1
|
Heckel R, Jacob M, Chaudhari A, Perlman O, Shimron E. Deep learning for accelerated and robust MRI reconstruction. MAGMA (NEW YORK, N.Y.) 2024; 37:335-368. [PMID: 39042206 DOI: 10.1007/s10334-024-01173-8] [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: 04/03/2024] [Revised: 05/24/2024] [Accepted: 05/28/2024] [Indexed: 07/24/2024]
Abstract
Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides a comprehensive overview of recent advances in DL for MRI reconstruction, and focuses on various DL approaches and architectures designed to improve image quality, accelerate scans, and address data-related challenges. It explores end-to-end neural networks, pre-trained and generative models, and self-supervised methods, and highlights their contributions to overcoming traditional MRI limitations. It also discusses the role of DL in optimizing acquisition protocols, enhancing robustness against distribution shifts, and tackling biases. Drawing on the extensive literature and practical insights, it outlines current successes, limitations, and future directions for leveraging DL in MRI reconstruction, while emphasizing the potential of DL to significantly impact clinical imaging practices.
Collapse
Affiliation(s)
- Reinhard Heckel
- Department of computer engineering, Technical University of Munich, Munich, Germany
| | - Mathews Jacob
- Department of Electrical and Computer Engineering, University of Iowa, Iowa, 52242, IA, USA
| | - Akshay Chaudhari
- Department of Radiology, Stanford University, Stanford, 94305, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, 94305, CA, USA
| | - Or Perlman
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Efrat Shimron
- Department of Electrical and Computer Engineering, Technion-Israel Institute of Technology, Haifa, 3200004, Israel.
- Department of Biomedical Engineering, Technion-Israel Institute of Technology, Haifa, 3200004, Israel.
| |
Collapse
|
2
|
Koç U, Sezer EA, Özkaya YA, Yarbay Y, Beşler MS, Taydaş O, Yalçın A, Evrimler Ş, Kızıloğlu HA, Kesimal U, Atasoy D, Oruç M, Ertuğrul M, Karakaş E, Karademir F, Sebik NB, Topuz Y, Aktan ME, Sezer Ö, Aydın Ş, Varlı S, Akdoğan E, Ülgü MM, Birinci Ş. Elevating healthcare through artificial intelligence: analyzing the abdominal emergencies data set (TR_ABDOMEN_RAD_EMERGENCY) at TEKNOFEST-2022. Eur Radiol 2024; 34:3588-3597. [PMID: 37947834 DOI: 10.1007/s00330-023-10391-y] [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: 01/17/2023] [Revised: 08/28/2023] [Accepted: 09/08/2023] [Indexed: 11/12/2023]
Abstract
OBJECTIVES The artificial intelligence competition in healthcare at TEKNOFEST-2022 provided a platform to address the complex multi-class classification challenge of abdominal emergencies using computer vision techniques. This manuscript aimed to comprehensively present the methodologies for data preparation, annotation procedures, and rigorous evaluation metrics. Moreover, it was conducted to introduce a meticulously curated abdominal emergencies data set to the researchers. METHODS The data set underwent a comprehensive central screening procedure employing diverse algorithms extracted from the e-Nabız (Pulse) and National Teleradiology System of the Republic of Türkiye, Ministry of Health. Full anonymization of the data set was conducted. Subsequently, the data set was annotated by a group of ten experienced radiologists. The evaluation process was executed by calculating F1 scores, which were derived from the intersection over union values between the predicted bounding boxes and the corresponding ground truth (GT) bounding boxes. The establishment of baseline performance metrics involved computing the average of the highest five F1 scores. RESULTS Observations indicated a progressive decline in F1 scores as the threshold value increased. Furthermore, it could be deduced that class 6 (abdominal aortic aneurysm/dissection) was relatively straightforward to detect compared to other classes, with class 5 (acute diverticulitis) presenting the most formidable challenge. It is noteworthy, however, that if all achieved outcomes for all classes were considered with a threshold of 0.5, the data set's complexity and associated challenges became pronounced. CONCLUSION This data set's significance lies in its pioneering provision of labels and GT-boxes for six classes, fostering opportunities for researchers. CLINICAL RELEVANCE STATEMENT The prompt identification and timely intervention in cases of emergent medical conditions hold paramount significance. The handling of patients' care can be augmented, while the potential for errors is minimized, particularly amidst high caseload scenarios, through the application of AI. KEY POINTS • The data set used in artificial intelligence competition in healthcare (TEKNOFEST-2022) provides a 6-class data set of abdominal CT images consisting of a great variety of abdominal emergencies. • This data set is compiled from the National Teleradiology System data repository of emergency radiology departments of 459 hospitals. • Radiological data on abdominal emergencies is scarce in literature and this annotated competition data set can be a valuable resource for further studies and new AI models.
