1
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Pensa J, Brisbane W, Kinnaird A, Kuppermann D, Hughes G, Ushko D, Priester A, Gonzalez S, Reiter R, Chin A, Sisk A, Felker E, Marks L, Geoghegan R. Evaluation of prostate cancer detection using micro-ultrasound versus MRI through co-registration to whole-mount pathology. Sci Rep 2024; 14:18910. [PMID: 39143293 PMCID: PMC11324719 DOI: 10.1038/s41598-024-69804-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 08/08/2024] [Indexed: 08/16/2024] Open
Abstract
Micro-ultrasound has recently been introduced as a low-cost alternative to multi-parametric MRI for imaging prostate cancer. Early clinical studies have demonstrated promising results; however, robust validation via comparison with whole-mount pathology has yet to be achieved. Due to micro-ultrasound probe design and tissue deformation during scanning, it is difficult to accurately correlate micro-ultrasound imaging planes with ground truth whole-mount pathology slides. In this study, we developed a multi-step methodology to co-register micro-ultrasound and MRI to whole-mount pathology. The three-step process had a registration error of 3.90 ± 0.11 mm and consists of: (1) micro-ultrasound image reconstruction, (2) 3D landmark registration of micro-ultrasound to MRI, and (3) 2D capsule registration of MRI to whole-mount pathology. This process was then used in a preliminary reader study to compare the diagnostic accuracy of micro-ultrasound and MRI in 15 patients who underwent radical prostatectomy for prostate cancer. Micro-ultrasound was found to have equivalent performance to retrospective MRI review for index lesion detection (91.7% vs. 80%), while demonstrating an increased detection of tumor extent (52.5% vs. 36.7%) with similar false positive regions-of-interest (38.3% vs. 40.8%). Prospective MRI review had reduced detection of index lesions (73.3%) and tumor extent (18.9%) but improved false positive regions-of-interest (22.7%) relative to micro-ultrasound and retrospective MRI. Further evaluation is needed with a larger sample size.
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Affiliation(s)
- Jake Pensa
- Department of Bioengineering, University of California Los Angeles, Los Angeles, USA.
- Department of Urology, University of California Los Angeles, Los Angeles, USA.
- Center for Advanced Surgical and Interventional Technology, University of California Los Angeles, Los Angeles, USA.
| | - Wayne Brisbane
- Department of Urology, University of California Los Angeles, Los Angeles, USA
| | - Adam Kinnaird
- Department of Urology, University of Alberta, Edmonton, Canada
| | - David Kuppermann
- Department of Urology, University of California Los Angeles, Los Angeles, USA
| | - Griffith Hughes
- Department of Bioengineering, University of California Los Angeles, Los Angeles, USA
- Department of Urology, University of California Los Angeles, Los Angeles, USA
- Center for Advanced Surgical and Interventional Technology, University of California Los Angeles, Los Angeles, USA
| | - Derrick Ushko
- Department of Urology, University of Alberta, Edmonton, Canada
| | - Alan Priester
- Department of Urology, University of California Los Angeles, Los Angeles, USA
- Center for Advanced Surgical and Interventional Technology, University of California Los Angeles, Los Angeles, USA
| | - Samantha Gonzalez
- Department of Urology, University of California Los Angeles, Los Angeles, USA
| | - Robert Reiter
- Department of Urology, University of California Los Angeles, Los Angeles, USA
| | - Arnold Chin
- Department of Urology, University of California Los Angeles, Los Angeles, USA
| | - Anthony Sisk
- Department of Urology, University of California Los Angeles, Los Angeles, USA
| | - Ely Felker
- Department of Urology, University of California Los Angeles, Los Angeles, USA
| | - Leonard Marks
- Department of Urology, University of California Los Angeles, Los Angeles, USA
| | - Rory Geoghegan
- Department of Urology, University of California Los Angeles, Los Angeles, USA
- Center for Advanced Surgical and Interventional Technology, University of California Los Angeles, Los Angeles, USA
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2
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Sarhan K, Khan N, Prezzi D, Antonelli M, Hyde E, MacAskill F, Bunton C, Byrne N, Diaz-Pinto A, Stabile A, Briganti A, Gandaglia G, Raison N, Montorsi F, Ourselin S, Dasgupta P, Granados A. Reduction of surgical complications via 3D models during robotic assisted radical prostatectomy: review of current evidence and meta-analysis. J Robot Surg 2024; 18:304. [PMID: 39105931 PMCID: PMC11303509 DOI: 10.1007/s11701-024-02041-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Accepted: 07/03/2024] [Indexed: 08/07/2024]
Abstract
The use of 3-dimensional (3D) technology has become increasingly popular across different surgical specialities to improve surgical outcomes. 3D technology has the potential to be applied to robotic assisted radical prostatectomy to visualise the patient's prostate anatomy to be used as a preoperative and peri operative surgical guide. This literature review aims to analyse all relevant pre-existing research on this topic. Following PRISMA guidelines, a search was carried out on PubMed, Medline, and Scopus. A total of seven studies were included in this literature review; two of which used printed-3D models and the remaining five using virtual augmented reality (AR) 3D models. Results displayed variation with select studies presenting that the use of 3D models enhances surgical outcomes and reduces complications whilst others displayed conflicting evidence. The use of 3D modelling within surgery has potential to improve various areas. This includes the potential surgical outcomes, including complication rates, due to improved planning and education.
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Affiliation(s)
| | - Nawal Khan
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Department of Urology, Guy's Hospital, London, UK
| | - Davide Prezzi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Department of Urology, Guy's Hospital, London, UK
| | - Michela Antonelli
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | | | | | - Christopher Bunton
- Medical Physics and Clinical Engineering, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Nick Byrne
- Medical Physics and Clinical Engineering, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Andres Diaz-Pinto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- NVIDIA, Santa Clara, CA, USA
| | | | | | | | - Nicholas Raison
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Department of Urology, Guy's Hospital, London, UK
| | | | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Prokar Dasgupta
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Department of Urology, Guy's Hospital, London, UK
| | - Alejandro Granados
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
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3
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Paverd H, Zormpas-Petridis K, Clayton H, Burge S, Crispin-Ortuzar M. Radiology and multi-scale data integration for precision oncology. NPJ Precis Oncol 2024; 8:158. [PMID: 39060351 PMCID: PMC11282284 DOI: 10.1038/s41698-024-00656-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 07/15/2024] [Indexed: 07/28/2024] Open
Abstract
In this Perspective paper we explore the potential of integrating radiological imaging with other data types, a critical yet underdeveloped area in comparison to the fusion of other multi-omic data. Radiological images provide a comprehensive, three-dimensional view of cancer, capturing features that would be missed by biopsies or other data modalities. This paper explores the complexities and challenges of incorporating medical imaging into data integration models, in the context of precision oncology. We present the different categories of imaging-omics integration and discuss recent progress, highlighting the opportunities that arise from bringing together spatial data on different scales.
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Affiliation(s)
- Hania Paverd
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
- Department of Oncology, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
| | | | - Hannah Clayton
- Department of Oncology, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
| | - Sarah Burge
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
| | - Mireia Crispin-Ortuzar
- Department of Oncology, University of Cambridge, Cambridge, UK.
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK.
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4
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Fidvi S, Holder J, Li H, Parnes GJ, Shamir SB, Wake N. Advanced 3D Visualization and 3D Printing in Radiology. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1406:103-138. [PMID: 37016113 DOI: 10.1007/978-3-031-26462-7_6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/06/2023]
Abstract
Since the discovery of X-rays in 1895, medical imaging systems have played a crucial role in medicine by permitting the visualization of internal structures and understanding the function of organ systems. Traditional imaging modalities including Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and Ultrasound (US) present fixed two-dimensional (2D) images which are difficult to conceptualize complex anatomy. Advanced volumetric medical imaging allows for three-dimensional (3D) image post-processing and image segmentation to be performed, enabling the creation of 3D volume renderings and enhanced visualization of pertinent anatomic structures in 3D. Furthermore, 3D imaging is used to generate 3D printed models and extended reality (augmented reality and virtual reality) models. A 3D image translates medical imaging information into a visual story rendering complex data and abstract ideas into an easily understood and tangible concept. Clinicians use 3D models to comprehend complex anatomical structures and to plan and guide surgical interventions more precisely. This chapter will review the volumetric radiological techniques that are commonly utilized for advanced 3D visualization. It will also provide examples of 3D printing and extended reality technology applications in radiology and describe the positive impact of advanced radiological image visualization on patient care.
