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Afat S, Wohlers J, Herrmann J, Brendlin AS, Gassenmaier S, Almansour H, Werner S, Brendel JM, Mika A, Scherieble C, Notohamiprodjo M, Gatidis S, Nikolaou K, Küstner T. Reducing energy consumption in musculoskeletal MRI using shorter scan protocols, optimized magnet cooling patterns, and deep learning sequences. Eur Radiol 2024:10.1007/s00330-024-11056-0. [PMID: 39242400 DOI: 10.1007/s00330-024-11056-0] [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: 03/15/2024] [Revised: 06/25/2024] [Accepted: 08/14/2024] [Indexed: 09/09/2024]
Abstract
OBJECTIVES The unprecedented surge in energy costs in Europe, coupled with the significant energy consumption of MRI scanners in radiology departments, necessitates exploring strategies to optimize energy usage without compromising efficiency or image quality. This study investigates MR energy consumption and identifies strategies for improving energy efficiency, focusing on musculoskeletal MRI. We assess the potential savings achievable through (1) optimizing protocols, (2) incorporating deep learning (DL) accelerated acquisitions, and (3) optimizing the cooling system. MATERIALS AND METHODS Energy consumption measurements were performed on two MRI scanners (1.5-T Aera, 1.5-T Sola) in practices in Munich, Germany, between December 2022 and March 2023. Three levels of energy reduction measures were implemented and compared to the baseline. Wilcoxon signed-rank test with Bonferroni correction was conducted to evaluate the impact of sequence scan times and energy consumption. RESULTS Our findings showed significant energy savings by optimizing protocol settings and implementing DL technologies. Across all body regions, the average reduction in energy consumption was 72% with DL and 31% with economic protocols, accompanied by time reductions of 71% (DL) and 18% (economic protocols) compared to baseline. Optimizing the cooling system during the non-scanning time showed a 30% lower energy consumption. CONCLUSION Implementing energy-saving strategies, including economic protocols, DL accelerated sequences, and optimized magnet cooling, can significantly reduce energy consumption in MRI scanners. Radiology departments and practices should consider adopting these strategies to improve energy efficiency and reduce costs. CLINICAL RELEVANCE STATEMENT MRI scanner energy consumption can be substantially reduced by incorporating protocol optimization, DL accelerated acquisition, and optimized magnetic cooling into daily practice, thereby cutting costs and environmental impact. KEY POINTS Optimization of protocol settings reduced energy consumption by 31% and imaging time by 18%. DL technologies led to a 72% reduction in energy consumption of and a 71% reduction in time, compared to the standard MRI protocol. During non-scanning times, activating Eco power mode (EPM) resulted in a 30% reduction in energy consumption, saving 4881 € ($5287) per scanner annually.
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Affiliation(s)
- Saif Afat
- Department of Radiology, Tuebingen University Hospital, University of Tuebingen, Tuebingen, Germany
| | - Julian Wohlers
- Department of Magnetic Resonance Product Management, Siemens Healthineers, Erlangen, Germany
| | - Judith Herrmann
- Department of Radiology, Tuebingen University Hospital, University of Tuebingen, Tuebingen, Germany
| | - Andreas S Brendlin
- Department of Radiology, Tuebingen University Hospital, University of Tuebingen, Tuebingen, Germany
| | - Sebastian Gassenmaier
- Department of Radiology, Tuebingen University Hospital, University of Tuebingen, Tuebingen, Germany
| | - Haidara Almansour
- Department of Radiology, Tuebingen University Hospital, University of Tuebingen, Tuebingen, Germany
| | - Sebastian Werner
- Department of Radiology, Tuebingen University Hospital, University of Tuebingen, Tuebingen, Germany
| | - Jan M Brendel
- Department of Radiology, Tuebingen University Hospital, University of Tuebingen, Tuebingen, Germany
| | - Alexander Mika
- Radiologische, Strahlentherapeutische und Nuklearmedizinische Partnerschaftsgesellschaft Muenchen, DIE RADIOLOGIE, Munich, Germany
| | - Christoph Scherieble
- Radiologische, Strahlentherapeutische und Nuklearmedizinische Partnerschaftsgesellschaft Muenchen, DIE RADIOLOGIE, Munich, Germany
| | - Mike Notohamiprodjo
- Radiologische, Strahlentherapeutische und Nuklearmedizinische Partnerschaftsgesellschaft Muenchen, DIE RADIOLOGIE, Munich, Germany
| | - Sergios Gatidis
- Department of Radiology, Tuebingen University Hospital, University of Tuebingen, Tuebingen, Germany
| | - Konstantin Nikolaou
- Department of Radiology, Tuebingen University Hospital, University of Tuebingen, Tuebingen, Germany
| | - Thomas Küstner
- Department of Radiology, Tuebingen University Hospital, University of Tuebingen, Tuebingen, Germany.
