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

JURKA, Martin, Iva MACOVA, Monika WAGNEROVA, Otakar CAPOUN, Roman JAKUBICEK, Petr OUŘEDNÍČEK, Lukas LAMBERT and Andrea BURGETOVA. 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. QUANTITATIVE IMAGING IN MEDICINE AND SURGERY. SHATIN: AME PUBL CO, 2024, vol. 14, No 5, p. 3534-3544. ISSN 2223-4292. Available from: https://dx.doi.org/10.21037/qims-23-1488.

Basic information

• Original name: 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
• Authors: JURKA, Martin (203 Czech Republic), Iva MACOVA (203 Czech Republic), Monika WAGNEROVA (203 Czech Republic), Otakar CAPOUN (203 Czech Republic), Roman JAKUBICEK (203 Czech Republic), Petr OUŘEDNÍČEK (203 Czech Republic, belonging to the institution), Lukas LAMBERT (203 Czech Republic) and Andrea BURGETOVA (203 Czech Republic).
• Edition: QUANTITATIVE IMAGING IN MEDICINE AND SURGERY, SHATIN, AME PUBL CO, 2024, 2223-4292.

Other information

• Original language: English
• Type of outcome: Article in a journal
• Field of Study: 30224 Radiology, nuclear medicine and medical imaging
• Country of publisher: Hong Kong
• Confidentiality degree: is not subject to a state or trade secret
• WWW: URL
• Impact factor: Impact factor: 2.800 in 2022
• Organization unit: Faculty of Medicine
• Doi: http://dx.doi.org/10.21037/qims-23-1488
• UT WoS: 001250132200023
• Keywords in English: Magnetic resonance imaging (MRI); prostate cancer; artificial intelligence (AI); image reconstruction
• Tags: 14110119, rivok
• Tags: International impact, Reviewed

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.