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DOI 10.1097/rli.0000000000000847

Медицинская библиотека · Investigative radiology · 2022

Impact of Deep Learning Reconstruction Combined With a Sharpening Filter on Single-Shot Fast Spin-Echo T2-Weighted Magnetic Resonance Imaging of the Uterus.

Tsuboyama Takahiro, Onishi Hiromitsu, Nakamoto Atsushi, Ogawa Kazuya, Koyama Yoshihiro, Tarewaki Hiroyuki, Tomiyama Noriyuki

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Аннотация

OBJECTIVE

This study aimed to evaluate the effects of deep learning (DL) reconstruction and a postprocessing sharpening filter on the image quality of single-shot fast spin-echo (SSFSE) T2-weighted imaging (T2WI) of the uterus.

MATERIALS AND

METHODS

Fifty consecutive patients who underwent pelvic magnetic resonance imaging were included. Parasagittal T2WI with a slice thickness of 4 mm was obtained with the periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) and SSFSE sequences (mean scan time, 204 and 22 seconds, respectively). The following 3 types of SSFSE images were reconstructed, and the signal-to-noise ratio (SNR) and tissue contrast were assessed: conventional reconstruction (SSFSE-C), DL reconstruction (SSFSE-DL), and DL with a sharpening filter (SSFSE-DLF). Three radiologists independently assessed image quality, and area under the visual grading characteristics curve (AUCVGC) analysis was performed to compare the SSFSE and PROPELLER images.

RESULTS

Compared with that of the PROPELLER images, the SNR of the SSFSE-C, SSFSE-DL, and SSFSE-DLF images was significantly lower (P < 0.05), significantly higher (P < 0.05), and equivalent, respectively. The SSFSE-DL images exhibited significantly lower contrast between the junctional zone and myometrium than those obtained with the other sequences (P < 0.05). In qualitative comparisons with the PROPELLER images, all 3 SSFSE sequences, SSFSE-DL, and SSFSE-DLF demonstrated significantly higher scores for artifacts, noise, and sharpness, respectively (P < 0.01). The overall image quality of SSFSE-C (mean AUCVGC, 0.03; P < 0.01) and SSFSE-DL (mean AUCVGC, 0.23; P < 0.01) was rated as significantly inferior, whereas that of SSFSE-DLF (mean AUCVGC, 0.69) was equivalent or significantly higher (P < 0.01).

CONCLUSION

Using a combination of DL and a sharpening filter markedly increases the image quality of SSFSE of the uterus to the level of the PROPELLER sequence.

Для цитирования: Tsuboyama Takahiro, Onishi Hiromitsu, Nakamoto Atsushi, Ogawa Kazuya, Koyama Yoshihiro, Tarewaki Hiroyuki, Tomiyama Noriyuki. Impact of Deep Learning Reconstruction Combined With a Sharpening Filter on Single-Shot Fast Spin-Echo T2-Weighted Magnetic Resonance Imaging of the Uterus.. Investigative radiology. 2022. DOI: 10.1097/rli.0000000000000847.

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