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DiffeoRaptor: diffeomorphic inter-modal image registration using RaPTOR. Int J Comput Assist Radiol Surg 2023; 18:367-377. [PMID: 36173541 DOI: 10.1007/s11548-022-02749-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 09/06/2022] [Indexed: 02/08/2023]
Abstract
PURPOSE Diffeomorphic image registration is essential in many medical imaging applications. Several registration algorithms of such type have been proposed, but primarily for intra-contrast alignment. Currently, efficient inter-modal/contrast diffeomorphic registration, which is vital in numerous applications, remains a challenging task. METHODS We proposed a novel inter-modal/contrast registration algorithm that leverages Robust PaTch-based cOrrelation Ratio metric to allow inter-modal/contrast image alignment and bandlimited geodesic shooting demonstrated in Fourier-Approximated Lie Algebras (FLASH) algorithm for fast diffeomorphic registration. RESULTS The proposed algorithm, named DiffeoRaptor, was validated with three public databases for the tasks of brain and abdominal image registration while comparing the results against three state-of-the-art techniques, including FLASH, NiftyReg, and Symmetric image Normalization (SyN). CONCLUSIONS Our results demonstrated that DiffeoRaptor offered comparable or better registration performance in terms of registration accuracy. Moreover, DiffeoRaptor produces smoother deformations than SyN in inter-modal and contrast registration. The code for DiffeoRaptor is publicly available at https://github.com/nimamasoumi/DiffeoRaptor .
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Baum ZMC, Hu Y, Barratt DC. Real-time multimodal image registration with partial intraoperative point-set data. Med Image Anal 2021; 74:102231. [PMID: 34583240 PMCID: PMC8566274 DOI: 10.1016/j.media.2021.102231] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 07/16/2021] [Accepted: 09/10/2021] [Indexed: 11/28/2022]
Abstract
We present Free Point Transformer (FPT) - a deep neural network architecture for non-rigid point-set registration. Consisting of two modules, a global feature extraction module and a point transformation module, FPT does not assume explicit constraints based on point vicinity, thereby overcoming a common requirement of previous learning-based point-set registration methods. FPT is designed to accept unordered and unstructured point-sets with a variable number of points and uses a "model-free" approach without heuristic constraints. Training FPT is flexible and involves minimizing an intuitive unsupervised loss function, but supervised, semi-supervised, and partially- or weakly-supervised training are also supported. This flexibility makes FPT amenable to multimodal image registration problems where the ground-truth deformations are difficult or impossible to measure. In this paper, we demonstrate the application of FPT to non-rigid registration of prostate magnetic resonance (MR) imaging and sparsely-sampled transrectal ultrasound (TRUS) images. The registration errors were 4.71 mm and 4.81 mm for complete TRUS imaging and sparsely-sampled TRUS imaging, respectively. The results indicate superior accuracy to the alternative rigid and non-rigid registration algorithms tested and substantially lower computation time. The rapid inference possible with FPT makes it particularly suitable for applications where real-time registration is beneficial.
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Affiliation(s)
- Zachary M C Baum
- Centre for Medical Image Computing, University College London, London, UK; Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK.
| | - Yipeng Hu
- Centre for Medical Image Computing, University College London, London, UK; Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Dean C Barratt
- Centre for Medical Image Computing, University College London, London, UK; Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
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Fu Y, Lei Y, Wang T, Patel P, Jani AB, Mao H, Curran WJ, Liu T, Yang X. Biomechanically constrained non-rigid MR-TRUS prostate registration using deep learning based 3D point cloud matching. Med Image Anal 2020; 67:101845. [PMID: 33129147 DOI: 10.1016/j.media.2020.101845] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 08/17/2020] [Accepted: 08/31/2020] [Indexed: 01/04/2023]
Abstract
A non-rigid MR-TRUS image registration framework is proposed for prostate interventions. The registration framework consists of a convolutional neural networks (CNN) for MR prostate segmentation, a CNN for TRUS prostate segmentation and a point-cloud based network for rapid 3D point cloud matching. Volumetric prostate point clouds were generated from the segmented prostate masks using tetrahedron meshing. The point cloud matching network was trained using deformation field that was generated by finite element analysis. Therefore, the network implicitly models the underlying biomechanical constraint when performing point cloud matching. A total of 50 patients' datasets were used for the network training and testing. Alignment of prostate shapes after registration was evaluated using three metrics including Dice similarity coefficient (DSC), mean surface distance (MSD) and Hausdorff distance (HD). Internal point-to-point registration accuracy was assessed using target registration error (TRE). Jacobian determinant and strain tensors of the predicted deformation field were calculated to analyze the physical fidelity of the deformation field. On average, the mean and standard deviation were 0.94±0.02, 0.90±0.23 mm, 2.96±1.00 mm and 1.57±0.77 mm for DSC, MSD, HD and TRE, respectively. Robustness of our method to point cloud noise was evaluated by adding different levels of noise to the query point clouds. Our results demonstrated that the proposed method could rapidly perform MR-TRUS image registration with good registration accuracy and robustness.
