1
|
Han Z, Dou Q. A review on organ deformation modeling approaches for reliable surgical navigation using augmented reality. Comput Assist Surg (Abingdon) 2024; 29:2357164. [PMID: 39253945 DOI: 10.1080/24699322.2024.2357164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/11/2024] Open
Abstract
Augmented Reality (AR) holds the potential to revolutionize surgical procedures by allowing surgeons to visualize critical structures within the patient's body. This is achieved through superimposing preoperative organ models onto the actual anatomy. Challenges arise from dynamic deformations of organs during surgery, making preoperative models inadequate for faithfully representing intraoperative anatomy. To enable reliable navigation in augmented surgery, modeling of intraoperative deformation to obtain an accurate alignment of the preoperative organ model with the intraoperative anatomy is indispensable. Despite the existence of various methods proposed to model intraoperative organ deformation, there are still few literature reviews that systematically categorize and summarize these approaches. This review aims to fill this gap by providing a comprehensive and technical-oriented overview of modeling methods for intraoperative organ deformation in augmented reality in surgery. Through a systematic search and screening process, 112 closely relevant papers were included in this review. By presenting the current status of organ deformation modeling methods and their clinical applications, this review seeks to enhance the understanding of organ deformation modeling in AR-guided surgery, and discuss the potential topics for future advancements.
Collapse
Affiliation(s)
- Zheng Han
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Qi Dou
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China
| |
Collapse
|
2
|
Bloemberg J, de Vries M, van Riel LAMJG, de Reijke TM, Sakes A, Breedveld P, van den Dobbelsteen JJ. Therapeutic prostate cancer interventions: a systematic review on pubic arch interference and needle positioning errors. Expert Rev Med Devices 2024; 21:625-641. [PMID: 38946519 DOI: 10.1080/17434440.2024.2374761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 06/27/2024] [Indexed: 07/02/2024]
Abstract
INTRODUCTION This study focuses on the quantification of and current guidelines on the hazards related to needle positioning in prostate cancer treatment: (1) access restrictions to the prostate gland by the pubic arch, so-called Pubic Arch Interference (PAI) and (2) needle positioning errors. Next, we propose solution strategies to mitigate these hazards. METHODS The literature search was executed in the Embase, Medline ALL, Web of Science Core Collection*, and Cochrane Central Register of Controlled Trials databases. RESULTS The literature search resulted in 50 included articles. PAI was reported in patients with various prostate volumes. The level of reported PAI varied between 0 and 22.3 mm, depending on the patient's position and the measuring method. Low-Dose-Rate Brachytherapy induced the largest reported misplacement errors, especially in the cranio-caudal direction (up to 10 mm) and the largest displacement errors were reported for High-Dose-Rate Brachytherapy in the cranio-caudal direction (up to 47 mm), generally increasing over time. CONCLUSIONS Current clinical guidelines related to prostate volume, needle positioning accuracy, and maximum allowable PAI are ambiguous, and compliance in the clinical setting differs between institutions. Solutions, such as steerable needles, assist in mitigating the hazards and potentially allow the physician to proceed with the procedure.This systematic review was performed in accordance with the PRISMA guidelines. The review was registered at Protocols.io (DOI: dx.doi.org/10.17504/protocols.io.6qpvr89eplmk/v1).
Collapse
Affiliation(s)
- Jette Bloemberg
- Department of Biomechanical Engineering, Faculty of Mechanical Engineering, Delft University of Technology, Delft, The Netherlands
| | - Martijn de Vries
- Department of Biomechanical Engineering, Faculty of Mechanical Engineering, Delft University of Technology, Delft, The Netherlands
| | - Luigi A M J G van Riel
- Department of Urology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Theo M de Reijke
- Department of Urology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Aimée Sakes
- Department of Biomechanical Engineering, Faculty of Mechanical Engineering, Delft University of Technology, Delft, The Netherlands
| | - Paul Breedveld
- Department of Biomechanical Engineering, Faculty of Mechanical Engineering, Delft University of Technology, Delft, The Netherlands
| | - John J van den Dobbelsteen
- Department of Biomechanical Engineering, Faculty of Mechanical Engineering, Delft University of Technology, Delft, The Netherlands
| |
Collapse
|
3
|
Lang J, McClure TD, Margolis DJA. MRI-Ultrasound Fused Approach for Prostate Biopsy-How It Is Performed. Cancers (Basel) 2024; 16:1424. [PMID: 38611102 PMCID: PMC11010881 DOI: 10.3390/cancers16071424] [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: 02/27/2024] [Revised: 04/02/2024] [Accepted: 04/03/2024] [Indexed: 04/14/2024] Open
Abstract
The use of MRI-ultrasound image fusion targeted biopsy of the prostate in the face of an elevated serum PSA is now recommended by multiple societies, and results in improved detection of clinically significant cancer and, potentially, decreased detection of indolent disease. This combines the excellent sensitivity of MRI for clinically significant prostate cancer and the real-time biopsy guidance and confirmation of ultrasound. Both transperineal and transrectal approaches can be implemented using cognitive fusion, mechanical fusion with an articulated arm and electromagnetic registration, or pure software registration. The performance has been shown comparable to in-bore MRI biopsy performance. However, a number of factors influence the performance of this technique, including the quality and interpretation of the MRI, the approach used for biopsy, and experience of the practitioner, with most studies showing comparable performance of MRI-ultrasound fusion to in-bore targeted biopsy. Future improvements including artificial intelligence promise to refine the performance of all approaches.
Collapse
Affiliation(s)
- Jacob Lang
- Department of Urology, Weill Cornell Medicine, New York, NY 10068, USA
| | - Timothy Dale McClure
- Department of Urology, Weill Cornell Medicine, New York, NY 10068, USA
- Department of Radiology, Weill Cornell Medicine, New York, NY 10068, USA
| | | |
Collapse
|
4
|
Sanhinova M, Haouchine N, Pieper SD, Wells WM, Balboni TA, Spektor A, Huynh MA, Guenette JP, Czajkowski B, Caplan S, Doyle P, Kang H, Hackney DB, Alkalay RN. Registration of Longitudinal Spine CTs for Monitoring Lesion Growth. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2024; 12926:129262O. [PMID: 39310216 PMCID: PMC11416858 DOI: 10.1117/12.3006621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/25/2024]
Abstract
Accurate and reliable registration of longitudinal spine images is essential for assessment of disease progression and surgical outcome. Implementing a fully automatic and robust registration is crucial for clinical use, however, it is challenging due to substantial change in shape and appearance due to lesions. In this paper we present a novel method to automatically align longitudinal spine CTs and accurately assess lesion progression. Our method follows a two-step pipeline where vertebrae are first automatically localized, labeled and 3D surfaces are generated using a deep learning model, then longitudinally aligned using a Gaussian mixture model surface registration. We tested our approach on 37 vertebrae, from 5 patients, with baseline CTs and 3, 6, and 12 months follow-ups leading to 111 registrations. Our experiment showed accurate registration with an average Hausdorff distance of 0.65 mm and average Dice score of 0.92.
