Joseph Ischia
- University of Melbourne, Melbourne, Victoria 3010, Australia
| | - Edward C Jones
- Vancouver General Hospital, Vancouver, British Columbia V5Z 1M9, Canada
| | - S Larry Goldenberg
- Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
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Tempany CMC, Jayender J, Kapur T, Bueno R, Golby A, Agar N, Jolesz FA. Multimodal imaging for improved diagnosis and treatment of cancers. Cancer 2014; 121:817-27. [PMID: 25204551 DOI: 10.1002/cncr.29012] [Citation(s) in RCA: 69] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2014] [Revised: 07/03/2014] [Accepted: 07/14/2014] [Indexed: 12/17/2022]
Abstract
The authors review methods for image-guided diagnosis and therapy that increase precision in the detection, characterization, and localization of many forms of cancer to achieve optimal target definition and complete resection or ablation. A new model of translational, clinical, image-guided therapy research is presented, and the Advanced Multimodality Image-Guided Operating (AMIGO) suite is described. AMIGO was conceived and designed to allow for the full integration of imaging in cancer diagnosis and treatment. Examples are drawn from over 500 procedures performed on brain, neck, spine, thorax (breast, lung), and pelvis (prostate and gynecologic) areas and are used to describe how they address some of the many challenges of treating brain, prostate, and lung tumors. Cancer 2015;121:817-827. © 2014 American Cancer Society.
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Affiliation(s)
- Clare M C Tempany
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
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