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Affiliation(s)
- Shabnam Fidvi
- Department of Radiology, Montefiore Medical Center, Bronx, NY, USA.
| | - Justin Holder
- Department of Radiology, Montefiore Medical Center, Bronx, NY, USA
| | - Hong Li
- Department of Radiology, Jacobi Medical Center, Bronx, NY, USA
| | | | | | - Nicole Wake
- GE Healthcare, Aurora, OH, USA
- Center for Advanced Imaging Innovation and Research, NYU Langone Health, New York, NY, USA
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5
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Sonni I, Felker ER, Lenis AT, Sisk AE, Bahri S, Allen-Auerbach M, Armstrong WR, Suvannarerg V, Tubtawee T, Grogan T, Elashoff D, Eiber M, Raman SS, Czernin J, Reiter RE, Calais J. Head-to-Head Comparison of 68Ga-PSMA-11 PET/CT and mpMRI with a Histopathology Gold Standard in the Detection, Intraprostatic Localization, and Determination of Local Extension of Primary Prostate Cancer: Results from a Prospective Single-Center Imaging Trial. J Nucl Med 2022; 63:847-854. [PMID: 34649942 PMCID: PMC9157724 DOI: 10.2967/jnumed.121.262398] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 09/26/2021] [Indexed: 01/19/2023] Open
Abstract
The role of prostate-specific membrane antigen (PSMA)-targeted PET in comparison to multiparametric MRI (mpMRI) in the evaluation of intraprostatic cancer foci is not well defined. The aim of our study was to compare the diagnostic performance of 68Ga-PSMA-11 PET/CT (PSMA PET/CT), mpMRI, and PSMA PET/CT + mpMRI using 3 independent masked readers for each modality and with histopathology as the gold standard in the detection, intraprostatic localization, and determination of local extension of primary prostate cancer. Methods: Patients with intermediate- or high-risk prostate cancer who underwent PSMA PET/CT as part of a prospective trial (NCT03368547) and mpMRI before radical prostatectomy were included. Each imaging modality was interpreted by 3 independent readers who were unaware of the other modality result. A central majority rule was applied (2:1). Pathologic examination of whole-mount slices was used as the gold standard. Imaging scans and whole-mount slices were interpreted using the same standardized approach on a segment level and a lesion level. A "neighboring" approach was used to define imaging-pathology correlation for the detection of individual prostate cancer foci. Accuracy in determining the location, extraprostatic extension (EPE), and seminal vesicle invasion (SVI) of prostate cancer foci was assessed using receiver-operating-characteristic curve analysis. Interreader agreement was calculated using intraclass correlation coefficient analysis. Results: The final analysis included 74 patients (14 [19%] with intermediate risk and 60 [81%] with high risk). The cancer detection rate (lesion-based analysis) was 85%, 83%, and 87% for PSMA PET/CT, mpMRI, and PSMA PET/CT + mpMRI, respectively. The change in AUC was statistically significant between PSMA PET/CT + mpMRI and the 2 imaging modalities alone for delineation of tumor localization (segment-based analysis) (P < 0.001) but not between PSMA PET/CT and mpMRI (P = 0.093). mpMRI outperformed PSMA PET/CT in detecting EPE (P = 0.002) and SVI (P = 0.001). In the segment-level analysis, intraclass correlation coefficient analysis showed moderate reliability among PSMA PET/CT and mpMRI readers using a 5-point Likert scale (range, 0.53-0.64). In the evaluation of T staging, poor reliability was found among PSMA PET/CT readers and poor to moderate reliability was found for mpMRI readers. Conclusion: PSMA PET/CT and mpMRI have similar accuracy in the detection and intraprostatic localization of prostate cancer foci. mpMRI performs better in identifying EPE and SVI. For the T-staging evaluation of intermediate to high-risk prostate cancer, mpMRI should still be considered the imaging modality of reference. Whenever available, PSMA PET/MRI or the coregistration or fusion of PSMA PET/CT and mpMRI (PSMA PET/CT + mpMRI) should be used as it improves tumor extent delineation.
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Affiliation(s)
- Ida Sonni
- Ahmanson Translational Theranostics Division, Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, UCLA, Los Angeles, California
| | - Ely R. Felker
- Department of Radiology, David Geffen School of Medicine, UCLA, Los Angeles, California
| | | | - Anthony E. Sisk
- Department of Pathology, David Geffen School of Medicine, UCLA, Los Angeles, California
| | - Shadfar Bahri
- Ahmanson Translational Theranostics Division, Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, UCLA, Los Angeles, California;,Institute of Urologic Oncology, David Geffen School of Medicine, UCLA, Los Angeles, California
| | - Martin Allen-Auerbach
- Ahmanson Translational Theranostics Division, Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, UCLA, Los Angeles, California;,Institute of Urologic Oncology, David Geffen School of Medicine, UCLA, Los Angeles, California
| | - Wesley R. Armstrong
- Ahmanson Translational Theranostics Division, Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, UCLA, Los Angeles, California
| | - Voraparee Suvannarerg
- Department of Radiology, David Geffen School of Medicine, UCLA, Los Angeles, California;,Department of Radiology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Teeravut Tubtawee
- Department of Radiology, David Geffen School of Medicine, UCLA, Los Angeles, California;,Department of Radiology, Prince of Songkla University, Hat Yai, Thailand
| | - Tristan Grogan
- Department of Medicine Statistics Core, UCLA, Los Angeles, California
| | - David Elashoff
- Department of Medicine Statistics Core, UCLA, Los Angeles, California
| | - Matthias Eiber
- Ahmanson Translational Theranostics Division, Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, UCLA, Los Angeles, California;,Department of Nuclear Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany; and
| | - Steven S. Raman
- Department of Radiology, David Geffen School of Medicine, UCLA, Los Angeles, California
| | - Johannes Czernin
- Ahmanson Translational Theranostics Division, Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, UCLA, Los Angeles, California;,Institute of Urologic Oncology, David Geffen School of Medicine, UCLA, Los Angeles, California;,Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, California
| | - Robert E. Reiter
- Department of Urology, UCLA, Los Angeles, California;,Institute of Urologic Oncology, David Geffen School of Medicine, UCLA, Los Angeles, California;,Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, California
| | - Jeremie Calais
- Ahmanson Translational Theranostics Division, Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, UCLA, Los Angeles, California;,Institute of Urologic Oncology, David Geffen School of Medicine, UCLA, Los Angeles, California;,Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, California
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6
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Bhattacharya I, Khandwala YS, Vesal S, Shao W, Yang Q, Soerensen SJ, Fan RE, Ghanouni P, Kunder CA, Brooks JD, Hu Y, Rusu M, Sonn GA. A review of artificial intelligence in prostate cancer detection on imaging. Ther Adv Urol 2022; 14:17562872221128791. [PMID: 36249889 PMCID: PMC9554123 DOI: 10.1177/17562872221128791] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 08/30/2022] [Indexed: 11/07/2022] Open
Abstract
A multitude of studies have explored the role of artificial intelligence (AI) in providing diagnostic support to radiologists, pathologists, and urologists in prostate cancer detection, risk-stratification, and management. This review provides a comprehensive overview of relevant literature regarding the use of AI models in (1) detecting prostate cancer on radiology images (magnetic resonance and ultrasound imaging), (2) detecting prostate cancer on histopathology images of prostate biopsy tissue, and (3) assisting in supporting tasks for prostate cancer detection (prostate gland segmentation, MRI-histopathology registration, MRI-ultrasound registration). We discuss both the potential of these AI models to assist in the clinical workflow of prostate cancer diagnosis, as well as the current limitations including variability in training data sets, algorithms, and evaluation criteria. We also discuss ongoing challenges and what is needed to bridge the gap between academic research on AI for prostate cancer and commercial solutions that improve routine clinical care.
