Clinical Implementation of Deep Learning MR reconstruction for TSE Sequences: Reduction of Acquisition Time and Maintenance

Herrmann, Judith; Gassenmaier, Sebastian; Küstner, Thomas; Kuendel, Matthias; Nickel, Dominik; Koerzdoerfer, Gregor; Mostapha, Mahmoud; Nadar, Mariappan S.; Othman, Ahmed A.

doi:10.21203/rs.3.rs-153369/v1

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2021

DOI: 10.21203/rs.3.rs-153369/v1

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Clinical Implementation of Deep Learning MR reconstruction for TSE Sequences: Reduction of Acquisition Time and Maintenance

Judith Herrmann

Sebastian Gassenmaier

Thomas Küstner

et al.

Abstract: Background: The application of Deep Learning (DL) in MR image reconstruction is increasingly gaining attention due to its potential of increasing image quality and reducing acquisition time. However, the technology hasn’t been yet implemented in clinical routine. The aim of this study was therefore to describe the implementation of this novel DL image reconstruction for turbo spin echo (TSE) sequences in clinical workflow including a thorough explanation of the required steps and an evaluation of the obtainabl… Show more

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Cited by 6 publications

References 12 publications

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Thin-Slice Prostate MRI Enabled by Deep Learning Image Reconstruction

Gassenmaier

Warm

Nickel

et al. 2023

Cancers

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Objectives: Thin-slice prostate MRI might be beneficial for prostate cancer diagnostics. However, prolongation of acquisition time is a major drawback of thin-slice imaging. Therefore, the purpose of this study was to investigate the impact of a thin-slice deep learning accelerated T2-weighted (w) TSE imaging sequence (T2DLR) of the prostate as compared to conventional T2w TSE imaging (T2S). Materials and Methods: Thirty patients were included in this prospective study at one university center after obtaining written informed consent. T2S (3 mm slice thickness) was acquired first in three orthogonal planes followed by thin-slice T2DLR (2 mm slice thickness) in axial plane. Acquisition time of axial conventional T2S was 4:12 min compared to 4:37 min for T2DLR. Imaging datasets were evaluated by two radiologists using a Likert-scale ranging from 1–4, with 4 being the best regarding the following parameters: sharpness, lesion detectability, artifacts, overall image quality, and diagnostic confidence. Furthermore, preference of T2S versus T2DLR was evaluated. Results: The mean patient age was 68 ± 8 years. Sharpness of images and lesion detectability were rated better in T2DLR with a median of 4 versus a median of 3 in T2S (p < 0.001 for both readers). Image noise was evaluated to be significantly worse in T2DLR as compared to T2S (p < 0.001 and p = 0.021, respectively). Overall image quality was also evaluated to be superior in T2DLR versus T2S with a median of 4 versus 3 (p < 0.001 for both readers). Both readers chose T2DLR in 29 cases as their preference. Conclusions: Thin-slice T2DLR of the prostate provides a significant improvement of image quality without significant prolongation of acquisition time.

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Thin-Slice Prostate MRI Enabled by Deep Learning Image Reconstruction

Gassenmaier

Warm

Nickel

et al. 2023

Cancers

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Speeding Up and Improving Image Quality in Glioblastoma MRI Protocol by Deep Learning Image Reconstruction

