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

J 2024

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 et. al.

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

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

30224 Radiology, nuclear medicine and medical imaging

Country of publisher

Hong Kong

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

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

Abstract

V originále

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.

Citovat

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.

@article{2417619,
   author = {Jurka, Martin and Macova, Iva and Wagnerova, Monika and Capoun, Otakar and Jakubicek, Roman and Ouředníček, Petr and Lambert, Lukas and Burgetova, Andrea},
   article_location = {SHATIN},
   article_number = {5},
   doi = {http://dx.doi.org/10.21037/qims-23-1488},
   keywords = {Magnetic resonance imaging (MRI); prostate cancer; artificial intelligence (AI); image reconstruction},
   language = {eng},
   issn = {2223-4292},
   journal = {QUANTITATIVE IMAGING IN MEDICINE AND SURGERY},
   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},
   url = {https://qims.amegroups.org/article/view/123434/html},
   volume = {14},
   year = {2024}
}

TY  - JOUR
ID  - 2417619
AU  - Jurka, Martin - Macova, Iva - Wagnerova, Monika - Capoun, Otakar - Jakubicek, Roman - Ouředníček, Petr - Lambert, Lukas - Burgetova, Andrea
PY  - 2024
TI  - 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
JF  - QUANTITATIVE IMAGING IN MEDICINE AND SURGERY
VL  - 14
IS  - 5
SP  - 3534-3544
EP  - 3534-3544
PB  - AME PUBL CO
SN  - 22234292
KW  - Magnetic resonance imaging (MRI)
KW  - prostate cancer
KW  - artificial intelligence (AI)
KW  - image reconstruction
UR  - https://qims.amegroups.org/article/view/123434/html
N2  - 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.
ER  -

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. \textit{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.

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