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Coelho FMA, Baroni RH. Strategies for improving image quality in prostate MRI. Abdom Radiol (NY) 2024:10.1007/s00261-024-04396-4. [PMID: 38940911 DOI: 10.1007/s00261-024-04396-4] [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: 03/31/2024] [Revised: 05/15/2024] [Accepted: 05/17/2024] [Indexed: 06/29/2024]
Abstract
Prostate magnetic resonance imaging (MRI) stands as the cornerstone in diagnosing prostate cancer (PCa), offering superior detection capabilities while minimizing unnecessary biopsies. Despite its critical role, global disparities in MRI diagnostic performance persist, stemming from variations in image quality and radiologist expertise. This manuscript reviews the challenges and strategies for enhancing image quality in prostate MRI, spanning patient preparation, MRI unit optimization, and radiology team engagement. Quality assurance (QA) and quality control (QC) processes are pivotal, emphasizing standardized protocols, meticulous patient evaluation, MRI unit workflow, and radiology team performance. Additionally, artificial intelligence (AI) advancements offer promising avenues for improving image quality and reducing acquisition times. The Prostate-Imaging Quality (PI-QUAL) scoring system emerges as a valuable tool for assessing MRI image quality. A comprehensive approach addressing technical, procedural, and interpretative aspects is essential to ensure consistent and reliable prostate MRI outcomes.
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Affiliation(s)
| | - Ronaldo Hueb Baroni
- Department of Radiology, Hospital Israelita Albert Einstein, 627 Albert Einstein Ave., Sao Paulo, SP, 05652-900, Brazil.
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2
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Küstner T, Qin C, Sun C, Ning L, Scannell CM. The intelligent imaging revolution: artificial intelligence in MRI and MRS acquisition and reconstruction. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01179-2. [PMID: 38900344 DOI: 10.1007/s10334-024-01179-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Accepted: 05/21/2024] [Indexed: 06/21/2024]
Affiliation(s)
- Thomas Küstner
- Medical Image and Data Analysis (MIDAS.Lab), Diagnostic and Interventional Radiology, University Hospital of Tuebingen, 72076, Tuebingen, Germany.
| | - Chen Qin
- Department of Electrical and Electronic Engineering, I-X Imperial College London, London, UK
| | - Changyu Sun
- Department of Chemical and Biomedical Engineering, Department of Radiology, University of Missouri-Columbia, 65201, Columbia, USA
| | - Lipeng Ning
- Brigham and Women' s Hospital, 02215, Boston, USA
| | - Cian M Scannell
- Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
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Lang M, Lo WC, Sharp A, Hartmann SP, Pianykh OS, Melski LM, Herrington JA, Sellers R, Deshpande V, Clifford B, Brink JA, Husseini JS, Harisinghani MG, Huang SY. Improving Workflow Efficiency at an Outpatient MRI Imaging Facility: A Case Study. J Am Coll Radiol 2024:S1546-1440(24)00531-3. [PMID: 38908740 DOI: 10.1016/j.jacr.2024.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 03/11/2024] [Accepted: 06/11/2024] [Indexed: 06/24/2024]
Affiliation(s)
- Min Lang
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Wei-Ching Lo
- Siemens Medical Solutions USA, Boston, Massachusetts
| | - Andrew Sharp
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Sean P Hartmann
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Oleg S Pianykh
- Director of Medical Analytics, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Lauren M Melski
- MRI Operations Manager, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Jeremy A Herrington
- Clinical Director, MRI & 3D Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | | | - Vibhas Deshpande
- Vice President, Sustainability Innovation and Strategic Research, Siemens Medical Solutions USA, Inc, Austin, Texas
| | | | - James A Brink
- Radiologist-in-Chief, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Jad S Husseini
- MRI Service Chief, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Mukesh G Harisinghani
- Director of Abdominal MRI, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Susie Y Huang
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts; Associate Chair of Faculty Affairs, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts; Director of Translational Neuro MR Imaging, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts.
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Gaviraghi M, Ricciardi A, Palesi F, Brownlee W, Vitali P, Prados F, Kanber B, Gandini Wheeler-Kingshott CAM. Finding the limits of deep learning clinical sensitivity with fractional anisotropy (FA) microstructure maps. Front Neuroinform 2024; 18:1415085. [PMID: 38933144 PMCID: PMC11199892 DOI: 10.3389/fninf.2024.1415085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Accepted: 05/29/2024] [Indexed: 06/28/2024] Open
Abstract
Background Quantitative maps obtained with diffusion weighted (DW) imaging, such as fractional anisotropy (FA) -calculated by fitting the diffusion tensor (DT) model to the data,-are very useful to study neurological diseases. To fit this map accurately, acquisition times of the order of several minutes are needed because many noncollinear DW volumes must be acquired to reduce directional biases. Deep learning (DL) can be used to reduce acquisition times by reducing the number of DW volumes. We already developed a DL network named "one-minute FA," which uses 10 DW volumes to obtain FA maps, maintaining the same characteristics and clinical sensitivity of the FA maps calculated with the standard method using more volumes. Recent publications have indicated that it is possible to train DL networks and obtain FA maps even with 4 DW input volumes, far less than the minimum number of directions for the mathematical estimation of the DT. Methods Here we investigated the impact of reducing the number of DW input volumes to 4 or 7, and evaluated the performance and clinical sensitivity of the corresponding DL networks trained to calculate FA, while comparing results also with those using our one-minute FA. Each network training was performed on the human connectome project open-access dataset that has a high resolution and many DW volumes, used to fit a ground truth FA. To evaluate the generalizability of each network, they were tested on two external clinical datasets, not seen during training, and acquired on different scanners with different protocols, as previously done. Results Using 4 or 7 DW volumes, it was possible to train DL networks to obtain FA maps with the same range of values as ground truth - map, only when using HCP test data; pathological sensitivity was lost when tested using the external clinical datasets: indeed in both cases, no consistent differences were found between patient groups. On the contrary, our "one-minute FA" did not suffer from the same problem. Conclusion When developing DL networks for reduced acquisition times, the ability to generalize and to generate quantitative biomarkers that provide clinical sensitivity must be addressed.
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Affiliation(s)
- Marta Gaviraghi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Antonio Ricciardi
- NMR Research Unit, Department of Neuroinflammation, Queen Square Multiple Sclerosis Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Fulvia Palesi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Wallace Brownlee
- NMR Research Unit, Department of Neuroinflammation, Queen Square Multiple Sclerosis Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Paolo Vitali
- Department of Radiology, IRCCS Policlinico San Donato, Milan, Italy
- Department of Biomedical Sciences for Health, Universitá degli Studi di Milano, Milan, Italy
| | - Ferran Prados
- NMR Research Unit, Department of Neuroinflammation, Queen Square Multiple Sclerosis Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing, University College London, London, United Kingdom
- E-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Baris Kanber
- NMR Research Unit, Department of Neuroinflammation, Queen Square Multiple Sclerosis Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Claudia A. M. Gandini Wheeler-Kingshott
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- NMR Research Unit, Department of Neuroinflammation, Queen Square Multiple Sclerosis Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Brain Connectivity Centre, IRCCS Mondino Foundation, Pavia, Italy
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Vashistha R, Almuqbel MM, Palmer NJ, Keenan RJ, Gilbert K, Wells S, Lynch A, Li A, Kingston-Smith S, Melzer TR, Koerzdoerfer G, O'Brien K. Evaluation of deep-learning TSE images in clinical musculoskeletal imaging. J Med Imaging Radiat Oncol 2024. [PMID: 38837669 DOI: 10.1111/1754-9485.13714] [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: 04/18/2023] [Accepted: 05/15/2024] [Indexed: 06/07/2024]
Abstract
In this study, we compared the fat-saturated (FS) and non-FS turbo spin echo (TSE) magnetic resonance imaging knee sequences reconstructed conventionally (conventional-TSE) against a deep learning-based reconstruction of accelerated TSE (DL-TSE) scans. A total of 232 conventional-TSE and DL-TSE image pairs were acquired for comparison. For each consenting patient, one of the clinically acquired conventional-TSE proton density-weighted sequences in the sagittal or coronal planes (FS and non-FS), or in the axial plane (non-FS), was repeated using a research DL-TSE sequence. The DL-TSE reconstruction resulted in an image resolution that increased by at least 45% and scan times that were up to 52% faster compared to the conventional TSE. All images were acquired on a MAGNETOM Vida 3T scanner (Siemens Healthineers AG, Erlangen, Germany). The reporting radiologists, blinded to the acquisition time, were requested to qualitatively compare the DL-TSE against the conventional-TSE reconstructions. Despite having a faster acquisition time, the DL-TSE was rated to depict smaller structures better for 139/232 (60%) cases, equivalent for 72/232 (31%) cases and worse for 21/232 (9%) cases compared to the conventional-TSE. Overall, the radiologists preferred the DL-TSE reconstruction in 124/232 (53%) cases and stated no preference, implying equivalence, for 65/232 (28%) cases. DL-TSE reconstructions enabled faster acquisition times while enhancing spatial resolution and preserving the image contrast. From these results, the DL-TSE provided added or comparable clinical value and utility in less time. DL-TSE offers the opportunity to further reduce the overall examination time and improve patient comfort with no loss in diagnostic accuracy.
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Affiliation(s)
- Rajat Vashistha
- ARC Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Queensland, Australia
- Siemens Healthcare Pty Ltd, Brisbane, Queensland, Australia
| | - Mustafa M Almuqbel
- Pacific Radiology Group, Christchurch, New Zealand
- New Zealand Brain Research Institute, Christchurch, New Zealand
- Department of Medicine, University of Otago, Christchurch, New Zealand
- South Australia Health and Medical Research Institute, Adelaide, South Australia, Australia
| | | | - Ross J Keenan
- Pacific Radiology Group, Christchurch, New Zealand
- Department of Radiology, Christchurch Hospital, Christchurch, New Zealand
| | | | - Scott Wells
- Pacific Radiology Group, Christchurch, New Zealand
| | - Andrew Lynch
- Pacific Radiology Group, Christchurch, New Zealand
| | - Andrew Li
- Pacific Radiology Group, Christchurch, New Zealand
| | | | - Tracy R Melzer
- New Zealand Brain Research Institute, Christchurch, New Zealand
- Department of Medicine, University of Otago, Christchurch, New Zealand
- School of Psychology, Speech and Hearing, University of Canterbury, Christchurch, New Zealand
| | | | - Kieran O'Brien
- ARC Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Queensland, Australia
- Siemens Healthcare Pty Ltd, Brisbane, Queensland, Australia
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Lee Y, Yoon S, Paek M, Han D, Choi MH, Park SH. Advanced MRI techniques in abdominal imaging. Abdom Radiol (NY) 2024:10.1007/s00261-024-04369-7. [PMID: 38802629 DOI: 10.1007/s00261-024-04369-7] [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: 03/19/2024] [Revised: 04/30/2024] [Accepted: 05/03/2024] [Indexed: 05/29/2024]
Abstract
Magnetic resonance imaging (MRI) is a crucial modality for abdominal imaging evaluation of focal lesions and tissue properties. However, several obstacles, such as prolonged scan times, limitations in patients' breath-hold capacity, and contrast agent-associated artifacts, remain in abdominal MR images. Recent techniques, including parallel imaging, three-dimensional acquisition, compressed sensing, and deep learning, have been developed to reduce the scan time while ensuring acceptable image quality or to achieve higher resolution without extending the scan duration. Quantitative measurements using MRI techniques enable the noninvasive evaluation of specific materials. A comprehensive understanding of these advanced techniques is essential for accurate interpretation of MRI sequences. Herein, we therefore review advanced abdominal MRI techniques.
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Affiliation(s)
- Yoonhee Lee
- Department of Radiology, Gil Medical Center, Gachon University College of Medicine, 21, Namdong-daero 774beon-gil, Namdong-gu, Incheon, 21565, Republic of Korea
| | - Sungjin Yoon
- Department of Radiology, Gil Medical Center, Gachon University College of Medicine, 21, Namdong-daero 774beon-gil, Namdong-gu, Incheon, 21565, Republic of Korea
| | | | - Dongyeob Han
- Siemens Healthineers Ltd, Seoul, Republic of Korea
| | - Moon Hyung Choi
- Department of Radiology, Catholic University of Korea Eunpyeong St Mary's Hospital, Seoul, Republic of Korea
| | - So Hyun Park
- Department of Radiology, Gil Medical Center, Gachon University College of Medicine, 21, Namdong-daero 774beon-gil, Namdong-gu, Incheon, 21565, Republic of Korea.