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Affiliation(s)
- Yabo Fu
- Department of Radiation Oncology, Emory University, 1365 Clifton Road NE, Atlanta, GA 30322, United States
| | - Yang Lei
- Department of Radiation Oncology, Emory University, 1365 Clifton Road NE, Atlanta, GA 30322, United States
| | - Tonghe Wang
- Department of Radiation Oncology, Emory University, 1365 Clifton Road NE, Atlanta, GA 30322, United States; Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States
| | - Pretesh Patel
- Department of Radiation Oncology, Emory University, 1365 Clifton Road NE, Atlanta, GA 30322, United States; Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States
| | - Ashesh B Jani
- Department of Radiation Oncology, Emory University, 1365 Clifton Road NE, Atlanta, GA 30322, United States; Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States
| | - Hui Mao
- Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States; Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA 30322, United States
| | - Walter J Curran
- Department of Radiation Oncology, Emory University, 1365 Clifton Road NE, Atlanta, GA 30322, United States; Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States
| | - Tian Liu
- Department of Radiation Oncology, Emory University, 1365 Clifton Road NE, Atlanta, GA 30322, United States; Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States
| | - Xiaofeng Yang
- Department of Radiation Oncology, Emory University, 1365 Clifton Road NE, Atlanta, GA 30322, United States; Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States.
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Sultana S, Song DY, Lee J. Deformable registration of PET/CT and ultrasound for disease-targeted focal prostate brachytherapy. J Med Imaging (Bellingham) 2019; 6:035003. [PMID: 31528661 PMCID: PMC6739636 DOI: 10.1117/1.jmi.6.3.035003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Accepted: 08/20/2019] [Indexed: 12/27/2022] Open
Abstract
We propose a deformable registration algorithm for prostate-specific membrane antigen (PSMA) PET/CT and transrectal ultrasound (TRUS) fusion. Accurate registration of PSMA PET to intraoperative TRUS will allow physicians to customize dose planning based on the regions involved. The inputs to the registration algorithm are the PET/CT and TRUS volumes as well as the prostate segmentations. PET/CT and TRUS volumes are first rigidly registered by maximizing the overlap between the segmented prostate binary masks. Three-dimensional anatomical landmarks are then automatically extracted from the boundary as well as within the prostate. Then, a deformable registration is performed using a regularized thin plate spline where the landmark localization error is optimized between the extracted landmarks that are in correspondence. The proposed algorithm was evaluated on 25 prostate cancer patients treated with low-dose-rate brachytherapy. We registered the postimplant CT to TRUS using the proposed algorithm and computed target registration errors (TREs) by comparing implanted seed locations. Our approach outperforms state-of-the-art methods, with significantly lower ( mean ± standard deviation ) TRE of 1.96 ± 1.29 mm while being computationally efficient (mean computation time of 38 s). The proposed landmark-based PET/CT-TRUS deformable registration algorithm is simple, computationally efficient, and capable of producing quality registration of the prostate boundary as well as the internal gland.
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Affiliation(s)
- Sharmin Sultana
- Johns Hopkins University, Department of Radiation Oncology and Molecular Radiation Sciences, Baltimore, Maryland, United States
| | - Daniel Y. Song
- Johns Hopkins University, Department of Radiation Oncology and Molecular Radiation Sciences, Baltimore, Maryland, United States
| | - Junghoon Lee
- Johns Hopkins University, Department of Radiation Oncology and Molecular Radiation Sciences, Baltimore, Maryland, United States
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Sultana S, Song DY, Lee J. A deformable multimodal image registration using PET/CT and TRUS for intraoperative focal prostate brachytherapy. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2019; 10951:109511I. [PMID: 32341619 PMCID: PMC7185222 DOI: 10.1117/12.2512996] [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: 06/11/2023]
Abstract
In this paper, a deformable registration method is proposed that enables automatic alignment of preoperative PET/CT to intraoperative ultrasound in order to achieve PET-determined focal prostate brachytherapy. Novel PET imaging agents such as prostate specific membrane antigen (PSMA) enables highly accurate identification of intra/extra-prostatic tumors. Incorporation of PSMA PET into the standard transrectal ultrasound (TRUS)-guided prostate brachytherapy will enable focal therapy, thus minimizing radiation toxicities. Our registration method requires PET/CT and TRUS volume as well as prostate segmentations. These input volumes are first rigidly registered by maximizing spatial overlap between the segmented prostate volumes, followed by the deformable registration. To achieve anatomically accurate deformable registration, we extract anatomical landmarks from both prostate boundary and inside the gland. Landmarks are extracted along the base-apex axes using two approaches: equiangular and equidistance. Three-dimensional thin-plate spline (TPS)-based deformable registration is then performed using the extracted landmarks as control points. Finally, the PET/CT images are deformed to the TRUS space by using the computed TPS transformation. The proposed method was validated on 10 prostate cancer patient datasets in which we registered post-implant CT to end-of-implantation TRUS. We computed target registration errors (TREs) by comparing the implanted seed positions (transformed CT seeds vs. intraoperatively identified TRUS seeds). The average TREs of the proposed method are 1.98±1.22 mm (mean±standard deviation) and 1.97±1.24 mm for equiangular and equidistance landmark extraction methods, respectively, which is better than or comparable to existing state-of-the-art methods while being computationally more efficient with an average computation time less than 40 seconds.
