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Flory MN, Napel S, Tsai EB. Artificial Intelligence in Radiology: Opportunities and Challenges. Semin Ultrasound CT MR 2024; 45:152-160. [PMID: 38403128 DOI: 10.1053/j.sult.2024.02.004] [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: 02/27/2024]
Abstract
Artificial intelligence's (AI) emergence in radiology elicits both excitement and uncertainty. AI holds promise for improving radiology with regards to clinical practice, education, and research opportunities. Yet, AI systems are trained on select datasets that can contain bias and inaccuracies. Radiologists must understand these limitations and engage with AI developers at every step of the process - from algorithm initiation and design to development and implementation - to maximize benefit and minimize harm that can be enabled by this technology.
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Affiliation(s)
- Marta N Flory
- Department of Radiology, Stanford University School of Medicine, Center for Academic Medicine, Palo Alto, CA
| | - Sandy Napel
- Department of Radiology, Stanford University School of Medicine, Center for Academic Medicine, Palo Alto, CA
| | - Emily B Tsai
- Department of Radiology, Stanford University School of Medicine, Center for Academic Medicine, Palo Alto, CA.
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2
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Yoon MA, Gold GE, Chaudhari AS. Accelerated Musculoskeletal Magnetic Resonance Imaging. J Magn Reson Imaging 2023. [PMID: 38156716 DOI: 10.1002/jmri.29205] [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: 10/24/2023] [Revised: 12/13/2023] [Accepted: 12/14/2023] [Indexed: 01/03/2024] Open
Abstract
With a substantial growth in the use of musculoskeletal MRI, there has been a growing need to improve MRI workflow, and faster imaging has been suggested as one of the solutions for a more efficient examination process. Consequently, there have been considerable advances in accelerated MRI scanning methods. This article aims to review the basic principles and applications of accelerated musculoskeletal MRI techniques including widely used conventional acceleration methods, more advanced deep learning-based techniques, and new approaches to reduce scan time. Specifically, conventional accelerated MRI techniques, including parallel imaging, compressed sensing, and simultaneous multislice imaging, and deep learning-based accelerated MRI techniques, including undersampled MR image reconstruction, super-resolution imaging, artifact correction, and generation of unacquired contrast images, are discussed. Finally, new approaches to reduce scan time, including synthetic MRI, novel sequences, and new coil setups and designs, are also reviewed. We believe that a deep understanding of these fast MRI techniques and proper use of combined acceleration methods will synergistically improve scan time and MRI workflow in daily practice. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Min A Yoon
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Garry E Gold
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
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Virtual CT Myelography: A Patch-Based Machine Learning Model to Improve Intraspinal Soft Tissue Visualization on Unenhanced Dual-Energy Lumbar Spine CT. INFORMATION 2022. [DOI: 10.3390/info13090412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Background: Distinguishing between the spinal cord and cerebrospinal fluid (CSF) non-invasively on CT is challenging due to their similar mass densities. We hypothesize that patch-based machine learning applied to dual-energy CT can accurately distinguish CSF from neural or other tissues based on the center voxel and neighboring voxels. Methods: 88 regions of interest (ROIs) from 12 patients’ dual-energy (100 and 140 kVp) lumbar spine CT exams were manually labeled by a neuroradiologist as one of 4 major tissue types (water, fat, bone, and nonspecific soft tissue). Four-class classifier convolutional neural networks were trained, validated, and tested on thousands of nonoverlapping patches extracted from 82 ROIs among 11 CT exams, with each patch representing pixel values (at low and high energies) of small, rectangular, 3D CT volumes. Different patch sizes were evaluated, ranging from 3 × 3 × 3 × 2 to 7 × 7 × 7 × 2. A final ensemble model incorporating all patch sizes was tested on patches extracted from six ROIs in a holdout patient. Results: Individual models showed overall test accuracies ranging from 99.8% for 3 × 3 × 3 × 2 patches (N = 19,423) to 98.1% for 7 × 7 × 7 × 2 patches (N = 1298). The final ensemble model showed 99.4% test classification accuracy, with sensitivities and specificities of 90% and 99.6%, respectively, for the water class and 98.6% and 100% for the soft tissue class. Conclusions: Convolutional neural networks utilizing local low-level features on dual-energy spine CT can yield accurate tissue classification and enhance the visualization of intraspinal neural tissue.