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Affiliation(s)
- Indrani Bhattacharya
- Department of Radiology, Stanford University School of Medicine, 1201 Welch Road, Stanford, CA 94305, USA
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
| | - Yash S. Khandwala
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
| | - Sulaiman Vesal
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
| | - Wei Shao
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Qianye Yang
- Centre for Medical Image Computing, University College London, London, UK
- Wellcome / EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Simon J.C. Soerensen
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Epidemiology & Population Health, Stanford University School of Medicine, Stanford, CA, USA
| | - Richard E. Fan
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
| | - Pejman Ghanouni
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
| | - Christian A. Kunder
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - James D. Brooks
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
| | - 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
| | - Mirabela Rusu
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Geoffrey A. Sonn
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
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7
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Selective identification and localization of indolent and aggressive prostate cancers via CorrSigNIA: an MRI-pathology correlation and deep learning framework. Med Image Anal 2022; 75:102288. [PMID: 34784540 PMCID: PMC8678366 DOI: 10.1016/j.media.2021.102288] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2020] [Revised: 09/02/2021] [Accepted: 10/20/2021] [Indexed: 01/03/2023]
Abstract
Automated methods for detecting prostate cancer and distinguishing indolent from aggressive disease on Magnetic Resonance Imaging (MRI) could assist in early diagnosis and treatment planning. Existing automated methods of prostate cancer detection mostly rely on ground truth labels with limited accuracy, ignore disease pathology characteristics observed on resected tissue, and cannot selectively identify aggressive (Gleason Pattern≥4) and indolent (Gleason Pattern=3) cancers when they co-exist in mixed lesions. In this paper, we present a radiology-pathology fusion approach, CorrSigNIA, for the selective identification and localization of indolent and aggressive prostate cancer on MRI. CorrSigNIA uses registered MRI and whole-mount histopathology images from radical prostatectomy patients to derive accurate ground truth labels and learn correlated features between radiology and pathology images. These correlated features are then used in a convolutional neural network architecture to detect and localize normal tissue, indolent cancer, and aggressive cancer on prostate MRI. CorrSigNIA was trained and validated on a dataset of 98 men, including 74 men that underwent radical prostatectomy and 24 men with normal prostate MRI. CorrSigNIA was tested on three independent test sets including 55 men that underwent radical prostatectomy, 275 men that underwent targeted biopsies, and 15 men with normal prostate MRI. CorrSigNIA achieved an accuracy of 80% in distinguishing between men with and without cancer, a lesion-level ROC-AUC of 0.81±0.31 in detecting cancers in both radical prostatectomy and biopsy cohort patients, and lesion-levels ROC-AUCs of 0.82±0.31 and 0.86±0.26 in detecting clinically significant cancers in radical prostatectomy and biopsy cohort patients respectively. CorrSigNIA consistently outperformed other methods across different evaluation metrics and cohorts. In clinical settings, CorrSigNIA may be used in prostate cancer detection as well as in selective identification of indolent and aggressive components of prostate cancer, thereby improving prostate cancer care by helping guide targeted biopsies, reducing unnecessary biopsies, and selecting and planning treatment.
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8
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Zimmerman BE, Johnson SL, Odéen HA, Shea JE, Factor RE, Joshi SC, Payne AH. Histology to 3D in vivo MR registration for volumetric evaluation of MRgFUS treatment assessment biomarkers. Sci Rep 2021; 11:18923. [PMID: 34556678 PMCID: PMC8460731 DOI: 10.1038/s41598-021-97309-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 08/24/2021] [Indexed: 11/09/2022] Open
Abstract
Advances in imaging and early cancer detection have increased interest in magnetic resonance (MR) guided focused ultrasound (MRgFUS) technologies for cancer treatment. MRgFUS ablation treatments could reduce surgical risks, preserve organ tissue and function, and improve patient quality of life. However, surgical resection and histological analysis remain the gold standard to assess cancer treatment response. For non-invasive ablation therapies such as MRgFUS, the treatment response must be determined through MR imaging biomarkers. However, current MR biomarkers are inconclusive and have not been rigorously evaluated against histology via accurate registration. Existing registration methods rely on anatomical features to directly register in vivo MR and histology. For MRgFUS applications in anatomies such as liver, kidney, or breast, anatomical features that are not caused by the treatment are often insufficient to drive direct registration. We present a novel MR to histology registration workflow that utilizes intermediate imaging and does not rely on anatomical MR features being visible in histology. The presented workflow yields an overall registration accuracy of 1.00 ± 0.13 mm. The developed registration pipeline is used to evaluate a common MRgFUS treatment assessment biomarker against histology. Evaluating MR biomarkers against histology using this registration pipeline will facilitate validating novel MRgFUS biomarkers to improve treatment assessment without surgical intervention. While the presented registration technique has been evaluated in a MRgFUS ablation treatment model, this technique could be potentially applied in any tissue to evaluate a variety of therapeutic options.
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Affiliation(s)
- Blake E Zimmerman
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA. .,Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA.
| | - Sara L Johnson
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA.,Utah Center for Advanced Imaging Research, University of Utah, Salt Lake City, UT, USA
| | - Henrik A Odéen
- Utah Center for Advanced Imaging Research, University of Utah, Salt Lake City, UT, USA
| | - Jill E Shea
- Department of Surgery, University of Utah, Salt Lake City, UT, USA
| | - Rachel E Factor
- Department of Pathology, University of Utah, Salt Lake City, UT, USA
| | - Sarang C Joshi
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA.,Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
| | - Allison H Payne
- Utah Center for Advanced Imaging Research, University of Utah, Salt Lake City, UT, USA
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9
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The impact of the co-registration technique and analysis methodology in comparison studies between advanced imaging modalities and whole-mount-histology reference in primary prostate cancer. Sci Rep 2021; 11:5836. [PMID: 33712662 PMCID: PMC7954803 DOI: 10.1038/s41598-021-85028-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Accepted: 02/24/2021] [Indexed: 12/17/2022] Open
Abstract
Comparison studies using histopathology as standard of reference enable a validation of the diagnostic performance of imaging methods. This study analysed (1) the impact of different image-histopathology co-registration pathways, (2) the impact of the applied data analysis method and (3) intraindividually compared multiparametric magnet resonance tomography (mpMRI) and prostate specific membrane antigen positron emission tomography (PSMA-PET) by using the different approaches. Ten patients with primary PCa who underwent mpMRI and [18F]PSMA-1007 PET/CT followed by prostatectomy were prospectively enrolled. We demonstrate that the choice of the intermediate registration step [(1) via ex-vivo CT or (2) mpMRI] does not significantly affect the performance of the registration framework. Comparison of analysis methods revealed that methods using high spatial resolutions e.g. quadrant-based slice-by-slice analysis are beneficial for a differentiated analysis of performance, compared to methods with a lower resolution (segment-based analysis with 6 or 18 segments and lesions-based analysis). Furthermore, PSMA-PET outperformed mpMRI for intraprostatic PCa detection in terms of sensitivity (median %: 83-85 vs. 60-69, p < 0.04) with similar specificity (median %: 74-93.8 vs. 100) using both registration pathways. To conclude, the choice of an intermediate registration pathway does not significantly affect registration performance, analysis methods with high spatial resolution are preferable and PSMA-PET outperformed mpMRI in terms of sensitivity in our cohort.