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Talyshinskii A, Hameed BMZ, Ravinder PP, Naik N, Randhawa P, Shah M, Rai BP, Tokas T, Somani BK. Catalyzing Precision Medicine: Artificial Intelligence Advancements in Prostate Cancer Diagnosis and Management. Cancers (Basel) 2024; 16:1809. [PMID: 38791888 PMCID: PMC11119252 DOI: 10.3390/cancers16101809] [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: 03/11/2024] [Revised: 04/29/2024] [Accepted: 05/07/2024] [Indexed: 05/26/2024] Open
Abstract
BACKGROUND The aim was to analyze the current state of deep learning (DL)-based prostate cancer (PCa) diagnosis with a focus on magnetic resonance (MR) prostate reconstruction; PCa detection/stratification/reconstruction; positron emission tomography/computed tomography (PET/CT); androgen deprivation therapy (ADT); prostate biopsy; associated challenges and their clinical implications. METHODS A search of the PubMed database was conducted based on the inclusion and exclusion criteria for the use of DL methods within the abovementioned areas. RESULTS A total of 784 articles were found, of which, 64 were included. Reconstruction of the prostate, the detection and stratification of prostate cancer, the reconstruction of prostate cancer, and diagnosis on PET/CT, ADT, and biopsy were analyzed in 21, 22, 6, 7, 2, and 6 studies, respectively. Among studies describing DL use for MR-based purposes, datasets with magnetic field power of 3 T, 1.5 T, and 3/1.5 T were used in 18/19/5, 0/1/0, and 3/2/1 studies, respectively, of 6/7 studies analyzing DL for PET/CT diagnosis which used data from a single institution. Among the radiotracers, [68Ga]Ga-PSMA-11, [18F]DCFPyl, and [18F]PSMA-1007 were used in 5, 1, and 1 study, respectively. Only two studies that analyzed DL in the context of DT met the inclusion criteria. Both were performed with a single-institution dataset with only manual labeling of training data. Three studies, each analyzing DL for prostate biopsy, were performed with single- and multi-institutional datasets. TeUS, TRUS, and MRI were used as input modalities in two, three, and one study, respectively. CONCLUSION DL models in prostate cancer diagnosis show promise but are not yet ready for clinical use due to variability in methods, labels, and evaluation criteria. Conducting additional research while acknowledging all the limitations outlined is crucial for reinforcing the utility and effectiveness of DL-based models in clinical settings.
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Affiliation(s)
- Ali Talyshinskii
- Department of Urology and Andrology, Astana Medical University, Astana 010000, Kazakhstan;
| | | | - Prajwal P. Ravinder
- Department of Urology, Kasturba Medical College, Mangaluru, Manipal Academy of Higher Education, Manipal 576104, India;
| | - Nithesh Naik
- Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India;
| | - Princy Randhawa
- Department of Mechatronics, Manipal University Jaipur, Jaipur 303007, India;
| | - Milap Shah
- Department of Urology, Aarogyam Hospital, Ahmedabad 380014, India;
| | - Bhavan Prasad Rai
- Department of Urology, Freeman Hospital, Newcastle upon Tyne NE7 7DN, UK;
| | - Theodoros Tokas
- Department of Urology, Medical School, University General Hospital of Heraklion, University of Crete, 14122 Heraklion, Greece;
| | - Bhaskar K. Somani
- Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India;
- Department of Urology, University Hospital Southampton NHS Trust, Southampton SO16 6YD, UK
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Jeong J, Yeom SK, Choi IY, Cha SH, Han JS, Lee CH, Sung DJ, Choi JW, Bae JH, Choi H, Kim I, Benkert T, Weiland E. Deep learning image reconstruction of diffusion-weighted imaging in evaluation of prostate cancer focusing on its clinical implications. Quant Imaging Med Surg 2024; 14:3432-3446. [PMID: 38720859 PMCID: PMC11074768 DOI: 10.21037/qims-23-1379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Accepted: 03/18/2024] [Indexed: 05/12/2024]
Abstract
Background Image-based assessment of prostate cancer (PCa) is increasingly emphasized in the diagnostic workflow for selecting biopsy targets and possibly predicting clinically significant prostate cancer (csPCa). Assessment is based on Prostate Imaging-Reporting and Data System (PI-RADS) which is largely dependent on T2-weighted image (T2WI) and diffusion weighted image (DWI). This study aims to determine whether deep learning reconstruction (DLR) can improve the image quality of DWI and affect the assessment of PI-RADS ≥4 in patients with PCa. Methods In this retrospective study, 3.0T post-biopsy prostate magnetic resonance imaging (MRI) of 70 patients with PCa in Korea University Ansan Hospital from November 2021 to July 2022 was reconstructed with and without using DLR. Four DWI image sets were made: (I) conventional DWI (CDWI): DWI with acceleration factor 2 and conventional parallel imaging reconstruction, (II) DL1: DWI with acceleration factor 2 using DLR, (III) DL2: DWI with acceleration factor 3 using DLR, and (IV) DL3: DWI with acceleration factor 3 and half average b-value using DLR. Apparent diffusion coefficient (ADC) value, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured by one reviewer, while two reviewers independently assessed overall image quality, noise, and lesion conspicuity using a four-point visual scoring system from each DWI image set. Two reviewers also performed PI-RADSv2.1 scoring on lesions suspected of malignancy. Results A total of 70 patients (mean age, 70.8±9.7 years) were analyzed. The image acquisition time was 4:46 min for CDWI and DL1, 3:40 min for DL2, and 2:00 min for DL3. DL1 and DL2 images resulted in better lesion conspicuity compared to CDWI images assessed by both readers (P<0.05). DLR resulted in a significant increase in SNR, from 38.4±14.7 in CDWI to 56.9±21.0 in DL1. CNR increased from 25.1±11.5 in CDWI to 43.1±17.8 in DL1 (P<0.001). PI-RADS v2.1 scoring for PCa lesions was more agreeable with the DL1 reconstruction method than with CDWI (κ value CDWI, DL1; 0.40, 0.61, respectively). A statistically significant number of lesions were upgraded from PI-RADS <4 in CDWI image to PI-RADS ≥4 in DL1 images for both readers (P<0.05). Most of the PI-RADS upgraded lesions were from higher than unfavorable intermediate-risk groups according to the 2023 National Comprehensive Cancer Network guidelines with statistically significant difference of marginal probability in DL1 and DL2 for both readers (P<0.05). Conclusions DLR in DWI for PCa can provide options for improving image quality with a significant impact on PI-RADS evaluation or about a 23% reduction in acquisition time without compromising image quality.
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Affiliation(s)
- Juhyun Jeong
- Department of Radiology, Korea University Ansan Hospital, Ansan-si, Gyeonggi-do, South Korea
| | - Suk Keu Yeom
- Department of Radiology, Korea University Ansan Hospital, Ansan-si, Gyeonggi-do, South Korea
| | - In Young Choi
- Department of Radiology, Korea University Ansan Hospital, Ansan-si, Gyeonggi-do, South Korea
| | - Sang Hoon Cha
- Department of Radiology, Korea University Ansan Hospital, Ansan-si, Gyeonggi-do, South Korea
| | - Ji Sung Han
- Department of Radiology, Korea University Ansan Hospital, Ansan-si, Gyeonggi-do, South Korea
| | - Chang Hee Lee
- Department of Radiology, Korea University Guro Hospital, Seoul, South Korea
| | - Deuk Jae Sung
- Department of Radiology, Korea University Anam Hospital, Seoul, South Korea
| | - Jung-Woo Choi
- Department of Pathology, Korea University Ansan Hospital, Ansan-si, Gyeonggi-do, South Korea
| | - Jae Hyun Bae
- Department of Urology, Korea University Ansan Hospital, Ansan-si, Gyeonggi-do, South Korea
| | - Hangseok Choi
- Biomedical Research Center, Korea University Ansan Hospital, Ansan-si, Gyeonggi-do, South Korea
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Ensle F, Kaniewska M, Lohezic M, Guggenberger R. Enhanced bone assessment of the shoulder using zero-echo time MRI with deep-learning image reconstruction. Skeletal Radiol 2024:10.1007/s00256-024-04690-8. [PMID: 38658419 DOI: 10.1007/s00256-024-04690-8] [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: 01/22/2024] [Revised: 04/07/2024] [Accepted: 04/18/2024] [Indexed: 04/26/2024]
Abstract
OBJECTIVES To assess a deep learning-based reconstruction algorithm (DLRecon) in zero echo-time (ZTE) MRI of the shoulder at 1.5 Tesla for improved delineation of osseous findings. METHODS In this retrospective study, 63 consecutive exams of 52 patients (28 female) undergoing shoulder MRI at 1.5 Tesla in clinical routine were included. Coronal 3D isotropic radial ZTE pulse sequences were acquired in the standard MR shoulder protocol. In addition to standard-of-care (SOC) image reconstruction, the same raw data was reconstructed with a vendor-supplied prototype DLRecon algorithm. Exams were classified into three subgroups: no pathological findings, degenerative changes, and posttraumatic changes, respectively. Two blinded readers performed bone assessment on a 4-point scale (0-poor, 3-perfect) by qualitatively grading image quality features and delineation of osseous pathologies including diagnostic confidence in the respective subgroups. Quantitatively, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of bone were measured. Qualitative variables were compared using the Wilcoxon signed-rank test for ordinal data and the McNemar test for dichotomous variables; quantitative measures were compared with Student's t-testing. RESULTS DLRecon scored significantly higher than SOC in all visual metrics of image quality (all, p < 0.03), except in the artifact category (p = 0.37). DLRecon also received superior qualitative scores for delineation of osseous pathologies and diagnostic confidence (p ≤ 0.03). Quantitatively, DLRecon achieved superior CNR (95 CI [1.4-3.1]) and SNR (95 CI [15.3-21.5]) of bone than SOC (p < 0.001). CONCLUSION DLRecon enhanced image quality in ZTE MRI and improved delineation of osseous pathologies, allowing for increased diagnostic confidence in bone assessment.
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Affiliation(s)
- Falko Ensle
- Diagnostic and Interventional Radiology, University Hospital Zurich, University Zurich, Zurich, Switzerland.
- University of Zurich (UZH), Raemistrasse 100, CH-8091, Zurich, Switzerland.
| | - Malwina Kaniewska
- Diagnostic and Interventional Radiology, University Hospital Zurich, University Zurich, Zurich, Switzerland
- University of Zurich (UZH), Raemistrasse 100, CH-8091, Zurich, Switzerland
| | | | - Roman Guggenberger
- Diagnostic and Interventional Radiology, University Hospital Zurich, University Zurich, Zurich, Switzerland
- University of Zurich (UZH), Raemistrasse 100, CH-8091, Zurich, Switzerland
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Kiso K, Tsuboyama T, Onishi H, Ogawa K, Nakamoto A, Tatsumi M, Ota T, Fukui H, Yano K, Honda T, Kakemoto S, Koyama Y, Tarewaki H, Tomiyama N. Effect of Deep Learning Reconstruction on Respiratory-triggered T2-weighted MR Imaging of the Liver: A Comparison between the Single-shot Fast Spin-echo and Fast Spin-echo Sequences. Magn Reson Med Sci 2024; 23:214-224. [PMID: 36990740 PMCID: PMC11024712 DOI: 10.2463/mrms.mp.2022-0111] [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: 09/07/2022] [Accepted: 03/07/2023] [Indexed: 03/30/2023] Open
Abstract
PURPOSE To compare the effects of deep learning reconstruction (DLR) on respiratory-triggered T2-weighted MRI of the liver between single-shot fast spin-echo (SSFSE) and fast spin-echo (FSE) sequences. METHODS Respiratory-triggered fat-suppressed liver T2-weighted MRI was obtained with the FSE and SSFSE sequences at the same spatial resolution in 55 patients. Conventional reconstruction (CR) and DLR were applied to each sequence, and the SNR and liver-to-lesion contrast were measured on FSE-CR, FSE-DLR, SSFSE-CR, and SSFSE-DLR images. Image quality was independently assessed by three radiologists. The results of the qualitative and quantitative analyses were compared among the four types of images using repeated-measures analysis of variance or Friedman's test for normally and non-normally distributed data, respectively, and a visual grading characteristics (VGC) analysis was performed to evaluate the image quality improvement by DLR on the FSE and SSFSE sequences. RESULTS The liver SNR was lowest on SSFSE-CR and highest on FSE-DLR and SSFSE-DLR (P < 0.01). The liver-to-lesion contrast did not differ significantly among the four types of images. Qualitatively, noise scores were worst on SSFSE-CR but best on SSFSE-DLR because DLR significantly reduced noise (P < 0.01). In contrast, artifact scores were worst both on FSE-CR and FSE-DLR (P < 0.01) because DLR did not reduce the artifacts. Lesion conspicuity was significantly improved by DLR compared with CR in the SSFSE (P < 0.01) but not in FSE sequences for all readers. Overall image quality was significantly improved by DLR compared with CR for all readers in the SSFSE (P < 0.01) but only one reader in the FSE (P < 0.01). The mean area under the VGC curve values for the FSE-DLR and SSFSE-DLR sequences were 0.65 and 0.94, respectively. CONCLUSION In liver T2-weighted MRI, DLR produced more marked improvements in image quality in SSFSE than in FSE.
