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Rajamohan N, Bagga B, Bansal B, Ginocchio L, Gupta A, Chandarana H. Deep Learning-accelerated MRI in Body and Chest. J Comput Assist Tomogr 2025:00004728-990000000-00459. [PMID: 40360272 DOI: 10.1097/rct.0000000000001762] [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: 12/23/2024] [Accepted: 03/27/2025] [Indexed: 05/15/2025]
Abstract
Deep learning reconstruction (DLR) provides an elegant solution for MR acceleration while preserving image quality. This advancement is crucial for body imaging, which is frequently marred by the increased likelihood of motion-related artifacts. Multiple vendor-specific models focusing on T2, T1, and diffusion-weighted imaging have been developed for the abdomen, pelvis, and chest, with the liver and prostate being the most well-studied organ systems. Variational networks with supervised DL models, including data consistency layers and regularizers, are the most common DLR methods. The common theme for all single-center studies on this subject has been noninferior or superior image quality metrics and lesion conspicuity to conventional sequences despite significant acquisition time reduction. DLR also provides a potential for denoising, artifact reduction, increased resolution, and increased signal-noise ratio (SNR) and contrast-to-noise ratio (CNR) that can be balanced with acceleration benefits depending on the imaged organ system. Some specific challenges faced by DLR include slightly reduced lesion detection, cardiac motion-related signal loss, regional SNR variations, and variabilities in ADC measurements as reported in different organ systems. Continued investigations with large-scale multicenter prospective clinical validation of DLR to document generalizability and demonstrate noninferior diagnostic accuracy with histopathologic correlation are the need of the hour. The creation of vendor-neutral solutions, open data sharing, and diversifying training data sets are also critical to strengthening model robustness.
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Affiliation(s)
- Naveen Rajamohan
- Department of Radiology, UT Southwestern Medical Center, Dallas, TX
| | - Barun Bagga
- Department of Radiology, NYU Grossman Long Island School of Medicine, Mineola, NY
| | - Bhavik Bansal
- All India Institute of Medical Sciences, New Delhi, India
| | - Luke Ginocchio
- Department of Radiology, NYU Grossman School of Medicine, New York, NY
| | - Amit Gupta
- All India Institute of Medical Sciences, New Delhi, India
| | - Hersh Chandarana
- Department of Radiology, Center for Advanced Imaging Innovation and Research, NYU Grossman School of Medicine, New York, NY
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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 2025; 35:1993-2004. [PMID: 39242400 PMCID: PMC11914325 DOI: 10.1007/s00330-024-11056-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [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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Kubicka F, Tan Q, Meyer T, Nickel D, Weiland E, Wagner M, Marticorena Garcia SR. Deep-Learning-Based Reconstruction of Single-Breath-Hold 3 mm HASTE Improves Abdominal Image Quality and Reduces Acquisition Time: A Quantitative Analysis. Curr Oncol 2025; 32:30. [PMID: 39851946 PMCID: PMC11763676 DOI: 10.3390/curroncol32010030] [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: 11/26/2024] [Revised: 12/24/2024] [Accepted: 12/31/2024] [Indexed: 01/26/2025] Open
Abstract
Purpose: Breath-hold T2-weighted half-Fourier acquisition single-shot turbo spin echo (HASTE) magnetic resonance imaging (MRI) of the upper abdomen with a slice thickness below 5 mm suffers from high image noise and blurring. The purpose of this prospective study was to improve image quality and accelerate imaging acquisition by using single-breath-hold T2-weighted HASTE with deep learning (DL) reconstruction (DL-HASTE) with a 3 mm slice thickness. Method: MRI of the upper abdomen with DL-HASTE was performed in 35 participants (5 healthy volunteers and 30 patients) at 3 Tesla. In a subgroup of five healthy participants, signal-to-noise ratio (SNR) analysis was used after DL reconstruction to identify the smallest possible layer thickness (1, 2, 3, 4, 5 mm). DL-HASTE was acquired with a 3 mm slice thickness (DL-HASTE-3 mm) in 30 patients and compared with 5 mm DL-HASTE (DL-HASTE-5 mm) and with standard HASTE (standard-HASTE-5 mm). Image quality and motion artifacts were assessed quantitatively using Laplacian variance and semi-quantitatively by two radiologists using five-point Likert scales. Results: In the five healthy participants, DL-HASTE-3 mm was identified as the optimal slice (SNR 23.227 ± 3.901). Both DL-HASTE-3 mm and DL-HASTE-5 