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10
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Shao W, Banh L, Kunder CA, Fan RE, Soerensen SJC, Wang JB, Teslovich NC, Madhuripan N, Jawahar A, Ghanouni P, Brooks JD, Sonn GA, Rusu M. ProsRegNet: A deep learning framework for registration of MRI and histopathology images of the prostate. Med Image Anal 2021; 68:101919. [PMID: 33385701 PMCID: PMC7856244 DOI: 10.1016/j.media.2020.101919] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Revised: 11/18/2020] [Accepted: 11/23/2020] [Indexed: 12/21/2022]
Abstract
Magnetic resonance imaging (MRI) is an increasingly important tool for the diagnosis and treatment of prostate cancer. However, interpretation of MRI suffers from high inter-observer variability across radiologists, thereby contributing to missed clinically significant cancers, overdiagnosed low-risk cancers, and frequent false positives. Interpretation of MRI could be greatly improved by providing radiologists with an answer key that clearly shows cancer locations on MRI. Registration of histopathology images from patients who had radical prostatectomy to pre-operative MRI allows such mapping of ground truth cancer labels onto MRI. However, traditional MRI-histopathology registration approaches are computationally expensive and require careful choices of the cost function and registration hyperparameters. This paper presents ProsRegNet, a deep learning-based pipeline to accelerate and simplify MRI-histopathology image registration in prostate cancer. Our pipeline consists of image preprocessing, estimation of affine and deformable transformations by deep neural networks, and mapping cancer labels from histopathology images onto MRI using estimated transformations. We trained our neural network using MR and histopathology images of 99 patients from our internal cohort (Cohort 1) and evaluated its performance using 53 patients from three different cohorts (an additional 12 from Cohort 1 and 41 from two public cohorts). Results show that our deep learning pipeline has achieved more accurate registration results and is at least 20 times faster than a state-of-the-art registration algorithm. This important advance will provide radiologists with highly accurate prostate MRI answer keys, thereby facilitating improvements in the detection of prostate cancer on MRI. Our code is freely available at https://github.com/pimed//ProsRegNet.
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Affiliation(s)
- Wei Shao
- Department of Radiology, Stanford University, Stanford, CA 94305, USA.
| | - Linda Banh
- Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA
| | | | - Richard E Fan
- Department of Urology, Stanford University, Stanford, CA 94305, USA
| | | | - Jeffrey B Wang
- School of Medicine, Stanford University, Stanford, CA 94305, USA
| | | | - Nikhil Madhuripan
- Department of Radiology, University of Colorado, Aurora, CO 80045, USA
| | | | - Pejman Ghanouni
- Department of Radiology, Stanford University, Stanford, CA 94305, USA
| | - James D Brooks
- Department of Urology, Stanford University, Stanford, CA 94305, USA
| | - Geoffrey A Sonn
- Department of Radiology, Stanford University, Stanford, CA 94305, USA; Department of Urology, Stanford University, Stanford, CA 94305, USA
| | - Mirabela Rusu
- Department of Radiology, Stanford University, Stanford, CA 94305, USA.
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11
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Sood RR, Shao W, Kunder C, Teslovich NC, Wang JB, Soerensen SJC, Madhuripan N, Jawahar A, Brooks JD, Ghanouni P, Fan RE, Sonn GA, Rusu M. 3D Registration of pre-surgical prostate MRI and histopathology images via super-resolution volume reconstruction. Med Image Anal 2021; 69:101957. [PMID: 33550008 DOI: 10.1016/j.media.2021.101957] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 12/23/2020] [Accepted: 01/04/2021] [Indexed: 12/15/2022]
Abstract
The use of MRI for prostate cancer diagnosis and treatment is increasing rapidly. However, identifying the presence and extent of cancer on MRI remains challenging, leading to high variability in detection even among expert radiologists. Improvement in cancer detection on MRI is essential to reducing this variability and maximizing the clinical utility of MRI. To date, such improvement has been limited by the lack of accurately labeled MRI datasets. Data from patients who underwent radical prostatectomy enables the spatial alignment of digitized histopathology images of the resected prostate with corresponding pre-surgical MRI. This alignment facilitates the delineation of detailed cancer labels on MRI via the projection of cancer from histopathology images onto MRI. We introduce a framework that performs 3D registration of whole-mount histopathology images to pre-surgical MRI in three steps. First, we developed a novel multi-image super-resolution generative adversarial network (miSRGAN), which learns information useful for 3D registration by producing a reconstructed 3D MRI. Second, we trained the network to learn information between histopathology slices to facilitate the application of 3D registration methods. Third, we registered the reconstructed 3D histopathology volumes to the reconstructed 3D MRI, mapping the extent of cancer from histopathology images onto MRI without the need for slice-to-slice correspondence. When compared to interpolation methods, our super-resolution reconstruction resulted in the highest PSNR relative to clinical 3D MRI (32.15 dB vs 30.16 dB for BSpline interpolation). Moreover, the registration of 3D volumes reconstructed via super-resolution for both MRI and histopathology images showed the best alignment of cancer regions when compared to (1) the state-of-the-art RAPSODI approach, (2) volumes that were not reconstructed, or (3) volumes that were reconstructed using nearest neighbor, linear, or BSpline interpolations. The improved 3D alignment of histopathology images and MRI facilitates the projection of accurate cancer labels on MRI, allowing for the development of improved MRI interpretation schemes and machine learning models to automatically detect cancer on MRI.
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Affiliation(s)
- Rewa R Sood
- Department of Electrical Engineering, Stanford University, 350 Jane Stanford Way, Stanford, CA 94305, USA
| | - Wei Shao
- Department of Radiology, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA
| | - Christian Kunder
- Department of Pathology, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA
| | - Nikola C Teslovich
- Department of Urology, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA
| | - Jeffrey B Wang
- Stanford School of Medicine, 291 Campus Drive, Stanford, CA 94305, USA
| | - Simon J C Soerensen
- Department of Urology, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA; Department of Urology, Aarhus University Hospital, Aarhus, Denmark
| | - Nikhil Madhuripan
- Department of Radiology, University of Colorado, Aurora, CO 80045, USA
| | | | - James D Brooks
- Department of Urology, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA
| | - Pejman Ghanouni
- Department of Radiology, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA
| | - Richard E Fan
- Department of Urology, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA
| | - Geoffrey A Sonn
- Department of Radiology, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA; Department of Urology, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA
| | - Mirabela Rusu
- Department of Radiology, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA.
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12
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Baboli M, Winters KV, Freed M, Zhang J, Kim SG. Evaluation of metronomic chemotherapy response using diffusion and dynamic contrast-enhanced MRI. PLoS One 2020; 15:e0241916. [PMID: 33237905 PMCID: PMC7688103 DOI: 10.1371/journal.pone.0241916] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 10/22/2020] [Indexed: 01/22/2023] Open
Abstract
PURPOSE To investigate the feasibility of using diffusion MRI (dMRI) and dynamic contrast-enhanced (DCE) MRI to evaluate the treatment response of metronomic chemotherapy (MCT) in the 4T1 mammary tumor model of locally advanced breast cancer. METHODS Twelve Balb/c mice with metastatic breast cancer were divided into treated and untreated (control) groups. The treated group (n = 6) received five treatments of anti-metabolite agent 5-Fluorouracil (5FU) in the span of two weeks. dMRI and DCE-MRI were acquired for both treated and control groups before and after MCT. Immunohistochemically staining and measurements were performed after the post-MRI measurements for comparison. RESULTS The control mice had significantly (p<0.005) larger tumors than the MCT treated mice. The DCE-MRI analysis showed a decrease in contrast enhancement for the control group, whereas the MCT mice had a more stable enhancement between the pre-chemo and post-chemo time points. This confirms the antiangiogenic effects of 5FU treatment. Comparing amplitude of enhancement revealed a significantly (p<0.05) higher enhancement in the MCT tumors than in the controls. Moreover, the MCT uptake rate was significantly (p<0.001) slower than the controls. dMRI analysis showed the MCT ADC values were significantly larger than the control group at the post-scan time point. CONCLUSION dMRI and DCE-MRI can be used as potential biomarkers for assessing the treatment response of MCT. The MRI and pathology observations suggested that in addition to the cytotoxic effect of cell kills, the MCT with a cytotoxic drug, 5FU, induced changes in the tumor vasculature similar to the anti-angiogenic effect.
