Deep learning-based low count whole-body positron emission tomography denoising incorporating computed tomography priors.

Background: Deep-learning-based denoising improves image quality and quantification accuracy for low count (LC) positron emission tomography (PET). Conventional deep-learning-based denoising methods only require single LC PET image input. This study aims to propose a deep-learning-based LC PET denoising method incorporating computed tomography (CT) priors to further reduce the dose level.

Partial volume correction for Lu-177-PSMA SPECT.

Background: The limited spatial resolution in SPECT images leads to partial volume effect (PVE), degrading the subsequent dosimetric accuracy. We aim to quantitatively evaluate PVE and partial volume corrections (PVC), i.e.

Ultrasound Neuromodulation for Sleep and Neurological Disorder Therapy: A Path to Clinical Translation.

Ultrasound neuromodulation is a promising noninvasive technique capable of penetrating the skull and precisely targeting deep brain regions with millimeter accuracy. Recent studies have demonstrated that transcranial ultrasound stimulation (TUS) of sleep-related brain areas can induce sleep in mice and even trigger a reversible, hibernation-like state without causing damage. Beyond its utility in preclinical models of central nervous system diseases, such as epilepsy, tremors, Alzheimer's disease, and depression, TUS holds significant potential for clinical translation.

Deep learning-based multi-frequency denoising for myocardial perfusion SPECT.

Background: Deep learning (DL)-based denoising has been proven to improve image quality and quantitation accuracy of low dose (LD) SPECT. However, conventional DL-based methods used SPECT images with mixed frequency components. This work aims to develop an integrated multi-frequency denoising network to further enhance LD myocardial perfusion (MP) SPECT denoising.