mm were assigned significantly higher overall image quality scores than standard-HASTE-5 mm (Laplacian variance, both p < 0.001; Likert scale, p < 0.001). Compared with DL-HASTE-5 mm (1.10 × 10-5 ± 6.93 × 10-6), DL-HASTE-3 mm (1.56 × 10-5 ± 8.69 × 10-6) provided a significantly higher SNR Laplacian variance (p < 0.001) and sharpness sub-scores for the intestinal tract, adrenal glands, and small anatomic structures (bile ducts, pancreatic ducts, and vessels; p < 0.05). Lesion detectability was rated excellent for both DL-HASTE-3 mm and DL-HASTE-5 mm (both: 5 [IQR4-5]) and was assigned higher scores than standard-HASTE-5 mm (4 [IQR4-5]; p < 0.001). DL-HASTE reduced the acquisition time by 63-69% compared with standard-HASTE-5 mm (p < 0.001). Conclusions: DL-HASTE is a robust abdominal MRI technique that improves image quality while at the same time reducing acquisition time compared with the routine clinical HASTE sequence. Using ultra-thin DL-HASTE-3 mm results in an even greater improvement with a similar SNR.
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Affiliation(s)
- Felix Kubicka
- Department of Radiology, Charité—Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany (M.W.); (S.R.M.G.)
| | - Qinxuan Tan
- Department of Radiology, Charité—Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany (M.W.); (S.R.M.G.)
| | - Tom Meyer
- Department of Radiology, Charité—Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany (M.W.); (S.R.M.G.)
| | - Dominik Nickel
- MR Applications Predevelopment, Siemens Healthcare GmbH, Allee am Roethelheimpark 2, 91052 Erlangen, Germany (E.W.)
| | - Elisabeth Weiland
- MR Applications Predevelopment, Siemens Healthcare GmbH, Allee am Roethelheimpark 2, 91052 Erlangen, Germany (E.W.)
| | - Moritz Wagner
- Department of Radiology, Charité—Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany (M.W.); (S.R.M.G.)
| | - Stephan Rodrigo Marticorena Garcia
- Department of Radiology, Charité—Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany (M.W.); (S.R.M.G.)
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Wilpert C, Schneider H, Rau A, Russe MF, Oerther B, Strecker R, Nickel MD, Weiland E, Haeger A, Benndorf M, Mayrhofer T, Weiss J, Bamberg F, Windfuhr-Blum M, Neubauer J. Faster Acquisition and Improved Image Quality of T2-Weighted Dixon Breast MRI at 3T Using Deep Learning: A Prospective Study. Korean J Radiol 2025; 26:29-42. [PMID: 39780629 PMCID: PMC11717867 DOI: 10.3348/kjr.2023.1303] [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: 12/29/2023] [Revised: 10/15/2024] [Accepted: 10/15/2024] [Indexed: 01/11/2025] Open
Abstract
OBJECTIVE The aim of this study was to compare image quality features and lesion characteristics between a faster deep learning (DL) reconstructed T2-weighted (T2-w) fast spin-echo (FSE) Dixon sequence with super-resolution (T2DL) and a conventional T2-w FSE Dixon sequence (T2STD) for breast magnetic resonance imaging (MRI). MATERIALS AND METHODS This prospective study was conducted between November 2022 and April 2023 using a 3T scanner. Both T2DL and T2STD sequences were acquired for each patient. Quantitative analysis was based on region-of-interest (ROI) measurements of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Qualitative analysis was performed independently by two radiologists using Likert scales to evaluate various image quality features, morphology, and diagnostic confidence for cysts and breast cancers. Reader preference between T2DL and T2STD was assessed via side-by-side comparison, and inter-reader reliability was also analyzed. RESULTS Total of 151 women were enrolled, with 140 women (mean age: 52 ± 14 years; 85 cysts and 31 breast cancers) included in the final analysis. The acquisition time was 110 s ± 0 for T2DL compared to 266 s ± 0 for T2STD. SNR and CNR were significantly higher in T2DL (P < 0.001). T2DL was associated with higher image quality scores, reduced noise, and fewer artifacts (P < 0.001). All evaluated anatomical regions (breast and axilla), breast implants, and bone margins were rated higher in T2DL (P ≤ 0.008), except for bone marrow, which scored higher in T2STD (P < 0.001). Scores for conspicuity, sharpness/margins, and microstructure of cysts and breast cancers were higher in T2DL (P ≤ 0.002). Diagnostic confidence for cysts was improved with T2DL (P < 0.001). Readers significantly preferred T2DL over T2STD in side-by-side comparisons (P < 0.001). CONCLUSION T2DL effectively corrected for SNR loss caused by accelerated image acquisition and provided a 58% reduction in acquisition time compared to T2STD. This led to fewer artifacts and improved overall image quality. Thus, T2DL is feasible and has the potential to replace conventional T2-w sequences for breast MRI examinations.
