Abstract
Purpose: To evaluate the effect of implementing two distinct commercially available deep learning reconstruction (DLR) algorithms on the efficiency of MRI examinations conducted in real clinical practice within an outpatient setting at a large, multicenter institution. Materials and Methods: This retrospective study included 7346 examinations from 10 clinical MRI scanners analyzed during the pre-and postimplementation periods of DLR methods. Two different types of DLR methods, namely Digital Imaging and Communications in Medicine (DICOM)–based and k-space–based methods, were implemented in half of the scanners (three DICOM-based and two k-space– based), while the remaining five scanners had no DLR method implemented. Scan and room times of each examination type during the pre-and postimplementation periods were compared among the different DLR methods using the Wilcoxon test. Results: The application of deep learning methods resulted in significant reductions in scan and room times for certain examination types. The DICOM-based method demonstrated up to a 53% reduction in scan times and a 41% reduction in room times for various study types. The k-space–based method demonstrated up to a 27% reduction in scan times but did not significantly reduce room times. Conclusion: DLR methods were associated with reductions in scan and room times in a clinical setting, though the effects were heterogeneous depending on examination type. Thus, potential adopters should carefully evaluate their case mix to determine the impact of integrating these tools.
Original language | English |
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Article number | e230181 |
Journal | Radiology: Artificial Intelligence |
Volume | 6 |
Issue number | 3 |
DOIs | |
State | Published - May 2024 |
Keywords
- DICOM-based Reconstruction
- Deep Learning MRI Reconstruction
- Reconstruction Algorithms
- k-Space–based Reconstruction