Deep learning image reconstruction method for limited-angle ultrasound tomography in prostate cancer

Alexis Cheng, Younsu Kim, Emran M.A. Anas, Arman Rahmim, Emad Boctor, Reza Seifabadi, Bradford J. Wood

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Problem: The gold standard for prostate cancer diagnosis is B-mode transrectal ultrasound-guided systematic core needle biopsy. However, cancer is indistinguishable under ultrasound and thus additional costly imaging methods are necessary to perform targeted biopsies. Speed of sound is a potential biomarker for prostate cancer and has the potential to be measured using ultrasound tomography. Given the physical constraints of the prostate's anatomy, this work explores a simulation study using deep learning for limited-angle ultrasound tomography to reconstruct speed of sound. Methods: A deep learning-based image reconstruction framework is used to address the limited-angle ultrasound tomography problem. The training data is generated using the k-wave acoustic simulation package. The general network structure is composed of a series of dense fully-connected layers followed by an encoder and a decoder network. The basic idea behind this neural network is to encode a time of flight map into a lower dimension representation that can then be decoded into a speed of sound image. Results and Conclusions: We show that limited-angle UST is feasible in simulation using an auto-encoder-like DL framework. There was a mean absolute error of 7.5 ± 8.1 m/s with a maximum absolute error of 139.3 m/s. Future validation on experimental data will further assess their ability in improving limited-angle ultrasound tomography.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2019
Subtitle of host publicationUltrasonic Imaging and Tomography
EditorsBrett C. Byram, Nicole V. Ruiter
PublisherSPIE
ISBN (Electronic)9781510625570
DOIs
StatePublished - Jan 1 2019
EventMedical Imaging 2019: Ultrasonic Imaging and Tomography - San Diego, United States
Duration: Feb 17 2019Feb 18 2019

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10955
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2019: Ultrasonic Imaging and Tomography
CountryUnited States
CitySan Diego
Period2/17/192/18/19

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ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

Cite this

Cheng, A., Kim, Y., Anas, E. M. A., Rahmim, A., Boctor, E., Seifabadi, R., & Wood, B. J. (2019). Deep learning image reconstruction method for limited-angle ultrasound tomography in prostate cancer. In B. C. Byram, & N. V. Ruiter (Eds.), Medical Imaging 2019: Ultrasonic Imaging and Tomography [1095516] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10955). SPIE. https://doi.org/10.1117/12.2512533