Collapse
Affiliation(s)
- Malika Sanhinova
- Harvard Medical School, Boston, MA, USA
- Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Nazim Haouchine
- Harvard Medical School, Boston, MA, USA
- Brigham and Women's Hospital, Boston, MA, USA
| | | | - William M Wells
- Harvard Medical School, Boston, MA, USA
- Brigham and Women's Hospital, Boston, MA, USA
| | - Tracy A Balboni
- Harvard Medical School, Boston, MA, USA
- Brigham and Women's Hospital, Boston, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
| | - Alexander Spektor
- Harvard Medical School, Boston, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
| | - Mai Anh Huynh
- Harvard Medical School, Boston, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
| | - Jeffrey P Guenette
- Harvard Medical School, Boston, MA, USA
- Brigham and Women's Hospital, Boston, MA, USA
| | - Bryan Czajkowski
- Harvard Medical School, Boston, MA, USA
- Brigham and Women's Hospital, Boston, MA, USA
| | - Sarah Caplan
- Harvard Medical School, Boston, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
| | - Patrick Doyle
- Harvard Medical School, Boston, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
| | - Heejoo Kang
- Harvard Medical School, Boston, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
| | - David B Hackney
- Harvard Medical School, Boston, MA, USA
- Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Ron N Alkalay
- Harvard Medical School, Boston, MA, USA
- Beth Israel Deaconess Medical Center, Boston, MA, USA
| |
Collapse
|
5
|
Wu M, He X, Li F, Zhu J, Wang S, Burstein PD. Weakly supervised volumetric prostate registration for MRI-TRUS image driven by signed distance map. Comput Biol Med 2023; 163:107150. [PMID: 37321103 DOI: 10.1016/j.compbiomed.2023.107150] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 05/24/2023] [Accepted: 06/07/2023] [Indexed: 06/17/2023]
Abstract
Image registration is a fundamental step for MRI-TRUS fusion targeted biopsy. Due to the inherent representational differences between these two image modalities, though, intensity-based similarity losses for registration tend to result in poor performance. To mitigate this, comparison of organ segmentations, functioning as a weak proxy measure of image similarity, has been proposed. Segmentations, though, are limited in their information encoding capabilities. Signed distance maps (SDMs), on the other hand, encode these segmentations into a higher dimensional space where shape and boundary information are implicitly captured, and which, in addition, yield high gradients even for slight mismatches, thus preventing vanishing gradients during deep-network training. Based on these advantages, this study proposes a weakly-supervised deep learning volumetric registration approach driven by a mixed loss that operates both on segmentations and their corresponding SDMs, and which is not only robust to outliers, but also encourages optimal global alignment. Our experimental results, performed on a public prostate MRI-TRUS biopsy dataset, demonstrate that our method outperforms other weakly-supervised registration approaches with a dice similarity coefficient (DSC), Hausdorff distance (HD) and mean surface distance (MSD) of 87.3 ± 11.3, 4.56 ± 1.95 mm, and 0.053 ± 0.026 mm, respectively. We also show that the proposed method effectively preserves the prostate gland's internal structure.
Collapse
Affiliation(s)
- Menglin Wu
- School of Computer Science and Technology, Nanjing Tech University, Nanjing, China.
| | - Xuchen He
- School of Computer Science and Technology, Nanjing Tech University, Nanjing, China
| | - Fan Li
- School of Computer Science and Technology, Nanjing Tech University, Nanjing, China
| | - Jie Zhu
- Senior Department of Urology, The Third Medical Center of Chinese People's Liberation Army General Hospital, Beijing, China.
| | | | | |
Collapse
|
6
|
Dossun C, Niederst C, Noel G, Meyer P. Evaluation of DIR algorithm performance in real patients for radiotherapy treatments: A systematic review of operator-dependent strategies. Phys Med 2022; 101:137-157. [PMID: 36007403 DOI: 10.1016/j.ejmp.2022.08.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 07/21/2022] [Accepted: 08/16/2022] [Indexed: 11/15/2022] Open
Abstract
PURPOSE The performance of deformable medical image registration (DIR) algorithms has become a major concern. METHODS We aimed to obtain updated information on DIR algorithm performance quantification through a literature review of articles published between 2010 and 2022. We focused only on studies using operator-based methods to treat real patients. The PubMed, Google Scholar and Embase databases were searched following PRISMA guidelines. RESULTS One hundred and seven articles were identified. The mean number of patients and registrations per publication was 20 and 63, respectively. We found 23 different geometric metrics appearing at least twice, and the dosimetric impact of DIR was quantified in 32 articles. Forty-eight different at-risk organs were described, and target volumes were studied in 43 publications. Prostate, head-and-neck and thoracic locations represented more than ¾ of the studied locations. We summarized the type of DIR and the images used, and other key elements. Intra/interobserver variability, threshold values and the correlation between metrics were also discussed. CONCLUSIONS This literature review covers the past decade and should facilitate the implementation of DIR algorithms in clinical practice by providing practical and pertinent information to quantify their performance on real patients.
Collapse
Affiliation(s)
- C Dossun
- Department of Radiotherapy, Institut de Cancerologie Strasbourg Europe (ICANS), Strasbourg, France
| | - C Niederst
- Department of Radiotherapy, Institut de Cancerologie Strasbourg Europe (ICANS), Strasbourg, France
| | - G Noel
- Department of Radiotherapy, Institut de Cancerologie Strasbourg Europe (ICANS), Strasbourg, France
| | - P Meyer
- Department of Radiotherapy, Institut de Cancerologie Strasbourg Europe (ICANS), Strasbourg, France; ICUBE, CNRS UMR 7357, Team IMAGES, Strasbourg, France.
| |
Collapse
|
7
|
A Fusion Biopsy Framework for Prostate Cancer Based on Deformable Superellipses and nnU-Net. Bioengineering (Basel) 2022; 9:bioengineering9080343. [PMID: 35892756 PMCID: PMC9394419 DOI: 10.3390/bioengineering9080343] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 07/13/2022] [Accepted: 07/21/2022] [Indexed: 11/24/2022] Open
Abstract
In prostate cancer, fusion biopsy, which couples magnetic resonance imaging (MRI) with transrectal ultrasound (TRUS), poses the basis for targeted biopsy by allowing the comparison of information coming from both imaging modalities at the same time. Compared with the standard clinical procedure, it provides a less invasive option for the patients and increases the likelihood of sampling cancerous tissue regions for the subsequent pathology analyses. As a prerequisite to image fusion, segmentation must be achieved from both MRI and TRUS domains. The automatic contour delineation of the prostate gland from TRUS images is a challenging task due to several factors including unclear boundaries, speckle noise, and the variety of prostate anatomical shapes. Automatic methodologies, such as those based on deep learning, require a huge quantity of training data to achieve satisfactory results. In this paper, the authors propose a novel optimization formulation to find the best superellipse, a deformable model that can accurately represent the prostate shape. The advantage of the proposed approach is that it does not require extensive annotations, and can be used independently of the specific transducer employed during prostate biopsies. Moreover, in order to show the clinical applicability of the method, this study also presents a module for the automatic segmentation of the prostate gland from MRI, exploiting the nnU-Net framework. Lastly, segmented contours from both imaging domains are fused with a customized registration algorithm in order to create a tool that can help the physician to perform a targeted prostate biopsy by interacting with the graphical user interface.
Collapse
|
8
|
Automatic 3D MRI-Ultrasound Registration for Image Guided Arthroscopy. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12115488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Registration of partial view intra-operative ultrasound (US) to pre-operative MRI is an essential step in image-guided minimally invasive surgery. In this paper, we present an automatic, landmark-free 3D multimodal registration of pre-operative MRI to 4D US (high-refresh-rate 3D-US) for enabling guidance in knee arthroscopy. We focus on the problem of initializing registration in the case of partial views. The proposed method utilizes a pre-initialization step of using the automatically segmented structures from both modalities to achieve a global geometric initialization. This is followed by computing distance maps of the procured segmentations for registration in the distance space. Following that, the final local refinement between the MRI-US volumes is achieved using the LC2 (Linear correlation of linear combination) metric. The method is evaluated on 11 cases spanning six subjects, with four levels of knee flexion. A best-case error of 1.41 mm and 2.34∘ and an average registration error of 3.45 mm and 7.76∘ is achieved in translation and rotation, respectively. An inter-observer variability study is performed, and a mean difference of 4.41 mm and 7.77∘ is reported. The errors obtained through the developed registration algorithm and inter-observer difference values are found to be comparable. We have shown that the proposed algorithm is simple, robust and allows for the automatic global registration of 3D US and MRI that can enable US based image guidance in minimally invasive procedures.
