Huaiqiang Sun

Abstract Motion artifacts remain a major challenge in magnetic resonance imaging (MRI), degrading image quality and compromising diagnostic reliability. Although deep learning–based retrospective correction methods have advanced for brain MRI, their development is constrained by training datasets dominated by synthetic or task-induced motion artifacts, which do not adequately represent real-world clinical motion.

Psychosocial deterioration over the course of illness in schizophrenia is well established (1–3). Several studies have reported progressive cerebral alterations as well (4, 5). However, the degree to which these latter changes are progressive over the longer-term course of illness and the degree to which they are secondary to antipsychotic treatment remain unclear.

Since absorption of X-ray radiation has the possibility of inducing cancerous, genetic and other diseases to patients, researches usually attempt to reduce the radiation dose. However, reduction of the radiation dose associated with CT scans will unavoidably increase the severity of noise and artifacts, which can seriously affect diagnostic confidence.