Deep Learning Reconstruction to Improve the Quality of MR Imaging: Evaluating the Best Sequence for T-category Assessment in Non-small Cell Lung Cancer Patients

Daisuke Takenaka; Yoshiyuki Ozawa; Kaori Yamamoto; Maiko Shinohara; Masato Ikedo; Masao Yui; Yuka Oshima; Nayu Hamabuchi; Hiroyuki Nagata; Takahiro Ueda; Hirotaka Ikeda; Akiyoshi Iwase; Takeshi Yoshikawa; Hiroshi Toyama; Yoshiharu Ohno

doi:10.2463/mrms.mp.2023-0068

Deep Learning Reconstruction to Improve the Quality of MR Imaging: Evaluating the Best Sequence for T-category Assessment in Non-small Cell Lung Cancer Patients

Magn Reson Med Sci. 2023 Sep 1. doi: 10.2463/mrms.mp.2023-0068. Online ahead of print.

Authors

Daisuke Takenaka^{1

2}, Yoshiyuki Ozawa¹, Kaori Yamamoto³, Maiko Shinohara³, Masato Ikedo³, Masao Yui³, Yuka Oshima¹, Nayu Hamabuchi¹, Hiroyuki Nagata⁴, Takahiro Ueda¹, Hirotaka Ikeda¹, Akiyoshi Iwase⁵, Takeshi Yoshikawa^{1

2}, Hiroshi Toyama¹, Yoshiharu Ohno^{4

6}

Affiliations

¹ Department of Radiology, Fujita Health University School of Medicine.
² Department of Diagnostic Radiology, Hyogo Cancer Center.
³ Canon Medical Systems Corporation.
⁴ Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine.
⁵ Department of Radiology, Fujita Health University Hospital.
⁶ Department of Diagnostic Radiology, Fujita Health University School of Medicine.

PMID: 37661425
DOI: 10.2463/mrms.mp.2023-0068

Abstract

Purpose: Deep learning reconstruction (DLR) has been recommended as useful for improving image quality. Moreover, compressed sensing (CS) or DLR has been proposed as useful for improving temporal resolution and image quality on MR sequences in different body fields. However, there have been no reports regarding the utility of DLR for image quality and T-factor assessment improvements on T2-weighted imaging (T2WI), short inversion time (TI) inversion recovery (STIR) imaging, and unenhanced- and contrast-enhanced (CE) 3D fast spoiled gradient echo (GRE) imaging with and without CS in comparison with thin-section multidetector-row CT (MDCT) for non-small cell lung cancer (NSCLC) patients. The purpose of this study was to determine the utility of DLR for improving image quality and the appropriate sequence for T-category assessment for NSCLC patients.

Methods: As subjects for this study, 213 pathologically diagnosed NSCLC patients who underwent thin-section MDCT and MR imaging as well as T-factor diagnosis were retrospectively enrolled. SNR of each tumor was calculated and compared by paired t-test for each sequence with and without DLR. T-factor for each patient was assessed with thin-section MDCT and all MR sequences, and the accuracy for T-factor diagnosis was compared among all sequences and thin-section CT by means of McNemar's test.

Results: SNRs of T2WI, STIR imaging, unenhanced thin-section Quick 3D imaging, and CE-thin-section Quick 3D imaging with DLR were significantly higher than SNRs of those without DLR (P < 0.05). Diagnostic accuracy of STIR imaging and CE-thick- or thin-section Quick 3D imaging was significantly higher than that of thin-section CT, T2WI, and unenhanced thick- or thin-section Quick 3D imaging (P < 0.05).

Conclusion: DLR is thus considered useful for image quality improvement on MR imaging. STIR imaging and CE-Quick 3D imaging with or without CS were validated as appropriate MR sequences for T-factor evaluation in NSCLC patients.

Keywords: deep learning reconstruction; lung cancer; magnetic resonance imaging; staging.