Collapse
|
9
|
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.
Collapse
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
| |
Collapse
|
10
|
McGeachy P, Watt E, Husain S, Martell K, Martinez P, Sawhney S, Thind K. MRI-TRUS registration methodology for TRUS-guided HDR prostate brachytherapy. J Appl Clin Med Phys 2021; 22:284-294. [PMID: 34318581 PMCID: PMC8364261 DOI: 10.1002/acm2.13292] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 04/26/2021] [Accepted: 05/05/2021] [Indexed: 11/15/2022] Open
Abstract
Purpose High‐dose‐rate (HDR) prostate brachytherapy is an established technique for whole‐gland treatment. For transrectal ultrasound (TRUS)‐guided HDR prostate brachytherapy, image fusion with a magnetic resonance image (MRI) can be performed to make use of its soft‐tissue contrast. The MIM treatment planning system has recently introduced image registration specifically for HDR prostate brachytherapy and has incorporated a Predictive Fusion workflow, which allows clinicians to attempt to compensate for differences in patient positioning between imaging modalities. In this study, we investigate the accuracy of the MIM algorithms for MRI‐TRUS fusion, including the Predictive Fusion workflow. Materials and Methods A radiation oncologist contoured the prostate gland on both TRUS and MRI. Four registration methodologies to fuse the MRI and the TRUS images were considered: rigid registration (RR), contour‐based (CB) deformable registration, Predictive Fusion followed by RR (pfRR), and Predictive Fusion followed by CB deformable registration (pfCB). Registrations were compared using the mean distance to agreement and the Dice similarity coefficient for the prostate as contoured on TRUS and the registered MRI prostate contour. Results Twenty patients treated with HDR prostate brachytherapy at our center were included in this retrospective evaluation. For the cohort, mean distance to agreement was 2.1 ± 0.8 mm, 0.60 ± 0.08 mm, 2.0 ± 0.5 mm, and 0.59 ± 0.06 mm for RR, CB, pfRR, and pfCB, respectively. Dice similarity coefficients were 0.80 ± 0.05, 0.93 ± 0.02, 0.81 ± 0.03, and 0.93 ± 0.01 for RR, CB, pfRR, and pfCB, respectively. The inclusion of the Predictive Fusion workflow did not significantly improve the quality of the registration. Conclusions The CB deformable registration algorithm in the MIM treatment planning system yielded the best geometric registration indices. MIM offers a commercial platform allowing for easier access and integration into clinical departments with the potential to play an integral role in future focal therapy applications for prostate cancer.
Collapse
Affiliation(s)
- Philip McGeachy
- Department of Medical Physics, Tom Baker Cancer Centre, Calgary, AB, Canada.,Department of Oncology, University of Calgary, Calgary, AB, Canada.,Department of Physics and Astronomy, University of Calgary, Calgary, AB, Canada
| | - Elizabeth Watt
- Department of Medical Physics, Tom Baker Cancer Centre, Calgary, AB, Canada.,Department of Oncology, University of Calgary, Calgary, AB, Canada
| | - Siraj Husain
- Department of Oncology, University of Calgary, Calgary, AB, Canada.,Department of Radiation Oncology, Tom Baker Cancer Centre, Calgary, AB, Canada
| | - Kevin Martell
- Department of Oncology, University of Calgary, Calgary, AB, Canada.,Department of Radiation Oncology, Tom Baker Cancer Centre, Calgary, AB, Canada
| | - Pedro Martinez
- Department of Medical Physics, Tom Baker Cancer Centre, Calgary, AB, Canada.,Department of Physics and Astronomy, University of Calgary, Calgary, AB, Canada
| | - Summit Sawhney
- Department of Radiology and Diagnostic Imaging, University of Calgary, Calgary, AB, Canada
| | - Kundan Thind
- Department of Medical Physics, Tom Baker Cancer Centre, Calgary, AB, Canada.,Department of Oncology, University of Calgary, Calgary, AB, Canada.,Department of Physics and Astronomy, University of Calgary, Calgary, AB, Canada
| |
Collapse
|
11
|
Wang W, Pan B, Fu Y, Liu Y. Development of a transperineal prostate biopsy robot guided by MRI-TRUS image. Int J Med Robot 2021; 17:e2266. [PMID: 33887097 DOI: 10.1002/rcs.2266] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 03/08/2021] [Accepted: 04/19/2021] [Indexed: 01/21/2023]
Abstract
BACKGROUND In the transrectal ultrasound (TRUS)-guided transperineal prostate biopsy, doctors determine the biopsy target by observing the prostate region in TRUS images. However, ultrasound images with low imaging quality make doctors easy to be interfered when determining the biopsy route, which reduces the biopsy success rate. METHODS This paper introduces the guidance method of magnetic resonance image (MRI) registration to ultrasound image and develops a 5-degrees of freedom robot for prostate biopsy guided by MRI-TRUS image. The robot uses a structure attached to the ultrasound probe to reduce the space occupied. By registering the posture relationship between MRI, TRUS image, ultrasonic probe and the robot base, the accurate localization of the suspected lesion area can be achieved with the preoperative MRIs. RESULTS The prostate phantom biopsy based on the robotic biopsy system in this paper, the average biopsy error is 1.44 mm, and the maximum biopsy error is 2.23 mm. CONCLUSIONS We build a robotic biopsy platform with prostate phantom, and evaluate the biopsy accuracy of MRI-TRUS guided prostate biopsy robot, the results meet clinical prostate biopsy requirements.
Collapse
Affiliation(s)
- Weirong Wang
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, Heilongjiang, China
| | - Bo Pan
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, Heilongjiang, China
| | - Yili Fu
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, Heilongjiang, China
| | - Yanjie Liu
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, Heilongjiang, China
| |
Collapse
|
12
|
Meyer A, Chlebus G, Rak M, Schindele D, Schostak M, van Ginneken B, Schenk A, Meine H, Hahn HK, Schreiber A, Hansen C. Anisotropic 3D Multi-Stream CNN for Accurate Prostate Segmentation from Multi-Planar MRI. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 200:105821. [PMID: 33218704 DOI: 10.1016/j.cmpb.2020.105821] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 10/26/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Accurate and reliable segmentation of the prostate gland in MR images can support the clinical assessment of prostate cancer, as well as the planning and monitoring of focal and loco-regional therapeutic interventions. Despite the availability of multi-planar MR scans due to standardized protocols, the majority of segmentation approaches presented in the literature consider the axial scans only. In this work, we investigate whether a neural network processing anisotropic multi-planar images could work in the context of a semantic segmentation task, and if so, how this additional information would improve the segmentation quality. METHODS We propose an anisotropic 3D multi-stream CNN architecture, which processes additional scan directions to produce a high-resolution isotropic prostate segmentation. We investigate two variants of our architecture, which work on two (dual-plane) and three (triple-plane) image orientations, respectively. The influence of additional information used by these models is evaluated by comparing them with a single-plane baseline processing only axial images. To realize a fair comparison, we employ a hyperparameter optimization strategy to select optimal configurations for the individual approaches. RESULTS Training and evaluation on two datasets spanning multiple sites show statistical significant improvement over the plain axial segmentation (p<0.05 on the Dice similarity coefficient). The improvement can be observed especially at the base (0.898 single-plane vs. 0.906 triple-plane) and apex (0.888 single-plane vs. 0.901 dual-plane). CONCLUSION This study indicates that models employing two or three scan directions are superior to plain axial segmentation. The knowledge of precise boundaries of the prostate is crucial for the conservation of risk structures. Thus, the proposed models have the potential to improve the outcome of prostate cancer diagnosis and therapies.
