DB-SPECT, a Fixed-Gantry SPECT Scanner for Dynamic Brain Imaging: Design Concept and First Results

Feng, Yuemeng; Hashemi, Amirreza; Soleymani, Sina; Ottensmeyer, Mark; Sabet, Hamid

doi:10.1109/nss/mic44845.2022.10399152

Cited by 1 publication

(1 citation statement)

References 12 publications

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“…Furthermore, the adoption of quantum principles aligns with the growing interest in quantum computing and offers promising avenues for addressing noise-related challenges across diverse imaging modalities both on system level and component level. The former refers to reconstructed image resolution in nuclear medicine modalities especially in count starved geometries such as limited angle tomography using time of ight Positron Emission Tomography 10,11,40,41 , organ-speci c Single Photon Emission Computed Tomography 14,42,43 . On detector level implementation, quantum-inspired denoising can play a signi cant role in nuclear imaging detectors during event positioning estimation 44 , and detector projection processing for novel unconventional detector designs [45][46][47][48] .…”

Section: Discussionmentioning

confidence: 99%

Quantum inspired approach for denoising with application to medical imaging

Hashemi,

Dutta,

Georgeot

et al. 2024

Preprint

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Background noise in many fields such as medical imaging poses significant challenges for accurate diagnosis, prompting the development of denoising algorithms. Traditional methodologies, however, often struggle to address the complexities of noisy environments in high dimensional imaging systems. This paper introduces a novel quantum-inspired approach for image denoising, drawing upon principles of quantum and condensed matter physics. Our approach views medical images as amorphous structures akin to those found in condensed matter physics and we propose an algorithm that incorporates the concept of mode resolved localization directly into the denoising process. Notably, our approach eliminates the need for hyperparameter tuning. The proposed method is a standalone algorithm with minimal manual intervention, demonstrating its potential to use quantum-based techniques in classical signal denoising. Through numerical validation, we showcase the effectiveness of our approach in addressing noise-related challenges in imaging and especially medical imaging, underscoring its relevance for possible quantum computing applications.

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