Gohla,

Hauser,

Bombach

et al. 2024

Cancers

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A fully diagnostic MRI glioma protocol is key to monitoring therapy assessment but is time-consuming and especially challenging in critically ill and uncooperative patients. Artificial intelligence demonstrated promise in reducing scan time and improving image quality simultaneously. The purpose of this study was to investigate the diagnostic performance, the impact on acquisition acceleration, and the image quality of a deep learning optimized glioma protocol of the brain. Thirty-three patients with histologically confirmed glioblastoma underwent standardized brain tumor imaging according to the glioma consensus recommendations on a 3-Tesla MRI scanner. Conventional and deep learning-reconstructed (DLR) fluid-attenuated inversion recovery, and T2- and T1-weighted contrast-enhanced Turbo spin echo images with an improved in-plane resolution, i.e., super-resolution, were acquired. Two experienced neuroradiologists independently evaluated the image datasets for subjective image quality, diagnostic confidence, tumor conspicuity, noise levels, artifacts, and sharpness. In addition, the tumor volume was measured in the image datasets according to Response Assessment in Neuro-Oncology (RANO) 2.0, as well as compared between both imaging techniques, and various clinical–pathological parameters were determined. The average time saving of DLR sequences was 30% per MRI sequence. Simultaneously, DLR sequences showed superior overall image quality (all p < 0.001), improved tumor conspicuity and image sharpness (all p < 0.001, respectively), and less image noise (all p < 0.001), while maintaining diagnostic confidence (all p > 0.05), compared to conventional images. Regarding RANO 2.0, the volume of non-enhancing non-target lesions (p = 0.963), enhancing target lesions (p = 0.993), and enhancing non-target lesions (p = 0.951) did not differ between reconstruction types. The feasibility of the deep learning-optimized glioma protocol was demonstrated with a 30% reduction in acquisition time on average and an increased in-plane resolution. The evaluated DLR sequences improved subjective image quality and maintained diagnostic accuracy in tumor detection and tumor classification according to RANO 2.0.

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Faster Elbow MRI with Deep Learning Reconstruction—Assessment of Image Quality, Diagnostic Confidence, and Anatomy Visualization Compared to Standard Imaging

Herrmann,

Afat,

Gassenmaier

et al. 2023

Diagnostics

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Objective: The objective of this study was to evaluate a deep learning (DL) reconstruction for turbo spin echo (TSE) sequences of the elbow regarding image quality and visualization of anatomy. Materials and Methods: Between October 2020 and June 2021, seventeen participants (eight patients, nine healthy subjects; mean age: 43 ± 16 (20–70) years, eight men) were prospectively included in this study. Each patient underwent two examinations: standard MRI, including TSE sequences reconstructed with a generalized autocalibrating partial parallel acquisition reconstruction (TSESTD), and prospectively undersampled TSE sequences reconstructed with a DL reconstruction (TSEDL). Two radiologists evaluated the images concerning image quality, noise, edge sharpness, artifacts, diagnostic confidence, and delineation of anatomical structures using a 5-point Likert scale, and rated the images concerning the detection of common pathologies. Results: Image quality was significantly improved in TSEDL (mean 4.35, IQR 4–5) compared to TSESTD (mean 3.76, IQR 3–4, p = 0.008). Moreover, TSEDL showed decreased noise (mean 4.29, IQR 3.5–5) compared to TSESTD (mean 3.35, IQR 3–4, p = 0.004). Ratings for delineation of anatomical structures, artifacts, edge sharpness, and diagnostic confidence did not differ significantly between TSEDL and TSESTD (p > 0.05). Inter-reader agreement was substantial to almost perfect (κ = 0.628–0.904). No difference was found concerning the detection of pathologies between the readers and between TSEDL and TSESTD. Using DL, the acquisition time could be reduced by more than 35% compared to TSESTD. Conclusion: TSEDL provided improved image quality and decreased noise while receiving equal ratings for edge sharpness, artifacts, delineation of anatomical structures, diagnostic confidence, and detection of pathologies compared to TSESTD. Providing more than a 35% reduction of acquisition time, TSEDL may be clinically relevant for elbow imaging due to increased patient comfort and higher patient throughput.

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Clinical Implementation of Deep Learning MR reconstruction for TSE Sequences: Reduction of Acquisition Time and Maintenance

Cited by 6 publications

References 12 publications

Thin-Slice Prostate MRI Enabled by Deep Learning Image Reconstruction

Thin-Slice Prostate MRI Enabled by Deep Learning Image Reconstruction

Speeding Up and Improving Image Quality in Glioblastoma MRI Protocol by Deep Learning Image Reconstruction

Faster Elbow MRI with Deep Learning Reconstruction—Assessment of Image Quality, Diagnostic Confidence, and Anatomy Visualization Compared to Standard Imaging

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