Collapse
Affiliation(s)
- Anneke Meyer
- Faculty of Computer Science and Research Campus STIMULATE, University of Magdeburg, Germany.
| | - Grzegorz Chlebus
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany; Radboud University Medical Center, Nijmegen, The Netherlands
| | - Marko Rak
- Faculty of Computer Science and Research Campus STIMULATE, University of Magdeburg, Germany
| | - Daniel Schindele
- Clinic of Urology and Pediatric Urology, University Hospital Magdeburg, Germany
| | - Martin Schostak
- Clinic of Urology and Pediatric Urology, University Hospital Magdeburg, Germany
| | - Bram van Ginneken
- Radboud University Medical Center, Nijmegen, The Netherlands; Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Andrea Schenk
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Hans Meine
- University of Bremen, Medical Image Computing Group, Bremen, Germany; Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Horst K Hahn
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | | | - Christian Hansen
- Faculty of Computer Science and Research Campus STIMULATE, University of Magdeburg, Germany
| |
Collapse
|
13
|
Quantitation of Tissue Resection Using a Brain Tumor Model and 7-T Magnetic Resonance Imaging Technology. World Neurosurg 2021; 148:e326-e339. [PMID: 33418122 DOI: 10.1016/j.wneu.2020.12.141] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 12/27/2020] [Accepted: 12/27/2020] [Indexed: 01/17/2023]
Abstract
BACKGROUND Animal brain tumor models can be useful educational tools for the training of neurosurgical residents in risk-free environments. Magnetic resonance imaging (MRI) technologies have not used these models to quantitate tumor, normal gray and white matter, and total tissue removal during complex neurosurgical procedures. This pilot study was carried out as a proof of concept to show the feasibility of using brain tumor models combined with 7-T MRI technology to quantitatively assess tissue removal during subpial tumor resection. METHODS Seven ex vivo calf brain hemispheres were used to develop the 7-T MRI segmentation methodology. Three brains were used to quantitate brain tissue removal using 7-T MRI segmentation methodology. Alginate artificial brain tumor was created in 4 calf brains to assess the ability of 7-T MRI segmentation methodology to quantitate tumor and gray and white matter along with total tissue volumes removal during a subpial tumor resection procedure. RESULTS Quantitative studies showed a correlation between removed brain tissue weights and volumes determined from segmented 7-T MRIs. Analysis of baseline and postresection alginate brain tumor segmented 7-T MRIs allowed quantification of tumor and gray and white matter along with total tissue volumes removed and detection of alterations in surrounding gray and white matter. CONCLUSIONS This pilot study showed that the use of animal tumor models in combination with 7-T MRI technology provides an opportunity to increase the granularity of data obtained from operative procedures and to improve the assessment and training of learners.
Collapse
|
14
|
Shaaer A, Paudel M, Davidson M, Semple M, Nicolae A, Mendez LC, Chung H, Loblaw A, Tseng CL, Morton G, Ravi A. Dosimetric evaluation of MRI-to-ultrasound automated image registration algorithms for prostate brachytherapy. Brachytherapy 2020; 19:599-606. [PMID: 32712028 DOI: 10.1016/j.brachy.2020.06.014] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 06/13/2020] [Accepted: 06/15/2020] [Indexed: 11/26/2022]
Abstract
PURPOSE Identifying dominant intraprostatic lesions (DILs) on transrectal ultrasound (TRUS) images during prostate high-dose-rate brachytherapy treatment planning remains a significant challenge. Multiparametric MRI (mpMRI) is the tool of choice for DIL identification; however, the geometry of the prostate on mpMRI and on the TRUS may differ significantly, requiring image registration. This study assesses the dosimetric impact attributed to differences in DIL contours generated using commonly available MRI to TRUS automated registration: rigid, semi-rigid, and deformable image registration, respectively. METHODS AND MATERIALS Ten patients, each with mpMRI and TRUS data sets, were included in this study. Five radiation oncologists with expertise in TRUS-based high-dose-rate brachytherapy were asked cognitively to transfer the DIL from the mpMRI images of each patient to the TRUS image. The contours were analyzed for concordance using simultaneous truth and performance level estimation (STAPLE) algorithm. The impact of DIL contour differences due to registration variability was evaluated by comparing the STAPLE-DIL dosimetry from the reference (STAPLE) plan with that from the evaluation plans (manual and automated registration) for each patient. The dosimetric impact of the automatic registration approach was also validated using a margin expansion that normalizes the volume of the autoregistered DILs to the volumes of the STAPLE-DILs. Dose metrics including D90, Dmean, V150, and V200 to the prostate and DIL were reported. For urethra and rectum, D10 and V80 were reported. RESULTS Significant differences in DIL coverage between reference and evaluation plans were found regardless of the algorithm methodology. No statistical difference was reported in STAPLE-DIL dosimetry when manual registration was used. A margin of 1.5 ± 0.8 mm, 1.1 ± 0.8 mm, and 2.5 ± 1.6 mm was required to be added for rigid, semi-rigid, and deformable registration, respectively, to mitigate the difference in STAPLE-DIL coverage between the evaluation and reference plans. CONCLUSION The dosimetric impact of integrating an MRI-delineated DIL into a TRUS-based brachytherapy workflow has been validated in this study. The results show that rigid, semi-rigid, and deformable registration algorithms lead to a significant undercoverage of the DIL D90 and Dmean. A margin of at least 1.5 ± 0.8 mm, 1.1 ± 0.8 mm, and 2.5 ± 1.6 mm is required to be added to the rigid, semi-rigid, and deformable DIL registration to be suitable for DIL-boosting during prostate brachytherapy.
Collapse
Affiliation(s)
- Amani Shaaer
- Department of physics, Ryerson University, Toronto, Ontario, Canada; Biomedical Physics Department, King Faisal Specialist Hospital & Research Centre, Riyadh, Saudi Arabia
| | - Moti Paudel
- Department of Medical Physics, University of Toronto, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Melanie Davidson
- Department of Medical Physics, University of Toronto, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Mark Semple
- Department of Medical Physics, University of Toronto, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Alexandru Nicolae
- Department of Medical Physics, University of Toronto, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Lucas Castro Mendez
- Department of Medical Physics, University of Toronto, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Hans Chung
- Department of Radiation Oncology, University of Toronto, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Andrew Loblaw
- Department of Radiation Oncology, University of Toronto, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Chia-Lin Tseng
- Department of Radiation Oncology, University of Toronto, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Gerard Morton
- Department of Radiation Oncology, University of Toronto, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Ananth Ravi
- Department of Medical Physics, University of Toronto, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.
| |
Collapse
|
15
|
Abstract
A wide range of medical devices have significant electronic components. Compared to open-source medical software, open (and open-source) electronic hardware has been less published in peer-reviewed literature. In this review, we explore the developments, significance, and advantages of using open platform electronic hardware for medical devices. Open hardware electronics platforms offer not just shorter development times, reduced costs, and customization; they also offer a key potential advantage which current commercial medical devices lack—seamless data sharing for machine learning and artificial intelligence. We explore how various electronic platforms such as microcontrollers, single board computers, field programmable gate arrays, development boards, and integrated circuits have been used by researchers to design medical devices. Researchers interested in designing low cost, customizable, and innovative medical devices can find references to various easily available electronic components as well as design methodologies to integrate those components for a successful design.