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Affiliation(s)
- Caroline Wilpert
- Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
| | - Hannah Schneider
- Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Alexander Rau
- Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Department of Neuroradiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Maximilian Frederic Russe
- Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Benedict Oerther
- Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Ralph Strecker
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
- EMEA Scientific Partnerships, Siemens Healthcare GmbH, Erlangen, Germany
| | | | - Elisabeth Weiland
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Alexa Haeger
- Department of Neurology, RWTH Aachen University, Aachen, Germany
- JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich GmbH and RWTH Aachen University, Aachen, Germany
| | - Matthias Benndorf
- Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Thomas Mayrhofer
- School of Business Studies, Stralsund University of Applied Sciences, Stralsund, Germany
- Cardiovascular Imaging Research Center, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Jakob Weiss
- Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Fabian Bamberg
- Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Marisa Windfuhr-Blum
- Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Jakob Neubauer
- Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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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; 49:3615-3636. [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] [MESH Headings] [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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Zhang X, Wang Y, Xu X, Zhang J, Sun Y, Hu M, Wang S, Li Y, Chen Y, Zhao X. Bladder MRI with deep learning-based reconstruction: a prospective evaluation of muscle invasiveness using VI-RADS. Abdom Radiol (NY) 2024; 49:1615-1625. [PMID: 38652125 DOI: 10.1007/s00261-024-04280-1] [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: 01/22/2024] [Revised: 03/03/2024] [Accepted: 03/05/2024] [Indexed: 04/25/2024]
Abstract
PURPOSE To investigate the influence of deep learning reconstruction (DLR) on bladder MRI, specifically examination time, image quality, and diagnostic performance of vesical imaging reporting and data system (VI-RADS) within a prospective clinical cohort. METHODS Seventy participants with bladder cancer who underwent MRI between August 2022 and February 2023 with a protocol containing standard T2-weighted imaging (T2WIS), standard diffusion-weighted imaging (DWIS), fast T2WI with DLR (T2WIDL), and fast DWI with DLR (DWIDL) were enrolled in this prospective study. Imaging quality was evaluated by measuring signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and qualitative image quality scoring. Additionally, the apparent diffusion coefficient (ADC) of bladder lesions derived from DWIS and DWIDL was measured and VI-RADS scoring was performed. Paired t-test or paired Wilcoxon signed-rank test were performed to compare image quality score, SNR, CNR, and ADC between standard sequences and fast sequences with DLR. The diagnostic performance for VI-RADS was assessed using the area under the receiver operating characteristic curve (AUC). RESULTS Compared to T2WIS and DWIS, T2WIDL and DWIDL reduced the acquisition time from 5:57 min to 3:13 min and showed significantly higher SNR, CNR, qualitative image quality score of overall image quality, image sharpness, and lesion conspicuity. There were no significant differences in ADC and AUC of VI-RADS between standard sequences and fast sequences with DLR. CONCLUSIONS The application of DLR to T2WI and DWI reduced examination time and significantly improved image quality, maintaining ADC and the diagnostic performance of VI-RADS for evaluating muscle invasion in bladder cancer.