Collapse
Affiliation(s)
- Ural Koç
- Department of Radiology, Ankara Bilkent City Hospital, Ankara, Türkiye.
| | - Ebru Akçapınar Sezer
- Artificial Intelligence Division, Department of Computer Engineering, Hacettepe University, Ankara, Türkiye
| | | | - Yasin Yarbay
- General Directorate of Health Information Systems, Ministry of Health, Ankara, Türkiye
| | | | - Onur Taydaş
- Department of Radiology, Faculty of Medicine, Sakarya University, Sakarya, Türkiye
| | - Ahmet Yalçın
- Department of Radiology, Faculty of Medicine, Erzurum Atatürk University, Erzurum, Türkiye
| | - Şehnaz Evrimler
- Department of Radiology, Ankara Etlik City Hospital, Ankara, Türkiye
| | | | - Uğur Kesimal
- Department of Radiology, Ankara Training and Research Hospital, Ankara, Türkiye
| | - Dilara Atasoy
- Department of Radiology, Sivas Numune State Hospital, Sivas, Türkiye
| | - Meltem Oruç
- Department of Radiology, Karaman Training and Research Hospital, Karaman, Türkiye
| | - Mustafa Ertuğrul
- Department of Radiology, Ürgüp State Hospital, Nevşehir, Türkiye
| | - Emrah Karakaş
- General Directorate of Health Information Systems, Ministry of Health, Ankara, Türkiye
| | | | - Nihat Barış Sebik
- General Directorate of Health Information Systems, Ministry of Health, Ankara, Türkiye
| | | | | | - Özgür Sezer
- General Directorate of Health Information Systems, Ministry of Health, Ankara, Türkiye
| | - Şahin Aydın
- General Directorate of Health Information Systems, Ministry of Health, Ankara, Türkiye
| | - Songül Varlı
- Health Institutes of Türkiye, İstanbul, Türkiye
- Department of Computer Engineering, Yıldız Technical University, İstanbul, Türkiye
| | - Erhan Akdoğan
- Health Institutes of Türkiye, İstanbul, Türkiye
- Department of Mechatronics Engineering, Faculty of Mechanical Engineering, Yıldız Technical University, İstanbul, Türkiye
| | - Mustafa Mahir Ülgü
- General Directorate of Health Information Systems, Ministry of Health, Ankara, Türkiye
| | | |
Collapse
|
3
|
Perchik JD, Rothenberg SA, Tridandapani S, Perchik LM, Smith AD. Artificial Intelligence in Body Imaging: An Overview of Commercially Available Tools. Semin Roentgenol 2023; 58:196-202. [PMID: 37087140 DOI: 10.1053/j.ro.2023.02.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 02/18/2023] [Accepted: 02/23/2023] [Indexed: 03/29/2023]
|
4
|
Hirvonen J, Heikkinen J, Nyman M, Happonen T, Velhonoja J, Irjala H, Soukka T, Mattila K, Nurminen J. MRI of acute neck infections: evidence summary and pictorial review. Insights Imaging 2023; 14:5. [PMID: 36617619 PMCID: PMC9826778 DOI: 10.1186/s13244-022-01347-9] [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: 09/19/2022] [Accepted: 12/04/2022] [Indexed: 01/09/2023] Open
Abstract
Infection of the deep neck spaces is a life-threatening acute illness that requires prompt diagnosis and treatment. Magnetic resonance imaging (MRI) offers unsurpassed soft tissue discrimination and is therefore well suited for imaging neck infections. Recently, the feasibility, diagnostic accuracy, and clinical significance of this method have been documented in patients with acute neck infections. This review article summarizes the scientific evidence, provides a practical guide to image acquisition and interpretation, reviews the most common imaging findings, and discusses some difficult diagnoses and pitfalls in acute neck infections, to help both radiologists and clinicians in managing these critically ill patients.