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Affiliation(s)
- Sharmin Sultana
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Daniel Y Song
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Junghoon Lee
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD, USA
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6
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Sánta Z, Kato Z. Elastic Alignment of Triangular Surface Meshes. Int J Comput Vis 2018. [DOI: 10.1007/s11263-018-1084-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Orczyk C, Rosenkrantz AB, Mikheev A, Villers A, Bernaudin M, Taneja SS, Valable S, Rusinek H. 3D Registration of mpMRI for Assessment of Prostate Cancer Focal Therapy. Acad Radiol 2017; 24:1544-1555. [PMID: 29122471 PMCID: PMC6025844 DOI: 10.1016/j.acra.2017.06.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2017] [Revised: 05/25/2017] [Accepted: 06/09/2017] [Indexed: 01/16/2023]
Abstract
RATIONALE AND OBJECTIVES This study aimed to assess a novel method of three-dimensional (3D) co-registration of prostate magnetic resonance imaging (MRI) examinations performed before and after prostate cancer focal therapy. MATERIALS AND METHODS We developed a software platform for automatic 3D deformable co-registration of prostate MRI at different time points and applied this method to 10 patients who underwent focal ablative therapy. MRI examinations were performed preoperatively, as well as 1 week and 6 months post treatment. Rigid registration served as reference for assessing co-registration accuracy and precision. RESULTS Segmentation of preoperative and postoperative prostate revealed a significant postoperative volume decrease of the gland that averaged 6.49 cc (P = .017). Applying deformable transformation based on mutual information from 120 pairs of MRI slices, we refined by 2.9 mm (max. 6.25 mm) the alignment of the ablation zone, segmented from contrast-enhanced images on the 1-week postoperative examination, to the 6-month postoperative T2-weighted images. This represented a 500% improvement over the rigid approach (P = .001), corrected by volume. The dissimilarity by Dice index of the mapped ablation zone using deformable transformation vs rigid control was significantly (P = .04) higher at the ablation site than in the whole gland. CONCLUSIONS Our findings illustrate our method's ability to correct for deformation at the ablation site. The preliminary analysis suggests that deformable transformation computed from mutual information of preoperative and follow-up MRI is accurate in co-registration of MRI examinations performed before and after focal therapy. The ability to localize the previously ablated tissue in 3D space may improve targeting for image-guided follow-up biopsy within focal therapy protocols.
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Affiliation(s)
- Clément Orczyk
- The Prostate Unit, Department of Urology, University College London Hospitals, London, United Kingdom; Division of Urologic Oncology, Department of Urology, New York University Langone Medical Center, New York, NY; Normandie Université, UNICAEN, CEA, CNRS, ISTCT/CERVOxy Group, 14000Caen, France; Department of Urology, University Hospital of Caen, Caen, France.
| | - Andrew B Rosenkrantz
- Department of Radiology, New York University Langone Medical Center, New York, NY
| | - Artem Mikheev
- Department of Radiology, New York University Langone Medical Center, New York, NY
| | - Arnauld Villers
- Department of Urology, Université Lille Nord de France, Lille, France
| | - Myriam Bernaudin
- Normandie Université, UNICAEN, CEA, CNRS, ISTCT/CERVOxy Group, 14000Caen, France
| | - Samir S Taneja
- Division of Urologic Oncology, Department of Urology, New York University Langone Medical Center, New York, NY; Department of Radiology, New York University Langone Medical Center, New York, NY
| | - Samuel Valable
- Normandie Université, UNICAEN, CEA, CNRS, ISTCT/CERVOxy Group, 14000Caen, France
| | - Henry Rusinek
- Department of Radiology, New York University Langone Medical Center, New York, NY
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Yang C, White J, Chen GP, Li XA. Deformable MRI-CT registration for breast cancer radiation treatment planning using a sequentially applied semi-physical model regularization method. Biomed Phys Eng Express 2017. [DOI: 10.1088/2057-1976/aa6dba] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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9
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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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10
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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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11
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Rhee H, Thomas P, Shepherd B, Gustafson S, Vela I, Russell P, Nelson C, Chung E, Wood G, Malone G, Wood S, Heathcote P. Prostate Specific Membrane Antigen Positron Emission Tomography May Improve the Diagnostic Accuracy of Multiparametric Magnetic Resonance Imaging in Localized Prostate Cancer. J Urol 2016; 196:1261-7. [DOI: 10.1016/j.juro.2016.02.3000] [Citation(s) in RCA: 88] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/25/2016] [Indexed: 12/12/2022]