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Affiliation(s)
- Mehran Baboli
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, New York, United States of America
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, United States of America
- * E-mail:
| | - Kerryanne V. Winters
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, New York, United States of America
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, United States of America
| | - Melanie Freed
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, New York, United States of America
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, United States of America
| | - Jin Zhang
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, New York, United States of America
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, United States of America
| | - Sungheon Gene Kim
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, New York, United States of America
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, United States of America
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13
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Alyami W, Kyme A, Bourne R. Histological Validation of MRI: A Review of Challenges in Registration of Imaging and Whole-Mount Histopathology. J Magn Reson Imaging 2020; 55:11-22. [PMID: 33128424 DOI: 10.1002/jmri.27409] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 10/07/2020] [Accepted: 10/09/2020] [Indexed: 12/20/2022] Open
Abstract
Rigorous validation with ground truth information such as histology is needed to reliably assess the current and potential value of MRI techniques to characterize tissue and identify disease-related tissue alterations. Commonly used methods that aim to directly correlate histology and MRI data generally fall short of this goal due to spatial errors that preclude direct matching. Errors result from tissue deformation, differences in spatial resolution and slice thickness, non-coplanar and/or nonintersecting plane orientations, and different image contrast mechanisms. Some of these problems arise from limitations in standard protocols for clinical tissue processing and histology-based pathology reporting, and to some extent can be addressed by modifications to standard protocols without compromising the clinical process. Typical modifications include ex vivo specimen MRI, block-face photography, addition of fiducial markers, and 3D printed molds to constrain tissue deformation and guide sectioning. This review summarizes the advantages and limitations of MRI validation techniques based on coregistration of MRI with whole-mount histology of tissue specimens. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Wadha Alyami
- Discipline of Medical Imaging Science, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.,Discipline of Medical Imaging Science, Faculty of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Andre Kyme
- School of Biomedical Engineering, Faculty of Engineering and IT, The University of Sydney, Sydney, New South Wales, Australia
| | - Roger Bourne
- Discipline of Medical Imaging Science, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
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14
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Rusu M, Shao W, Kunder CA, Wang JB, Soerensen SJC, Teslovich NC, Sood RR, Chen LC, Fan RE, Ghanouni P, Brooks JD, Sonn GA. Registration of presurgical MRI and histopathology images from radical prostatectomy via RAPSODI. Med Phys 2020; 47:4177-4188. [PMID: 32564359 PMCID: PMC7586964 DOI: 10.1002/mp.14337] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 05/17/2020] [Accepted: 06/08/2020] [Indexed: 01/29/2023] Open
Abstract
PURPOSE Magnetic resonance imaging (MRI) has great potential to improve prostate cancer diagnosis; however, subtle differences between cancer and confounding conditions render prostate MRI interpretation challenging. The tissue collected from patients who undergo radical prostatectomy provides a unique opportunity to correlate histopathology images of the prostate with preoperative MRI to accurately map the extent of cancer from histopathology images onto MRI. We seek to develop an open-source, easy-to-use platform to align presurgical MRI and histopathology images of resected prostates in patients who underwent radical prostatectomy to create accurate cancer labels on MRI. METHODS Here, we introduce RAdiology Pathology Spatial Open-Source multi-Dimensional Integration (RAPSODI), the first open-source framework for the registration of radiology and pathology images. RAPSODI relies on three steps. First, it creates a three-dimensional (3D) reconstruction of the histopathology specimen as a digital representation of the tissue before gross sectioning. Second, RAPSODI registers corresponding histopathology and MRI slices. Third, the optimized transforms are applied to the cancer regions outlined on the histopathology images to project those labels onto the preoperative MRI. RESULTS We tested RAPSODI in a phantom study where we simulated various conditions, for example, tissue shrinkage during fixation. Our experiments showed that RAPSODI can reliably correct multiple artifacts. We also evaluated RAPSODI in 157 patients from three institutions that underwent radical prostatectomy and have very different pathology processing and scanning. RAPSODI was evaluated in 907 corresponding histpathology-MRI slices and achieved a Dice coefficient of 0.97 ± 0.01 for the prostate, a Hausdorff distance of 1.99 ± 0.70 mm for the prostate boundary, a urethra deviation of 3.09 ± 1.45 mm, and a landmark deviation of 2.80 ± 0.59 mm between registered histopathology images and MRI. CONCLUSION Our robust framework successfully mapped the extent of cancer from histopathology slices onto MRI providing labels from training machine learning methods to detect cancer on MRI.
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Affiliation(s)
- Mirabela Rusu
- Department of RadiologySchool of MedicineStanford UniversityStanfordCA94305USA
| | - Wei Shao
- Department of RadiologySchool of MedicineStanford UniversityStanfordCA94305USA
| | - Christian A. Kunder
- Department of PathologySchool of MedicineStanford UniversityStanfordCA94305USA
| | | | - Simon J. C. Soerensen
- Department of UrologySchool of MedicineStanford UniversityStanfordCA94305USA
- Department of UrologyAarhus University HospitalAarhusDenmark
| | - Nikola C. Teslovich
- Department of UrologySchool of MedicineStanford UniversityStanfordCA94305USA
| | - Rewa R. Sood
- Department of Electrical EngineeringStanford UniversityStanfordCA94305USA
| | - Leo C. Chen
- Department of UrologySchool of MedicineStanford UniversityStanfordCA94305USA
| | - Richard E. Fan
- Department of UrologySchool of MedicineStanford UniversityStanfordCA94305USA
| | - Pejman Ghanouni
- Department of RadiologySchool of MedicineStanford UniversityStanfordCA94305USA
| | - James D. Brooks
- Department of UrologySchool of MedicineStanford UniversityStanfordCA94305USA
| | - Geoffrey A. Sonn
- Department of RadiologySchool of MedicineStanford UniversityStanfordCA94305USA
- Department of UrologySchool of MedicineStanford UniversityStanfordCA94305USA
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15
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Chen MY, Woodruff MA, Dasgupta P, Rukin NJ. Variability in accuracy of prostate cancer segmentation among radiologists, urologists, and scientists. Cancer Med 2020; 9:7172-7182. [PMID: 32810385 PMCID: PMC7541146 DOI: 10.1002/cam4.3386] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 07/19/2020] [Accepted: 07/27/2020] [Indexed: 12/11/2022] Open
Abstract
Background There is increasing research in using segmentation of prostate cancer to create a digital 3D model from magnetic resonance imaging (MRI) scans for purposes of education or surgical planning. However, the variation in segmentation of prostate cancer among users and potential inaccuracy has not been studied. Methods Four consultant radiologists, four consultant urologists, four urology trainees, and four nonclinician segmentation scientists were asked to segment a single slice of a lateral T3 prostate tumor on MRI (“Prostate 1”), an anterior zone prostate tumor MRI (“Prostate 2”), and a kidney tumor computed tomography (CT) scan (“Kidney”). Time taken and self‐rated subjective accuracy out of a maximum score of 10 were recorded. Root mean square error, Dice coefficient, Matthews correlation coefficient, Jaccard index, specificity, and sensitivity were calculated using the radiologists as the ground truth. Results There was high variance among the radiologists in segmentation of Prostate 1 and 2 tumors with mean Dice coefficients of 0.81 and 0.58, respectively, compared to 0.96 for the kidney tumor. Urologists and urology trainees had similar accuracy, while nonclinicians had the lowest accuracy scores for Prostate 1 and 2 tumors (0.60 and 0.47) but similar for kidney tumor (0.95). Mean sensitivity in Prostate 1 (0.63) and Prostate 2 (0.61) was lower than specificity (0.92 and 0.93) suggesting under‐segmentation of tumors in the non‐radiologist groups. Participants spent less time on the kidney tumor segmentation and self‐rated accuracy was higher than both prostate tumors. Conclusion Segmentation of prostate cancers is more difficult than other anatomy such as kidney tumors. Less experienced participants appear to under‐segment models and underestimate the size of prostate tumors. Segmentation of prostate cancer is highly variable even among radiologists, and 3D modeling for clinical use must be performed with caution. Further work to develop a methodology to maximize segmentation accuracy is needed.
