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

Deep-learning-based direct synthesis of low-energy virtual monoenergetic images with multi-energy CT Abstract.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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Medical Imaging SPIE

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

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Publisher
SPIE
Copyright
© 2021 Society of Photo-Optical Instrumentation Engineers (SPIE)
ISSN
2329-4302
eISSN
2329-4310
DOI
10.1117/1.jmi.8.5.052104
Publisher site
See Article on Publisher Site

Abstract

Abstract.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.

Journal

Journal of Medical ImagingSPIE

Published: Sep 1, 2021

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

References