Pinnock, Mark A;
Hu, Yipeng;
Bandula, Steve;
Barratt, Dean C;
(2023)
Multi-phase synthetic contrast enhancement in interventional computed tomography for guiding renal cryotherapy.
International Journal of Computer Assisted Radiology and Surgery
, 18
pp. 1437-1449.
10.1007/s11548-023-02843-z.
Preview |
Text
s11548-023-02843-z.pdf - Published Version Download (5MB) | Preview |
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.
| Type: | Article |
|---|---|
| Title: | Multi-phase synthetic contrast enhancement in interventional computed tomography for guiding renal cryotherapy |
| Location: | Germany |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s11548-023-02843-z |
| Publisher version: | https://doi.org/10.1007/s11548-023-02843-z |
| Language: | English |
| Additional information: | Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| Keywords: | Computed tomography, Contrast enhancement, Convolutional neural network, Deep learning, Interventional radiology |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10165327 |
Archive Staff Only
 |
View Item |