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Affiliation(s)
- Kengo Kiso
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Takahiro Tsuboyama
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Hiromitsu Onishi
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Kazuya Ogawa
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Atsushi Nakamoto
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Mitsuaki Tatsumi
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Takashi Ota
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Hideyuki Fukui
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Keigo Yano
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Toru Honda
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Shinji Kakemoto
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Yoshihiro Koyama
- Department of Radiology, Osaka University Hospital, Suita, Osaka, Japan
| | - Hiroyuki Tarewaki
- Department of Radiology, Osaka University Hospital, Suita, Osaka, Japan
| | - Noriyuki Tomiyama
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
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11
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Estler A, Hauser TK, Brunnée M, Zerweck L, Richter V, Knoppik J, Örgel A, Bürkle E, Adib SD, Hengel H, Nikolaou K, Ernemann U, Gohla G. Deep learning-accelerated image reconstruction in back pain-MRI imaging: reduction of acquisition time and improvement of image quality. LA RADIOLOGIA MEDICA 2024; 129:478-487. [PMID: 38349416 PMCID: PMC10943137 DOI: 10.1007/s11547-024-01787-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 01/15/2024] [Indexed: 03/16/2024]
Abstract
INTRODUCTION Low back pain is a global health issue causing disability and missed work days. Commonly used MRI scans including T1-weighted and T2-weighted images provide detailed information of the spine and surrounding tissues. Artificial intelligence showed promise in improving image quality and simultaneously reducing scan time. This study evaluates the performance of deep learning (DL)-based T2 turbo spin-echo (TSE, T2DLR) and T1 TSE (T1DLR) in lumbar spine imaging regarding acquisition time, image quality, artifact resistance, and diagnostic confidence. MATERIAL AND METHODS This retrospective monocentric study included 60 patients with lower back pain who underwent lumbar spinal MRI between February and April 2023. MRI parameters and DL reconstruction (DLR) techniques were utilized to acquire images. Two neuroradiologists independently evaluated image datasets based on various parameters using a 4-point Likert scale. RESULTS Accelerated imaging showed significantly less image noise and artifacts, as well as better image sharpness, compared to standard imaging. Overall image quality and diagnostic confidence were higher in accelerated imaging. Relevant disk herniations and spinal fractures were detected in both DLR and conventional images. Both readers favored accelerated imaging in the majority of examinations. The lumbar spine examination time was cut by 61% in accelerated imaging compared to standard imaging. CONCLUSION In conclusion, the utilization of deep learning-based image reconstruction techniques in lumbar spinal imaging resulted in significant time savings of up to 61% compared to standard imaging, while also improving image quality and diagnostic confidence. These findings highlight the potential of these techniques to enhance efficiency and accuracy in clinical practice for patients with lower back pain.
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Affiliation(s)
- Arne Estler
- Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Tuebingen, Hoppe-Seyler-Str. 3, 72076, Tübingen, Baden-Württemberg, Germany
| | - Till-Karsten Hauser
- Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Tuebingen, Hoppe-Seyler-Str. 3, 72076, Tübingen, Baden-Württemberg, Germany
| | - Merle Brunnée
- Department of Neuroradiology, Neurological University Clinic, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
| | - Leonie Zerweck
- Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Tuebingen, Hoppe-Seyler-Str. 3, 72076, Tübingen, Baden-Württemberg, Germany.
| | - Vivien Richter
- Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Tuebingen, Hoppe-Seyler-Str. 3, 72076, Tübingen, Baden-Württemberg, Germany
| | - Jessica Knoppik
- Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Tuebingen, Hoppe-Seyler-Str. 3, 72076, Tübingen, Baden-Württemberg, Germany
| | - Anja Örgel
- Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Tuebingen, Hoppe-Seyler-Str. 3, 72076, Tübingen, Baden-Württemberg, Germany
| | - Eva Bürkle
- Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Tuebingen, Hoppe-Seyler-Str. 3, 72076, Tübingen, Baden-Württemberg, Germany
| | - Sasan Darius Adib
- Department of Neurosurgery, University of Tübingen, 72076, Tübingen, Germany
| | - Holger Hengel
- Department of Neurology and Hertie-Institute for Clinical Brain Research, University of Tübingen, 72076, Tübingen, Germany
| | - Konstantin Nikolaou
- Department of Diagnostic and Interventional Radiology, University of Tuebingen, Hoppe-Seyler-Str. 3, 72076, Tuebingen, Germany
| | - Ulrike Ernemann
- Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Tuebingen, Hoppe-Seyler-Str. 3, 72076, Tübingen, Baden-Württemberg, Germany
| | - Georg Gohla
- Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Tuebingen, Hoppe-Seyler-Str. 3, 72076, Tübingen, Baden-Württemberg, Germany
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12
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Estler A, Zerweck L, Brunnée M, Estler B, Richter V, Örgel A, Bürkle E, Becker H, Hurth H, Stahl S, Konrad EM, Kelbsch C, Ernemann U, Hauser TK, Gohla G. Deep learning-accelerated image reconstruction in MRI of the orbit to shorten acquisition time and enhance image quality. J Neuroimaging 2024; 34:232-240. [PMID: 38195858 DOI: 10.1111/jon.13187] [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: 10/18/2023] [Revised: 12/02/2023] [Accepted: 12/22/2023] [Indexed: 01/11/2024] Open
Abstract
BACKGROUND AND PURPOSE This study explores the use of deep learning (DL) techniques in MRI of the orbit to enhance imaging. Standard protocols, although detailed, have lengthy acquisition times. We investigate DL-based methods for T2-weighted and T1-weighted, fat-saturated, contrast-enhanced turbo spin echo (TSE) sequences, aiming to improve image quality, reduce acquisition time, minimize artifacts, and enhance diagnostic confidence in orbital imaging. METHODS In a 3-Tesla MRI study of 50 patients evaluating orbital diseases from March to July 2023, conventional (TSES ) and DL TSE sequences (TSEDL ) were used. Two neuroradiologists independently assessed the image datasets for image quality, diagnostic confidence, noise levels, artifacts, and image sharpness using a randomized and blinded 4-point Likert scale. RESULTS TSEDL significantly reduced image noise and artifacts, enhanced image sharpness, and decreased scan time, outperforming TSES (p < .05). TSEDL showed superior overall image quality and diagnostic confidence, with relevant findings effectively detected in both DL-based and conventional images. In 94% of cases, readers preferred accelerated imaging. CONCLUSION The study proved that using DL for MRI image reconstruction in orbital scans significantly cut acquisition time by 69%. This approach also enhanced image quality, reduced image noise, sharpened images, and boosted diagnostic confidence.
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Affiliation(s)
- Arne Estler
- Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Tuebingen, Tübingen, Germany
| | - Leonie Zerweck
- Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Tuebingen, Tübingen, Germany
| | - Merle Brunnée
- Department of Neuroradiology, Neurological University Clinic, Heidelberg University Hospital, Heidelberg, Germany
| | - Bent Estler
- Department of Cardiology, Angiology, and Pneumology, Heidelberg University Hospital, Heidelberg, Germany
| | - Vivien Richter
- Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Tuebingen, Tübingen, Germany
| | - Anja Örgel
- Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Tuebingen, Tübingen, Germany
| | - Eva Bürkle
- Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Tuebingen, Tübingen, Germany
| | - Hannes Becker
- Department of Neurosurgery, University of Tübingen, Tübingen, Germany
| | - Helene Hurth
- Department of Neurosurgery, University of Tübingen, Tübingen, Germany
| | | | - Eva-Maria Konrad
- Center for Ophthalmology, University Eye Hospital, University of Tübingen, Tübingen, Germany
| | - Carina Kelbsch
- Center for Ophthalmology, University Eye Hospital, University of Tübingen, Tübingen, Germany
| | - Ulrike Ernemann
- Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Tuebingen, Tübingen, Germany
| | - Till-Karsten Hauser
- Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Tuebingen, Tübingen, Germany
| | - Georg Gohla
- Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Tuebingen, Tübingen, Germany
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Lee Y, Yoon S, Park SH, Nickel MD. Advanced Abdominal MRI Techniques and Problem-Solving Strategies. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2024; 85:345-362. [PMID: 38617869 PMCID: PMC11009130 DOI: 10.3348/jksr.2023.0067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 10/04/2023] [Accepted: 10/14/2023] [Indexed: 04/16/2024]
Abstract
MRI plays an important role in abdominal imaging because of its ability to detect and characterize focal lesions. However, MRI examinations have several challenges, such as comparatively long scan times and motion management through breath-holding maneuvers. Techniques for reducing scan time with acceptable image quality, such as parallel imaging, compressed sensing, and cutting-edge deep learning techniques, have been developed to enable problem-solving strategies. Additionally, free-breathing techniques for dynamic contrast-enhanced imaging, such as extra-dimensional-volumetric interpolated breath-hold examination, golden-angle radial sparse parallel, and liver acceleration volume acquisition Star, can help patients with severe dyspnea or those under sedation to undergo abdominal MRI. We aimed to present various advanced abdominal MRI techniques for reducing the scan time while maintaining image quality and free-breathing techniques for dynamic imaging and illustrate cases using the techniques mentioned above. A review of these advanced techniques can assist in the appropriate interpretation of sequences.
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14
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Tian Y, Nayak KS. New clinical opportunities of low-field MRI: heart, lung, body, and musculoskeletal. MAGMA (NEW YORK, N.Y.) 2024; 37:1-14. [PMID: 37902898 PMCID: PMC10876830 DOI: 10.1007/s10334-023-01123-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 09/28/2023] [Accepted: 10/05/2023] [Indexed: 11/01/2023]
Abstract
Contemporary whole-body low-field MRI scanners (< 1 T) present new and exciting opportunities for improved body imaging. The fundamental reason is that the reduced off-resonance and reduced SAR provide substantially increased flexibility in the design of MRI pulse sequences. Promising body applications include lung parenchyma imaging, imaging adjacent to metallic implants, cardiac imaging, and dynamic imaging in general. The lower cost of such systems may make MRI favorable for screening high-risk populations and population health research, and the more open configurations allowed may prove favorable for obese subjects and for pregnant women. This article summarizes promising body applications for contemporary whole-body low-field MRI systems, with a focus on new platforms developed within the past 5 years. This is an active area of research, and one can expect many improvements as MRI physicists fully explore the landscape of pulse sequences that are feasible, and as clinicians apply these to patient populations.
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Affiliation(s)
- Ye Tian
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, 3740 McClintock Ave, EEB 406, Los Angeles, CA, 90089-2564, USA.
| | - Krishna S Nayak
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, 3740 McClintock Ave, EEB 406, Los Angeles, CA, 90089-2564, USA
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15
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Kim M, Kim SH, Hong S, Kim YJ, Kim HR, Kim JY. Evaluation of Extra-Prostatic Extension on Deep Learning-Reconstructed High-Resolution Thin-Slice T2-Weighted Images in Patients with Prostate Cancer. Cancers (Basel) 2024; 16:413. [PMID: 38254901 PMCID: PMC10814256 DOI: 10.3390/cancers16020413] [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: 12/15/2023] [Revised: 01/12/2024] [Accepted: 01/16/2024] [Indexed: 01/24/2024] Open
Abstract
The aim of this study was to compare diagnostic performance for extra-prostatic extension (EPE) and image quality among three image datasets: conventional T2-weighted images (T2WIconv, slice thickness, 3 mm) and high-resolution thin-slice T2WI (T2WIHR, 2 mm), with and without deep learning reconstruction (DLR) in patients with prostatic cancer (PCa). A total of 88 consecutive patients (28 EPE-positive and 60 negative) diagnosed with PCa via radical prostatectomy who had undergone 3T-MRI were included. Two independent reviewers performed a crossover review in three sessions, in which each reviewer recorded five-point confidence scores for the presence of EPE and image quality using a five-point Likert scale. Pathologic topographic maps served as the reference standard. For both reviewers, T2WIconv showed better diagnostic performance than T2WIHR with and without DLR (AUCs, in order, for reviewer 1, 0.883, 0.806, and 0.772, p = 0.0006; for reviewer 2, 0.803, 0.762, and 0.745, p = 0.022). The image quality was also the best in T2WIconv, followed by T2WIHR with DLR and T2WIHR without DLR for both reviewers (median, in order, 3, 4, and 5, p < 0.0001). In conclusion, T2WIconv was optimal in regard to image quality and diagnostic performance for the evaluation of EPE in patients with PCa.