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Affiliation(s)
- Xinxin Zhang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Yichen Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Xiaojuan Xu
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Jie Zhang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Yuying Sun
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Mancang Hu
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Sicong Wang
- GE Healthcare, MR Research China, Tongji South Road No1, Beijing, 100176, China
| | - Yi Li
- School of Statistics and Mathematics, Nanjing Audit University, Nanjing, 211815, China
| | - Yan Chen
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
| | - Xinming Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
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Pierre K, Haneberg AG, Kwak S, Peters KR, Hochhegger B, Sananmuang T, Tunlayadechanont P, Tighe PJ, Mancuso A, Forghani R. Applications of Artificial Intelligence in the Radiology Roundtrip: Process Streamlining, Workflow Optimization, and Beyond. Semin Roentgenol 2023; 58:158-169. [PMID: 37087136 DOI: 10.1053/j.ro.2023.02.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Accepted: 02/14/2023] [Indexed: 04/24/2023]
Abstract
There are many impactful applications of artificial intelligence (AI) in the electronic radiology roundtrip and the patient's journey through the healthcare system that go beyond diagnostic applications. These tools have the potential to improve quality and safety, optimize workflow, increase efficiency, and increase patient satisfaction. In this article, we review the role of AI for process improvement and workflow enhancement which includes applications beginning from the time of order entry, scan acquisition, applications supporting the image interpretation task, and applications supporting tasks after image interpretation such as result communication. These non-diagnostic workflow and process optimization tasks are an important part of the arsenal of potential AI tools that can streamline day to day clinical practice and patient care.
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Affiliation(s)
- Kevin Pierre
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Department of Radiology, University of Florida College of Medicine, Gainesville, FL
| | - Adam G Haneberg
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Division of Medical Physics, Department of Radiology, University of Florida College of Medicine, Gainesville, FL
| | - Sean Kwak
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL
| | - Keith R Peters
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Department of Radiology, University of Florida College of Medicine, Gainesville, FL
| | - Bruno Hochhegger
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Department of Radiology, University of Florida College of Medicine, Gainesville, FL
| | - Thiparom Sananmuang
- Department of Diagnostic and Therapeutic Radiology and Research, Faculty of Medicine Ramathibodi Hospital, Ratchathewi, Bangkok, Thailand
| | - Padcha Tunlayadechanont
- Department of Diagnostic and Therapeutic Radiology and Research, Faculty of Medicine Ramathibodi Hospital, Ratchathewi, Bangkok, Thailand
| | - Patrick J Tighe
- Departments of Anesthesiology & Orthopaedic Surgery, University of Florida College of Medicine, Gainesville, FL
| | - Anthony Mancuso
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Department of Radiology, University of Florida College of Medicine, Gainesville, FL
| | - Reza Forghani
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL; Department of Radiology, University of Florida College of Medicine, Gainesville, FL; Division of Medical Physics, Department of Radiology, University of Florida College of Medicine, Gainesville, FL.
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Application of a Deep Learning Algorithm for Combined Super-Resolution and Partial Fourier Reconstruction Including Time Reduction in T1-Weighted Precontrast and Postcontrast Gradient Echo Imaging of Abdominopelvic MR Imaging. Diagnostics (Basel) 2022; 12:diagnostics12102370. [PMID: 36292057 PMCID: PMC9600324 DOI: 10.3390/diagnostics12102370] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 09/25/2022] [Accepted: 09/26/2022] [Indexed: 11/17/2022] Open
Abstract
Purpose: The purpose of this study was to test the technical feasibility and the impact on the image quality of a deep learning-based super-resolution reconstruction algorithm in 1.5 T abdominopelvic MR imaging. Methods: 44 patients who underwent abdominopelvic MRI were retrospectively included, of which 4 had to be subsequently excluded. After the acquisition of the conventional volume interpolated breath-hold examination (VIBEStd), images underwent postprocessing, using a deep learning-based iterative denoising super-resolution reconstruction algorithm for partial Fourier acquisitions (VIBESR). Image analysis of 40 patients with a mean age of 56 years (range 18−84 years) was performed qualitatively by two radiologists independently using a Likert scale ranging from 1 to 5, where 5 was considered the best rating. Results: Image analysis showed an improvement of image quality, noise, sharpness of the organs and lymph nodes, and sharpness of the intestine for pre- and postcontrast images in VIBESR compared to VIBEStd (each p < 0.001). Lesion detectability was better for VIBESR (p < 0.001), while there were no differences concerning the number of lesions. Average acquisition time was 16 s (±1) for the upper abdomen and 15 s (±1) for the pelvis for VIBEStd, and 15 s (±1) for the upper abdomen and 14 s (±1) for the pelvis for VIBESR. Conclusion: This study demonstrated the technical feasibility of a deep learning-based super-resolution algorithm including partial Fourier technique in abdominopelvic MR images and illustrated a significant improvement of image quality, noise, and sharpness while reducing TA.
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