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Affiliation(s)
- Michael Y Chen
- Science and Engineering Faculty, Queensland University of Technology, Brisbane, Queensland, Australia.,Redcliffe Hospital, Metro North Hospital and Health Service, Herston, Queensland, Australia.,School of Medicine, University of Queensland, Brisbane, Queensland, Australia.,Herston Biofabrication Institute, Metro North Hospital and Health Service, Brisbane, Australia
| | - Maria A Woodruff
- Science and Engineering Faculty, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Prokar Dasgupta
- King's College London, Guy's Hospital, London, United Kingdom
| | - Nicholas J Rukin
- Science and Engineering Faculty, Queensland University of Technology, Brisbane, Queensland, Australia.,Redcliffe Hospital, Metro North Hospital and Health Service, Herston, Queensland, Australia.,School of Medicine, University of Queensland, Brisbane, Queensland, Australia.,Herston Biofabrication Institute, Metro North Hospital and Health Service, Brisbane, Australia
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16
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Affiliation(s)
- Susanna I. Lee
- From the Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, White Bldg, Room 270, Boston, MA 02114 (S.I.L.); and Department of Radiology, Weill Cornell Medical College, New York, NY (S.J.H.)
| | - Stefanie J. Hectors
- From the Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, White Bldg, Room 270, Boston, MA 02114 (S.I.L.); and Department of Radiology, Weill Cornell Medical College, New York, NY (S.J.H.)
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17
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Chen MY, Skewes J, Daley R, Woodruff MA, Rukin NJ. Three-dimensional printing versus conventional machining in the creation of a meatal urethral dilator: development and mechanical testing. Biomed Eng Online 2020; 19:55. [PMID: 32611431 PMCID: PMC7329536 DOI: 10.1186/s12938-020-00799-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 06/23/2020] [Indexed: 01/01/2023] Open
Abstract
Background Three-dimensional (3D) printing is a promising technology, but the limitations are often poorly understood. We compare different 3D printing methods with conventional machining techniques in manufacturing meatal urethral dilators which were recently removed from the Australian market. Methods A prototype dilator was 3D printed vertically orientated on a low-cost fused deposition modelling (FDM) 3D printer in polylactic acid (PLA) and acrylonitrile butadiene styrene (ABS). It was also 3D printed horizontally orientated in ABS on a high-end FDM 3D printer with soluble support material, as well as on an SLS 3D printer in medical nylon. The dilator was also machined in stainless steel using a lathe. All dilators were tested mechanically in a custom rig by hanging calibrated weights from the handle until the dilator snapped. Results The horizontally printed ABS dilator experienced failure at a greater load than the vertically printed PLA and ABS dilators, respectively (503 g vs 283 g vs 163 g, p < 0.001). The SLS nylon dilator and machined steel dilator did not fail. The steel dilator is the most expensive with a quantity of five at 98 USD each, but this decreases to 30 USD each for a quantity of 1000. In contrast, the cost for the SLS dilator is 33 USD each for five and 27 USD each for 1000. Conclusions Low-cost FDM 3D printing is not a replacement for conventional manufacturing. 3D printing is best used for patient-specific parts, prototyping or manufacturing complex parts that have additional functionality that cannot otherwise be achieved.
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Affiliation(s)
- Michael Y Chen
- Science and Engineering Faculty, Queensland University of Technology, Brisbane, Australia. .,Department of Urology, Redcliffe Hospital, Anzac Ave, Redcliffe, QLD, 4020, Australia. .,School of Medicine, University of Queensland, Brisbane, QLD, Australia. .,Herston Biofabrication Institute, Metro North Hospital and Health Service, Brisbane, Australia.
| | - Jacob Skewes
- Science and Engineering Faculty, Queensland University of Technology, Brisbane, Australia
| | - Ryan Daley
- Science and Engineering Faculty, Queensland University of Technology, Brisbane, Australia
| | - Maria A Woodruff
- Science and Engineering Faculty, Queensland University of Technology, Brisbane, Australia
| | - Nicholas J Rukin
- Science and Engineering Faculty, Queensland University of Technology, Brisbane, Australia.,Department of Urology, Redcliffe Hospital, Anzac Ave, Redcliffe, QLD, 4020, Australia.,School of Medicine, University of Queensland, Brisbane, QLD, Australia.,Herston Biofabrication Institute, Metro North Hospital and Health Service, Brisbane, Australia
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18
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Chen MY, Skewes J, Woodruff MA, Dasgupta P, Rukin NJ. Multi-colour extrusion fused deposition modelling: a low-cost 3D printing method for anatomical prostate cancer models. Sci Rep 2020; 10:10004. [PMID: 32561811 PMCID: PMC7305153 DOI: 10.1038/s41598-020-67082-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 06/01/2020] [Indexed: 01/17/2023] Open
Abstract
Three-dimensional (3D) printed prostate cancer models are an emerging adjunct for urological surgical planning and patient education, however published methods are costly which limits their translation into clinical practice. Multi-colour extrusion fused deposition modelling (FDM) can be used to create 3D prostate cancer models of a quality comparable to more expensive techniques at a fraction of the cost. Three different 3D printing methods were used to create the same 3D prostate model: FDM, colour jet printing (CJP) and material jetting (MJ), with a calculated cost per model of USD 20, USD 200 and USD 250 respectively. When taking into account the cost, the FDM prostate models are the most preferred 3D printing method by surgeons. This method could be used to manufacture low-cost 3D printed models across other medical disciplines.
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Affiliation(s)
- Michael Y Chen
- Redcliffe Hospital, Metro North Hospital Health Service, Queensland, Australia. .,University of Queensland, School of Medicine, Queensland, Australia. .,Queensland University of Technology, Science and Engineering Faculty, Queensland, Australia.