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2
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Sauer ST, Christner SA, Lois AM, Woznicki P, Curtaz C, Kunz AS, Weiland E, Benkert T, Bley TA, Baeßler B, Grunz JP. Deep Learning k-Space-to-Image Reconstruction Facilitates High Spatial Resolution and Scan Time Reduction in Diffusion-Weighted Imaging Breast MRI. J Magn Reson Imaging 2024; 60:1190-1200. [PMID: 37974498 DOI: 10.1002/jmri.29139] [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: 07/14/2023] [Revised: 11/03/2023] [Accepted: 11/04/2023] [Indexed: 11/19/2023] Open
Abstract
BACKGROUND For time-consuming diffusion-weighted imaging (DWI) of the breast, deep learning-based imaging acceleration appears particularly promising. PURPOSE To investigate a combined k-space-to-image reconstruction approach for scan time reduction and improved spatial resolution in breast DWI. STUDY TYPE Retrospective. POPULATION 133 women (age 49.7 ± 12.1 years) underwent multiparametric breast MRI. FIELD STRENGTH/SEQUENCE 3.0T/T2 turbo spin echo, T1 3D gradient echo, DWI (800 and 1600 sec/mm2). ASSESSMENT DWI data were retrospectively processed using deep learning-based k-space-to-image reconstruction (DL-DWI) and an additional super-resolution algorithm (SRDL-DWI). In addition to signal-to-noise ratio and apparent diffusion coefficient (ADC) comparisons among standard, DL- and SRDL-DWI, a range of quantitative similarity (e.g., structural similarity index [SSIM]) and error metrics (e.g., normalized root mean square error [NRMSE], symmetric mean absolute percent error [SMAPE], log accuracy error [LOGAC]) was calculated to analyze structural variations. Subjective image evaluation was performed independently by three radiologists on a seven-point rating scale. STATISTICAL TESTS Friedman's rank-based analysis of variance with Bonferroni-corrected pairwise post-hoc tests. P < 0.05 was considered significant. RESULTS Both DL- and SRDL-DWI allowed for a 39% reduction in simulated scan time over standard DWI (5 vs. 3 minutes). The highest image quality ratings were assigned to SRDL-DWI with good interreader agreement (ICC 0.834; 95% confidence interval 0.818-0.848). Irrespective of b-value, both standard and DL-DWI produced superior SNR compared to SRDL-DWI. ADC values were slightly higher in SRDL-DWI (+0.5%) and DL-DWI (+3.4%) than in standard DWI. Structural similarity was excellent between DL-/SRDL-DWI and standard DWI for either b value (SSIM ≥ 0.86). Calculation of error metrics (NRMSE ≤ 0.05, SMAPE ≤ 0.02, and LOGAC ≤ 0.04) supported the assumption of low voxel-wise error. DATA CONCLUSION Deep learning-based k-space-to-image reconstruction reduces simulated scan time of breast DWI by 39% without influencing structural similarity. Additionally, super-resolution interpolation allows for substantial improvement of subjective image quality. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Stephanie Tina Sauer
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Sara Aniki Christner
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Anna-Maria Lois
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Piotr Woznicki
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Carolin Curtaz
- Department of Obstetrics and Gynecology, University Hospital Würzburg, Würzburg, Germany
| | - Andreas Steven Kunz
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Elisabeth Weiland
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Thomas Benkert
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Thorsten Alexander Bley