Collapse
|
16
|
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.
Collapse
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
| |
Collapse
|
17
|
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.
Collapse
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
| |
Collapse
|
18
|
Clinical evaluation of an MRI-to-ultrasound deformable image registration algorithm for prostate brachytherapy. Brachytherapy 2019; 18:95-102. [DOI: 10.1016/j.brachy.2018.08.006] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Revised: 07/16/2018] [Accepted: 08/08/2018] [Indexed: 11/21/2022]
|
19
|
Mathur S, O'Malley ME, Ghai S, Jhaveri K, Sreeharsha B, Margolis M, Zhong L, Maan H, Toi A. Correlation of 3T multiparametric prostate MRI using prostate imaging reporting and data system (PIRADS) version 2 with biopsy as reference standard. Abdom Radiol (NY) 2019; 44:252-258. [PMID: 30032385 DOI: 10.1007/s00261-018-1696-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
OBJECTIVE To correlate the findings on 3T multiparametric prostate MRI using PIRADS version 2 with prostate biopsy results as the standard of reference. MATERIALS AND METHODS 134 consecutive treatment naive patients (mean age 64 years, range 41-82 years) underwent MRI-directed prostate biopsy. MRI-TRUS fusion biopsy was used for 77 (77/134 = 57.5%) patients, cognitive fusion for 51 (51/134 = 38.0%) patients, and 6 patients (6/134 = 4.5%) without a target nodule had systematic biopsy only. Out of the 1676 biopsy sites, 237 (237/1676 = 14.1%) were positive on MRI for a PIRADS 3, 4, or 5 nodule. Fifty-eight (58/134, 43.3%) patients had clinically significant prostate cancer (csPCa). The findings on MRI using PIRADS version 2 were correlated with the biopsy results. RESULTS The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of PIRADS ≥ 3 for csPCa were 89%, 76.5%, 89.7%, 31.7%, and 98.4%, respectively. The detection rates of csPCa for PIRADS 3, 4, and 5 nodules were 6.1% (4/66), 33.3% (42/126), and 64.4% (29/45), respectively. MRI did not identify a nodule in 23/1676 (1.4%) biopsy sites that contained csPCa. The MRI reader, biopsy operator, method of fusion biopsy, and zonal location of prostate nodule did not significantly affect the odds of having a biopsy result positive for csPCa. CONCLUSION PIRADS ≥ 3 had high specificity and high negative predictive value for csPCa using biopsy results as the standard of reference. The presence of csPCa from a biopsy site was highly unlikely in the absence of a corresponding PIRADS ≥ 3 nodule.
Collapse
Affiliation(s)
- Shobhit Mathur
- Joint Department of Medical Imaging, Toronto General Hospital, University of Toronto, 585 University Ave., Toronto, ON, M5G 2N2, Canada.
| | - Martin E O'Malley
- Joint Department of Medical Imaging, Princess Margaret Hospital, University of Toronto, 610 University Avenue, Toronto, ON, M5G 2M9, Canada
| | - Sangeet Ghai
- Joint Department of Medical Imaging, Toronto General Hospital, University of Toronto, 585 University Ave., Toronto, ON, M5G 2N2, Canada
| | - Kartik Jhaveri
- Joint Department of Medical Imaging, Princess Margaret Hospital, University of Toronto, 610 University Avenue, Toronto, ON, M5G 2M9, Canada
| | - Boraiah Sreeharsha
- Joint Department of Medical Imaging, Princess Margaret Hospital, University of Toronto, 610 University Avenue, Toronto, ON, M5G 2M9, Canada
| | - Myles Margolis
- Joint Department of Medical Imaging, Mount Sinai Hospital, University of Toronto, 600 University Ave., Toronto, ON, M5G 1X5, Canada
| | - Lehang Zhong
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, 155 College St, Toronto, ON, M5T 3M7, Canada
| | - Hassan Maan
- Joint Department of Medical Imaging, Toronto General Hospital, University of Toronto, 585 University Ave., Toronto, ON, M5G 2N2, Canada
| | - Ants Toi
- Joint Department of Medical Imaging, Mount Sinai Hospital, University of Toronto, 600 University Ave., Toronto, ON, M5G 1X5, Canada
| |
Collapse
|
20
|
Cornud F, Bomers J, Futterer J, Ghai S, Reijnen J, Tempany C. MR imaging-guided prostate interventional imaging: Ready for a clinical use? Diagn Interv Imaging 2018; 99:743-753. [DOI: 10.1016/j.diii.2018.08.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Accepted: 08/08/2018] [Indexed: 01/22/2023]
|
21
|
Haskins G, Kruecker J, Kruger U, Xu S, Pinto PA, Wood BJ, Yan P. Learning deep similarity metric for 3D MR-TRUS image registration. Int J Comput Assist Radiol Surg 2018; 14:417-425. [PMID: 30382457 DOI: 10.1007/s11548-018-1875-7] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Accepted: 10/14/2018] [Indexed: 11/26/2022]
Abstract
PURPOSE The fusion of transrectal ultrasound (TRUS) and magnetic resonance (MR) images for guiding targeted prostate biopsy has significantly improved the biopsy yield of aggressive cancers. A key component of MR-TRUS fusion is image registration. However, it is very challenging to obtain a robust automatic MR-TRUS registration due to the large appearance difference between the two imaging modalities. The work presented in this paper aims to tackle this problem by addressing two challenges: (i) the definition of a suitable similarity metric and (ii) the determination of a suitable optimization strategy. METHODS This work proposes the use of a deep convolutional neural network to learn a similarity metric for MR-TRUS registration. We also use a composite optimization strategy that explores the solution space in order to search for a suitable initialization for the second-order optimization of the learned metric. Further, a multi-pass approach is used in order to smooth the metric for optimization. RESULTS The learned similarity metric outperforms the classical mutual information and also the state-of-the-art MIND feature-based methods. The results indicate that the overall registration framework has a large capture range. The proposed deep similarity metric-based approach obtained a mean TRE of 3.86 mm (with an initial TRE of 16 mm) for this challenging problem. CONCLUSION A similarity metric that is learned using a deep neural network can be used to assess the quality of any given image registration and can be used in conjunction with the aforementioned optimization framework to perform automatic registration that is robust to poor initialization.
