Abstract #1670

Deep Learning Reconstruction: Capability for Image Quality and Staging Accuracy Improvements on Chest MRI in NSCLC Patients

Yoshiharu Ohno^1,2, Kaori Yamamoto³, Masato Ikedo³, Masao Yui³, Akiyoshi Iwase⁴, Yuka Oshima⁵, Nayu Hamabuchi⁵, Satomu Hanamatsu⁵, Hiroyuki Nagata², Takahiro Ueda¹, Hirotaka Ikeda¹, Takeshi Yoshikawa^1,6, Daisuke Takenaka^1,6, Yoshiyuki Ozawa¹, and Hiroshi Toyama¹

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

Synopsis

Keywords: Cancer, Lung, StagingDeep learning reconstruction (DLR) has been applied in routine clinical practice and started to demonstrate its’ potential in different MR examinations. However, no one have evaluated the utility of DLR for chest MRI, yet. We hypothesize that DLR method is useful for chest MRI and improve image quality and diagnostic performance for T and N factor evaluations in non-small cell lung cancer (NSCLC) patients. The purpose of this study was to determine the influence of DLR method on image quality and diagnostic performance for T and N factor evaluations at chest MRI in NSCLC patients.

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