Ziru Sang | Searcharxiv

arXiv2023

Synthesizing PET images from High-field and Ultra-high-field MR images Using Joint Diffusion Attention Model

MRI and PET are crucial diagnostic tools for brain diseases, as they provide complementary information on brain structure and function. However, PET scanning is costly and involves radioactive exposure, resulting in a lack of PET. Moreover, simultaneous PET and MRI at ultra-high-field are currently hardly infeasible. Ultra-high-field imaging has unquestionably proven valuable in both clinical and academic settings, especially in the field of cognitive neuroimaging. These motivate us to propose a method for synthetic PET from high-filed MRI and ultra-high-field MRI. From a statistical perspective, the joint probability distribution (JPD) is the most direct and fundamental means of portraying the correlation between PET and MRI. This paper proposes a novel joint diffusion attention model which has the joint probability distribution and attention strategy, named JDAM. JDAM has a diffusion process and a sampling process. The diffusion process involves the gradual diffusion of PET to Gaussian noise by adding Gaussian noise, while MRI remains fixed. JPD of MRI and noise-added PET was learned in the diffusion process. The sampling process is a predictor-corrector. PET images were generated from MRI by JPD of MRI and noise-added PET. The predictor is a reverse diffusion process and the corrector is Langevin dynamics. Experimental results on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrate that the proposed method outperforms state-of-the-art CycleGAN for high-field MRI (3T MRI). Finally, synthetic PET images from the ultra-high-field (5T MRI and 7T MRI) be attempted, providing a possibility for ultra-high-field PET-MRI imaging.

cs.LG↗

arXiv2019

Front-end electronic readout system for the Belle II imaging Time-Of-Propagation detector

The Time-Of-Propagation detector is a Cherenkov particle identification detector based on quartz radiator bars for the Belle II experiment at the SuperKEKB electron-positron collider. The purpose of the detector is to identify the type of charged hadrons produced in electron-positron collisions, and requires a single photon timing resolution below 100 picoseconds. A novel front-end electronic system was designed, built, and integrated to acquire data from the 8192 microchannel plate photomultiplier tube channels in the detector. Waveform sampling of these analog signals is done by switched-capacitor array application-specific integrated circuits. The processes of triggering, digitization of windows of interest, readout, and data transfer to the Belle II data acquisition system are managed by Xilinx Zynq-7000 programmable system on a chip devices.

physics.ins-det↗

arXiv2015

Characterization of a Serializer ASIC chip for the upgrade of the ATLAS muon detector

We report on the design of a serializer ASIC to be used in the ATLAS forward muon detector for trigger data transmission. We discuss the performance of a prototype chip covering power dissipation, latency and stable operating line rate. Tests show that the serializer is capable of running at least at 5.76 Gbps with a bit error ratio below 1x10^{-15}, and a power consumption of 200 mW running at 4.8 Gbps. The latency between the start of loading 30 bits into the serializer to the transmission of the first bit from the serializer is measured to be about 6 ns.

physics.ins-det↗