Collapse
Affiliation(s)
- Grant Haskins
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA
| | | | - Uwe Kruger
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA
| | - Sheng Xu
- Center for Interventional Oncology, Radiology & Imaging Sciences, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Peter A Pinto
- Center for Interventional Oncology, Radiology & Imaging Sciences, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Brad J Wood
- Center for Interventional Oncology, Radiology & Imaging Sciences, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Pingkun Yan
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.
| |
Collapse
|
22
|
Vanden Berg RNW, McClure TD, Margolis DJA. A Review of Prostate Biopsy Techniques. Semin Roentgenol 2018; 53:213-218. [PMID: 30031414 DOI: 10.1053/j.ro.2018.04.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
| | - Timothy D McClure
- Department of Urology, New York Presbyterian Hospital, Weill Cornell Medical College, New York, NY; Department of Radiology, New York Presbyterian Hospital, Weill Cornell Medical College, New York, NY
| | - Daniel J A Margolis
- Department of Radiology, New York Presbyterian Hospital, Weill Cornell Medical College, New York, NY
| |
Collapse
|
23
|
Favazza CP, Gorny KR, Callstrom MR, Kurup AN, Washburn M, Trester PS, Fowler CL, Hangiandreou NJ. Development of a robust MRI fiducial system for automated fusion of MR-US abdominal images. J Appl Clin Med Phys 2018; 19:261-270. [PMID: 29785834 PMCID: PMC6036384 DOI: 10.1002/acm2.12352] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2017] [Revised: 03/30/2018] [Accepted: 04/05/2018] [Indexed: 11/17/2022] Open
Abstract
We present the development of a two‐component magnetic resonance (MR) fiducial system, that is, a fiducial marker device combined with an auto‐segmentation algorithm, designed to be paired with existing ultrasound probe tracking and image fusion technology to automatically fuse MR and ultrasound (US) images. The fiducial device consisted of four ~6.4 mL cylindrical wells filled with 1 g/L copper sulfate solution. The algorithm was designed to automatically segment the device in clinical abdominal MR images. The algorithm's detection rate and repeatability were investigated through a phantom study and in human volunteers. The detection rate was 100% in all phantom and human images. The center‐of‐mass of the fiducial device was robustly identified with maximum variations of 2.9 mm in position and 0.9° in angular orientation. In volunteer images, average differences between algorithm‐measured inter‐marker spacings and actual separation distances were 0.53 ± 0.36 mm. “Proof‐of‐concept” automatic MR‐US fusions were conducted with sets of images from both a phantom and volunteer using a commercial prototype system, which was built based on the above findings. Image fusion accuracy was measured to be within 5 mm for breath‐hold scanning. These results demonstrate the capability of this approach to automatically fuse US and MR images acquired across a wide range of clinical abdominal pulse sequences.
Collapse
Affiliation(s)
| | | | | | - Anil N. Kurup
- Department of Radiology; Mayo Clinic; Rochester MN USA
| | | | | | | | | |
Collapse
|
24
|
Abstract
The use of magnetic resonance imaging (MRI) for image-guided intervention poses both great opportunity and challenges. Although MRI is distinguished by its excellent contrast resolution and lack of ionizing radiation, it was not till the 1990s that technologic innovations allowed for adoption of MRI as a guidance modality for intervention. With advances in magnet, protocol, coil, biopsy needle, and ablation probe design, MRI has emerged as a viable, and increasingly, preferable alternative to other image guidance modalities. With the development of targeting software, augmented reality, robotic assist devices, and MR thermometry, the future of MRI-guided interventions remains promising.
Collapse
Affiliation(s)
- Farzad Sedaghat
- Division of Abdominal Imaging and Intervention, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | | |
Collapse
|
25
|
Poulin E, Boudam K, Pinter C, Kadoury S, Lasso A, Fichtinger G, Ménard C. Validation of MRI to TRUS registration for high-dose-rate prostate brachytherapy. Brachytherapy 2018; 17:283-290. [PMID: 29331575 DOI: 10.1016/j.brachy.2017.11.018] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2017] [Revised: 11/27/2017] [Accepted: 11/30/2017] [Indexed: 11/26/2022]
Abstract
PURPOSE The objective of this study was to develop and validate an open-source module for MRI to transrectal ultrasound (TRUS) registration to support tumor-targeted prostate brachytherapy. METHODS AND MATERIALS In this study, 15 patients with prostate cancer lesions visible on multiparametric MRI were selected for the validation. T2-weighted images with 1-mm isotropic voxel size and diffusion weighted images were acquired on a 1.5T Siemens imager. Three-dimensional (3D) TRUS images with 0.5-mm slice thickness were acquired. The investigated registration module was incorporated in the open-source 3D Slicer platform, which can compute rigid and deformable transformations. An extension of 3D Slicer, SlicerRT, allows import of and export to DICOM-RT formats. For validation, similarity indices, prostate volumes, and centroid positions were determined in addition to registration errors for common 3D points identified by an experienced radiation oncologist. RESULTS The average time to compute the registration was 35 ± 3 s. For the rigid and deformable registration, respectively, Dice similarity coefficients were 0.87 ± 0.05 and 0.93 ± 0.01 while the 95% Hausdorff distances were 4.2 ± 1.0 and 2.2 ± 0.3 mm. MRI volumes obtained after the rigid and deformable registration were not statistically different (p > 0.05) from reference TRUS volumes. For the rigid and deformable registration, respectively, 3D distance errors between reference and registered centroid positions were 2.1 ± 1.0 and 0.4 ± 0.1 mm while registration errors between common points were 3.5 ± 3.2 and 2.3 ± 1.1 mm. Deformable registration was found significantly better (p < 0.05) than rigid registration for all parameters. CONCLUSIONS An open-source MRI to TRUS registration platform was validated for integration in the brachytherapy workflow.
Collapse
|
26
|
Jadvar H. Multimodal Imaging in Focal Therapy Planning and Assessment in Primary Prostate Cancer. Clin Transl Imaging 2017; 5:199-208. [PMID: 28713796 DOI: 10.1007/s40336-017-0228-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
PURPOSE There is increasing interest in focal therapy (male lumpectomy) of localized low-intermediate risk prostate cancer. Focal therapy is typically associated with low morbidity and provides the possibility of retreatment. Imaging is pivotal in stratification of men with localized prostate cancer for active surveillance, focal therapy or radical intervention. This article provides a concise review of focal therapy and the evolving role of imaging in this clinical setting. METHODS We performed a narrative and critical literature review by searching PubMed/Medline database from January 1997 to January 2017 for articles in the English language and the use of search keywords "focal therapy", "prostate cancer", and "imaging". RESULTS Most imaging studies are based on multiparametric magnetic resonance imaging. Transrectal ultrasound is inadequate independently but multiparametric ultrasound may provide new prospects. Positron emission tomography with radiotracers targeted to various underlying tumor biological features may provide unprecedented new opportunities. Multimodal Imaging appears most useful in localization of intraprostatic dominant index lesions amenable to focal therapy, in early assessment of therapeutic efficacy and potential need for additional focal treatments or transition to whole-gland therapy, and in predicting short-term and long-term outcomes. CONCLUSION Multimodal imaging is anticipated to play an increasing role in the focal therapy planning and assessment of low-intermediate risk prostate cancer and thereby moving this form of treatment option forward in the clinic.
Collapse
Affiliation(s)
- Hossein Jadvar
- Division of Nuclear Medicine, Department of Radiology, University of Southern California, Los Angeles, California, USA
| |
Collapse
|
27
|
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.
Collapse
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.
| |
Collapse
|
28
|
Ménard C, Pambrun JF, Kadoury S. The utilization of magnetic resonance imaging in the operating room. Brachytherapy 2017; 16:754-760. [PMID: 28139421 DOI: 10.1016/j.brachy.2016.12.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Revised: 12/12/2016] [Accepted: 12/12/2016] [Indexed: 11/26/2022]
Abstract
Online image guidance in the operating room using ultrasound imaging led to the resurgence of prostate brachytherapy in the 1980s. Here we describe the evolution of integrating MRI technology in the brachytherapy suite or operating room. Given the complexity, cost, and inherent safety issues associated with MRI system integration, first steps focused on the computational integration of images rather than systems. This approach has broad appeal given minimal infrastructure costs and efficiencies comparable with standard care workflows. However, many concerns remain regarding accuracy of registration through the course of a brachytherapy procedure. In selected academic institutions, MRI systems have been integrated in or near the brachytherapy suite in varied configurations to improve the precision and quality of treatments. Navigation toolsets specifically adapted to prostate brachytherapy are in development and are reviewed.