Affiliation(s)
- H. Rhee
- Department of Urology, Princess Alexandra Hospital, Queensland, Australia
- Australian Prostate Cancer Research Centre–Queensland, Institute of Health and Biomedical Innovation, Faculty of Health, Queensland University of Technology, Princess Alexandra Hospital, Translational Research Institute, Queensland, Australia
| | - P. Thomas
- Department of Nuclear Medicine, Royal Brisbane and Women’s Hospital, Queensland, Australia
| | - B. Shepherd
- Pathology Queensland, Princess Alexandra Hospital, Queensland, Australia
| | - S. Gustafson
- Department of Radiology, Princess Alexandra Hospital, Queensland, Australia
| | - I. Vela
- Department of Urology, Princess Alexandra Hospital, Queensland, Australia
- Australian Prostate Cancer Research Centre–Queensland, Institute of Health and Biomedical Innovation, Faculty of Health, Queensland University of Technology, Princess Alexandra Hospital, Translational Research Institute, Queensland, Australia
| | - P.J. Russell
- Australian Prostate Cancer Research Centre–Queensland, Institute of Health and Biomedical Innovation, Faculty of Health, Queensland University of Technology, Princess Alexandra Hospital, Translational Research Institute, Queensland, Australia
| | - C. Nelson
- Australian Prostate Cancer Research Centre–Queensland, Institute of Health and Biomedical Innovation, Faculty of Health, Queensland University of Technology, Princess Alexandra Hospital, Translational Research Institute, Queensland, Australia
| | - E. Chung
- Department of Urology, Princess Alexandra Hospital, Queensland, Australia
| | - G. Wood
- Department of Urology, Greenslopes Private Hospital, Queensland, Australia
| | - G. Malone
- Department of Urology, Princess Alexandra Hospital, Queensland, Australia
- Department of Urology, Greenslopes Private Hospital, Queensland, Australia
| | - S. Wood
- Department of Urology, Princess Alexandra Hospital, Queensland, Australia
- Department of Urology, Greenslopes Private Hospital, Queensland, Australia
| | - P. Heathcote
- Department of Urology, Princess Alexandra Hospital, Queensland, Australia
- Department of Urology, Greenslopes Private Hospital, Queensland, Australia
- Australian Prostate Cancer Research Centre–Queensland, Institute of Health and Biomedical Innovation, Faculty of Health, Queensland University of Technology, Princess Alexandra Hospital, Translational Research Institute, Queensland, Australia
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12
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Sparks R, Bloch BN, Feleppa E, Barratt D, Moses D, Ponsky L, Madabhushi A. Multiattribute probabilistic prostate elastic registration (MAPPER): application to fusion of ultrasound and magnetic resonance imaging. Med Phys 2016; 42:1153-63. [PMID: 25735270 DOI: 10.1118/1.4905104] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Transrectal ultrasound (TRUS)-guided needle biopsy is the current gold standard for prostate cancer diagnosis. However, up to 40% of prostate cancer lesions appears isoechoic on TRUS. Hence, TRUS-guided biopsy has a high false negative rate for prostate cancer diagnosis. Magnetic resonance imaging (MRI) is better able to distinguish prostate cancer from benign tissue. However, MRI-guided biopsy requires special equipment and training and a longer procedure time. MRI-TRUS fusion, where MRI is acquired preoperatively and then aligned to TRUS, allows for advantages of both modalities to be leveraged during biopsy. MRI-TRUS-guided biopsy increases the yield of cancer positive biopsies. In this work, the authors present multiattribute probabilistic postate elastic registration (MAPPER) to align prostate MRI and TRUS imagery. METHODS MAPPER involves (1) segmenting the prostate on MRI, (2) calculating a multiattribute probabilistic map of prostate location on TRUS, and (3) maximizing overlap between the prostate segmentation on MRI and the multiattribute probabilistic map on TRUS, thereby driving registration of MRI onto TRUS. MAPPER represents a significant advancement over the current state-of-the-art as it requires no user interaction during the biopsy procedure by leveraging texture and spatial information to determine the prostate location on TRUS. Although MAPPER requires manual interaction to segment the prostate on MRI, this step is performed prior to biopsy and will not substantially increase biopsy procedure time. RESULTS MAPPER was evaluated on 13 patient studies from two independent datasets—Dataset 1 has 6 studies acquired with a side-firing TRUS probe and a 1.5 T pelvic phased-array coil MRI; Dataset 2 has 7 studies acquired with a volumetric end-firing TRUS probe and a 3.0 T endorectal coil MRI. MAPPER has a root-mean-square error (RMSE) for expert selected fiducials of 3.36 ± 1.10 mm for Dataset 1 and 3.14 ± 0.75 mm for Dataset 2. State-of-the-art MRI-TRUS fusion methods report RMSE of 3.06-2.07 mm. CONCLUSIONS MAPPER aligns MRI and TRUS imagery without manual intervention ensuring efficient, reproducible registration. MAPPER has a similar RMSE to state-of-the-art methods that require manual intervention.