| | - Jacob Skewes
- Queensland University of Technology, Science and Engineering Faculty, Queensland, Australia
| | - Maria A Woodruff
- Queensland University of Technology, Science and Engineering Faculty, Queensland, Australia
| | - Prokar Dasgupta
- King's College London, Guy's Hospital, London, United Kingdom
| | - Nicholas J Rukin
- Redcliffe Hospital, Metro North Hospital Health Service, Queensland, Australia.,University of Queensland, School of Medicine, Queensland, Australia.,Queensland University of Technology, Science and Engineering Faculty, Queensland, Australia
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19
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Zhang Z, Wu HH, Priester A, Magyar C, Afshari Mirak S, Shakeri S, Mohammadian Bajgiran A, Hosseiny M, Azadikhah A, Sung K, Reiter RE, Sisk AE, Raman S, Enzmann DR. Prostate Microstructure in Prostate Cancer Using 3-T MRI with Diffusion-Relaxation Correlation Spectrum Imaging: Validation with Whole-Mount Digital Histopathology. Radiology 2020; 296:348-355. [PMID: 32515678 DOI: 10.1148/radiol.2020192330] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Background Microstructural MRI has the potential to improve diagnosis and characterization of prostate cancer (PCa), but validation with histopathology is lacking. Purpose To validate ex vivo diffusion-relaxation correlation spectrum imaging (DR-CSI) in the characterization of microstructural tissue compartments in prostate specimens from men with PCa by using registered whole-mount digital histopathology (WMHP) as the reference standard. Materials and Methods Men with PCa who underwent 3-T MRI and robotic-assisted radical prostatectomy between June 2018 and January 2019 were prospectively studied. After prostatectomy, the fresh whole prostate specimens were imaged in patient-specific three-dimensionally printed molds by using 3-T MRI with DR-CSI and were then sliced to create coregistered WMHP slides. The DR-CSI spectral signal component fractions (fA, fB, fC) were compared with epithelial, stromal, and luminal area fractions (fepithelium, fstroma, flumen) quantified in PCa and benign tissue regions. A linear mixed-effects model assessed the correlations between (fA, fB, fC) and (fepithelium, fstroma, flumen), and the strength of correlations was evaluated by using Spearman correlation coefficients. Differences between PCa and benign tissues in terms of DR-CSI signal components and microscopic tissue compartments were assessed using two-sided t tests. Results Prostate specimens from nine men (mean age, 65 years ± 7 [standard deviation]) were evaluated; 20 regions from 17 PCas, along with 20 benign tissue regions of interest, were analyzed. Three DR-CSI spectral signal components (spectral peaks) were consistently identified. The fA, fB, and fC were correlated with fepithelium, fstroma, and flumen (all P < .001), with Spearman correlation coefficients of 0.74 (95% confidence interval [CI]: 0.62, 0.83), 0.80 (95% CI: 0.66, 0.89), and 0.67 (95% CI: 0.51, 0.81), respectively. PCa exhibited differences compared with benign tissues in terms of increased fA (PCa vs benign, 0.37 ± 0.05 vs 0.27 ± 0.06; P < .001), decreased fC (PCa vs benign, 0.18 ± 0.06 vs 0.31 ± 0.13; P = .01), increased fepithelium (PCa vs benign, 0.44 ± 0.13 vs 0.26 ± 0.16; P < .001), and decreased flumen (PCa vs benign, 0.14 ± 0.08 vs 0.27 ± 0.18; P = .004). Conclusion Diffusion-relaxation correlation spectrum imaging signal components correlate with microscopic tissue compartments in the prostate and differ between cancer and benign tissue. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Lee and Hectors in this issue.
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Affiliation(s)
- Zhaohuan Zhang
- From the Department of Radiological Sciences, David Geffen School of Medicine (Z.Z., H.H.W., S.A.M., S.S., A.M.B., M.H., A.A., K.S., S.R., D.R.E.), Department of Bioengineering (Z.Z., H.H.W.), Department of Urology (A.P., R.E.R.), and Department of Pathology and Laboratory Medicine (C.M., A.E.S.), University of California, Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA 90095
| | - Holden H Wu
- From the Department of Radiological Sciences, David Geffen School of Medicine (Z.Z., H.H.W., S.A.M., S.S., A.M.B., M.H., A.A., K.S., S.R., D.R.E.), Department of Bioengineering (Z.Z., H.H.W.), Department of Urology (A.P., R.E.R.), and Department of Pathology and Laboratory Medicine (C.M., A.E.S.), University of California, Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA 90095
| | - Alan Priester
- From the Department of Radiological Sciences, David Geffen School of Medicine (Z.Z., H.H.W., S.A.M., S.S., A.M.B., M.H., A.A., K.S., S.R., D.R.E.), Department of Bioengineering (Z.Z., H.H.W.), Department of Urology (A.P., R.E.R.), and Department of Pathology and Laboratory Medicine (C.M., A.E.S.), University of California, Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA 90095
| | - Clara Magyar
- From the Department of Radiological Sciences, David Geffen School of Medicine (Z.Z., H.H.W., S.A.M., S.S., A.M.B., M.H., A.A., K.S., S.R., D.R.E.), Department of Bioengineering (Z.Z., H.H.W.), Department of Urology (A.P., R.E.R.), and Department of Pathology and Laboratory Medicine (C.M., A.E.S.), University of California, Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA 90095
| | - Sohrab Afshari Mirak
- From the Department of Radiological Sciences, David Geffen School of Medicine (Z.Z., H.H.W., S.A.M., S.S., A.M.B., M.H., A.A., K.S., S.R., D.R.E.), Department of Bioengineering (Z.Z., H.H.W.), Department of Urology (A.P., R.E.R.), and Department of Pathology and Laboratory Medicine (C.M., A.E.S.), University of California, Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA 90095
| | - Sepideh Shakeri
- From the Department of Radiological Sciences, David Geffen School of Medicine (Z.Z., H.H.W., S.A.M., S.S., A.M.B., M.H., A.A., K.S., S.R., D.R.E.), Department of Bioengineering (Z.Z., H.H.W.), Department of Urology (A.P., R.E.R.), and Department of Pathology and Laboratory Medicine (C.M., A.E.S.), University of California, Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA 90095
| | - Amirhossein Mohammadian Bajgiran
- From the Department of Radiological Sciences, David Geffen School of Medicine (Z.Z., H.H.W., S.A.M., S.S., A.M.B., M.H., A.A., K.S., S.R., D.R.E.), Department of Bioengineering (Z.Z., H.H.W.), Department of Urology (A.P., R.E.R.), and Department of Pathology and Laboratory Medicine (C.M., A.E.S.), University of California, Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA 90095
| | - Melina Hosseiny
- From the Department of Radiological Sciences, David Geffen School of Medicine (Z.Z., H.H.W., S.A.M., S.S., A.M.B., M.H., A.A., K.S., S.R., D.R.E.), Department of Bioengineering (Z.Z., H.H.W.), Department of Urology (A.P., R.E.R.), and Department of Pathology and Laboratory Medicine (C.M., A.E.S.), University of California, Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA 90095
| | - Afshin Azadikhah
- From the Department of Radiological Sciences, David Geffen School of Medicine (Z.Z., H.H.W., S.A.M., S.S., A.M.B., M.H., A.A., K.S., S.R., D.R.E.), Department of Bioengineering (Z.Z., H.H.W.), Department of Urology (A.P., R.E.R.), and Department of Pathology and Laboratory Medicine (C.M., A.E.S.), University of California, Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA 90095
| | - Kyunghyun Sung
- From the Department of Radiological Sciences, David Geffen School of Medicine (Z.Z., H.H.W., S.A.M., S.S., A.M.B., M.H., A.A., K.S., S.R., D.R.E.), Department of Bioengineering (Z.Z., H.H.W.), Department of Urology (A.P., R.E.R.), and Department of Pathology and Laboratory Medicine (C.M., A.E.S.), University of California, Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA 90095
| | - Robert E Reiter
- From the Department of Radiological Sciences, David Geffen School of Medicine (Z.Z., H.H.W., S.A.M., S.S., A.M.B., M.H., A.A., K.S., S.R., D.R.E.), Department of Bioengineering (Z.Z., H.H.W.), Department of Urology (A.P., R.E.R.), and Department of Pathology and Laboratory Medicine (C.M., A.E.S.), University of California, Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA 90095
| | - Anthony E Sisk
- From the Department of Radiological Sciences, David Geffen School of Medicine (Z.Z., H.H.W., S.A.M., S.S., A.M.B., M.H., A.A., K.S., S.R., D.R.E.), Department of Bioengineering (Z.Z., H.H.W.), Department of Urology (A.P., R.E.R.), and Department of Pathology and Laboratory Medicine (C.M., A.E.S.), University of California, Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA 90095
| | - Steven Raman
- From the Department of Radiological Sciences, David Geffen School of Medicine (Z.Z., H.H.W., S.A.M., S.S., A.M.B., M.H., A.A., K.S., S.R., D.R.E.), Department of Bioengineering (Z.Z., H.H.W.), Department of Urology (A.P., R.E.R.), and Department of Pathology and Laboratory Medicine (C.M., A.E.S.), University of California, Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA 90095
| | - Dieter R Enzmann
- From the Department of Radiological Sciences, David Geffen School of Medicine (Z.Z., H.H.W., S.A.M., S.S., A.M.B., M.H., A.A., K.S., S.R., D.R.E.), Department of Bioengineering (Z.Z., H.H.W.), Department of Urology (A.P., R.E.R.), and Department of Pathology and Laboratory Medicine (C.M., A.E.S.), University of California, Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA 90095
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20
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Liver-specific 3D sectioning molds for correlating in vivo CT and MRI with tumor histopathology in woodchucks (Marmota monax). PLoS One 2020; 15:e0230794. [PMID: 32214365 PMCID: PMC7098627 DOI: 10.1371/journal.pone.0230794] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Accepted: 03/08/2020] [Indexed: 12/19/2022] Open
Abstract
Purpose To evaluate the spatial registration and correlation of liver and tumor histopathology sections with corresponding in vivo CT and MRI using 3D, liver-specific cutting molds in a woodchuck (Marmota monax) hepatic tumor model. Methods Five woodchucks chronically infected with woodchuck hepatitis virus following inoculation at birth and with confirmed hepatic tumors were imaged by contrast enhanced CT or MRI. Virtual 3D liver or tumor models were generated by segmentation of in vivo CT or MR imaging. A specimen-specific cavity was created inside a block containing cutting slots aligned with an imaging plane using computer-aided design software, and the final cutting molds were fabricated using a 3D printer. Livers were resected two days after initial imaging, fixed with formalin or left unfixed, inserted into the 3D molds, and cut into parallel pieces by passing a sharp blade through the parallel slots in the mold. Histopathology sections were acquired and their spatial overlap with in vivo image slices was quantified using the Dice similarity coefficient (DSC). Results Imaging of the woodchucks revealed heterogeneous hepatic tumors of varying size, number, and location. Specimen-specific 3D molds provided accurate co-localization of histopathology of whole livers, liver lobes, and pedunculated tumors with in vivo CT and MR imaging, with or without tissue fixation. Visual inspection of histopathology sections and corresponding in vivo image slices revealed spatial registration of analogous pathologic features. The mean DSC for all specimens was 0.83+/-0.05. Conclusion Use of specimen-specific 3D molds for en bloc liver dissection provided strong spatial overlap and feature correspondence between in vivo image slices and histopathology sections.