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Affiliation(s)
- Mingyu Kim
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan 48108, Republic of Korea
| | - Seung Ho Kim
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan 48108, Republic of Korea
| | - Sujin Hong
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan 48108, Republic of Korea
| | - Yeon Jung Kim
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan 48108, Republic of Korea
| | - Hye Ri Kim
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan 48108, Republic of Korea
| | - Joo Yeon Kim
- Department of Pathology, Haeundae Paik Hospital, Inje University College of Medicine, Busan 48108, Republic of Korea
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16
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Estler A, Hauser TK, Mengel A, Brunnée M, Zerweck L, Richter V, Zuena M, Schuhholz M, Ernemann U, Gohla G. Deep Learning Accelerated Image Reconstruction of Fluid-Attenuated Inversion Recovery Sequence in Brain Imaging: Reduction of Acquisition Time and Improvement of Image Quality. Acad Radiol 2024; 31:180-186. [PMID: 37280126 DOI: 10.1016/j.acra.2023.05.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 05/08/2023] [Accepted: 05/08/2023] [Indexed: 06/08/2023]
Abstract
RATIONALE AND OBJECTIVES Fluid-attenuated inversion recovery (FLAIR) imaging is playing an increasingly significant role in the detection of brain metastases with a concomitant increase in the number of magnetic resonance imaging (MRI) examinations. Therefore, the purpose of this study was to investigate the impact on image quality and diagnostic confidence of an innovative deep learning-based accelerated FLAIR (FLAIRDLR) sequence of the brain compared to conventional (standard) FLAIR (FLAIRS) imaging. MATERIALS AND METHODS Seventy consecutive patients with staging cerebral MRIs were retrospectively enrolled in this single-center study. The FLAIRDLR was conducted using the same MRI acquisition parameters as the FLAIRS sequence, except for a higher acceleration factor for parallel imaging (from 2 to 4), which resulted in a shorter acquisition time of 1:39 minute instead of 2:40 minutes (-38%). Two specialized neuroradiologists evaluated the imaging datasets using a Likert scale that ranged from 1 to 4, with 4 indicating the best score for the following parameters: sharpness, lesion demarcation, artifacts, overall image quality, and diagnostic confidence. Additionally, the image preference of the readers and the interreader agreement were assessed. RESULTS The average age of the patients was 63 ± 11years. FLAIRDLR exhibited significantly less image noise than FLAIRS, with P-values of< .001 and< .05, respectively. The sharpness of the images and the ability to detect lesions were rated higher in FLAIRDLR, with a median score of 4 compared to a median score of 3 in FLAIRS (P-values of<.001 for both readers). In terms of overall image quality, FLAIRDLR was rated superior to FLAIRS, with a median score of 4 vs 3 (P-values of<.001 for both readers). Both readers preferred FLAIRDLR in 68/70 cases. CONCLUSION The feasibility of deep learning FLAIR brain imaging was shown with additional 38% reduction in examination time compared to standard FLAIR imaging. Furthermore, this technique has shown improvement in image quality, noise reduction, and lesion demarcation.
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Affiliation(s)
- Arne Estler
- Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Tuebingen, Hoppe-Seyler-Str. 3, 72076 Tübingen, Baden-Württemberg, Germany (A.E., T.-K.H., L.Z., V.R., M.Z., U.E., G.G.).
| | - Till-Karsten Hauser
- Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Tuebingen, Hoppe-Seyler-Str. 3, 72076 Tübingen, Baden-Württemberg, Germany (A.E., T.-K.H., L.Z., V.R., M.Z., U.E., G.G.)
| | - Annerose Mengel
- Department of Neurology & Stroke, Eberhard-Karls University of Tübingen, Tuebingen, Germany (A.M.)
| | - Merle Brunnée
- Department of Neuroradiology, Neurological University Clinic, Heidelberg University Hospital, Heidelberg, Germany (M.B.)
| | - Leonie Zerweck
- Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Tuebingen, Hoppe-Seyler-Str. 3, 72076 Tübingen, Baden-Württemberg, Germany (A.E., T.-K.H., L.Z., V.R., M.Z., U.E., G.G.)
| | - Vivien Richter
- Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Tuebingen, Hoppe-Seyler-Str. 3, 72076 Tübingen, Baden-Württemberg, Germany (A.E., T.-K.H., L.Z., V.R., M.Z., U.E., G.G.)
| | - Mario Zuena
- Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Tuebingen, Hoppe-Seyler-Str. 3, 72076 Tübingen, Baden-Württemberg, Germany (A.E., T.-K.H., L.Z., V.R., M.Z., U.E., G.G.)
| | - Martin Schuhholz
- Faculty of Medicine, University of Tuebingen, Tübingen, Germany (M.S.)
| | - Ulrike Ernemann
- Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Tuebingen, Hoppe-Seyler-Str. 3, 72076 Tübingen, Baden-Württemberg, Germany (A.E., T.-K.H., L.Z., V.R., M.Z., U.E., G.G.)
| | - Georg Gohla
- Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Tuebingen, Hoppe-Seyler-Str. 3, 72076 Tübingen, Baden-Württemberg, Germany (A.E., T.-K.H., L.Z., V.R., M.Z., U.E., G.G.)
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Barrett T, Lee KL, de Rooij M, Giganti F. Update on Optimization of Prostate MR Imaging Technique and Image Quality. Radiol Clin North Am 2024; 62:1-15. [PMID: 37973236 DOI: 10.1016/j.rcl.2023.06.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Abstract
Prostate MR imaging quality has improved dramatically over recent times, driven by advances in hardware, software, and improved functional imaging techniques. MRI now plays a key role in prostate cancer diagnostic work-up, but outcomes of the MRI-directed pathway are heavily dependent on image quality and optimization. MR sequences can be affected by patient-related degradations relating to motion and susceptibility artifacts which may enable only partial mitigation. In this Review, we explore issues relating to prostate MRI acquisition and interpretation, mitigation strategies at a patient and scanner level, PI-QUAL reporting, and future directions in image quality, including artificial intelligence solutions.
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Affiliation(s)
- Tristan Barrett
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge, Cambridge, UK.
| | - Kang-Lung Lee
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge, Cambridge, UK; Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Maarten de Rooij
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, Netherlands
| | - Francesco Giganti
- Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK; Division of Surgery and Interventional Science, University College London, London, UK
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18
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Shetty AS, Ludwig DR, Ippolito JE, Andrews TJ, Narra VR, Fraum TJ. Low-Field-Strength Body MRI: Challenges and Opportunities at 0.55 T. Radiographics 2023; 43:e230073. [PMID: 37917537 DOI: 10.1148/rg.230073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2023]
Abstract
Advances in MRI technology have led to the development of low-field-strength (hereafter, "low-field") (0.55 T) MRI systems with lower weight, fewer shielding requirements, and lower cost than those of traditional (1.5-3 T) systems. The trade-offs of lower signal-to-noise ratio (SNR) at 0.55 T are partially offset by patient safety and potential comfort advantages (eg, lower specific absorption rate and a more cost-effective larger bore diameter) and physical advantages (eg, decreased T2* decay, shorter T1 relaxation times). Image reconstruction advances leveraging developing technologies (such as deep learning-based denoising) can be paired with traditional techniques (such as increasing the number of signal averages) to improve SNR. The overall image quality produced by low-field MRI systems, although perhaps somewhat inferior to 1.5-3 T MRI systems in terms of SNR, is nevertheless diagnostic for a broad variety of body imaging applications. Effective low-field body MRI requires (a) an understanding of the trade-offs resulting from lower field strengths, (b) an approach to modifying routine sequences to overcome SNR challenges, and (c) a workflow for carefully selecting appropriate patients. The authors describe the rationale, opportunities, and challenges of low-field body MRI; discuss important considerations for low-field imaging with common body MRI sequences; and delineate a variety of use cases for low-field body MRI. The authors also include lessons learned from their preliminary experience with a new low-field MRI system at a tertiary care center. Finally, they explore the future of low-field MRI, summarizing current limitations and potential future developments that may enhance the clinical adoption of this technology. ©RSNA, 2023 Supplemental material is available for this article. Quiz questions for this article are available through the Online Learning Center. See the invited commentary by Venkatesh in this issue.
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Affiliation(s)
- Anup S Shetty
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St. Louis, MO 63110
| | - Daniel R Ludwig
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St. Louis, MO 63110
| | - Joseph E Ippolito
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St. Louis, MO 63110
| | - Trevor J Andrews
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St. Louis, MO 63110
| | - Vamsi R Narra
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St. Louis, MO 63110
| | - Tyler J Fraum
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St. Louis, MO 63110
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Taouli B, Ba-Ssalamah A, Chapiro J, Chhatwal J, Fowler K, Kang TW, Knobloch G, Koh DM, Kudo M, Lee JM, Murakami T, Pinato DJ, Ringe KI, Song B, Tabrizian P, Wang J, Yoon JH, Zeng M, Zhou J, Vilgrain V. Consensus report from the 10th Global Forum for Liver Magnetic Resonance Imaging: developments in HCC management. Eur Radiol 2023; 33:9152-9166. [PMID: 37500964 PMCID: PMC10730664 DOI: 10.1007/s00330-023-09928-y] [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: 01/03/2023] [Revised: 05/15/2023] [Accepted: 05/23/2023] [Indexed: 07/29/2023]
Abstract
The 10th Global Forum for Liver Magnetic Resonance Imaging (MRI) was held as a virtual 2-day meeting in October 2021, attended by delegates from North and South America, Asia, Australia, and Europe. Most delegates were radiologists with experience in liver MRI, with representation also from specialists in liver surgery, oncology, and hepatology. Presentations, discussions, and working groups at the Forum focused on the following themes: • Gadoxetic acid in clinical practice: Eastern and Western perspectives on current uses and challenges in hepatocellular carcinoma (HCC) screening/surveillance, diagnosis, and management • Economics and outcomes of HCC imaging • Radiomics, artificial intelligence (AI) and deep learning (DL) applications of MRI in HCC. These themes are the subject of the current manuscript. A second manuscript discusses multidisciplinary tumor board perspectives: how to approach early-, mid-, and late-stage HCC management from the perspectives of a liver surgeon, interventional radiologist, and oncologist (Taouli et al, 2023). Delegates voted on consensus statements that were developed by working groups on these meeting themes. A consensus was considered to be reached if at least 80% of the voting delegates agreed on the statements. CLINICAL RELEVANCE STATEMENT: This review highlights the clinical applications of gadoxetic acid-enhanced MRI for liver cancer screening and diagnosis, as well as its cost-effectiveness and the applications of radiomics and AI in patients with liver cancer. KEY POINTS: • Interpretation of gadoxetic acid-enhanced MRI differs slightly between Eastern and Western guidelines, reflecting different regional requirements for sensitivity vs specificity. • Emerging data are encouraging for the cost-effectiveness of gadoxetic acid-enhanced MRI in HCC screening and diagnosis, but more studies are required. • Radiomics and artificial intelligence are likely, in the future, to contribute to the detection, staging, assessment of treatment response and prediction of prognosis of HCC-reducing the burden on radiologists and other specialists and supporting timely and targeted treatment for patients.