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Bettina Baeßler
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Jan-Peter Grunz
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
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Olthof SC, Weiland E, Benkert T, Wessling D, Leyhr D, Afat S, Nikolaou K, Preibsch H. Optimizing Image Quality with High-Resolution, Deep-Learning-Based Diffusion-Weighted Imaging in Breast Cancer Patients at 1.5 T. Diagnostics (Basel) 2024; 14:1742. [PMID: 39202230 PMCID: PMC11353399 DOI: 10.3390/diagnostics14161742] [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: 06/11/2024] [Revised: 08/01/2024] [Accepted: 08/08/2024] [Indexed: 09/03/2024] Open
Abstract
The objective of this study was to evaluate a high-resolution deep-learning (DL)-based diffusion-weighted imaging (DWI) sequence for breast magnetic resonance imaging (MRI) in comparison to a standard DWI sequence (DWIStd) at 1.5 T. It is a prospective study of 38 breast cancer patients, who were scanned with DWIStd and DWIDL. Both DWI sequences were scored for image quality, sharpness, artifacts, contrast, noise, and diagnostic confidence with a Likert-scale from 1 (non-diagnostic) to 5 (excellent). The lesion diameter was evaluated on b 800 DWI, apparent diffusion coefficient (ADC), and the second subtraction (SUB) of the contrast-enhanced T1 VIBE. SNR was also calculated. Statistics included correlation analyses and paired t-tests. High-resolution DWIDL offered significantly superior image quality, sharpness, noise, contrast, and diagnostic confidence (each p < 0.02)). Artifacts were significantly higher in DWIDL by one reader (M = 4.62 vs. 4.36 Likert scale, p < 0.01) without affecting the diagnostic confidence. SNR was higher in DWIDL for b 50 and ADC maps (each p = 0.07). Acquisition time was reduced by 22% in DWIDL. The lesion diameters in DWI b 800DL and Std and ADCDL and Std were respectively 6% lower compared to the 2nd SUB. A DL-based diffusion sequence at 1.5 T in breast MRI offers a higher resolution and a faster acquisition, including only minimally more artefacts without affecting the diagnostic confidence.
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Affiliation(s)
- Susann-Cathrin Olthof
- Department of Diagnostic and Interventional Radiology, University Hospital of Tuebingen, 72076 Tuebingen, Germany; (S.A.); (K.N.); (H.P.)
| | - Elisabeth Weiland
- MR Application Predevelopment, Siemens Healthineers AG, 91052 Erlangen, Germany; (E.W.); (T.B.)
| | - Thomas Benkert
- MR Application Predevelopment, Siemens Healthineers AG, 91052 Erlangen, Germany; (E.W.); (T.B.)
| | - Daniel Wessling
- Department of Neuroradiology, University Hospital of Heidelberg, 69120 Heidelberg, Germany;
| | - Daniel Leyhr
- Faculty of Economics and Social Sciences, Institute of Sports Science & Methods Center, University of Tuebingen, 72074 Tuebingen, Germany;
| | - Saif Afat
- Department of Diagnostic and Interventional Radiology, University Hospital of Tuebingen, 72076 Tuebingen, Germany; (S.A.); (K.N.); (H.P.)
| | - Konstantin Nikolaou
- Department of Diagnostic and Interventional Radiology, University Hospital of Tuebingen, 72076 Tuebingen, Germany; (S.A.); (K.N.); (H.P.)
- Cluster of Excellence iFIT (EXC 2180) “Image Guided and Functionally Instructed Tumor Therapies”, University of Tuebingen, 72074 Tuebingen, Germany
| | - Heike Preibsch
- Department of Diagnostic and Interventional Radiology, University Hospital of Tuebingen, 72076 Tuebingen, Germany; (S.A.); (K.N.); (H.P.)