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Affiliation(s)
- Rachel Sparks
- Centre for Medical Image Computing, University College London, London WC1E 6BT, United Kingdom
| | - B Nicolas Bloch
- Department of Radiology, Boston Medical Center and Boston University, Boston, Massachusetts 02118
| | - Ernest Feleppa
- Lizzi Center for Biomedical Engineering, Riverside Research Institute, New York, New York 10038
| | - Dean Barratt
- Centre for Medical Image Computing, University College London, London WC1E 6BT, United Kingdom
| | - Daniel Moses
- South Western Sydney Clinical School, University of New South Wales, Sydney NSW 2052, Australia
| | - Lee Ponsky
- Department of Urology, University Hospitals Case Medical Center, Cleveland, Ohio 44106
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106
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13
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Mayer A, Zholkover A, Portnoy O, Raviv G, Konen E, Symon Z. Deformable registration of trans-rectal ultrasound (TRUS) and magnetic resonance imaging (MRI) for focal prostate brachytherapy. Int J Comput Assist Radiol Surg 2016; 11:1015-23. [PMID: 27017500 DOI: 10.1007/s11548-016-1380-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2016] [Accepted: 03/08/2016] [Indexed: 11/29/2022]
Abstract
PURPOSE Focal therapy in low-risk prostate cancer may provide the best balance between cancer control and quality of life preservation. As a minimally invasive approach performed under TRUS guidance, brachytherapy is an appealing framework for focal therapy. However, the contrast in TRUS images is generally insufficient to distinguish the target lesion from normal prostate tissue. MRI usually offers a much better contrast between the lesion and surrounding tissues. Registration between TRUS and MRI may therefore significantly improve lesion targeting capability in focal prostate brachytherapy. In this paper, we present a deformable registration framework for the accurate fusion of TRUS and MRI prostate volumes under large deformations arising from dissimilarities in diameter, shape and orientation between endorectal coils and TRUS probes. METHODS Following pose correction by a RANSAC implementation of the ICP algorithm, TRUS and MRI Prostate contour points are represented by a 3D extension of the shape-context descriptor and matched by the Hungarian algorithm. Eventually, a smooth free-form warping is computed by fitting a 3D B-spline mesh to the set of matched points. RESULTS Quantitative validation of the registration accuracy is provided on a retrospective set of ten real cases, using as landmarks either brachytherapy seeds (six cases) or external beam radiotherapy fiducials (four cases) implanted and visible in both modalities. The average registration error between the landmarks was 2.49 and 3.20 mm, for the brachytherapy and external beam sets, respectively, that is less than the MRI voxels' long axis length ([Formula: see text]). The overall average registration error (for brachytherapy and external beam datasets together) was 2.56 mm. CONCLUSIONS The proposed method provides a promising framework for TRUS-MRI registration in focal prostate brachytherapy.
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Affiliation(s)
- Arnaldo Mayer
- Diagnostic Imaging Institute, Sheba Medical Center, Ramat Gan, Israel. .,Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel.
| | - Adi Zholkover
- Diagnostic Imaging Institute, Sheba Medical Center, Ramat Gan, Israel
| | - Orith Portnoy
- Diagnostic Imaging Institute, Sheba Medical Center, Ramat Gan, Israel.,Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Gil Raviv
- Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel.,Department of Urology, Sheba Medical Center, Ramat Gan, Israel
| | - Eli Konen
- Diagnostic Imaging Institute, Sheba Medical Center, Ramat Gan, Israel.,Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Zvi Symon
- Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel.,Department of Radiation Oncology, Sheba Medical Center, Ramat Gan, Israel
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14
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van de Ven WJM, Hu Y, Barentsz JO, Karssemeijer N, Barratt D, Huisman HJ. Biomechanical modeling constrained surface-based image registration for prostate MR guided TRUS biopsy. Med Phys 2016; 42:2470-81. [PMID: 25979040 DOI: 10.1118/1.4917481] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
PURPOSE Adding magnetic resonance (MR)-derived information to standard transrectal ultrasound (TRUS) images for guiding prostate biopsy is of substantial clinical interest. A tumor visible on MR images can be projected on ultrasound (US) by using MR-US registration. A common approach is to use surface-based registration. The authors hypothesize that biomechanical modeling will better control deformation inside the prostate than a regular nonrigid surface-based registration method. The authors developed a novel method by extending a nonrigid surface-based registration algorithm with biomechanical finite element (FE) modeling to better predict internal deformations of the prostate. METHODS Data were collected from ten patients and the MR and TRUS images were rigidly registered to anatomically align prostate orientations. The prostate was manually segmented in both images and corresponding surface meshes were generated. Next, a tetrahedral volume mesh was generated from the MR image. Prostate deformations due to the TRUS probe were simulated using the surface displacements as the boundary condition. A three-dimensional thin-plate spline deformation field was calculated by registering the mesh vertices. The target registration errors (TREs) of 35 reference landmarks determined by surface and volume mesh registrations were compared. RESULTS The median TRE of a surface-based registration with biomechanical regularization was 2.76 (0.81-7.96) mm. This was significantly different than the median TRE of 3.47 (1.05-7.80) mm for regular surface-based registration without biomechanical regularization. CONCLUSIONS Biomechanical FE modeling has the potential to improve the accuracy of multimodal prostate registration when comparing it to a regular nonrigid surface-based registration algorithm and can help to improve the effectiveness of MR guided TRUS biopsy procedures.
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Affiliation(s)
- Wendy J M van de Ven
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen 6525 GA, The Netherlands
| | - Yipeng Hu
- Centre for Medical Image Computing, University College London, London WC1E 6BT, United Kingdom
| | - Jelle O Barentsz
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen 6525 GA, The Netherlands
| | - Nico Karssemeijer
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen 6525 GA, The Netherlands
| | - Dean Barratt
- Centre for Medical Image Computing, University College London, London WC1E 6BT, United Kingdom
| | - Henkjan J Huisman
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen 6525 GA, The Netherlands
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15
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Wang Y, Cheng JZ, Ni D, Lin M, Qin J, Luo X, Xu M, Xie X, Heng PA. Towards Personalized Statistical Deformable Model and Hybrid Point Matching for Robust MR-TRUS Registration. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:589-604. [PMID: 26441446 DOI: 10.1109/tmi.2015.2485299] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Registration and fusion of magnetic resonance (MR) and 3D transrectal ultrasound (TRUS) images of the prostate gland can provide high-quality guidance for prostate interventions. However, accurate MR-TRUS registration remains a challenging task, due to the great intensity variation between two modalities, the lack of intrinsic fiducials within the prostate, the large gland deformation caused by the TRUS probe insertion, and distinctive biomechanical properties in patients and prostate zones. To address these challenges, a personalized model-to-surface registration approach is proposed in this study. The main contributions of this paper can be threefold. First, a new personalized statistical deformable model (PSDM) is proposed with the finite element analysis and the patient-specific tissue parameters measured from the ultrasound elastography. Second, a hybrid point matching method is developed by introducing the modality independent neighborhood descriptor (MIND) to weight the Euclidean distance between points to establish reliable surface point correspondence. Third, the hybrid point matching is further guided by the PSDM for more physically plausible deformation estimation. Eighteen sets of patient data are included to test the efficacy of the proposed method. The experimental results demonstrate that our approach provides more accurate and robust MR-TRUS registration than state-of-the-art methods do. The averaged target registration error is 1.44 mm, which meets the clinical requirement of 1.9 mm for the accurate tumor volume detection. It can be concluded that the presented method can effectively fuse the heterogeneous image information in the elastography, MR, and TRUS to attain satisfactory image alignment performance.