Collapse
Affiliation(s)
- Jussi Hirvonen
- grid.1374.10000 0001 2097 1371Department of Radiology, University of Turku and Turku University Hospital, Turku, Finland ,grid.412330.70000 0004 0628 2985Medical Imaging Center, Department of Radiology, Tampere University and Tampere University Hospital, Tampere, Finland
| | - Jaakko Heikkinen
- grid.1374.10000 0001 2097 1371Department of Radiology, University of Turku and Turku University Hospital, Turku, Finland
| | - Mikko Nyman
- grid.1374.10000 0001 2097 1371Department of Radiology, University of Turku and Turku University Hospital, Turku, Finland
| | - Tatu Happonen
- grid.1374.10000 0001 2097 1371Department of Radiology, University of Turku and Turku University Hospital, Turku, Finland
| | - Jarno Velhonoja
- grid.1374.10000 0001 2097 1371Department of Otorhinolaryngology-Head and Neck Surgery, University of Turku and Turku University Hospital, Turku, Finland
| | - Heikki Irjala
- grid.1374.10000 0001 2097 1371Department of Otorhinolaryngology-Head and Neck Surgery, University of Turku and Turku University Hospital, Turku, Finland
| | - Tero Soukka
- grid.1374.10000 0001 2097 1371Department of Oral and Maxillofacial Surgery, University of Turku, Turku, Finland
| | - Kimmo Mattila
- grid.1374.10000 0001 2097 1371Department of Radiology, University of Turku and Turku University Hospital, Turku, Finland
| | - Janne Nurminen
- grid.1374.10000 0001 2097 1371Department of Radiology, University of Turku and Turku University Hospital, Turku, Finland
| |
Collapse
|
5
|
Irmici G, Cè M, Caloro E, Khenkina N, Della Pepa G, Ascenti V, Martinenghi C, Papa S, Oliva G, Cellina M. Chest X-ray in Emergency Radiology: What Artificial Intelligence Applications Are Available? Diagnostics (Basel) 2023; 13:diagnostics13020216. [PMID: 36673027 PMCID: PMC9858224 DOI: 10.3390/diagnostics13020216] [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: 12/10/2022] [Revised: 12/28/2022] [Accepted: 01/03/2023] [Indexed: 01/11/2023] Open
Abstract
Due to its widespread availability, low cost, feasibility at the patient's bedside and accessibility even in low-resource settings, chest X-ray is one of the most requested examinations in radiology departments. Whilst it provides essential information on thoracic pathology, it can be difficult to interpret and is prone to diagnostic errors, particularly in the emergency setting. The increasing availability of large chest X-ray datasets has allowed the development of reliable Artificial Intelligence (AI) tools to help radiologists in everyday clinical practice. AI integration into the diagnostic workflow would benefit patients, radiologists, and healthcare systems in terms of improved and standardized reporting accuracy, quicker diagnosis, more efficient management, and appropriateness of the therapy. This review article aims to provide an overview of the applications of AI for chest X-rays in the emergency setting, emphasizing the detection and evaluation of pneumothorax, pneumonia, heart failure, and pleural effusion.
Collapse
Affiliation(s)
- Giovanni Irmici
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Maurizio Cè
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Elena Caloro
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Natallia Khenkina
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Gianmarco Della Pepa
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Velio Ascenti
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Carlo Martinenghi
- Radiology Department, San Raffaele Hospital, Via Olgettina 60, 20132 Milan, Italy
| | - Sergio Papa
- Unit of Diagnostic Imaging and Stereotactic Radiosurgery, Centro Diagnostico Italiano, Via Saint Bon 20, 20147 Milan, Italy
| | - Giancarlo Oliva
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121 Milan, Italy
| | - Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121 Milan, Italy
| |
Collapse
|