MUST 2024

Session

Machine Learning

Jun 12, 2024, 2:20 PM

Eulerzaal (Centrum Wiskunde & Informatica)

5. Breast Reflection Ultrasound Computed Tomography Reconstruction with Self-Supervised Blind Sequence Network

Lei He

6/12/24, 2:20 PM

oral presentations

Reflection ultrasound computed tomography (RUCT) is emerging as an essential tool for clinical breast cancer screening. However, a persistent challenge in ultrasonic imaging lies in the degradation of image quality due to local sound speed variations in breast tissue and random noise in the circuitry. RUCT imaging is based on the classical delay and sum (DAS) algorithm. Its pixel value is...

8. Coordinate-based implicit neural representation for full waveform inversion in ultrasound computed tomography

Weicheng Yan (Huazhong University of Science and Technology，College of Life Science and Technology)

6/12/24, 2:40 PM

oral presentations

Background:
Sound speed imaging is an important characteristic of ultrasound computed tomography (USCT). Full waveform inversion (FWI) is regarded as the most promising algorithm for high-resolution sound speed imaging. However, FWI may encounter the phenomenon of cycle-skipping, which means falling into local optimal solutions. Implicit neural representation (INR) is a popular technique...

10. Neural Network-Based Data Recovery Using Spiral Sub-Sampling for Ultrasound Computed Tomography

Hantao Yang (Department of Biomedical Engineering, Huazhong University of Science and Technology)

6/12/24, 3:00 PM

oral presentations

Ultrasound computed tomography (USCT) has the potential for clinical applications due to its standardized operations and multi-modality. However, obtaining high-quality images requires a complete dataset including all transmitting-receiving pairs, resulting in a time-consuming scanning process and substantial data-processing demands. The limitation restricts the clinical applications of USCT....

37. ASPIRE: Iterative Normalizing Flows for Fast Transcranial Ultrasound with Uncertainty Quantification

Rafael Orozco (Georgia Institute of Technology)

6/12/24, 3:20 PM

oral presentations

Transcranial ultrasound computed tomography using Full-Waveform Inversion presents a unique challenge due to the non-linear physics and the computational expense of wave physics. We address this challenge with a probabilistic framework that learns to sample the Bayesian posterior of brain parameters that match the data. To scale to realistic parameter sizes, we use normalizing flows and...