lei hu1, jungong Zhao1,2, Caixia fu3, and Thomas Benkert4
1Department of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixt, 上海, China, 2Department of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixt, shanghai, China, 3MR Application Development, Siemens Shenzhen magnetic Resonance Ltd, shanghai, China, 4MR Application Predevelopment, Siemens Healthcare, Erlangen, Gernmany, Erlangen, Germany
1Department of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixt, 上海, China, 2Department of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixt, shanghai, China, 3MR Application Development, Siemens Shenzhen magnetic Resonance Ltd, shanghai, China, 4MR Application Predevelopment, Siemens Healthcare, Erlangen, Gernmany, Erlangen, Germany
A deep learning framework based on GAN is a promising
method to synthesize realistic high-b-value DWI sets with good image quality
and accuracy in PCa detection.
Fig.1 Violin plots of distributions of the
quantitative metrics between M0 and Mcyc.
Fig.2 T2WI, ADC,
a-DWIb1000, a-DWIb1500, cal-DWIb1500, non-optimized
syn-DWIb1500 vs. optimized syn-DWIb1500 of four
different patients.
Patient A: A 76-year-old man with chronic prostatitis. Patient B: A 63-year-old man with prostatic hyperplasia. Patient C: An 87-year-old
man with prostatic cancer
in the right transition zone. (Gleason
score, 4+3). Patient D: A 69-year-old man with prostatic cancer
in the peripheral zone invading the rectum (Gleason score 4+5).