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Affiliation(s)
- Bachir Taouli
- Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Ahmed Ba-Ssalamah
- Department of Biomedical Imaging and Image-guided therapy, Medical University of Vienna, Vienna, Austria
| | - Julius Chapiro
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Jagpreet Chhatwal
- Department of Radiology, Institute for Technology Assessment, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Kathryn Fowler
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Tae Wook Kang
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Gesine Knobloch
- Global Medical and Clinical Affairs and Digital Development, Radiology, Bayer Pharmaceuticals, Berlin, Germany
| | - Dow-Mu Koh
- Department of Diagnostic Radiology, Royal Marsden Hospital, Sutton, UK
| | - Masatoshi Kudo
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka, Japan
| | - Jeong Min Lee
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Seoul, South Korea
| | - Takamichi Murakami
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - David J Pinato
- Department of Surgery & Cancer, Imperial College London, Hammersmith Hospital, London, UK
- Division of Oncology, Department of Translational Medicine, University of Piemonte Orientale, Novara, Italy
| | - Kristina I Ringe
- Department of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Parissa Tabrizian
- Recanati/Miller Transplantation Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jin Wang
- Department of Radiology, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China
- Liver Disease Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Jeong Hee Yoon
- Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Seoul, South Korea
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Jian Zhou
- Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Valérie Vilgrain
- Université Paris Cité and Department of Radiology, Assistance-Publique Hôpitaux de Paris, APHP Nord, Hôpital Beaujon, Clichy, France
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20
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Abel F, Fiore J, Belanger M, Sneag DB, Lebl DR, Tan ET. Lumbar dorsal root ganglion displacement between supine and prone positions evaluated with 3D MRI. Magn Reson Imaging 2023; 104:29-38. [PMID: 37769881 DOI: 10.1016/j.mri.2023.09.006] [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: 01/16/2023] [Revised: 09/18/2023] [Accepted: 09/25/2023] [Indexed: 10/02/2023]
Abstract
OBJECTIVE Pre-operative lumbar spine MRI is usually acquired with the patient supine, whereas lumbar spine surgery is most commonly performed prone. For MRI to be used reliably and safely for intra-operative navigation for foraminal and extraforaminal decompression, the magnitude of dorsal root ganglion (DRG) displacement between supine and prone positions needs to be understood. METHODS A prospective study of a degenerative lumbar spine cohort of 18 subjects indicated for lumbar spine surgery. Three-dimensional T2-weighted fast spin echo and T1-weighted spoiled gradient echo sequences were acquired at 3 T. Displacement and cross-sectional area (CSA) of the bilateral DRGs at 5 motion levels (L1-2 to L5-S1) were determined via 3D segmentation by 2 independent evaluators. Wilcoxon rank-sum tests without correction for multiple comparison were performed against hypothesized 1-mm absolute displacement and corresponding 24% CSA change. RESULTS DRG mean absolute displacement was <1 mm (p > 0.99, mean = 0.707 mm, 95% confidence interval (CI) = 0.659 to 0.755 mm), with the largest directional displacement in the dorsal-to-ventral direction from supine to prone (mean = 0.141 mm, 95% CI = 0.082 to 0.200 mm). Directional displacements caudal-to-cephalad were 0.087 mm (95% CI = 0.022 to 0.151 mm), and left-right were -0.030 mm (95%CI = -0.059 to -0.001 mm). Mean CSA change was within 24% (p > 0.99, mean = -8.30%, 95% CI = -10.5 to -6.09%). Mean absolute displacement was largest for the L1 (mean = 0.811 mm) and L2 (mean = 0.829 mm) DRGs. CONCLUSIONS Minimal, non-statistically significant soft tissue displacement and morphological area differences were demonstrated between supine and prone positions during 3D lumbar spine MRI.
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Affiliation(s)
- Frederik Abel
- Department of Radiology and Imaging, Hospital for Special Surgery, New York, NY, USA; Department of Spine Surgery, Hospital for Special Surgery, New York, NY, USA
| | - Jake Fiore
- Department of Radiology and Imaging, Hospital for Special Surgery, New York, NY, USA
| | - Marianne Belanger
- Department of Radiology and Imaging, Hospital for Special Surgery, New York, NY, USA
| | - Darryl B Sneag
- Department of Radiology and Imaging, Hospital for Special Surgery, New York, NY, USA
| | - Darren R Lebl
- Department of Spine Surgery, Hospital for Special Surgery, New York, NY, USA
| | - Ek T Tan
- Department of Radiology and Imaging, Hospital for Special Surgery, New York, NY, USA.
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21
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Lee EJ, Hwang J, Park S, Bae SH, Lim J, Chang YW, Hong SS, Oh E, Nam BD, Jeong J, Sung JK, Nickel D. Utility of accelerated T2-weighted turbo spin-echo imaging with deep learning reconstruction in female pelvic MRI: a multi-reader study. Eur Radiol 2023; 33:7697-7706. [PMID: 37314472 DOI: 10.1007/s00330-023-09781-z] [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: 10/28/2022] [Revised: 03/24/2023] [Accepted: 03/28/2023] [Indexed: 06/15/2023]
Abstract
OBJECTIVES To determine the clinical feasibility of T2-weighted turbo spin-echo (T2-TSE) imaging with deep learning reconstruction (DLR) in female pelvic MRI compared with conventional T2 TSE in terms of image quality and scan time. METHODS Between May 2021 and September 2021, 52 women (mean age, 44 years ± 12) who underwent 3-T pelvic MRI with additional T2-TSE using a DLR algorithm were included in this single-center prospective study with patient's informed consents. Conventional, DLR, and DLR T2-TSE images with reduced scan times were independently assessed and compared by four radiologists. The overall image quality, differentiation of anatomic details, lesion conspicuity, and artifacts were evaluated using a 5-point scale. Inter-observer agreement of the qualitative scores was compared and reader protocol preferences were then evaluated. RESULTS In the qualitative analysis of all readers, fast DLR T2-TSE showed significantly better overall image quality, differentiation of anatomic regions, lesion conspicuity, and lesser artifacts than conventional T2-TSE and DLR T2-TSE, despite approximately 50% reduction in scan time (all p < 0.05). The inter-reader agreement for the qualitative analysis was moderate to good. All readers preferred DLR over conventional T2-TSE regardless of scan time and preferred fast DLR T2-TSE (57.7-78.8%), except for one who preferred DLR over fast DLR T2-TSE (53.8% vs. 46.1%). CONCLUSION In female pelvic MRI, image quality and accelerated image acquisition for T2-TSE can be significantly improved by using DLR compared to conventional T2-TSE. Fast DLR T2-TSE was non-inferior to DLR T2-TSE in terms of reader preference and image quality. CLINICAL RELEVANCE STATEMENT DLR of T2-TSE in female pelvic MRI enables fast imaging along with maintaining optimal image quality compared with parallel imaging-based conventional T2-TSE. KEY POINTS • Conventional T2 turbo spin-echo based on parallel imaging has limitations for accelerated image acquisition while maintaining good image quality. • Deep learning image reconstruction showed better image quality in both images obtained using the same or accelerated image acquisition parameters compared with conventional T2 turbo spin-echo in female pelvic MRI. • Deep learning image reconstruction enables accelerated image acquisition while maintaining good image quality in the T2-TSE of female pelvic MRI.
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Affiliation(s)
- Eun Ji Lee
- Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, 59 Daesakwan-Ro, Yongsan-Ku, Seoul, 04401, Korea
| | - Jiyoung Hwang
- Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, 59 Daesakwan-Ro, Yongsan-Ku, Seoul, 04401, Korea.
| | - Suyeon Park
- Department of Biostatistics, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea
| | - Sung Hwan Bae
- Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, 59 Daesakwan-Ro, Yongsan-Ku, Seoul, 04401, Korea
| | - Jiyun Lim
- Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, 59 Daesakwan-Ro, Yongsan-Ku, Seoul, 04401, Korea
| | - Yun-Woo Chang
- Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, 59 Daesakwan-Ro, Yongsan-Ku, Seoul, 04401, Korea
| | - Seong Sook Hong
- Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, 59 Daesakwan-Ro, Yongsan-Ku, Seoul, 04401, Korea
| | - Eunsun Oh
- Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, 59 Daesakwan-Ro, Yongsan-Ku, Seoul, 04401, Korea
| | - Bo Da Nam
- Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, 59 Daesakwan-Ro, Yongsan-Ku, Seoul, 04401, Korea
| | - Jewon Jeong
- Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, 59 Daesakwan-Ro, Yongsan-Ku, Seoul, 04401, Korea
| | | | - Dominik Nickel
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
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22
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Shen X, Luo J, Tang X, Chen B, Qin Y, Zhou Y, Xiao J. Deep Learning Approach for Diagnosing Early Osteonecrosis of the Femoral Head Based on Magnetic Resonance Imaging. J Arthroplasty 2023; 38:2044-2050. [PMID: 36243276 DOI: 10.1016/j.arth.2022.10.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 09/28/2022] [Accepted: 10/03/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND The diagnosis of early osteonecrosis of the femoral head (ONFH) based on magnetic resonance imaging (MRI) is challenging due to variability in the surgeon's experience level. This study developed an MRI-based deep learning system to detect early ONFH and evaluated its feasibility in the clinic. METHODS We retrospectively evaluated clinical MRIs of the hips that were performed in our institution from January 2019 to June 2022 and collected all MRIs diagnosed with early ONFH. An advanced convolutional neural network (CNN) was trained and optimized; then, the diagnostic performance of the CNN was evaluated according to its accuracy, sensitivity, and specificity. We also further compared the CNN's performance with that of orthopaedic surgeons. RESULTS Overall, 11,061 images were retrospectively included in the present study and were divided into three datasets with ratio 7:2:1. The area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity of the CNN model for identifying early ONFH were 0.98, 98.4, 97.6, and 98.6%, respectively. In our review panel, the averaged accuracy, sensitivity, and specificity for identifying ONFH were 91.7, 87.0, and 94.1% for attending orthopaedic surgeons; 87.1, 84.0, and 89.3% for resident orthopaedic surgeons; and 97.1, 96.0, and 97.9% for deputy chief orthopaedic surgeons, respectively. CONCLUSION The deep learning system showed a comparable performance to that of deputy chief orthopaedic surgeons in identifying early ONFH. The success of deep learning diagnosis of ONFH might be conducive to assisting less-experienced surgeons, especially in large-scale medical imaging screening and community scenarios lacking consulting experts.
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Affiliation(s)
- Xianyue Shen
- Department of Orthopedics, The Second Hospital of Jilin University
| | - Jia Luo
- College of software, Jilin University
| | - Xiongfeng Tang
- Department of Orthopedics, The Second Hospital of Jilin University
| | - Bo Chen
- Department of Orthopedics, The Second Hospital of Jilin University
| | - Yanguo Qin
- Department of Orthopedics, The Second Hospital of Jilin University
| | - You Zhou
- College of software, Jilin University
| | - Jianlin Xiao
- Department of Orthopedics, China-Japan Union Hospital of Jilin University, Changchun, Jilin province, PR China
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23
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Zbinden L, Catucci D, Suter Y, Hulbert L, Berzigotti A, Brönnimann M, Ebner L, Christe A, Obmann VC, Sznitman R, Huber AT. Automated liver segmental volume ratio quantification on non-contrast T1-Vibe Dixon liver MRI using deep learning. Eur J Radiol 2023; 167:111047. [PMID: 37690351 DOI: 10.1016/j.ejrad.2023.111047] [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: 04/04/2023] [Revised: 07/29/2023] [Accepted: 08/13/2023] [Indexed: 09/12/2023]
Abstract
PURPOSE To evaluate the effectiveness of automated liver segmental volume quantification and calculation of the liver segmental volume ratio (LSVR) on a non-contrast T1-vibe Dixon liver MRI sequence using a deep learning segmentation pipeline. METHOD A dataset of 200 liver MRI with a non-contrast 3 mm T1-vibe Dixon sequence was manually labeledslice-by-sliceby an expert for Couinaud liver segments, while portal and hepatic veins were labeled separately. A convolutional neural networkwas trainedusing 170 liver MRI for training and 30 for evaluation. Liver segmental volumes without liver vessels were retrieved and LSVR was calculated as the liver segmental volumes I-III divided by the liver segmental volumes IV-VIII. LSVR was compared with the expert manual LSVR calculation and the LSVR calculated on CT scans in 30 patients with CT and MRI within 6 months. RESULTS Theconvolutional neural networkclassified the Couinaud segments I-VIII with an average Dice score of 0.770 ± 0.03, ranging between 0.726 ± 0.13 (segment IVb) and 0.810 ± 0.09 (segment V). The calculated mean LSVR with liver MRI unseen by the model was 0.32 ± 0.14, as compared with manually quantified LSVR of 0.33 ± 0.15, resulting in a mean absolute error (MAE) of 0.02. A comparable LSVR of 0.35 ± 0.14 with a MAE of 0.04 resulted with the LSRV retrieved from the CT scans. The automated LSVR showed significant correlation with the manual MRI LSVR (Spearman r = 0.97, p < 0.001) and CT LSVR (Spearman r = 0.95, p < 0.001). CONCLUSIONS A convolutional neural network allowed for accurate automated liver segmental volume quantification and calculation of LSVR based on a non-contrast T1-vibe Dixon sequence.
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Affiliation(s)
- Lukas Zbinden
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland; Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Damiano Catucci
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, Bern, Switzerland; Graduate School for Health Sciences, University of Bern, Switzerland
| | - Yannick Suter
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Leona Hulbert
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Annalisa Berzigotti
- Hepatology, Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Michael Brönnimann
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Lukas Ebner
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Andreas Christe
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Verena Carola Obmann
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Raphael Sznitman
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Adrian Thomas Huber
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, Bern, Switzerland.