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Stamatelatou A, Scheenen TWJ, Heerschap A. Developments in proton MR spectroscopic imaging of prostate cancer. MAGMA (NEW YORK, N.Y.) 2022; 35:645-665. [PMID: 35445307 PMCID: PMC9363347 DOI: 10.1007/s10334-022-01011-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 03/04/2022] [Accepted: 03/22/2022] [Indexed: 10/25/2022]
Abstract
In this paper, we review the developments of 1H-MR spectroscopic imaging (MRSI) methods designed to investigate prostate cancer, covering key aspects such as specific hardware, dedicated pulse sequences for data acquisition and data processing and quantification techniques. Emphasis is given to recent advancements in MRSI methodologies, as well as future developments, which can lead to overcome difficulties associated with commonly employed MRSI approaches applied in clinical routine. This includes the replacement of standard PRESS sequences for volume selection, which we identified as inadequate for clinical applications, by sLASER sequences and implementation of 1H MRSI without water signal suppression. These may enable a new evaluation of the complementary role and significance of MRSI in prostate cancer management.
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Affiliation(s)
- Angeliki Stamatelatou
- Department of Medical Imaging (766), Radboud University Medical Center Nijmegen, Geert Grooteplein 10, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands.
| | - Tom W J Scheenen
- Department of Medical Imaging (766), Radboud University Medical Center Nijmegen, Geert Grooteplein 10, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands
| | - Arend Heerschap
- Department of Medical Imaging (766), Radboud University Medical Center Nijmegen, Geert Grooteplein 10, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands
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Huber FA, Guggenberger R. AI MSK clinical applications: spine imaging. Skeletal Radiol 2022; 51:279-291. [PMID: 34263344 PMCID: PMC8692301 DOI: 10.1007/s00256-021-03862-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 06/28/2021] [Accepted: 07/03/2021] [Indexed: 02/02/2023]
Abstract
Recent investigations have focused on the clinical application of artificial intelligence (AI) for tasks specifically addressing the musculoskeletal imaging routine. Several AI applications have been dedicated to optimizing the radiology value chain in spine imaging, independent from modality or specific application. This review aims to summarize the status quo and future perspective regarding utilization of AI for spine imaging. First, the basics of AI concepts are clarified. Second, the different tasks and use cases for AI applications in spine imaging are discussed and illustrated by examples. Finally, the authors of this review present their personal perception of AI in daily imaging and discuss future chances and challenges that come along with AI-based solutions.
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Affiliation(s)
- Florian A. Huber
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091 Zurich, Switzerland
| | - Roman Guggenberger
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091 Zurich, Switzerland
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Koktzoglou I, Huang R, Ankenbrandt WJ, Walker MT, Edelman RR. Super-resolution head and neck MRA using deep machine learning. Magn Reson Med 2021; 86:335-345. [PMID: 33619802 DOI: 10.1002/mrm.28738] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 01/25/2021] [Accepted: 01/26/2021] [Indexed: 12/29/2022]
Abstract
PURPOSE To probe the feasibility of deep learning-based super-resolution (SR) reconstruction applied to nonenhanced MR angiography (MRA) of the head and neck. METHODS High-resolution 3D thin-slab stack-of-stars quiescent interval slice-selective (QISS) MRA of the head and neck was obtained in eight subjects (seven healthy volunteers, one patient) at 3T. The spatial resolution of high-resolution ground-truth MRA data in the slice-encoding direction was reduced by factors of 2 to 6. Four deep neural network (DNN) SR reconstructions were applied, with two based on U-Net architectures (2D and 3D) and two (2D and 3D) consisting of serial convolutions with a residual connection. SR images were compared to ground-truth high-resolution data using Dice similarity coefficient (DSC), structural similarity index measure (SSIM), arterial diameter, and arterial sharpness measurements. Image review of the optimal DNN SR reconstruction was done by two experienced neuroradiologists. RESULTS DNN SR of up to twofold and fourfold lower-resolution (LR) input volumes provided images that resembled those of the original high-resolution ground-truth volumes for intracranial and extracranial arterial segments, and improved DSC, SSIM, arterial diameters, and arterial sharpness relative to LR volumes (P < .001). The 3D DNN SR outperformed 2D DNN SR reconstruction. According to two neuroradiologists, 3D DNN SR reconstruction consistently improved image quality with respect to LR input volumes (P < .001). CONCLUSION DNN-based SR reconstruction of 3D head and neck QISS MRA offers the potential for up to fourfold reduction in acquisition time for neck vessels without the need to commensurately sacrifice spatial resolution.