Collapse
Affiliation(s)
- C Ménard
- University of Montréal Hospital Research Centre (CRCHUM), Montréal, QC, Canada; TECHNA Institute, University of Toronto, Toronto, ON, Canada; Princess Margaret Cancer Center, Toronto, ON, Canada.
| | - J-F Pambrun
- University of Montréal Hospital Research Centre (CRCHUM), Montréal, QC, Canada; École polytechnique de Montréal, Montréal, QC, Canada
| | - S Kadoury
- University of Montréal Hospital Research Centre (CRCHUM), Montréal, QC, Canada; École polytechnique de Montréal, Montréal, QC, Canada
| |
Collapse
|
29
|
Abstract
The leading application of multiparametric magnetic resonance imaging (mpMRI) of the prostate is for lesion detection with the intention of tissue sampling (biopsy). Although direct in-bore magnetic resonance (MR)-guided biopsy allows for confirmation of the biopsy site, this can be expensive, time-consuming, and most importantly limited in availability. MR-transrectal ultrasound (MR-TRUS) image fusion targeted biopsy (TBx) allows for lesions identified on MRI to be targeted with the ease, efficiency, and availability of ultrasound.The learning objectives are optimized mpMRI protocol and reporting for image fusion targeted biopsy; methods of TRUS TBx; performance and limitations of MR-TRUS TBx; future improvements and applications.
Collapse
|
30
|
Rosenkrantz AB, Ginocchio LA, Cornfeld D, Froemming AT, Gupta RT, Turkbey B, Westphalen AC, Babb JS, Margolis DJ. Interobserver Reproducibility of the PI-RADS Version 2 Lexicon: A Multicenter Study of Six Experienced Prostate Radiologists. Radiology 2016; 280:793-804. [PMID: 27035179 DOI: 10.1148/radiol.2016152542] [Citation(s) in RCA: 354] [Impact Index Per Article: 44.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Purpose To determine the interobserver reproducibility of the Prostate Imaging Reporting and Data System (PI-RADS) version 2 lexicon. Materials and Methods This retrospective HIPAA-compliant study was institutional review board-approved. Six radiologists from six separate institutions, all experienced in prostate magnetic resonance (MR) imaging, assessed prostate MR imaging examinations performed at a single center by using the PI-RADS lexicon. Readers were provided screen captures that denoted the location of one specific lesion per case. Analysis entailed two sessions (40 and 80 examinations per session) and an intersession training period for individualized feedback and group discussion. Percent agreement (fraction of pairwise reader combinations with concordant readings) was compared between sessions. κ coefficients were computed. Results No substantial difference in interobserver agreement was observed between sessions, and the sessions were subsequently pooled. Agreement for PI-RADS score of 4 or greater was 0.593 in peripheral zone (PZ) and 0.509 in transition zone (TZ). In PZ, reproducibility was moderate to substantial for features related to diffusion-weighted imaging (κ = 0.535-0.619); fair to moderate for features related to dynamic contrast material-enhanced (DCE) imaging (κ = 0.266-0.439); and fair for definite extraprostatic extension on T2-weighted images (κ = 0.289). In TZ, reproducibility for features related to lesion texture and margins on T2-weighted images ranged from 0.136 (moderately hypointense) to 0.529 (encapsulation). Among 63 lesions that underwent targeted biopsy, classification as PI-RADS score of 4 or greater by a majority of readers yielded tumor with a Gleason score of 3+4 or greater in 45.9% (17 of 37), without missing any tumor with a Gleason score of 3+4 or greater. Conclusion Experienced radiologists achieved moderate reproducibility for PI-RADS version 2, and neither required nor benefitted from a training session. Agreement tended to be better in PZ than TZ, although was weak for DCE in PZ. The findings may help guide future PI-RADS lexicon updates. (©) RSNA, 2016 Online supplemental material is available for this article.
Collapse
Affiliation(s)
- Andrew B Rosenkrantz
- From the Department of Radiology, NYU School of Medicine, NYU Langone Medical Center, 660 1st Ave, Third Floor, New York, NY 10016 (A.B.R., L.A.G., J.S.B.); Department of Radiology, Yale School of Medicine, New Haven, Conn (D.C.); Department of Radiology, Mayo Clinic, Rochester, Minn (A.T.F.); Department of Radiology, Duke University Medical Center, Duke Cancer Institute, Durham, NC (R.T.G.); Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Md (B.T.); Departments of Radiology and Biomedical Imaging and Urology, University of California-San Francisco, San Francisco, Calif (A.C.W.); and Department of Radiology, David Geffen School of Medicine at UCLA, Los Angeles, Calif (D.J.M.)
| | - Luke A Ginocchio
- From the Department of Radiology, NYU School of Medicine, NYU Langone Medical Center, 660 1st Ave, Third Floor, New York, NY 10016 (A.B.R., L.A.G., J.S.B.); Department of Radiology, Yale School of Medicine, New Haven, Conn (D.C.); Department of Radiology, Mayo Clinic, Rochester, Minn (A.T.F.); Department of Radiology, Duke University Medical Center, Duke Cancer Institute, Durham, NC (R.T.G.); Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Md (B.T.); Departments of Radiology and Biomedical Imaging and Urology, University of California-San Francisco, San Francisco, Calif (A.C.W.); and Department of Radiology, David Geffen School of Medicine at UCLA, Los Angeles, Calif (D.J.M.)
| | - Daniel Cornfeld
- From the Department of Radiology, NYU School of Medicine, NYU Langone Medical Center, 660 1st Ave, Third Floor, New York, NY 10016 (A.B.R., L.A.G., J.S.B.); Department of Radiology, Yale School of Medicine, New Haven, Conn (D.C.); Department of Radiology, Mayo Clinic, Rochester, Minn (A.T.F.); Department of Radiology, Duke University Medical Center, Duke Cancer Institute, Durham, NC (R.T.G.); Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Md (B.T.); Departments of Radiology and Biomedical Imaging and Urology, University of California-San Francisco, San Francisco, Calif (A.C.W.); and Department of Radiology, David Geffen School of Medicine at UCLA, Los Angeles, Calif (D.J.M.)
| | - Adam T Froemming
- From the Department of Radiology, NYU School of Medicine, NYU Langone Medical Center, 660 1st Ave, Third Floor, New York, NY 10016 (A.B.R., L.A.G., J.S.B.); Department of Radiology, Yale School of Medicine, New Haven, Conn (D.C.); Department of Radiology, Mayo Clinic, Rochester, Minn (A.T.F.); Department of Radiology, Duke University Medical Center, Duke Cancer Institute, Durham, NC (R.T.G.); Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Md (B.T.); Departments of Radiology and Biomedical Imaging and Urology, University of California-San Francisco, San Francisco, Calif (A.C.W.); and Department of Radiology, David Geffen School of Medicine at UCLA, Los Angeles, Calif (D.J.M.)
| | - Rajan T Gupta
- From the Department of Radiology, NYU School of Medicine, NYU Langone Medical Center, 660 1st Ave, Third Floor, New York, NY 10016 (A.B.R., L.A.G., J.S.B.); Department of Radiology, Yale School of Medicine, New Haven, Conn (D.C.); Department of Radiology, Mayo Clinic, Rochester, Minn (A.T.F.); Department of Radiology, Duke University Medical Center, Duke Cancer Institute, Durham, NC (R.T.G.); Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Md (B.T.); Departments of Radiology and Biomedical Imaging and Urology, University of California-San Francisco, San Francisco, Calif (A.C.W.); and Department of Radiology, David Geffen School of Medicine at UCLA, Los Angeles, Calif (D.J.M.)