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Gudmundson AT, Davies-Jenkins CW, Özdemir İ, Murali-Manohar S, Zöllner HJ, Song Y, Hupfeld KE, Schnitzler A, Oeltzschner G, Stark CEL, Edden RAE. Application of a 1H Brain MRS Benchmark Dataset to Deep Learning for Out-of-Voxel Artifacts. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.08.539813. [PMID: 37215030 PMCID: PMC10197548 DOI: 10.1101/2023.05.08.539813] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Neural networks are potentially valuable for many of the challenges associated with MRS data. The purpose of this manuscript is to describe the AGNOSTIC dataset, which contains 259,200 synthetic 1H MRS examples for training and testing neural networks. AGNOSTIC was created using 270 basis sets that were simulated across 18 field strengths and 15 echo times. The synthetic examples were produced to resemble in vivo brain data with combinations of metabolite, macromolecule, residual water signals, and noise. To demonstrate the utility, we apply AGNOSTIC to train two Convolutional Neural Networks (CNNs) to address out-of-voxel (OOV) echoes. A Detection Network was trained to identify the point-wise presence of OOV echoes, providing proof of concept for real-time detection. A Prediction Network was trained to reconstruct OOV echoes, allowing subtraction during post-processing. Complex OOV signals were mixed into 85% of synthetic examples to train two separate CNNs for the detection and prediction of OOV signals. AGNOSTIC is available through Dryad and all Python 3 code is available through GitHub. The Detection network was shown to perform well, identifying 95% of OOV echoes. Traditional modeling of these detected OOV signals was evaluated and may prove to be an effective method during linear-combination modeling. The Prediction Network greatly reduces OOV echoes within FIDs and achieved a median log10 normed-MSE of -1.79, an improvement of almost two orders of magnitude.
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Affiliation(s)
- Aaron T. Gudmundson
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Christopher W. Davies-Jenkins
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - İpek Özdemir
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Saipavitra Murali-Manohar
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Helge J. Zöllner
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Yulu Song
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Kathleen E. Hupfeld
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Alfons Schnitzler
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
| | - Craig E. L. Stark
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA
| | - Richard A. E. Edden
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins School of Medicine
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute
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Herrmann J, Afat S, Gassenmaier S, Grunz JP, Koerzdoerfer G, Lingg A, Almansour H, Nickel D, Patzer TS, Werner S. Faster Elbow MRI with Deep Learning Reconstruction-Assessment of Image Quality, Diagnostic Confidence, and Anatomy Visualization Compared to Standard Imaging. Diagnostics (Basel) 2023; 13:2747. [PMID: 37685285 PMCID: PMC10486923 DOI: 10.3390/diagnostics13172747] [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: 07/04/2023] [Revised: 08/21/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023] Open
Abstract
OBJECTIVE The objective of this study was to evaluate a deep learning (DL) reconstruction for turbo spin echo (TSE) sequences of the elbow regarding image quality and visualization of anatomy. MATERIALS AND METHODS Between October 2020 and June 2021, seventeen participants (eight patients, nine healthy subjects; mean age: 43 ± 16 (20-70) years, eight men) were prospectively included in this study. Each patient underwent two examinations: standard MRI, including TSE sequences reconstructed with a generalized autocalibrating partial parallel acquisition reconstruction (TSESTD), and prospectively undersampled TSE sequences reconstructed with a DL reconstruction (TSEDL). Two radiologists evaluated the images concerning image quality, noise, edge sharpness, artifacts, diagnostic confidence, and delineation of anatomical structures using a 5-point Likert scale, and rated the images concerning the detection of common pathologies. RESULTS Image quality was significantly improved in TSEDL (mean 4.35, IQR 4-5) compared to TSESTD (mean 3.76, IQR 3-4, p = 0.008). Moreover, TSEDL showed decreased noise (mean 4.29, IQR 3.5-5) compared to TSESTD (mean 3.35, IQR 3-4, p = 0.004). Ratings for delineation of anatomical structures, artifacts, edge sharpness, and diagnostic confidence did not differ significantly between TSEDL and TSESTD (p > 0.05). Inter-reader agreement was substantial to almost perfect (κ = 0.628-0.904). No difference was found concerning the detection of pathologies between the readers and between TSEDL and TSESTD. Using DL, the acquisition time could be reduced by more than 35% compared to TSESTD. CONCLUSION TSEDL provided improved image quality and decreased noise while receiving equal ratings for edge sharpness, artifacts, delineation of anatomical structures, diagnostic confidence, and detection of pathologies compared to TSESTD. Providing more than a 35% reduction of acquisition time, TSEDL may be clinically relevant for elbow imaging due to increased patient comfort and higher patient throughput.
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Affiliation(s)
- Judith Herrmann
- Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, 72076 Tübingen, Germany (S.G.); (A.L.); (H.A.); (S.W.)
| | - Saif Afat
- Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, 72076 Tübingen, Germany (S.G.); (A.L.); (H.A.); (S.W.)
| | - Sebastian Gassenmaier
- Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, 72076 Tübingen, Germany (S.G.); (A.L.); (H.A.); (S.W.)
| | - Jan-Peter Grunz
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, 97080 Würzburg, Germany; (J.-P.G.); (T.S.P.)
| | - Gregor Koerzdoerfer
- MR Application Predevelopment, Siemens Healthcare GmbH, 91052 Erlangen, Germany; (G.K.); (D.N.)
| | - Andreas Lingg
- Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, 72076 Tübingen, Germany (S.G.); (A.L.); (H.A.); (S.W.)
| | - Haidara Almansour
- Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, 72076 Tübingen, Germany (S.G.); (A.L.); (H.A.); (S.W.)
| | - Dominik Nickel
- MR Application Predevelopment, Siemens Healthcare GmbH, 91052 Erlangen, Germany; (G.K.); (D.N.)
| | - Theresa Sophie Patzer
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, 97080 Würzburg, Germany; (J.-P.G.); (T.S.P.)
| | - Sebastian Werner
- Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, 72076 Tübingen, Germany (S.G.); (A.L.); (H.A.); (S.W.)
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26
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Ursprung S, Herrmann J, Joos N, Weiland E, Benkert T, Almansour H, Lingg A, Afat S, Gassenmaier S. Accelerated diffusion-weighted imaging of the prostate using deep learning image reconstruction: A retrospective comparison with standard diffusion-weighted imaging. Eur J Radiol 2023; 165:110953. [PMID: 37399667 DOI: 10.1016/j.ejrad.2023.110953] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 06/27/2023] [Indexed: 07/05/2023]
Abstract
PURPOSE Routine multiparametric MRI of the prostate reduces overtreatment and increases sensitivity in the diagnosis of the most common solid cancer in men. However, the capacity of MRI systems is limited. Here we investigate the ability of deep learning image reconstruction to accelerate time consuming diffusion-weighted imaging (DWI) acquisition while maintaining diagnostic image quality. METHOD In this retrospective study, raw data of DWI sequences of consecutive patients undergoing MRI of the prostate at a tertiary care hospital in Germany were reconstructed using standard and deep learning reconstruction. To simulate a shortening of acquisition times by 39 %, one instead of two and six instead of ten averages were used in the reconstruction of b = 0 and 1000 s/mm2 images, respectively. Image quality was assessed by three radiologists and objective image quality metrics. RESULTS After the application of exclusion criteria, 35 out of 147 patients examined between September 2022 and January 2023 were included in this study. The radiologists perceived less image noise on deep learning reconstructed images at b = 0 s/mm2 images and ADC maps with good inter-reader agreement. Signal-to-noise ratios were similar overall with discretely reduced values in the transitional zone after deep learning reconstruction. CONCLUSIONS An acquisition time reduction of 39 % without loss in image quality is feasible in DWI of the prostate when using deep learning image reconstruction.
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Affiliation(s)
- Stephan Ursprung
- Department of Radiology, University Hospital Tuebingen, Eberhard Karls University of Tuebingen, Tuebingen, Germany
| | - Judith Herrmann
- Department of Radiology, University Hospital Tuebingen, Eberhard Karls University of Tuebingen, Tuebingen, Germany
| | - Natalie Joos
- Department of Radiology, University Hospital Tuebingen, Eberhard Karls University of Tuebingen, Tuebingen, Germany
| | - Elisabeth Weiland
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Thomas Benkert
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Haidara Almansour
- Department of Radiology, University Hospital Tuebingen, Eberhard Karls University of Tuebingen, Tuebingen, Germany
| | - Andreas Lingg
- Department of Radiology, University Hospital Tuebingen, Eberhard Karls University of Tuebingen, Tuebingen, Germany
| | - Saif Afat
- Department of Radiology, University Hospital Tuebingen, Eberhard Karls University of Tuebingen, Tuebingen, Germany.
| | - Sebastian Gassenmaier
- Department of Radiology, University Hospital Tuebingen, Eberhard Karls University of Tuebingen, Tuebingen, Germany
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Potočnik J, Foley S, Thomas E. Current and potential applications of artificial intelligence in medical imaging practice: A narrative review. J Med Imaging Radiat Sci 2023; 54:376-385. [PMID: 37062603 DOI: 10.1016/j.jmir.2023.03.033] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 03/25/2023] [Accepted: 03/28/2023] [Indexed: 04/18/2023]
Abstract
BACKGROUND AND PURPOSE Artificial intelligence (AI) is present in many areas of our lives. Much of the digital data generated in health care can be used for building automated systems to bring improvements to existing workflows and create a more personalised healthcare experience for patients. This review outlines select current and potential AI applications in medical imaging practice and provides a view of how diagnostic imaging suites will operate in the future. Challenges associated with potential applications will be discussed and healthcare staff considerations necessary to benefit from AI-enabled solutions will be outlined. METHODS Several electronic databases, including PubMed, ScienceDirect, Google Scholar, and University College Dublin Library Database, were used to identify relevant articles with a Boolean search strategy. Textbooks, government sources and vendor websites were also considered. RESULTS/DISCUSSION Many AI-enabled solutions in radiographic practice are available with more automation on the horizon. Traditional workflow will become faster, more effective, and more user friendly. AI can handle administrative or technical types of work, meaning it is applicable across all aspects of medical imaging practice. CONCLUSION AI offers significant potential to automate most of the manual tasks, ensure service consistency, and improve patient care. Radiographers, radiation therapists, and clinicians should ensure they have adequate understanding of the technology to enable ethical oversight of its implementation.
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Affiliation(s)
- Jaka Potočnik
- University College Dublin School of Medicine, Radiography & Diagnostic Imaging, Room A223, Belfield, Dublin 4, Ireland.
| | - Shane Foley
- University College Dublin School of Medicine, Radiography & Diagnostic Imaging, Room A223, Belfield, Dublin 4, Ireland
| | - Edel Thomas
- University College Dublin School of Medicine, Radiography & Diagnostic Imaging, Room A223, Belfield, Dublin 4, Ireland
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Lin Y, Yilmaz EC, Belue MJ, Turkbey B. Prostate MRI and image Quality: It is time to take stock. Eur J Radiol 2023; 161:110757. [PMID: 36870241 PMCID: PMC10493032 DOI: 10.1016/j.ejrad.2023.110757] [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: 01/28/2023] [Revised: 02/15/2023] [Accepted: 02/21/2023] [Indexed: 03/06/2023]
Abstract
Multiparametric magnetic resonance imaging (mpMRI) plays a vital role in prostate cancer diagnosis and management. With the increase in use of mpMRI, obtaining the best possible quality images has become a priority. The Prostate Imaging Reporting and Data System (PI-RADS) was introduced to standardize and optimize patient preparation, scanning techniques, and interpretation. However, the quality of the MRI sequences depends not only on the hardware/software and scanning parameters, but also on patient-related factors. Common patient-related factors include bowel peristalsis, rectal distension, and patient motion. There is currently no consensus regarding the best approaches to address these issues and improve the quality of mpMRI. New evidence has been accrued since the release of PI-RADS, and this review aims to explore the key strategies which aim to improve prostate MRI quality, such as imaging techniques, patient preparation methods, the new Prostate Imaging Quality (PI-QUAL) criteria, and artificial intelligence on prostate MRI quality.
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Affiliation(s)
- Yue Lin
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Enis C Yilmaz
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Mason J Belue
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States.
