Ph.D. Dissertation Defense: Minjie Zhu

Tuesday, November 21, 2023
1:00 p.m.
AVW 2460
Maria Hoo
301 405 3681
mch@umd.edu

ANNOUNCEMENT: Ph.D. Dissertation Defense 


Name:  Minjie Zhu
 
Committee:
Professor Behtash Babadi, Chair/Advisor
Professor Dirk Mayer
Professor Jonathan Z. Simon
Professor Gang Qu
Professor Luiz Pessoa, Dean's Representative 
 
Date/Time:  Tuesday, November 21st, 2023 at 1pm 
 
Location:  AVW 2460
 
Title: Image Reconstruction for Hyperpolarized Carbon-13 Metabolic Magnetic Resonance Imaging with Iterative Methods
 
Abstract:
Magnetic resonance imaging (MRI) with hyperpolarized carbon-13(13C) agents is an emerging in vivo medical imaging technique.  13C MRI gives a series of images that show the evolution of the injected substrate and its metabolic products in the imaging volume, which leads to various medical applications including monitoring tumor progression and post-treatment response in both animal models and clinical trials. This dissertation focuses on the application of novel iterative image reconstruction methods for 13C MRI that aim to improve image quality and temporal resolution.

One of the challenges for the existing 13C MRI reconstruction method is the difficulty in quantification of lower intensity metabolites due to noise and overlapping peaks in the aliased spectrum. In the first part of the dissertation, a model-based iterative reconstruction method is proposed to overcome such difficulty. The proposed method utilizes prior knowledge of the properties of the metabolites in the imaging volume, including off-resonance frequency, T2* decay constants, and the image acquisition trajectory in frequency domain. Metabolic images are reconstructed through solving the linear equation between acquired signal and images with least square error estimation. Digital phantom simulation demonstrates that the proposed method has higher image SNR compared with the conventional reconstruction method. The reconstruction results on in vivo imaging data sets demonstrate that the proposed method can separate two overlapped peaks in an aliased spectrum while the conventional method fails.

Another challenge for 13C MRI is to reconstruct metabolic images from under-sampled acquisitions. Due to the short lifetime of the injected substrate and the physical limitation of the MRI scanner, only a few temporal frames can be acquired for 13C MRI with one injection. Under-sampling in the image acquisition can provide more frames, but certain reconstruction methods are required to remove the artifacts from direct reconstruction on the under-sampled data. In the second part of the dissertation, a customized low-rank plus sparse (L+S) reconstruction method is proposed to produce artifact-free images from under-sampled data. Digital phantom simulations are performed to evaluate the optimal reconstruction parameters. Retrospective study and prospective study on the 2D and 3D dynamic imaging data demonstrate the effectiveness in image acceleration without introducing image artifacts using the proposed reconstruction method.

In the third part of the dissertation, we present a preclinical application of 13C MRI to study brain metabolism and identify the source of metabolic products based on the metabolic images derived. In vivo metabolic imaging with different flow-suppression levels was performed on rats in the brain region. Results show that metabolic product, lactate, has no significant dependence on the level of suppression while the substrate pyruvate is strongly dependent. This supports our hypothesis that lactate seen in metabolic images is generated in the brain. An additional high-resolution metabolic imaging was performed and our proposed L+S reconstruction method was used to derive metabolic images with reduced background noise. The high-resolution images show clearly different signal distributions for pyruvate and lactate across the temporal frames, further supporting our hypothesis.
 

Audience: Graduate  Faculty 

remind we with google calendar

 

May 2024

SU MO TU WE TH FR SA
28 29 30 1 2 3 4
5 6 7 8 9 10 11
12 13 14 15 16 17 18
19 20 21 22 23 24 25
26 27 28 29 30 31 1
Submit an Event