MAR-DTN: Metal Artifact Reduction using Domain Transformation Network for Radiotherapy Planning

• URL: http://arxiv.org/abs/2409.15155v1
• Date: Mon, 23 Sep 2024 16:04:00 GMT
• Title: MAR-DTN: Metal Artifact Reduction using Domain Transformation Network for Radiotherapy Planning
• Authors: Belén Serrano-Antón, Mubashara Rehman, Niki Martinel, Michele Avanzo, Riccardo Spizzo, Giuseppe Fanetti, Alberto P. Muñuzuri, Christian Micheloni,
• Abstract summary: In patients with head and neck cancer, quality of standard CT scans generated using kilo-Voltage (kVCT) tube potentials is severely degraded by streak artifacts. Some radiotherapy devices offer the possibility of acquiring Mega-Voltage CT (MVCT) for daily patient setup verification. We propose a deep learning-based approach capable of generating artifact-free MVCT images from acquired kVCT images.
• Score: 10.515417851330877
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: For the planning of radiotherapy treatments for head and neck cancers, Computed Tomography (CT) scans of the patients are typically employed. However, in patients with head and neck cancer, the quality of standard CT scans generated using kilo-Voltage (kVCT) tube potentials is severely degraded by streak artifacts occurring in the presence of metallic implants such as dental fillings. Some radiotherapy devices offer the possibility of acquiring Mega-Voltage CT (MVCT) for daily patient setup verification, due to the higher energy of X-rays used, MVCT scans are almost entirely free from artifacts making them more suitable for radiotherapy treatment planning. In this study, we leverage the advantages of kVCT scans with those of MVCT scans (artifact-free). We propose a deep learning-based approach capable of generating artifact-free MVCT images from acquired kVCT images. The outcome offers the benefits of artifact-free MVCT images with enhanced soft tissue contrast, harnessing valuable information obtained through kVCT technology for precise therapy calibration. Our proposed method employs UNet-inspired model, and is compared with adversarial learning and transformer networks. This first and unique approach achieves remarkable success, with PSNR of 30.02 dB across the entire patient volume and 27.47 dB in artifact-affected regions exclusively. It is worth noting that the PSNR calculation excludes the background, concentrating solely on the region of interest.

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