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Afat S, Herrmann J, Almansour H, Benkert T, Weiland E, Hölldobler T, Nikolaou K, Gassenmaier S. Acquisition time reduction of diffusion-weighted liver imaging using deep learning image reconstruction. Diagn Interv Imaging 2023; 104:178-184. [PMID: 36787419 DOI: 10.1016/j.diii.2022.11.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 11/01/2022] [Accepted: 11/08/2022] [Indexed: 12/12/2022]
Abstract
PURPOSE The purpose of this study was to investigate the impact of deep learning accelerated diffusion-weighted imaging (DWIDL) in 1.5-T liver MRI on image quality, sharpness, and diagnostic confidence. MATERIALS AND METHODS One-hundred patients who underwent liver MRI at 1.5-T including DWI with two different b-values (50 and 800 s/mm²) between February and April 2022 were retrospectively included. There were 54 men and 46 women, with a mean age of 59 ± 14 (SD) years (range: 21-88 years). The single average raw data were retrospectively processed using a deep learning (DL) image reconstruction algorithm leading to a simulated acquisition time of 1 min 28 s for DWIDL as compared to 2 min 31 s for standard DWI (DWIStd) via reduction of signal averages. All DWI datasets were reviewed by four radiologists using a Likert scale ranging from 1-4 using the following criteria: noise level, extent of artifacts, sharpness, overall image quality, and diagnostic confidence. Furthermore, quantitative assessment of noise and signal-to-noise ratio (SNR) was performed via regions of interest. RESULTS No significant differences were found regarding artifacts and overall image quality (P > 0.05). Noise measurements for the spleen, liver, and erector spinae muscles revealed significantly lower noise for DWIDL versus DWIStd (P < 0.001). SNR measurements in the above-mentioned tissues also showed significantly superior results for DWIDL versus DWIStd for b = 50 s/mm² and ADC maps (all P < 0.001). For b = 800 s/mm², significantly superior results were found for the spleen, right hemiliver, and erector spinae muscles. CONCLUSIONS DL image reconstruction of liver DWI at 1.5-T is feasible including significant reduction of acquisition time without compromised image quality.
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Affiliation(s)
- Saif Afat
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, Tuebingen 72076, Germany.
| | - Judith Herrmann
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, Tuebingen 72076, Germany
| | - Haidara Almansour
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, Tuebingen 72076, Germany
| | - Thomas Benkert
- MR Applications Predevelopment, Siemens Healthcare GmbH, Allee am Roethelheimpark 2, Erlangen 91052, Germany
| | - Elisabeth Weiland
- MR Applications Predevelopment, Siemens Healthcare GmbH, Allee am Roethelheimpark 2, Erlangen 91052, Germany
| | - Thomas Hölldobler
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, Tuebingen 72076, Germany
| | - Konstantin Nikolaou
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, Tuebingen 72076, Germany; Cluster of Excellence iFIT (EXC 2180) "Image Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tuebingen, Germany
| | - Sebastian Gassenmaier
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, Tuebingen 72076, Germany
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Chaika M, Afat S, Wessling D, Afat C, Nickel D, Kannengiesser S, Herrmann J, Almansour H, Männlin S, Othman AE, Gassenmaier S. Deep learning-based super-resolution gradient echo imaging of the pancreas: Improvement of image quality and reduction of acquisition time. Diagn Interv Imaging 2023; 104:53-59. [PMID: 35843839 DOI: 10.1016/j.diii.2022.06.006] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 06/16/2022] [Accepted: 06/17/2022] [Indexed: 02/07/2023]
Abstract
PURPOSE The purpose of this study was to evaluate the impact of a deep learning-based super-resolution technique on T1-weighted gradient-echo acquisitions (volumetric interpolated breath-hold examination; VIBE) on the assessment of pancreatic MRI at 1.5 T compared to standard VIBE imaging (VIBESTD). MATERIALS AND METHODS This retrospective single-center study was conducted between April 2021 and October 2021. Fifty patients with a total of 50 detectable pancreatic lesion entities were included in this study. There were 27 men and 23 women, with a mean age of 69 ± 13 (standard deviation [SD]) years (age range: 33-89 years). VIBESTD (precontrast, dynamic, postcontrast) was retrospectively processed with a deep learning-based super-resolution algorithm including a more aggressive partial Fourier setting leading to a simulated acquisition time reduction (VIBESR). Image analysis was performed by two radiologists regarding lesion detectability, noise levels, sharpness and contrast of pancreatic edges, as well as regarding diagnostic confidence using a 5-point Likert-scale with 5 being the best. RESULTS VIBESR was rated better than VIBESTD by both readers regarding lesion detectability (5 [IQR: 5, 5] vs. 5 [IQR: 4, 5], for reader 1; 5 [IQR: 5, 5] vs. 4 [IQR: 4, 5]) for reader 2; both P <0.001), noise levels (5 [IQR: 5, 5] vs. 5 [IQR: 4, 5] for reader 1; 5 [IQR: 5, 5] vs. 4 [IQR: 4, 5] for reader 2; both P <0.001), sharpness and contrast of pancreatic edges (5 [IQR: 5, 5] vs. 5 [IQR: 4, 5] for reader 1; 5 [IQR: 5, 5] vs. 4 [IQR: 4, 5] for reader 2; both P <0.001), as well as regarding diagnostic confidence (5 [IQR: 5, 5] vs. 5 [IQR: 4, 5] for reader 1; 5 [IQR: 5, 5] vs. 4 [IQR: 4, 5] for reader 2; both P <0.001). There were no significant differences between lesion sizes as measured by the two readers on VIBESR and VIBESTD images (P > 0.05). The mean acquisition time for VIBESTD (15 ± 1 [SD] s; range: 11-16 s) was longer than that for VIBESR (13 ± 1 [SD] s; range: 11-14 s) (P < 0.001). CONCLUSION Our results indicate that the newly developed deep learning-based super-resolution algorithm adapted to partial Fourier acquisitions has a positive influence not only on shortening the examination time but also on improvement of image quality in pancreatic MRI.
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Affiliation(s)
- Maryanna Chaika
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany
| | - Saif Afat
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany
| | - Daniel Wessling
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany
| | - Carmen Afat
- Department of Internal Medicine I, Otfried-Müller-Straße 10, Eberhard Karls University Tuebingen, 72076, Tuebingen, Germany
| | - Dominik Nickel
- MR Applications Predevelopment, Siemens Healthcare GmbH, Allee am Roethelheimpark 2, 91052, Erlangen, Germany
| | - Stephan Kannengiesser
- MR Applications Predevelopment, Siemens Healthcare GmbH, Allee am Roethelheimpark 2, 91052, Erlangen, Germany
| | - Judith Herrmann
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany
| | - Haidara Almansour
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany
| | - Simon Männlin
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany
| | - Ahmed E Othman
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany; Department of Neuroradiology, University Medical Center, 55131, Mainz, Germany
| | - Sebastian Gassenmaier
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany.
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Huang X, Dong K, Chen R, Liao G. Editorial for "Perfusion Maps Acquired from Dynamic Angiography MRI Using Deep Learning Approaches". J Magn Reson Imaging 2023; 57:470-471. [PMID: 35733276 DOI: 10.1002/jmri.28313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 05/04/2022] [Indexed: 01/20/2023] Open
Affiliation(s)
- Xuanyu Huang
- Institute of Cell Biology and Neurobiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Kailin Dong
- Department of Basic Medical Sciences, Fujian Medical University, Fuzhou, People's Republic of China
| | - Ru Chen
- Department of Basic Medical Sciences, Fujian Medical University, Fuzhou, People's Republic of China
| | - Guangyuan Liao
- Department of Basic Medical Sciences, Fujian Medical University, Fuzhou, People's Republic of China
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Gassenmaier S, Warm V, Nickel D, Weiland E, Herrmann J, Almansour H, Wessling D, Afat S. Thin-Slice Prostate MRI Enabled by Deep Learning Image Reconstruction. Cancers (Basel) 2023; 15:cancers15030578. [PMID: 36765539 PMCID: PMC9913660 DOI: 10.3390/cancers15030578] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 01/08/2023] [Accepted: 01/13/2023] [Indexed: 01/20/2023] Open
Abstract
OBJECTIVES Thin-slice prostate MRI might be beneficial for prostate cancer diagnostics. However, prolongation of acquisition time is a major drawback of thin-slice imaging. Therefore, the purpose of this study was to investigate the impact of a thin-slice deep learning accelerated T2-weighted (w) TSE imaging sequence (T2DLR) of the prostate as compared to conventional T2w TSE imaging (T2S). MATERIALS AND METHODS Thirty patients were included in this prospective study at one university center after obtaining written informed consent. T2S (3 mm slice thickness) was acquired first in three orthogonal planes followed by thin-slice T2DLR (2 mm slice thickness) in axial plane. Acquisition time of axial conventional T2S was 4:12 min compared to 4:37 min for T2DLR. Imaging datasets were evaluated by two radiologists using a Likert-scale ranging from 1-4, with 4 being the best regarding the following parameters: sharpness, lesion detectability, artifacts, overall image quality, and diagnostic confidence. Furthermore, preference of T2S versus T2DLR was evaluated. RESULTS The mean patient age was 68 ± 8 years. Sharpness of images and lesion detectability were rated better in T2DLR with a median of 4 versus a median of 3 in T2S (p < 0.001 for both readers). Image noise was evaluated to be significantly worse in T2DLR as compared to T2S (p < 0.001 and p = 0.021, respectively). Overall image quality was also evaluated to be superior in T2DLR versus T2S with a median of 4 versus 3 (p < 0.001 for both readers). Both readers chose T2DLR in 29 cases as their preference. CONCLUSIONS Thin-slice T2DLR of the prostate provides a significant improvement of image quality without significant prolongation of acquisition time.
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Affiliation(s)
- Sebastian Gassenmaier
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany
- Correspondence: ; Tel.: +49-7071-29-68111
| | - Verena Warm
- Institute for Pathology and Neuropathology, University Hospital of Tuebingen, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany
| | - Dominik Nickel
- MR Applications Predevelopment, Siemens Healthcare GmbH, Allee am Roethelheimpark 2, 91052 Erlangen, Germany
| | - Elisabeth Weiland
- MR Applications Predevelopment, Siemens Healthcare GmbH, Allee am Roethelheimpark 2, 91052 Erlangen, Germany
| | - Judith Herrmann
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany
| | - Haidara Almansour
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany
| | - Daniel Wessling
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany
| | - Saif Afat
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany
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Usefulness of deep learning-based noise reduction for 1.5 T MRI brain images. Clin Radiol 2023; 78:e13-e21. [PMID: 36116967 DOI: 10.1016/j.crad.2022.08.127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Revised: 08/04/2022] [Accepted: 08/04/2022] [Indexed: 01/07/2023]
Abstract
AIM To evaluate 1.5 T magnetic resonance imaging (MRI) brain images with denoising procedures using deep learning-based reconstruction (dDLR) relative to the original 1.5 and 3 T images. MATERIALS AND METHODS Eleven volunteers underwent MRI at 3 and 1.5 T. Two-dimensional fast spin-echo T2-weighted imaging (T2WI), fluid-attenuated inversion recovery (FLAIR) imaging and diffusion-weighted imaging (DWI) sequences were performed. The dDLR method was applied to the 1.5 T data (dDLR-1.5 T), then the image quality of the dDLR-1.5 T data relative to the original 1.5 T and 3 T data was qualitatively and quantitatively assessed based on the structure similarity (SSIM) index; the signal-to-noise ratios (SNRs) of the grey matter (GM) and white matter (WM); and the contrast-to-noise ratios (CNRs) between the GM and WM (CNRgm-wm) and between the striatum (ST) and WM (CNRst-wm). RESULTS The perceived image quality, and SNRs and CNRs were significantly higher for the dDLR-1.5 T images versus the 1.5 T images for all sequences and almost comparable or even superior to those of the 3 T images. For DWI, the SNRs and CNRst-wm were significantly higher for the dDLR-1.5 T images versus the 3 T images. CONCLUSION The dDLR technique improved the image quality of 1.5 T brain MRI images. With respect to qualitative and quantitative measurements, the denoised 1.5 T brain images were almost equivalent or even superior to the 3 T brain images.
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Application of deep learning-based super-resolution to T1-weighted postcontrast gradient echo imaging of the chest. LA RADIOLOGIA MEDICA 2023; 128:184-190. [PMID: 36609662 PMCID: PMC9938811 DOI: 10.1007/s11547-022-01587-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Accepted: 12/30/2022] [Indexed: 01/09/2023]
Abstract
OBJECTIVES A deep learning-based super-resolution for postcontrast volume-interpolated breath-hold examination (VIBE) of the chest was investigated in this study. Aim was to improve image quality, noise, artifacts and diagnostic confidence without change of acquisition parameters. MATERIALS AND METHODS Fifty patients who received VIBE postcontrast imaging of the chest at 1.5 T were included in this retrospective study. After acquisition of the standard VIBE (VIBES), a novel deep learning-based algorithm and a denoising algorithm were applied, resulting in enhanced images (VIBEDL). Two radiologists qualitatively evaluated both datasets independently, rating sharpness of soft tissue, vessels, bronchial structures, lymph nodes, artifacts, cardiac motion artifacts, noise levels and overall diagnostic confidence, using a Likert scale ranging from 1 to 4. In the presence of lung lesions, the largest lesion was rated regarding sharpness and diagnostic confidence using the same Likert scale as mentioned above. Additionally, the largest diameter of the lesion was measured. RESULTS The sharpness of soft tissue, vessels, bronchial structures and lymph nodes as well as the diagnostic confidence, the extent of artifacts, the extent of cardiac motion artifacts and noise levels were rated superior in VIBEDL (all P < 0.001). There was no significant difference in the diameter or the localization of the largest lung lesion in VIBEDL compared to VIBES. Lesion sharpness as well as detectability was rated significantly better by both readers with VIBEDL (both P < 0.001). CONCLUSION The application of a novel deep learning-based super-resolution approach in T1-weighted VIBE postcontrast imaging resulted in an improvement in image quality, noise levels and diagnostic confidence as well as in a shortened acquisition time.
