DeepAI AI Chat
Log In Sign Up

Feasibility of Material Decomposition-based Photon-counting CT Thermometry

by   Nathan Wang, et al.

Over the past decades, thermal ablation procedures such as high intensity focused ultrasound (HIFU) have been developed vaporize cancerous tissues in a focal area. Thermal ablation has the potential to non-invasively eliminate tumors and treat other medical conditions with high efficacy and better patient outcomes. However, it is necessary for treatment to be coupled with real time temperature monitoring in order to deliver sufficient thermal dosage to the target while sparing surrounding, healthy tissues. To this end, computed tomography (CT) has been explored to offer fine spatial resolution at a rapid speed. However, current CT thermometry techniques are sensitive to heterogeneous tissue properties, imaging noise, and artifacts. To address this challenge, this paper utilizes the emerging photon-counting CT technology, performs tomographic thermometry based on material decomposition, and obtains excellent results in realistic numerical simulation. Specifically, three algorithms were designed to decouple material composition and thermal expansion from spectral CT reconstruction and compared in a comparative study. The best algorithm, referred to as the one-step algorithm, uniquely finds both material decomposition and the associated temperature at the same time in a closed form solution, which is robust to changes in tissue composition and generates temperature predictions with centigrade accuracy under realistic CT image noise levels.


Block Matching Frame based Material Reconstruction for Spectral CT

Spectral computed tomography (CT) has a great potential in material iden...

Synthesis and Inpainting-Based MR-CT Registration for Image-Guided Thermal Ablation of Liver Tumors

Thermal ablation is a minimally invasive procedure for treat-ing small o...

MRI-Guided High Intensity Focused Ultrasound of Liver and Kidney

High Intensity Focused Ultrasound (HIFU) can be used to achieve a local ...

DLIMD: Dictionary Learning based Image-domain Material Decomposition for spectral CT

The potential huge advantage of spectral computed tomography (CT) is its...

DECT-MULTRA: Dual-Energy CT Image Decomposition With Learned Mixed Material Models and Efficient Clustering

Dual energy computed tomography (DECT) imaging plays an important role i...

Deep correction of breathing-related artifacts in MR-thermometry

Real-time MR-imaging has been clinically adapted for monitoring thermal ...