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16
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Zhang S, Jiang S, Yang Z, Liu R. 2D Ultrasound and 3D MR Image Registration of the Prostate for Brachytherapy Surgical Navigation. Medicine (Baltimore) 2015; 94:e1643. [PMID: 26448009 PMCID: PMC4616768 DOI: 10.1097/md.0000000000001643] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2015] [Revised: 08/26/2015] [Accepted: 08/27/2015] [Indexed: 01/22/2023] Open
Abstract
Two-dimensional (2D) ultrasound (US) images are widely used in minimally invasive prostate procedure for its noninvasive nature and convenience. However, the poor quality of US image makes it difficult to be used as guiding utility. To improve the limitation, we propose a multimodality image guided navigation module that registers 2D US images with magnetic resonance imaging (MRI) based on high quality preoperative models. A 2-step spatial registration method is used to complete the procedure which combines manual alignment and rapid mutual information (MI) optimize algorithm. In addition, a 3-dimensional (3D) reconstruction model of prostate with surrounding organs is employed to combine with the registered images to conduct the navigation. Registration accuracy is measured by calculating the target registration error (TRE). The results show that the error between the US and preoperative MR images of a polyvinyl alcohol hydrogel model phantom is 1.37 ± 0.14 mm, with a similar performance being observed in patient experiments.
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Affiliation(s)
- Shihui Zhang
- From the Center for Advanced Mechanisms and Robotics, School of Mechanical Engineering, Tianjin University (SZ, SJ, ZY); and Tianjin Institute of Urology and Department of Urology, Second Hospital of Tianjin Medical University, Hexi District, Tianjin, China (RL)
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17
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Mapping and characterizing endometrial implants by registering 2D transvaginal ultrasound to 3D pelvic magnetic resonance images. Comput Med Imaging Graph 2015; 45:11-25. [DOI: 10.1016/j.compmedimag.2015.07.007] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2014] [Revised: 06/26/2015] [Accepted: 07/13/2015] [Indexed: 11/23/2022]
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18
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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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19
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Multimodal image-guided prostate fusion biopsy based on automatic deformable registration. Int J Comput Assist Radiol Surg 2015; 10:1997-2007. [DOI: 10.1007/s11548-015-1233-y] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2015] [Accepted: 05/29/2015] [Indexed: 10/23/2022]
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20
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Sun Y, Yuan J, Qiu W, Rajchl M, Romagnoli C, Fenster A. Three-Dimensional Nonrigid MR-TRUS Registration Using Dual Optimization. IEEE TRANSACTIONS ON MEDICAL IMAGING 2015; 34:1085-1095. [PMID: 25438308 DOI: 10.1109/tmi.2014.2375207] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
In this study, we proposed an efficient nonrigid magnetic resonance (MR) to transrectal ultrasound (TRUS) deformable registration method in order to improve the accuracy of targeting suspicious regions during a three dimensional (3-D) TRUS guided prostate biopsy. The proposed deformable registration approach employs the multi-channel modality independent neighborhood descriptor (MIND) as the local similarity feature across the two modalities of MR and TRUS, and a novel and efficient duality-based convex optimization-based algorithmic scheme was introduced to extract the deformations and align the two MIND descriptors. The registration accuracy was evaluated using 20 patient images by calculating the TRE using manually identified corresponding intrinsic fiducials in the whole gland and peripheral zone. Additional performance metrics [Dice similarity coefficient (DSC), mean absolute surface distance (MAD), and maximum absolute surface distance (MAXD)] were also calculated by comparing the MR and TRUS manually segmented prostate surfaces in the registered images. Experimental results showed that the proposed method yielded an overall median TRE of 1.76 mm. The results obtained in terms of DSC showed an average of 80.8±7.8% for the apex of the prostate, 92.0±3.4% for the mid-gland, 81.7±6.4% for the base and 85.7±4.7% for the whole gland. The surface distance calculations showed an overall average of 1.84±0.52 mm for MAD and 6.90±2.07 mm for MAXD.
