Detail publikace

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, M. MACOVÁ, I. WAGNEROVÁ, M. ČAPOUN, O. JAKUBÍČEK, R. OUŘEDNÍČEK, P. LAMBERT, L. BURGETOVÁ, A.

Originální název

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

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

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

Autoři

JURKA, M.; MACOVÁ, I.; WAGNEROVÁ, M.; ČAPOUN, O.; JAKUBÍČEK, R.; OUŘEDNÍČEK, P.; LAMBERT, L.; BURGETOVÁ, A.

Vydáno

11. 4. 2024

Nakladatel

AME PUBLISHING COMPANY

Místo

SHATIN

ISSN

2223-4292

Periodikum

Quantitative Imaging in Medicine and Surgery

Ročník

14

Číslo

5

Stát

Čínská lidová republika

Strany od

3534

Strany do

3544

Strany počet

11

URL

https://qims.amegroups.org/article/view/123434/pdf

BibTex

@article{BUT188900,
  author="Martin {Jurka} and Iva {Macová} and Monika {Wagnerová} and Otakar {Čapoun} and Roman {Jakubíček} and Petr {Ouředníček} and Lukáš {Lambert} and Andrea {Burgetová}",
  title="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",
  journal="Quantitative Imaging in Medicine and Surgery",
  year="2024",
  volume="14",
  number="5",
  pages="3534--3544",
  doi="10.21037/qims-23-1488",
  issn="2223-4292",
  url="https://qims.amegroups.org/article/view/123434/pdf"
}