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International Journal of Computer Assisted Radiology and Surgery
Published in: International Journal of Computer Assisted Radiology and Surgery 8/2023

Open Access 15-02-2023 | Computed Tomography | Original Article

Multi-phase synthetic contrast enhancement in interventional computed tomography for guiding renal cryotherapy

Authors: Mark A. Pinnock, Yipeng Hu, Steve Bandula, Dean C. Barratt

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 8/2023

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Abstract

Purpose

Minimally invasive treatments for renal carcinoma offer a low rate of complications and quick recovery. One drawback of the use of computed tomography (CT) for needle guidance is the use of iodinated contrast agents, which require an increased X-ray dose and can potentially cause adverse reactions. The purpose of this work is to generalise the problem of synthetic contrast enhancement to allow the generation of multiple phases on non-contrast CT data from a real-world, clinical dataset without training multiple convolutional neural networks.

Methods

A framework for switching between contrast phases by conditioning the network on the phase information is proposed and compared with separately trained networks. We then examine how the degree of supervision affects the generated contrast by evaluating three established architectures: U-Net (fully supervised), Pix2Pix (adversarial with supervision), and CycleGAN (fully adversarial).

Results

We demonstrate that there is no performance loss when testing the proposed method against separately trained networks. Of the training paradigms investigated, the fully adversarial CycleGAN performs the worst, while the fully supervised U-Net generates more realistic voxel intensities and performed better than Pix2Pix in generating contrast images for use in a downstream segmentation task. Lastly, two models are shown to generalise to intra-procedural data not seen during the training process, also enhancing features such as needles and ice balls relevant to interventional radiological procedures.

Conclusion

The proposed contrast switching framework is a feasible option for generating multiple contrast phases without the overhead of training multiple neural networks, while also being robust towards unseen data and enhancing contrast in features relevant to clinical practice.
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Metadata
Title
Multi-phase synthetic contrast enhancement in interventional computed tomography for guiding renal cryotherapy
Authors
Mark A. Pinnock
Yipeng Hu
Steve Bandula
Dean C. Barratt
Publication date
15-02-2023
Publisher
Springer International Publishing
Published in
International Journal of Computer Assisted Radiology and Surgery / Issue 8/2023
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-023-02843-z

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