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21
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Lemaître G, Martí R, Freixenet J, Vilanova JC, Walker PM, Meriaudeau F. Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: a review. Comput Biol Med 2015; 60:8-31. [PMID: 25747341 DOI: 10.1016/j.compbiomed.2015.02.009] [Citation(s) in RCA: 121] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2014] [Revised: 02/11/2015] [Accepted: 02/12/2015] [Indexed: 12/30/2022]
Abstract
Prostate cancer is the second most diagnosed cancer of men all over the world. In the last few decades, new imaging techniques based on Magnetic Resonance Imaging (MRI) have been developed to improve diagnosis. In practise, diagnosis can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. In this regard, computer-aided detection and computer-aided diagnosis systems have been designed to help radiologists in their clinical practice. Research on computer-aided systems specifically focused for prostate cancer is a young technology and has been part of a dynamic field of research for the last 10 years. This survey aims to provide a comprehensive review of the state-of-the-art in this lapse of time, focusing on the different stages composing the work-flow of a computer-aided system. We also provide a comparison between studies and a discussion about the potential avenues for future research. In addition, this paper presents a new public online dataset which is made available to the research community with the aim of providing a common evaluation framework to overcome some of the current limitations identified in this survey.
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Affiliation(s)
- Guillaume Lemaître
- LE2I-UMR CNRS 6306, Université de Bourgogne, 12 rue de la Fonderie, 71200 Le Creusot, France; ViCOROB, Universitat de Girona, Campus Montilivi, Edifici P4, 17071 Girona, Spain.
| | - Robert Martí
- ViCOROB, Universitat de Girona, Campus Montilivi, Edifici P4, 17071 Girona, Spain.
| | - Jordi Freixenet
- ViCOROB, Universitat de Girona, Campus Montilivi, Edifici P4, 17071 Girona, Spain.
| | - Joan C Vilanova
- Department of Magnetic Resonance, Clínica Girona, Lorenzana 36, 17002 Girona, Spain
| | - Paul M Walker
- LE2I-UMR CNRS 6306, Université de Bourgogne, Avenue Alain Savary, 21000 Dijon, France.
| | - Fabrice Meriaudeau
- LE2I-UMR CNRS 6306, Université de Bourgogne, 12 rue de la Fonderie, 71200 Le Creusot, France.
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22
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Shah A, Zettinig O, Maurer T, Precup C, Schulte zu Berge C, Weiss J, Frisch B, Navab N. An Open Source Multimodal Image-Guided Prostate Biopsy Framework. ACTA ACUST UNITED AC 2014. [DOI: 10.1007/978-3-319-13909-8_1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
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23
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Kalpathy-Cramer J, Awan M, Bedrick S, Rasch CRN, Rosenthal DI, Fuller CD. Development of a software for quantitative evaluation radiotherapy target and organ-at-risk segmentation comparison. J Digit Imaging 2014; 27:108-19. [PMID: 24043593 DOI: 10.1007/s10278-013-9633-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Modern radiotherapy requires accurate region of interest (ROI) inputs for plan optimization and delivery. Target delineation, however, remains operator-dependent and potentially serves as a major source of treatment delivery error. In order to optimize this critical, yet observer-driven process, a flexible web-based platform for individual and cooperative target delineation analysis and instruction was developed in order to meet the following unmet needs: (1) an open-source/open-access platform for automated/semiautomated quantitative interobserver and intraobserver ROI analysis and comparison, (2) a real-time interface for radiation oncology trainee online self-education in ROI definition, and (3) a source for pilot data to develop and validate quality metrics for institutional and cooperative group quality assurance efforts. The resultant software, Target Contour Testing/Instructional Computer Software (TaCTICS), developed using Ruby on Rails, has since been implemented and proven flexible, feasible, and useful in several distinct analytical and research applications.
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Affiliation(s)
- Jayashree Kalpathy-Cramer
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Department of Radiology and Neuroscience, Massachusetts General Hospital, Charlestown, MA, USA
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24
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Chilali O, Ouzzane A, Diaf M, Betrouni N. A survey of prostate modeling for image analysis. Comput Biol Med 2014; 53:190-202. [PMID: 25156801 DOI: 10.1016/j.compbiomed.2014.07.019] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2013] [Revised: 06/22/2014] [Accepted: 07/23/2014] [Indexed: 11/18/2022]
Affiliation(s)
- O Chilali
- Inserm U703, 152, rue du Docteur Yersin, Lille University Hospital, 59120 Loos, France; Automatic Department, Mouloud Mammeri University, Tizi-Ouzou, Algeria
| | - A Ouzzane
- Inserm U703, 152, rue du Docteur Yersin, Lille University Hospital, 59120 Loos, France; Urology Department, Claude Huriez Hospital, Lille University Hospital, France
| | - M Diaf
- Automatic Department, Mouloud Mammeri University, Tizi-Ouzou, Algeria
| | - N Betrouni
- Inserm U703, 152, rue du Docteur Yersin, Lille University Hospital, 59120 Loos, France.