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Affiliation(s)
- Ioannis Koktzoglou
- Department of Radiology, NorthShore University HealthSystem, Evanston, Illinois, USA.,Pritzker School of Medicine, University of Chicago, Chicago, Illinois, USA
| | - Rong Huang
- Department of Radiology, NorthShore University HealthSystem, Evanston, Illinois, USA
| | - William J Ankenbrandt
- Department of Radiology, NorthShore University HealthSystem, Evanston, Illinois, USA.,Pritzker School of Medicine, University of Chicago, Chicago, Illinois, USA
| | - Matthew T Walker
- Department of Radiology, NorthShore University HealthSystem, Evanston, Illinois, USA.,Pritzker School of Medicine, University of Chicago, Chicago, Illinois, USA
| | - Robert R Edelman
- Department of Radiology, NorthShore University HealthSystem, Evanston, Illinois, USA.,Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
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Oztek MA, Brunnquell CL, Hoff MN, Boulter DJ, Mossa-Basha M, Beauchamp LH, Haynor DL, Nguyen XV. Practical Considerations for Radiologists in Implementing a Patient-friendly MRI Experience. Top Magn Reson Imaging 2021; 29:181-186. [PMID: 32511199 DOI: 10.1097/rmr.0000000000000247] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
For many patients, numerous unpleasant features of the magnetic resonance imaging (MRI) experience such as scan duration, auditory noise, spatial confinement, and motion restrictions can lead to premature termination or low diagnostic quality of imaging studies. This article discusses practical, patient-oriented considerations that are helpful for radiologists contemplating ways to improve the MRI experience for patients. Patient friendly scanner properties are discussed, with an emphasis on literature findings of effectiveness in mitigating patient claustrophobia, other anxiety, or motion and on reducing scan incompletion rates or need for sedation. As shorter scanning protocols designed to answer specific diagnostic questions may be more practical and tolerable to the patient than a full-length standard-of-care examination, a few select protocol adjustments potentially useful for specific clinical settings are discussed. In addition, adjunctive devices such as audiovisual or other sensory aides that can be useful distractive approaches to reduce patient discomfort are considered. These modifications to the MRI scanning process not only allow for a more pleasant experience for patients, but they may also increase patient compliance and decrease patient movement to allow more efficient acquisition of diagnostic-quality images.
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Affiliation(s)
- Murat Alp Oztek
- Department of Radiology, University of Washington School of Medicine, Seattle, WA.,Department of Radiology, Seattle Children's Hospital, Seattle, WA
| | | | - Michael N Hoff
- Department of Radiology, University of Washington School of Medicine, Seattle, WA
| | - Daniel J Boulter
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH
| | - Mahmud Mossa-Basha
- Department of Radiology, University of Washington School of Medicine, Seattle, WA
| | - Luke H Beauchamp
- Michigan State University College of Human Medicine, East Lansing, MI
| | - David L Haynor
- Department of Radiology, University of Washington School of Medicine, Seattle, WA
| | - Xuan V Nguyen
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH
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Davendralingam N, Sebire NJ, Arthurs OJ, Shelmerdine SC. Artificial intelligence in paediatric radiology: Future opportunities. Br J Radiol 2021; 94:20200975. [PMID: 32941736 PMCID: PMC7774693 DOI: 10.1259/bjr.20200975] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 09/04/2020] [Indexed: 12/13/2022] Open
Abstract
Artificial intelligence (AI) has received widespread and growing interest in healthcare, as a method to save time, cost and improve efficiencies. The high-performance statistics and diagnostic accuracies reported by using AI algorithms (with respect to predefined reference standards), particularly from image pattern recognition studies, have resulted in extensive applications proposed for clinical radiology, especially for enhanced image interpretation. Whilst certain sub-speciality areas in radiology, such as those relating to cancer screening, have received wide-spread attention in the media and scientific community, children's imaging has been hitherto neglected.In this article, we discuss a variety of possible 'use cases' in paediatric radiology from a patient pathway perspective where AI has either been implemented or shown early-stage feasibility, while also taking inspiration from the adult literature to propose potential areas for future development. We aim to demonstrate how a 'future, enhanced paediatric radiology service' could operate and to stimulate further discussion with avenues for research.
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Affiliation(s)
- Natasha Davendralingam
- Department of Radiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK
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Artificial Intelligence in Pediatrics. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_316-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Brunnquell CL, Hoff MN, Balu N, Nguyen XV, Oztek MA, Haynor DR. Making Magnets More Attractive: Physics and Engineering Contributions to Patient Comfort in MRI. Top Magn Reson Imaging 2020; 29:167-174. [PMID: 32541257 DOI: 10.1097/rmr.0000000000000246] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Patient comfort is an important factor of a successful magnetic resonance (MR) examination, and improvements in the patient's MR scanning experience can contribute to improved image quality, diagnostic accuracy, and efficiency in the radiology department, and therefore reduced cost. Magnet designs that are more open and accessible, reduced auditory noise of MR examinations, light and flexible radiofrequency (RF) coils, and faster motion-insensitive imaging techniques can all significantly improve the patient experience in MR imaging. In this work, we review the design, development, and implementation of these physics and engineering approaches to improve patient comfort.