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Gullo RL, Brunekreef J, Marcus E, Han LK, Eskreis-Winkler S, Thakur SB, Mann R, Lipman KG, Teuwen J, Pinker K. AI Applications to Breast MRI: Today and Tomorrow. J Magn Reson Imaging 2024:10.1002/jmri.29358. [PMID: 38581127 PMCID: PMC11452568 DOI: 10.1002/jmri.29358] [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: 12/06/2023] [Revised: 03/07/2024] [Accepted: 03/09/2024] [Indexed: 04/08/2024] Open
Abstract
In breast imaging, there is an unrelenting increase in the demand for breast imaging services, partly explained by continuous expanding imaging indications in breast diagnosis and treatment. As the human workforce providing these services is not growing at the same rate, the implementation of artificial intelligence (AI) in breast imaging has gained significant momentum to maximize workflow efficiency and increase productivity while concurrently improving diagnostic accuracy and patient outcomes. Thus far, the implementation of AI in breast imaging is at the most advanced stage with mammography and digital breast tomosynthesis techniques, followed by ultrasound, whereas the implementation of AI in breast magnetic resonance imaging (MRI) is not moving along as rapidly due to the complexity of MRI examinations and fewer available dataset. Nevertheless, there is persisting interest in AI-enhanced breast MRI applications, even as the use of and indications of breast MRI continue to expand. This review presents an overview of the basic concepts of AI imaging analysis and subsequently reviews the use cases for AI-enhanced MRI interpretation, that is, breast MRI triaging and lesion detection, lesion classification, prediction of treatment response, risk assessment, and image quality. Finally, it provides an outlook on the barriers and facilitators for the adoption of AI in breast MRI. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 6.
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Affiliation(s)
- Roberto Lo Gullo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Joren Brunekreef
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Eric Marcus
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Lynn K Han
- Weill Cornell Medical College, New York-Presbyterian Hospital, New York, NY, USA
| | - Sarah Eskreis-Winkler
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Sunitha B Thakur
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Ritse Mann
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Kevin Groot Lipman
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Jonas Teuwen
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
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Dietzel M, Laun FB, Heiß R, Wenkel E, Bickelhaupt S, Hack C, Uder M, Ohlmeyer S. Initial experience with a next-generation low-field MRI scanner: Potential for breast imaging? Eur J Radiol 2024; 173:111352. [PMID: 38330534 DOI: 10.1016/j.ejrad.2024.111352] [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: 01/27/2023] [Revised: 01/24/2024] [Accepted: 01/29/2024] [Indexed: 02/10/2024]
Abstract
PURPOSE Broader clinical adoption of breast magnetic resonance imaging (MRI) faces challenges such as limited availability and high procedural costs. Low-field technology has shown promise in addressing these challenges. We report our initial experience using a next-generation scanner for low-field breast MRI at 0.55T. METHODS This initial cases series was part of an institutional review board-approved prospective study using a 0.55T scanner (MAGNETOM Free.Max, Siemens Healthcare, Erlangen/Germany: height < 2 m, weight < 3.2 tons, no quench pipe) equipped with a seven-channel breast coil (Noras, Höchberg/Germany). A multiparametric breast MRI protocol consisting of dynamic T1-weighted, T2-weighted, and diffusion-weighted sequences was optimized for 0.55T. Two radiologists with 12 and 20 years of experience in breast MRI evaluated the examinations. RESULTS Twelve participants (mean age: 55.3 years, range: 36-78 years) were examined. The image quality was diagnostic in all examinations and not impaired by relevant artifacts. Typical imaging phenotypes were visualized. The scan time for a complete, non-abbreviated breast MRI protocol ranged from 10:30 to 18:40 min. CONCLUSION This initial case series suggests that low-field breast MRI is feasible at diagnostic image quality within an acceptable examination time.
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Affiliation(s)
- Matthias Dietzel
- Department of Radiology, University Hospital Erlangen, Maximiliansplatz 3, 91054 Erlangen, Germany.
| | - Frederik B Laun
- Department of Radiology, University Hospital Erlangen, Maximiliansplatz 3, 91054 Erlangen, Germany.
| | - Rafael Heiß
- Department of Radiology, University Hospital Erlangen, Maximiliansplatz 3, 91054 Erlangen, Germany.
| | - Evelyn Wenkel
- Radiologie München, Burgstrasse 7, 80331 München, Germany.
| | - Sebastian Bickelhaupt
- Department of Radiology, University Hospital Erlangen, Maximiliansplatz 3, 91054 Erlangen, Germany.
| | - Carolin Hack
- Department of Gynecology, Erlangen University Hospital, Friedrich Alexander University of Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Universitätsstraße 21/23, 91054 Erlangen, Germany.
| | - Michael Uder
- Department of Radiology, University Hospital Erlangen, Maximiliansplatz 3, 91054 Erlangen, Germany.
| | - Sabine Ohlmeyer
- Department of Radiology, University Hospital Erlangen, Maximiliansplatz 3, 91054 Erlangen, Germany.
