Development of fast ultrasound super-resolution microvessel imaging

Ultrasound localization microscopy (ULM) based on microbubble (MB) localization was recently introduced to overcome the resolution limit of conventional ultrasound. However, ULM is currently challenged by the requirement for long data acquisition times to accumulate adequate MB events to fully reconstruct vasculature. In practice, ULM is limited by the need for contrast injections, long data acquisition, and computationally expensive post-processing times. To achieve successful ULM with accurate vascular reconstruction, it is necessary to accumulate ample amount of spatially isolated MB events to facilitate robust localization. This requirement inevitably imposes a trade-off between imaging speed and localization accuracy. A lower MB concentration leads to less MB signal overlap and higher localization accuracy, but with the cost of a longer required data acquisition time to ensure complete MB perfusion of the microvasculature. These limitations make it challenging to apply ULM for many practical applications. One of them is functional ultrasound localization microscopy (fULM). Originated from ultrasound localization microscopy (ULM), fULM evaluates the variations in blood flow associated with neural activities by measuring the number of intravenously injected microbubbles at each vessel location. To accurately estimate the dynamic blood volume variation, fULM requires an adequate number of MBs to be detected within each functional stimulation period. However, an elevated MB number or concentration negatively impacts the MB localization and tracking performance, resulting in suboptimal ULM reconstruction and imaging quality. In this thesis, I will first introduce a curvelet transform-based sparsity promoting (CTSP) algorithm that improves ULM imaging speed by recovering missing MB localization signal from data with very short acquisition times. Next, I will introduce a contrast-free super-resolution Doppler (CS Doppler) technique that uses deep generative networks to achieve super-resolution with short data acquisition. Finally, I will introduce a systematical analysis about the imaging sensitivity of fULM and its relationship with imaging spatial resolution, and a new method that combines MB backscattering intensity with MB count to improve the sensitivity of fULM.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Bioengineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2024

Author dc:creator

• You, Qi

Contributors dc:contributor

• Song, Pengfei
• Anastasio, Mark
• Oelze, Michael
• Lam, Fan

• Super Resolution
• Ultrasound Imaging

Statement dc:rights

• Copyright 2024 Qi You

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/127253

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F127253