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Deep Learning-Enhanced Parallel Imaging and Simultaneous Multislice Acceleration Reconstruction in Knee MRI. Invest Radiol 2022; 57:826-833. [PMID: 35776434 DOI: 10.1097/rli.0000000000000900] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
OBJECTIVES This study aimed to examine various combinations of parallel imaging (PI) and simultaneous multislice (SMS) acceleration imaging using deep learning (DL)-enhanced and conventional reconstruction. The study also aimed at comparing the diagnostic performance of the various combinations in internal knee derangement and provided a quantitative evaluation of image sharpness and noise using edge rise distance (ERD) and noise power (NP), respectively. MATERIALS AND METHODS The data from adult patients who underwent knee magnetic resonance imaging using various DL-enhanced acquisitions between June 2021 and January 2022 were retrospectively analyzed. The participants underwent conventional 2-fold PI and DL protocols with 4- to 8-fold acceleration imaging (P2S2 [2-fold PI with 2-fold SMS], P3S2, and P4S2). Three readers evaluated the internal knee derangement and the overall image quality. The diagnostic performance was calculated using consensus reading as a standard reference, and we conducted comparative evaluations. We calculated the ERD and NP for quantitative evaluations of image sharpness and noise, respectively. Interreader and intermethod agreements were calculated using Fleiss κ. RESULTS A total of 33 patients (mean age, 49 ± 19 years; 20 women) were included in this study. The diagnostic performance for internal knee derangement and the overall image quality were similar among the evaluated protocols. The NP values were significantly lower using the DL protocols than with conventional imaging ( P < 0.001), whereas the ERD values were similar among these methods ( P > 0.12). Interreader and intermethod agreements were moderate-to-excellent (κ = 0.574-0.838) and good-to-excellent (κ = 0.755-1.000), respectively. In addition, the mean acquisition time was reduced by 47% when using DL with P2S2, by 62% with P3S2, and by 71% with P4S2, compared with conventional P2 imaging (2 minutes and 55 seconds). CONCLUSIONS The combined use of DL-enhanced 8-fold acceleration imaging (4-fold PI with 2-fold SMS) showed comparable performance with conventional 2-fold PI for the evaluation of internal knee derangement, with a 71% reduction in acquisition time.
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Biparametric prostate MRI: impact of a deep learning-based software and of quantitative ADC values on the inter-reader agreement of experienced and inexperienced readers. Radiol Med 2022; 127:1245-1253. [PMID: 36114928 PMCID: PMC9587977 DOI: 10.1007/s11547-022-01555-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 08/30/2022] [Indexed: 11/17/2022]
Abstract
Objective To investigate the impact of an artificial intelligence (AI) software and quantitative ADC (qADC) on the inter-reader agreement, diagnostic performance, and reporting times of prostate biparametric MRI (bpMRI) for experienced and inexperienced readers. Materials and methods A total of 170 multiparametric MRI (mpMRI) of patients with suspicion of prostate cancer (PCa) were retrospectively reviewed by one experienced and one inexperienced reader three times, following a wash-out period. First, only the bpMRI sequences, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) sequences, and apparent diffusion coefficient (ADC) maps, were used. Then, bpMRI and quantitative ADC values were used. Lastly, bpMRI and the AI software were used. Inter-reader agreement between the two readers and between each reader and the mpMRI original reports was calculated. Detection rates and reporting times were calculated for each group. Results Inter-reader agreement with respect to mpMRI was moderate for bpMRI, Quantib, and qADC for both the inexperienced (weighted k of 0.42, 0.45, and 0.41, respectively) and the experienced radiologists (weighted k of 0.44, 0.46, and 0.42, respectively). Detection rate of PCa was similar between the inexperienced (0.24, 0.26, and 0.23) and the experienced reader (0.26, 0.27 and 0.27), for bpMRI, Quantib, and qADC, respectively. Reporting times were lower for Quantib (8.23, 7.11, and 9.87 min for the inexperienced reader and 5.62, 5.07, and 6.21 min for the experienced reader, for bpMRI, Quantib, and qADC, respectively). Conclusions AI and qADC did not have a significant impact on the diagnostic performance of both readers. The use of Quantib was associated with lower reporting times.
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Wessling D, Herrmann J, Afat S, Nickel D, Othman AE, Almansour H, Gassenmaier S. Reduction in Acquisition Time and Improvement in Image Quality in T2-Weighted MR Imaging of Musculoskeletal Tumors of the Extremities Using a Novel Deep Learning-Based Reconstruction Technique in a Turbo Spin Echo (TSE) Sequence. Tomography 2022; 8:1759-1769. [PMID: 35894013 PMCID: PMC9326558 DOI: 10.3390/tomography8040148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 06/23/2022] [Accepted: 06/30/2022] [Indexed: 11/23/2022] Open
Abstract
Background: The aim of this study was to assess the technical feasibility and the impact on image quality and acquisition time of a deep learning-accelerated fat-saturated T2-weighted turbo spin echo sequence in musculoskeletal imaging of the extremities. Methods: Twenty-three patients who underwent MRI of the extremities were prospectively included. Standard T2w turbo inversion recovery magnitude (TIRMStd) imaging was compared to a deep learning-accelerated T2w TSE (TSEDL) sequence. Image analysis of 23 patients with a mean age of 60 years (range 30−86) was performed regarding image quality, noise, sharpness, contrast, artifacts, lesion detectability and diagnostic confidence. Pathological findings were documented measuring the maximum diameter. Results: The analysis showed a significant improvement for the T2 TSEDL with regard to image quality, noise, contrast, sharpness, lesion detectability, and diagnostic confidence, as compared to T2 TIRMStd (each p < 0.001). There were no differences in the number of detected lesions. The time of acquisition (TA) could be reduced by 52−59%. Interrater agreement was almost perfect (κ = 0.886). Conclusion: Accelerated T2 TSEDL was technically feasible and superior to conventionally applied T2 TIRMStd. Concurrently, TA could be reduced by 52−59%. Therefore, deep learning-accelerated MR imaging is a promising and applicable method in musculoskeletal imaging.
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Affiliation(s)
- Daniel Wessling
- Department of Diagnostic and Interventional Radiology, University Hospital of Tuebingen, 72076 Tuebingen, Germany; (D.W.); (J.H.); (H.A.); (S.G.)
| | - Judith Herrmann
- Department of Diagnostic and Interventional Radiology, University Hospital of Tuebingen, 72076 Tuebingen, Germany; (D.W.); (J.H.); (H.A.); (S.G.)
| | - Saif Afat
- Department of Diagnostic and Interventional Radiology, University Hospital of Tuebingen, 72076 Tuebingen, Germany; (D.W.); (J.H.); (H.A.); (S.G.)
- Correspondence:
| | - Dominik Nickel
- MR Application Predevelopment, Siemens Healthcare GmbH, 91052 Erlangen, Germany;
| | - Ahmed E. Othman
- Department of Diagnostic and Interventional Neuroradiology, University Hospital of Mainz, 55131 Mainz, Germany;
| | - Haidara Almansour
- Department of Diagnostic and Interventional Radiology, University Hospital of Tuebingen, 72076 Tuebingen, Germany; (D.W.); (J.H.); (H.A.); (S.G.)
| | - Sebastian Gassenmaier
- Department of Diagnostic and Interventional Radiology, University Hospital of Tuebingen, 72076 Tuebingen, Germany; (D.W.); (J.H.); (H.A.); (S.G.)
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Almansour H, Herrmann J, Gassenmaier S, Lingg A, Nickel MD, Kannengiesser S, Arberet S, Othman AE, Afat S. Combined Deep Learning-based Super-Resolution and Partial Fourier Reconstruction for Gradient Echo Sequences in Abdominal MRI at 3 Tesla: Shortening Breath-Hold Time and Improving Image Sharpness and Lesion Conspicuity. Acad Radiol 2022; 30:863-872. [PMID: 35810067 DOI: 10.1016/j.acra.2022.06.003] [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: 04/06/2022] [Revised: 05/20/2022] [Accepted: 06/04/2022] [Indexed: 11/29/2022]
Abstract
RATIONALE AND OBJECTIVES To investigate the impact of a prototypical deep learning-based super-resolution reconstruction algorithm tailored to partial Fourier acquisitions on acquisition time and image quality for abdominal T1-weighted volume-interpolated breath-hold examination (VIBESR) at 3 Tesla. The standard T1-weighted images were used as the reference standard (VIBESD). MATERIALS AND METHODS Patients with diverse abdominal pathologies, who underwent a clinically indicated contrast-enhanced abdominal VIBE magnetic resonance imaging at 3T between March and June 2021 were retrospectively included. Following the acquisition of the standard VIBESD sequences, additional images for the non-contrast, dynamic contrast-enhanced and post-contrast T1-weighted VIBE acquisition were retrospectively reconstructed using the same raw data and employing a prototypical deep learning-based super-resolution reconstruction algorithm. The algorithm was designed to enhance edge sharpness by avoiding conventional k-space filtering and to perform a partial Fourier reconstruction in the slice phase-encoding direction for a predefined asymmetric sampling ratio. In the retrospective reconstruction, the asymmetric sampling was realized by omitting acquired samples at the end of the acquisition and therefore corresponding to a shorter acquisition. Four radiologists independently analyzed the image datasets (VIBESR and VIBESD) in a blinded manner. Outcome measures were: sharpness of abdominal organs, sharpness of vessels, image contrast, noise, hepatic lesion conspicuity and size, overall image quality and diagnostic confidence. These parameters were statistically compared and interrater reliability was computed using Fleiss' Kappa and intraclass correlation coefficient (ICC). Finally, the rate of detection of hepatic lesions was documented and was statistically compared using the paired Wilcoxon test. RESULTS A total of 32 patients aged 59 ± 16 years (23 men (72%), 9 women (28%)) were included. For VIBESR, breath-hold time was significantly reduced by approximately 13.6% (VIBESR 11.9 ± 1.2 seconds vs. VIBESD: 13.9 ± 1.4 seconds, p < 0.001). All readers rated sharpness of abdominal organs, sharpness of vessels to be superior in images with VIBESR (p values ranged between p = 0.005 and p < 0.001). Despite reduction of acquisition time, image contrast, noise, overall image quality and diagnostic confidence were not compromised, as there was no evidence of a difference between VIBESR and VIBESD (p > 0.05). The inter-reader agreement was substantial with a Fleiss' Kappa of >0.7 in all contrast phases. A total of 13 hepatic lesions were analyzed. The four readers observed a superior lesion conspicuity in VIBESR than in VIBESD (p values ranged between p = 0.046 and p < 0.001). In terms of lesion size, there was no significant difference between VIBESD and VIBESR for all readers. Finally, there was an excellent inter-reader agreement regarding lesion size (ICC > 0.9). For all readers, no statistically significant difference was observed regarding detection of hepatic lesions between VIBESD and VIBESR. CONCLUSION The deep learning-based super-resolution reconstruction with partial Fourier in the slice phase-encoding direction enabled a reduction of breath-hold time and improved image sharpness and lesion conspicuity in T1-weighted gradient echo sequences in abdominal magnetic resonance imaging at 3 Tesla. Faster acquisition time without compromising image quality or diagnostic confidence was possible by using this deep learning-based reconstruction technique.
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Affiliation(s)
- Haidara Almansour
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University, Tuebingen University Hospital, Tuebingen, Germany
| | - Judith Herrmann
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University, Tuebingen University Hospital, Tuebingen, Germany
| | - Sebastian Gassenmaier
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University, Tuebingen University Hospital, Tuebingen, Germany
| | - Andreas Lingg
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University, Tuebingen University Hospital, Tuebingen, Germany
| | | | | | - Simon Arberet
- Digital Technology & Innovation, Siemens Healthineers, Princeton, New Jersey
| | - Ahmed E Othman
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University, Tuebingen University Hospital, Tuebingen, Germany; Department of Neuroradiology, University Medical Center Mainz, Mainz, Germany.
| | - Saif Afat
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University, Tuebingen University Hospital, Tuebingen, Germany
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