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Zhang Z, Moulin K, Aliotta E, Shakeri S, Afshari Mirak S, Hosseiny M, Raman S, Ennis DB, Wu HH. Prostate diffusion MRI with minimal echo time using eddy current nulled convex optimized diffusion encoding. J Magn Reson Imaging 2019; 51:1526-1539. [PMID: 31625663 DOI: 10.1002/jmri.26960] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Accepted: 09/20/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Prostate diffusion-weighted imaging (DWI) using monopolar encoding is sensitive to eddy-current-induced distortion artifacts. Twice-refocused bipolar encoding suppresses eddy current artifacts, but increases echo time (TE), leading to lower signal-to-noise ratio (SNR). Optimization of the diffusion encoding might improve prostate DWI. PURPOSE To evaluate eddy current nulled convex optimized diffusion encoding (ENCODE) for prostate DWI with minimal TE. STUDY TYPE Prospective cohort study. POPULATION A diffusion phantom, an ex vivo prostate specimen, 10 healthy male subjects (27 ± 3 years old), and five prostate cancer patients (62 ± 7 years old). FIELD STRENGTH/SEQUENCE 3T; single-shot spin-echo echoplanar DWI. ASSESSMENT Eddy-current artifacts, TE, SNR, apparent diffusion coefficient (ADC), and image quality scores from three independent readers were compared between monopolar, bipolar, and ENCODE prostate DWI for standard-resolution (1.6 × 1.6 mm2 , partial Fourier factor [pF] = 6/8) and higher-resolution protocols (1.6 × 1.6 mm2 , pF = off; 1.0 × 1.0 mm2 , pF = 6/8). STATISTICAL TESTING SNR and ADC differences between techniques were tested with Kruskal-Wallis and Wilcoxon signed-rank tests (P < 0.05 considered significant). RESULTS Eddy current suppression with ENCODE was comparable to bipolar encoding (mean coefficient of variation across three diffusion directions of 9.4% and 9%). For a standard-resolution protocol, ENCODE achieved similar TE as monopolar and reduced TE by 14 msec compared to bipolar, resulting in 27% and 29% higher mean SNR in prostate transition zone (TZ) and peripheral zone (PZ) (P < 0.05) compared to bipolar, respectively. For higher-resolution protocols, ENCODE achieved the shortest TE (67 msec), with 17-21% and 58-70% higher mean SNR compared to monopolar (TE = 77 msec) and bipolar (TE = 102 msec) in PZ and TZ (P < 0.05). No significant differences were found in mean TZ (P = 0.91) and PZ ADC (P = 0.94) between the three techniques. ENCODE achieved similar or higher image quality scores than bipolar DWI in patients, with mean intraclass correlation coefficient of 0.77 for overall quality between three independent readers. DATA CONCLUSION ENCODE minimizes TE (improves SNR) and reduces eddy-current distortion for prostate DWI compared to monopolar and bipolar encoding. LEVEL OF EVIDENCE 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:1526-1539.
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Affiliation(s)
- Zhaohuan Zhang
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA.,Department of Bioengineering, University of California, Los Angeles, California, USA
| | - Kevin Moulin
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Eric Aliotta
- Department of Radiation Oncology, University of Virginia, Charlottesville, Virginia, USA
| | - Sepideh Shakeri
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA
| | - Sohrab Afshari Mirak
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA
| | - Melina Hosseiny
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA
| | - Steven Raman
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA
| | - Daniel B Ennis
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Holden H Wu
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA.,Department of Bioengineering, University of California, Los Angeles, California, USA
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22
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Cork TE, Perotti LE, Verzhbinsky IA, Loecher M, Ennis DB. High-Resolution Ex Vivo Microstructural MRI After Restoring Ventricular Geometry via 3D Printing. FUNCTIONAL IMAGING AND MODELING OF THE HEART : ... INTERNATIONAL WORKSHOP, FIMH ..., PROCEEDINGS. FIMH 2019; 11504:177-186. [PMID: 31432042 DOI: 10.1007/978-3-030-21949-9_20] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Computational modeling of the heart requires accurately incorporating both gross anatomical detail and local microstructural information. Together, these provide the necessary data to build 3D meshes for simulation of cardiac mechanics and electrophysiology. Recent MRI advances make it possible to measure detailed heart motion in vivo, but in vivo microstructural imaging of the heart remains challenging. Consequently, the most detailed measurements of microstructural organization and microanatomical infarct details are obtained ex vivo. The objective of this work was to develop and evaluate a new method for restoring ex vivo ventricular geometry to match the in vivo configuration. This approach aids the integration of high-resolution ex vivo microstructural information with in vivo motion measurements. The method uses in vivo cine imaging to generate surface meshes, then creates a 3D printed left ventricular (LV) blood pool cast and a pericardial mold to restore the ex vivo cardiac geometry to a mid-diastasis reference configuration. The method was evaluated in healthy (N = 7) and infarcted (N = 3) swine. Dice similarity coefficients were calculated between in vivo and ex vivo images for the LV cavity (0.93 ± 0.01), right ventricle (RV) cavity (0.80 ± 0.05), and the myocardium (0.72 ± 0.04). The R 2 coefficient between in vivo and ex vivo LV and RV cavity volumes were 0.95 and 0.91, respectively. These results suggest that this method adequately restores ex vivo geometry to match in vivo geometry. This approach permits a more precise incorporation of high-resolution ex vivo anatomical and microstructural data into computational models that use in vivo data for simulation of cardiac mechanics and electrophysiology.
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Affiliation(s)
- Tyler E Cork
- Department of Radiology, Stanford University, Stanford, CA 94305, USA.,Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Luigi E Perotti
- Department of Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL 32816, USA
| | | | - Michael Loecher
- Department of Radiology, Stanford University, Stanford, CA 94305, USA
| | - Daniel B Ennis
- Department of Radiology, Stanford University, Stanford, CA 94305, USA
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