Deep-learning-based reconstruction of T2-weighted magnetic resonance imaging of the prostate accelerated by compressed sensing provides improved image quality at half the acquisition time

Martin Jurka; Iva Macova; Monika Wagnerova; Otakar Capoun; Roman Jakubicek; Petr Ourednicek; Lukas Lambert; Andrea Burgetova

doi:10.21037/qims-23-1488

Deep-learning-based reconstruction of T2-weighted magnetic resonance imaging of the prostate accelerated by compressed sensing provides improved image quality at half the acquisition time

Quant Imaging Med Surg. 2024 May 1;14(5):3534-3543. doi: 10.21037/qims-23-1488. Epub 2024 Apr 11.

Authors

Martin Jurka¹, Iva Macova¹, Monika Wagnerova¹, Otakar Capoun², Roman Jakubicek³, Petr Ourednicek⁴, Lukas Lambert¹, Andrea Burgetova¹

Affiliations

¹ Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic.
² Department of Urology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic.
³ Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czech Republic.
⁴ Department of Medical Imaging, St. Anna University Hospital Brno and Faculty of Medicine, Masaryk University, Brno, Czech Republic.

Abstract

Background: Deep-learning-based reconstruction (DLR) improves the quality of magnetic resonance (MR) images which allows faster acquisitions. The aim of this study was to compare the image quality of standard and accelerated T2 weighted turbo-spin-echo (TSE) images of the prostate reconstructed with and without DLR and to find associations between perceived image quality and calculated image characteristics.

Methods: In a cohort of 47 prospectively enrolled consecutive patients referred for bi-parametric prostate magnetic resonance imaging (MRI), two T2-TSE acquisitions in the transverse plane were acquired on a 3T scanner-a standard T2-TSE sequence and a short sequence accelerated by a factor of two using compressed sensing (CS). The images were reconstructed with and without DLR in super-resolution mode. The image quality was rated in six domains. Signal-to-noise ratio (SNR), and image sharpness were measured.

Results: The mean acquisition time was 281±23 s for the standard and 140±12 s for the short acquisition (P<0.0001). DLR images had higher sharpness compared to non-DLR (P<0.001). Short and short-DLR had lower SNR than the standard and standard-DLR (P<0.001). The perceived image quality of short-DLR was rated better in all categories compared to the standard sequence (P<0.001 to P=0.004). All domains of subjective evaluation were correlated with measured image sharpness (P<0.001).

Conclusions: T2-TSE acquisition of the prostate accelerated using CS combined with DLR reconstruction provides images with increased sharpness that have a superior quality as perceived by human readers compared to standard T2-TSE. The perceived image quality is correlated with measured image contrast.

Keywords: Magnetic resonance imaging (MRI); artificial intelligence (AI); image reconstruction; prostate cancer.