Virtual digital subtraction angiography using multizone patch-based U-Net

Ryusei Kimura, Atsushi Teramoto, Tomoyuki Ohno, Kuniaki Saito, Hiroshi Fujita

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Digital subtraction angiography (DSA) is a powerful technique for visualizing blood vessels from X-ray images. However, the subtraction images obtained with this technique suffer from artifacts caused by patient motion. To avoid these artifacts, a new method called “Virtual DSA” is proposed, which generates DSA images directly from a single live image without using a mask image. The proposed Virtual DSA method was developed using the U-Net deep learning architecture. In the proposed method, a virtual DSA image only containing the extracted blood vessels was generated by inputting a single live image into U-Net. To extract the blood vessels more accurately, U-Net operates on each small area via a patch-based process. In addition, a different network was used for each zone to use the local information. The evaluation of the live images of the head confirmed accurate blood vessel extraction without artifacts in the virtual DSA image generated with the proposed method. In this study, the NMSE, PSNR, and SSIM indices were 8.58%, 33.86 dB, and 0.829, respectively. These results indicate that the proposed method can visualize blood vessels without motion artifacts from a single live image.

Original languageEnglish
Pages (from-to)1305-1315
Number of pages11
JournalPhysical and Engineering Sciences in Medicine
Issue number4
Publication statusPublished - 12-2020

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Radiological and Ultrasound Technology
  • Biophysics
  • Biomedical Engineering
  • Instrumentation
  • Radiology Nuclear Medicine and imaging


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