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25
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A pre-operative CT and non-contrast-enhanced C-arm CT registration framework for trans-catheter aortic valve implantation. Comput Med Imaging Graph 2014; 38:683-95. [PMID: 25158997 DOI: 10.1016/j.compmedimag.2014.06.021] [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: 10/29/2013] [Revised: 05/26/2014] [Accepted: 06/27/2014] [Indexed: 11/21/2022]
Abstract
Contrast-enhanced C-arm CT is routinely used for intra-operative guidance during the trans-catheter aortic valve implantation (TAVI); however, the requirement for contrast agent injection is not preferable, especially for patients with renal insufficiencies. To address this problem, we present a novel framework for fully automatic registration of pre-operative CT and non-contrast-enhanced C-arm CT. The proposed framework provides an improved workflow and minimizes the usage of contrast agent in the TAVI procedure. Our framework consists of three steps: coarse rigid-body alignment, anatomical knowledge-based prior deformation field generation, and fine deformable registration. We validated the proposed framework on 20 real patient data sets. Based on the 20 data sets, the mesh-to-mesh errors at the aortic root from different methods are measured. Our proposed method significantly outperforms the other state-of-the-art methods. Specifically, we achieve the registration accuracy at 1.76±0.43 mm which is clinically plausible. Quantitative evaluation on real non-contrast enhanced C-arm CT data sets confirms the applicability in the clinical usage. The proposed heart registration method is generic and hence can be easily applied to other cardiac applications.
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26
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A supervised learning framework of statistical shape and probability priors for automatic prostate segmentation in ultrasound images. Med Image Anal 2013; 17:587-600. [DOI: 10.1016/j.media.2013.04.001] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2012] [Revised: 02/05/2013] [Accepted: 04/01/2013] [Indexed: 11/21/2022]
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27
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Sparks R, Bloch BN, Feleppa E, Barratt D, Madabhushi A. Fully Automated Prostate Magnetic Resonance Imaging and Transrectal Ultrasound Fusion via a Probabilistic Registration Metric. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2013; 8671. [PMID: 24353393 DOI: 10.1117/12.2007610] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
In this work, we present a novel, automated, registration method to fuse magnetic resonance imaging (MRI) and transrectal ultrasound (TRUS) images of the prostate. Our methodology consists of: (1) delineating the prostate on MRI, (2) building a probabilistic model of prostate location on TRUS, and (3) aligning the MRI prostate segmentation to the TRUS probabilistic model. TRUS-guided needle biopsy is the current gold standard for prostate cancer (CaP) diagnosis. Up to 40% of CaP lesions appear isoechoic on TRUS, hence TRUS-guided biopsy cannot reliably target CaP lesions and is associated with a high false negative rate. MRI is better able to distinguish CaP from benign prostatic tissue, but requires special equipment and training. MRI-TRUS fusion, whereby MRI is acquired pre-operatively and aligned to TRUS during the biopsy procedure, allows for information from both modalities to be used to help guide the biopsy. The use of MRI and TRUS in combination to guide biopsy at least doubles the yield of positive biopsies. Previous work on MRI-TRUS fusion has involved aligning manually determined fiducials or prostate surfaces to achieve image registration. The accuracy of these methods is dependent on the reader's ability to determine fiducials or prostate surfaces with minimal error, which is a difficult and time-consuming task. Our novel, fully automated MRI-TRUS fusion method represents a significant advance over the current state-of-the-art because it does not require manual intervention after TRUS acquisition. All necessary preprocessing steps (i.e. delineation of the prostate on MRI) can be performed offline prior to the biopsy procedure. We evaluated our method on seven patient studies, with B-mode TRUS and a 1.5 T surface coil MRI. Our method has a root mean square error (RMSE) for expertly selected fiducials (consisting of the urethra, calcifications, and the centroids of CaP nodules) of 3.39 ± 0.85 mm.
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Affiliation(s)
- Rachel Sparks
- Department of Biomedical Engineering, Rutgers University ; Department of Biomedical Engineering, Case Western Reserve University
| | - B Nicolas Bloch
- Department of Radiology, Boston Medical Center & Boston University
| | - Ernest Feleppa
- Lizzi Center for Biomedical Engineering, Riverside Research
| | - Dean Barratt
- Centre for Medical Image Computing, University College London
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University
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Sun Y, Yuan J, Rajchl M, Qiu W, Romagnoli C, Fenster A. Efficient convex optimization approach to 3D non-rigid MR-TRUS registration. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2013; 16:195-202. [PMID: 24505666 DOI: 10.1007/978-3-642-40811-3_25] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
In this study, we propose an efficient non-rigid MR-TRUS deformable registration method to improve the accuracy of targeting suspicious locations during a 3D ultrasound (US) guided prostate biopsy. The proposed deformable registration approach employs the multi-channel modality independent neighbourhood descriptor (MIND) as the local similarity feature across the two modalities of MR and TRUS, and a novel and efficient duality-based convex optimization based algorithmic scheme is introduced to extract the deformations which align the two MIND descriptors. The registration accuracy was evaluated using 10 patient images by measuring the TRE of manually identified corresponding intrinsic fiducials in the whole gland and peripheral zone, and performance metrics (DSC, MAD and MAXD) for the apex, mid-gland and base of the prostate were also calculated by comparing two manually segmented prostate surfaces in the registered 3D MR and TRUS images. Experimental results show that the proposed method yielded an overall mean TRE of 1.74 mm, which is favorably comparable to a clinical requirement for an error of less than 2.5 mm.
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Affiliation(s)
- Yue Sun
- Imaging Research Labs, Robarts Research Institute, Western University, Canada
| | - Jing Yuan
- Imaging Research Labs, Robarts Research Institute, Western University, Canada
| | - Martin Rajchl
- Imaging Research Labs, Robarts Research Institute, Western University, Canada
| | - Wu Qiu
- Imaging Research Labs, Robarts Research Institute, Western University, Canada
| | | | - Aaron Fenster
- Imaging Research Labs, Robarts Research Institute, Western University, Canada
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