Impact of Deep Learning Image Reconstruction Methods on MRI Throughput

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. Keywords: Deep Learning MRI Reconstruction, Reconstruction Algorithms, DICOM-based Reconstruction, k-Space-based Reconstruction © RSNA, 2024 See also the commentary by GharehMohammadi in this issue.

Keywords: DICOM-based Reconstruction; Deep Learning MRI Reconstruction; Reconstruction Algorithms; k-Space–based Reconstruction.

Graphical abstract

Figure 1:

Workflow of DICOM-based and k-space–based reconstruction methods (in red). The DICOM-based reconstruction method uses the original DICOM format images, which are then processed on a server for the final images that are exported to PACS. The k-space–based reconstruction method processes the k-space data on an integrated internal processor to obtain the processed images. DICOM = Digital Imaging and Communications in Medicine, PACS = picture archiving and communication system.

Figure 2:

Boxplot compares the distribution of scan times of DICOM-based MRI scanners between the pre- and postimplementation periods. The box represents the IQR, with the central line inside indicating the median. The whiskers extend up to 1.5 times the IQR from the upper and lower quartiles, and any points beyond this range are considered outliers. DICOM = Digital Imaging and Communications in Medicine.

Figure 3:

Boxplot compares the distribution of room times of DICOM-based MRI scanners between the pre- and postimplementation periods. The box represents the IQR, with the central line inside indicating the median. The whiskers extend up to 1.5 times the IQR from the upper and lower quartiles, and any points beyond this range are considered outliers. DICOM = Digital Imaging and Communications in Medicine.

Figure 4:

Boxplot demonstrates the distribution of scan times by DLR method across various examination types in the postimplementation period. The box represents the IQR, with the central line inside indicating the median. The whiskers extend up to 1.5 times the IQR from the upper and lower quartiles, and any points beyond this range are considered outliers. DICOM = Digital Imaging and Communications in Medicine.

Figure 5:

Boxplot demonstrates the distribution of room times by DLR methods across various examination types in the postimplementation period. The box represents the IQR, with the central line inside indicating the median. The whiskers extend up to 1.5 times the IQR from the upper and lower quartiles, and any points beyond this range are considered outliers. DICOM = Digital Imaging and Communications in Medicine.