| | - Baris Turkbey
- From the Department of Radiology, NYU School of Medicine, NYU Langone Medical Center, 660 1st Ave, Third Floor, New York, NY 10016 (A.B.R., L.A.G., J.S.B.); Department of Radiology, Yale School of Medicine, New Haven, Conn (D.C.); Department of Radiology, Mayo Clinic, Rochester, Minn (A.T.F.); Department of Radiology, Duke University Medical Center, Duke Cancer Institute, Durham, NC (R.T.G.); Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Md (B.T.); Departments of Radiology and Biomedical Imaging and Urology, University of California-San Francisco, San Francisco, Calif (A.C.W.); and Department of Radiology, David Geffen School of Medicine at UCLA, Los Angeles, Calif (D.J.M.)
| | - Antonio C Westphalen
- From the Department of Radiology, NYU School of Medicine, NYU Langone Medical Center, 660 1st Ave, Third Floor, New York, NY 10016 (A.B.R., L.A.G., J.S.B.); Department of Radiology, Yale School of Medicine, New Haven, Conn (D.C.); Department of Radiology, Mayo Clinic, Rochester, Minn (A.T.F.); Department of Radiology, Duke University Medical Center, Duke Cancer Institute, Durham, NC (R.T.G.); Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Md (B.T.); Departments of Radiology and Biomedical Imaging and Urology, University of California-San Francisco, San Francisco, Calif (A.C.W.); and Department of Radiology, David Geffen School of Medicine at UCLA, Los Angeles, Calif (D.J.M.)
| | - James S Babb
- From the Department of Radiology, NYU School of Medicine, NYU Langone Medical Center, 660 1st Ave, Third Floor, New York, NY 10016 (A.B.R., L.A.G., J.S.B.); Department of Radiology, Yale School of Medicine, New Haven, Conn (D.C.); Department of Radiology, Mayo Clinic, Rochester, Minn (A.T.F.); Department of Radiology, Duke University Medical Center, Duke Cancer Institute, Durham, NC (R.T.G.); Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Md (B.T.); Departments of Radiology and Biomedical Imaging and Urology, University of California-San Francisco, San Francisco, Calif (A.C.W.); and Department of Radiology, David Geffen School of Medicine at UCLA, Los Angeles, Calif (D.J.M.)
| | - Daniel J Margolis
- From the Department of Radiology, NYU School of Medicine, NYU Langone Medical Center, 660 1st Ave, Third Floor, New York, NY 10016 (A.B.R., L.A.G., J.S.B.); Department of Radiology, Yale School of Medicine, New Haven, Conn (D.C.); Department of Radiology, Mayo Clinic, Rochester, Minn (A.T.F.); Department of Radiology, Duke University Medical Center, Duke Cancer Institute, Durham, NC (R.T.G.); Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Md (B.T.); Departments of Radiology and Biomedical Imaging and Urology, University of California-San Francisco, San Francisco, Calif (A.C.W.); and Department of Radiology, David Geffen School of Medicine at UCLA, Los Angeles, Calif (D.J.M.)
| |
Collapse
|
31
|
Sabater S, Pastor-Juan MDR, Berenguer R, Andres I, Sevillano M, Lozano-Setien E, Jimenez-Jimenez E, Rovirosa A, Sanchez-Prieto R, Arenas M. Analysing the integration of MR images acquired in a non-radiotherapy treatment position into the radiotherapy workflow using deformable and rigid registration. Radiother Oncol 2016; 119:179-84. [DOI: 10.1016/j.radonc.2016.02.032] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2015] [Revised: 02/18/2016] [Accepted: 02/28/2016] [Indexed: 12/22/2022]
|
32
|
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.
Collapse
|
33
|
Rosenkrantz AB, Meng X, Ream JM, Babb JS, Deng FM, Rusinek H, Huang WC, Lepor H, Taneja SS. Likert score 3 prostate lesions: Association between whole-lesion ADC metrics and pathologic findings at MRI/ultrasound fusion targeted biopsy. J Magn Reson Imaging 2015; 43:325-32. [PMID: 26131965 DOI: 10.1002/jmri.24983] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2015] [Accepted: 06/04/2015] [Indexed: 11/07/2022] Open
Abstract
BACKGROUND To assess associations between whole-lesion apparent diffusion coefficient (ADC) metrics and pathologic findings of Likert score 3 prostate lesions at MRI/ultrasound fusion targeted biopsy. METHODS This retrospective Institutional Review Board-approved study received a waiver of consent. We identified patients receiving a highest lesion score of 3 on 3 Tesla multiparametric MRI reviewed by a single experienced radiologist using a 5-point Likert scale and who underwent fusion biopsy. A total of 188 score 3 lesions in 158 patients were included. Three-dimensional volumes-of-interest encompassing each lesion were traced on ADC maps. Logistic regression was used to predict biopsy results based on whole-lesion ADC metrics and patient biopsy history. Biopsy yield was compared between metrics. RESULTS By lesion, targeted biopsy identified tumor in 22.3% and Gleason score (GS) > 6 tumor in 8.5%, although results varied by biopsy history: biopsy-naïve (n = 80), 20.0%/8.8%; prior negative biopsy (n = 53), 9.4%/1.9%; prior positive biopsy (n = 55): 40.0%/14.5%. Biopsy history, whole-lesion mean ADC, whole-lesion ADC10-25 , and whole-lesion ADC25-50 were each significantly associated with tumor or GS > 6 tumor at fusion biopsy (P ≤ 0.047). In men without prior negative prostate biopsy, whole-lesion ADC25-50 ≤ 1.04*10(-3) mm2 /s achieved 90.0% sensitivity and 50.0% specificity for GS > 6 tumor, which was significantly higher (P < 0.001) than specificity of PSA (17.5%) at identical sensitivity. CONCLUSION For score 3 lesions in patients without prior negative biopsy, whole-lesion ADC metrics help detect GS > 6 cancer while avoiding negative biopsies. However, deferral of fusion biopsy may be considered for score 3 lesions in patients with prior negative biopsy (without applying whole-lesion ADC metrics) given exceedingly low (∼ 2%) frequency of GS > 6 tumor in this group.
Collapse
Affiliation(s)
- Andrew B Rosenkrantz
- Department of Radiology, NYU School of Medicine, NYU Langone Medical Center, New York, New York, USA
| | - Xiaosong Meng
- Department of Urology, Division of Urologic Oncology, NYU School of Medicine, NYU Langone Medical Center, New York, New York, USA
| | - Justin M Ream
- Department of Radiology, NYU School of Medicine, NYU Langone Medical Center, New York, New York, USA
| | - James S Babb
- Department of Radiology, NYU School of Medicine, NYU Langone Medical Center, New York, New York, USA
| | - Fang-Ming Deng
- Department of Pathology, NYU School of Medicine, NYU Langone Medical Center, New York, New York, USA
| | - Henry Rusinek
- Department of Radiology, NYU School of Medicine, NYU Langone Medical Center, New York, New York, USA
| | - William C Huang
- Department of Urology, Division of Urologic Oncology, NYU School of Medicine, NYU Langone Medical Center, New York, New York, USA
| | - Herbert Lepor
- Department of Urology, Division of Urologic Oncology, NYU School of Medicine, NYU Langone Medical Center, New York, New York, USA
| | - Samir S Taneja
- Department of Urology, Division of Urologic Oncology, NYU School of Medicine, NYU Langone Medical Center, New York, New York, USA
| |
Collapse
|