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Herrmann J, Benkert T, Brendlin A, Gassenmaier S, Hölldobler T, Maennlin S, Almansour H, Lingg A, Weiland E, Afat S. Shortening Acquisition Time and Improving Image Quality for Pelvic MRI Using Deep Learning Reconstruction for Diffusion-Weighted Imaging at 1.5 T. Acad Radiol 2024; 31:921-928. [PMID: 37500416 DOI: 10.1016/j.acra.2023.06.035] [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: 06/06/2023] [Revised: 06/28/2023] [Accepted: 06/30/2023] [Indexed: 07/29/2023]
Abstract
RATIONALE AND OBJECTIVES To determine the impact on acquisition time reduction and image quality of a deep learning (DL) reconstruction for accelerated diffusion-weighted imaging (DWI) of the pelvis at 1.5 T compared to standard DWI. MATERIALS AND METHODS A total of 55 patients (mean age, 61 ± 13 years; range, 27-89; 20 men, 35 women) were consecutively included in this retrospective, monocentric study between February and November 2022. Inclusion criteria were (1) standard DWI (DWIS) in clinically indicated magnetic resonance imaging (MRI) at 1.5 T and (2) DL-reconstructed DWI (DWIDL). All patients were examined using the institution's standard MRI protocol according to their diagnosis including DWI with two different b-values (0 and 800 s/mm2) and calculation of apparent diffusion coefficient (ADC) maps. Image quality was qualitatively assessed by four radiologists using a visual 5-point Likert scale (5 = best) for the following criteria: overall image quality, noise level, extent of artifacts, sharpness, and diagnostic confidence. The qualitative scores for DWIS and DWIDL were compared with the Wilcoxon signed-rank test. RESULTS The overall image quality was evaluated to be significantly superior in DWIDL compared to DWIS for b = 0 s/mm2, b = 800 s/mm2, and ADC maps by all readers (P < .05). The extent of noise was evaluated to be significantly less in DWIDL compared to DWIS for b = 0 s/mm2, b = 800 s/mm2, and ADC maps by all readers (P < .001). No significant differences were found regarding artifacts, lesion detectability, sharpness of organs, and diagnostic confidence (P > .05). Acquisition time for DWIS was 2:06 minutes, and simulated acquisition time for DWIDL was 1:12 minutes. CONCLUSION DL image reconstruction improves image quality, and simulation results suggest that a reduction in acquisition time for diffusion-weighted MRI of the pelvis at 1.5 T is possible.
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Affiliation(s)
- Judith Herrmann
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany
| | - Thomas Benkert
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Andreas Brendlin
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany
| | - Sebastian Gassenmaier
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany
| | - Thomas Hölldobler
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany
| | - Simon Maennlin
- 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
| | - Andreas Lingg
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany
| | - Elisabeth Weiland
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Saif Afat
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany.
