Deep-learning-based direct synthesis of low-energy virtual monoenergetic images with multi-energy CT

Hao Gong, Jeffrey F. Marsh, Karen N. D'souza, Nathan R. Huber, Kishore Rajendran, Joel G. Fletcher, Cynthia H. Mccollough, Shuai Leng

Research output: Contribution to journalArticlepeer-review


Purpose: We developed a deep learning method to reduce noise and beam-hardening artifact in virtual monoenergetic image (VMI) at low x-ray energy levels. Approach: An encoder-decoder type convolutional neural network was implemented with customized inception modules and in-house-designed training loss (denoted as Incept-net), to directly estimate VMI from multi-energy CT images. Images of an abdomen-sized water phantom with varying insert materials were acquired from a research photon-counting-detector CT. The Incept-net was trained with image patches (64 × 64 pixels) extracted from the phantom data, as well as synthesized, random-shaped numerical insert materials. The whole CT images (512 × 512 pixels) with the remaining real insert materials that were unseen in network training were used for testing. Seven contrast-enhanced abdominal CT exams were used for preliminary evaluation of Incept-net generalizability over anatomical background. Mean absolute percentage error (MAPE) was used to evaluate CT number accuracy. Results: Compared to commercial VMI software, Incept-net largely suppressed beam-hardening artifact and reduced noise (53%) in phantom study. Incept-net presented comparable CT number accuracy at higher-density (P-value [0.0625, 0.999]) and improved it at lower-density inserts (P-value = 0.0313) with overall MAPE: Incept-net [2.9%, 4.6%]; commercial-VMI [6.7%, 10.9%]. In patient images, Incept-net suppressed beam-hardening artifact and reduced noise (up to 50%, P-value = 0.0156). Conclusion: In this preliminary study, Incept-net presented the potential to improve low-energy VMI quality.

Original languageEnglish (US)
Article number052104
JournalJournal of Medical Imaging
Issue number5
StatePublished - Sep 1 2021


  • artifact reduction
  • convolutional neural network
  • deep learning
  • dual-energy CT
  • noise reduction
  • photon counting detector
  • virtual monoenergetic image

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging


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