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Abstract #2273

Conjugate Gradient and DL Reconstructions for UTE-MRI: Reducing Scan Time without Image Quality and Nodule Detection Capability Degradations

Masahiro Endo1,2, Yoshiharu Ohno2,3, Kaori Yamamoto4, Yuichiro Sano4, Masato Ikedo4, Masanori Ozaki4, Masao Yui4, Hiroyuki Nagata3, Takahiro Ueda2, Masahiko Nomura2, Takeshi Yoshikawa2, Daisuke Takenaka2, and Yoshiyuki Ozawa2
1Diagnostic Radiology, Shizuoka Cancer Center, Sunto-gun, Japan, 2Diagnostic Radiology, Fujita Health University School of Medicine, Toyoake, Japan, 3Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, Toyoake, Japan, 4Canon Medical Systems Corporation, Otawara, Japan

Synopsis

Keywords: Lung, Lung, Ultra-Short TE

Motivation: Conjugate gradient reconstruction (CG Recon) and DLR can reduce scan time without nodule detection performance degradation than grid reconstruction method (Grid Recon) on UTE-MRI.

Goal(s): The goal was to determine utilities of CG Recon and DLR for scan time reduction without nodule detection performance degradation as compared with Grid Recon without DLR.

Approach: This clinical study examined 35 patients by UTE-MRIs, and nodule detection performance was statistically compared.

Results: Az of both UTEoriginals reconstructed with DLR and UTE1/2 reduced as 1/2 and reconstructed with CG Recon and DLR were significantly larger than that of UTEoriginal reconstructed with Grid Recon without DLR (p=0.03).

Impact: When compared with conventional grid reconstruction, conjugate gradient reconstruction and deep learning reconstruction are useful for reducing acquisition time without degradations of image quality and nodule detection on pulmonary MRI with ultra-short TE.

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Keywords