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Lemainque T. Editorial for "Effect of Phase Encoding Direction on Image Quality in Single-Shot EPI Diffusion-Weighted Imaging of the Breast". J Magn Reson Imaging 2024. [PMID: 38420670 DOI: 10.1002/jmri.29309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 12/06/2023] [Indexed: 03/02/2024] Open
Affiliation(s)
- Teresa Lemainque
- Department of Diagnostic and Interventional Radiology, Medical Faculty, RWTH Aachen University, Aachen, Germany
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Allen TJ, Henze Bancroft LC, Unal O, Estkowski LD, Cashen TA, Korosec F, Strigel RM, Kelcz F, Fowler AM, Gegios A, Thai J, Lebel RM, Holmes JH. Evaluation of a Deep Learning Reconstruction for High-Quality T2-Weighted Breast Magnetic Resonance Imaging. Tomography 2023; 9:1949-1964. [PMID: 37888744 PMCID: PMC10611328 DOI: 10.3390/tomography9050152] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 10/16/2023] [Accepted: 10/16/2023] [Indexed: 10/28/2023] Open
Abstract
Deep learning (DL) reconstruction techniques to improve MR image quality are becoming commercially available with the hope that they will be applicable to multiple imaging application sites and acquisition protocols. However, before clinical implementation, these methods must be validated for specific use cases. In this work, the quality of standard-of-care (SOC) T2w and a high-spatial-resolution (HR) imaging of the breast were assessed both with and without prototype DL reconstruction. Studies were performed using data collected from phantoms, 20 retrospectively collected SOC patient exams, and 56 prospectively acquired SOC and HR patient exams. Image quality was quantitatively assessed via signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and edge sharpness. Qualitatively, all in vivo images were scored by either two or four radiologist readers using 5-point Likert scales in the following categories: artifacts, perceived sharpness, perceived SNR, and overall quality. Differences in reader scores were tested for significance. Reader preference and perception of signal intensity changes were also assessed. Application of the DL resulted in higher average SNR (1.2-2.8 times), CNR (1.0-1.8 times), and image sharpness (1.2-1.7 times). Qualitatively, the SOC acquisition with DL resulted in significantly improved image quality scores in all categories compared to non-DL images. HR acquisition with DL significantly increased SNR, sharpness, and overall quality compared to both the non-DL SOC and the non-DL HR images. The acquisition time for the HR data only required a 20% increase compared to the SOC acquisition and readers typically preferred DL images over non-DL counterparts. Overall, the DL reconstruction demonstrated improved T2w image quality in clinical breast MRI.
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Affiliation(s)
- Timothy J. Allen
- Department of Medical Physics, University of Wisconsin-Madison, 1111 Highland Avenue, Madison, WI 53705, USA
| | - Leah C. Henze Bancroft
- Department of Radiology, University of Wisconsin-Madison, 600 Highland Avenue, Madison, WI 53792, USA
| | - Orhan Unal
- Department of Medical Physics, University of Wisconsin-Madison, 1111 Highland Avenue, Madison, WI 53705, USA
- Department of Radiology, University of Wisconsin-Madison, 600 Highland Avenue, Madison, WI 53792, USA
| | | | - Ty A. Cashen
- GE Healthcare, 3000 N Grandview Blvd, Waukesha, WI 53188, USA (R.M.L.)
| | - Frank Korosec
- Department of Radiology, University of Wisconsin-Madison, 600 Highland Avenue, Madison, WI 53792, USA
| | - Roberta M. Strigel
- Department of Medical Physics, University of Wisconsin-Madison, 1111 Highland Avenue, Madison, WI 53705, USA
- Department of Radiology, University of Wisconsin-Madison, 600 Highland Avenue, Madison, WI 53792, USA
- Carbone Cancer Center, University of Wisconsin-Madison, 600 Highland Avenue, Madison, WI 53792, USA
| | - Frederick Kelcz
- Department of Radiology, University of Wisconsin-Madison, 600 Highland Avenue, Madison, WI 53792, USA
| | - Amy M. Fowler
- Department of Medical Physics, University of Wisconsin-Madison, 1111 Highland Avenue, Madison, WI 53705, USA
- Department of Radiology, University of Wisconsin-Madison, 600 Highland Avenue, Madison, WI 53792, USA
- Carbone Cancer Center, University of Wisconsin-Madison, 600 Highland Avenue, Madison, WI 53792, USA
| | - Alison Gegios
- Department of Radiology, University of Wisconsin-Madison, 600 Highland Avenue, Madison, WI 53792, USA
| | - Janice Thai
- Department of Radiology, University of Wisconsin-Madison, 600 Highland Avenue, Madison, WI 53792, USA
| | - R. Marc Lebel
- GE Healthcare, 3000 N Grandview Blvd, Waukesha, WI 53188, USA (R.M.L.)
| | - James H. Holmes
- Department of Radiology, University of Iowa, 169 Newton Road, Iowa City, IA 52242, USA
- Department of Biomedical Engineering, University of Iowa, 3100 Seamans Center, Iowa City, IA 52242, USA
- Holden Comprehensive Cancer Center, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA
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