Deep Learning Convolutional Neural Network Reconstruction and Radial k-Space Acquisition MR Technique for Enhanced Detection of Retropatellar Cartilage Lesions of the Knee Joint

Malwina Kaniewska; Eva Deininger-Czermak; Maelene Lohezic; Falko Ensle; Roman Guggenberger

doi:10.3390/diagnostics13142438

Deep Learning Convolutional Neural Network Reconstruction and Radial k-Space Acquisition MR Technique for Enhanced Detection of Retropatellar Cartilage Lesions of the Knee Joint

Diagnostics (Basel). 2023 Jul 21;13(14):2438. doi: 10.3390/diagnostics13142438.

Authors

Malwina Kaniewska^{1

2}, Eva Deininger-Czermak^{1

2

3}, Maelene Lohezic⁴, Falko Ensle^{1

2}, Roman Guggenberger^{1

2}

Affiliations

¹ Institute of Diagnostic and Interventional Radiology, University Hospital Zurich (USZ), Raemistrasse 100, 8091 Zurich, Switzerland.
² Institute of Diagnostic and Interventional Radiology, University of Zurich (UZH), Raemistrasse 100, 8091 Zurich, Switzerland.
³ Department of Forensic Medicine and Imaging, Institute of Forensic Medicine, University of Zurich, 8152 Zurich, Switzerland.
⁴ Advanced Technology, Science and Technology Organization, GE HealthCare, 8152 Zurich, Switzerland.

Abstract

Objectives: To assess diagnostic performance of standard radial k-space (PROPELLER) MRI sequences and compare with accelerated acquisitions combined with a deep learning-based convolutional neural network (DL-CNN) reconstruction for evaluation of the knee joint.

Methods: Thirty-five patients undergoing MR imaging of the knee at 1.5 T were prospectively included. Two readers evaluated image quality and diagnostic confidence of standard and DL-CNN accelerated PROPELLER MR sequences using a four-point Likert scale. Pathological findings of bone, cartilage, cruciate and collateral ligaments, menisci, and joint space were analyzed. Inter-reader agreement (IRA) for image quality and diagnostic confidence was assessed using intraclass coefficients (ICC). Cohen's Kappa method was used for evaluation of IRA and consensus between sequences in assessing different structures. In addition, image quality was quantitatively evaluated by signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) measurements.

Results: Mean acquisition time of standard vs. DL-CNN sequences was 10 min 3 s vs. 4 min 45 s. DL-CNN sequences showed significantly superior image quality and diagnostic confidence compared to standard MR sequences. There was moderate and good IRA for assessment of image quality in standard and DL-CNN sequences with ICC of 0.524 and 0.830, respectively. Pathological findings of the knee joint could be equally well detected in both sequences (κ-value of 0.8). Retropatellar cartilage could be significantly better assessed on DL-CNN sequences. SNR and CNR was significantly higher for DL-CNN sequences (both p < 0.05).

Conclusions: In MR imaging of the knee, DL-CNN sequences showed significantly higher image quality and diagnostic confidence compared to standard PROPELLER sequences, while reducing acquisition time substantially. Both sequences perform comparably in the detection of knee-joint pathologies, while DL-CNN sequences are superior for evaluation of retropatellar cartilage lesions.

Keywords: MRI; PROPELLER technique; artificial intelligence; knee.

Grants and funding

This research received no external funding.