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Affiliation(s)
- Christina L Brunnquell
- Department of Radiology, University of Washington, Seattle, WA Department of Radiology, The Ohio State University Wexler Medical Center, Columbus, OH
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Mayr NA, Yuh WTC, Oztek MA, Nguyen XV. Human Touch for High-Tech Imaging and Imaging-Guided Procedures: Integrative Medicine Strategies for Patient-Centered Nonpharmacologic Approaches: Part 1: Challenges for High-Tech Imaging and Procedures: How Can Integrative Medicine Impact Quality and Operations? Top Magn Reson Imaging 2020; 29:123-124. [PMID: 32568973 DOI: 10.1097/rmr.0000000000000240] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Nina A Mayr
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, WA
| | - William T C Yuh
- Department of Radiology, University of Washington School of Medicine, Seattle, WA
| | - Murat A Oztek
- Department of Radiology, University of Washington School of Medicine, Seattle, WA
| | - Xuan V Nguyen
- Department of Radiology, The Ohio State University College of Medicine, Columbus, OH
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Human Touch for High-Tech Imaging and Imaging-Guided Procedures Integrative Medicine Strategies for Patient-Centered Nonpharmacologic Approaches: Part 2: Overcoming Anxiety in Imaging and Invasive Procedures: What can Physics, Technology, and Integrative Medicine Do for Us? Top Magn Reson Imaging 2020; 29:165-166. [PMID: 32511196 DOI: 10.1097/rmr.0000000000000250] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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13
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Ajam AA, Tahir S, Makary MS, Longworth S, Lang EV, Krishna NG, Mayr NA, Nguyen XV. Communication and Team Interactions to Improve Patient Experiences, Quality of Care, and Throughput in MRI. Top Magn Reson Imaging 2020; 29:131-134. [PMID: 32568975 DOI: 10.1097/rmr.0000000000000242] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Patients undergoing MRI may experience fear, claustrophobia, or other anxiety manifestations due to the typically lengthy, spatially constrictive, and noisy MRI acquisition process and in some cases are not able to tolerate completion of the study. This article discusses several patient-centered aspects of radiology practice that emphasize interpersonal interactions. Patient education and prescan communication represent 1 way to increase patients' awareness of what to expect during MRI and therefore mitigate anticipatory anxiety. Some patient interaction strategies to promote relaxation or calming effects are also discussed. Staff teamwork and staff training in communication and interpersonal skills are also described, along with literature evidence of effectiveness with respect to patient satisfaction and productivity endpoints. Attention to how radiologists, nurses, technologists, and other members of the radiology team interact with patients before or during the MRI scan could improve patients' motivation and ability to cooperate with the MRI scanning process as well as their subjective perceptions of the quality of their care. The topics discussed in this article are relevant not only to MRI operations but also to other clinical settings in which patient anxiety or motion represent impediments to optimal workflow.
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Affiliation(s)
- Amna A Ajam
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH
| | | | - Mina S Makary
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH
| | - Sandra Longworth
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH
| | | | - Nidhi G Krishna
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH
| | - Nina A Mayr
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, WA
| | - Xuan V Nguyen
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH
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Nguyen XV, Tahir S, Bresnahan BW, Andre JB, Lang EV, Mossa-Basha M, Mayr NA, Bourekas EC. Prevalence and Financial Impact of Claustrophobia, Anxiety, Patient Motion, and Other Patient Events in Magnetic Resonance Imaging. Top Magn Reson Imaging 2020; 29:125-130. [PMID: 32568974 DOI: 10.1097/rmr.0000000000000243] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Claustrophobia, other anxiety reactions, excessive motion, and other unanticipated patient events in magnetic resonance imaging (MRI) not only delay or preclude diagnostic-quality imaging but can also negatively affect the patient experience. In addition, by impeding MRI workflow, they may affect the finances of an imaging practice. This review article offers an overview of the various types of patient-related unanticipated events that occur in MRI, along with estimates of their frequency of occurrence as documented in the available literature. In addition, the financial implications of these events are discussed from a microeconomic perspective, primarily from the point of view of a radiology practice or hospital, although associated limitations and other economic viewpoints are also included. Efforts to minimize these unanticipated patient events can potentially improve not only patient satisfaction and comfort but also an imaging practice's operational efficiency and diagnostic capabilities.
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Affiliation(s)
- Xuan V Nguyen
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH
| | | | - Brian W Bresnahan
- Department of Radiology, University of Washington School of Medicine, Seattle, WA
| | - Jalal B Andre
- Department of Radiology, University of Washington School of Medicine, Seattle, WA
| | | | - Mahmud Mossa-Basha
- Department of Radiology, University of Washington School of Medicine, Seattle, WA
| | - Nina A Mayr
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, WA
| | - Eric C Bourekas
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH
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