Fetal MRI Artifacts: Semi-Supervised Generative Adversarial Neural Network for Motion Artifacts Reducing in Fetal Magnetic Resonance Images

This study addresses challenges in fetal magnetic resonance imaging (MRI) related to motion artifacts, maternal respiration, and hardware limitations. To enhance MRI quality, we employ deep learning techniques, specifically utilizing Cycle GAN. Synthetic pairs of images, simulating artifacts in fetal MRI, are generated to train the model. Our primary contribution is the use of Cycle GAN for fetal MRI restoration, augmented by artificially corrupted data. We compare three approaches (supervised Cycle GAN, Pix2Pix, and Mobile Unet) for artifact removal. Experimental results demonstrate that the proposed supervised Cycle GAN effectively removes artifacts while preserving image details, as validated through Structural Similarity Index Measure (SSIM) and normalized Mean Absolute Error (MAE). The method proves comparable to alternatives but avoids the generation of spurious regions, which is crucial for medical accuracy.

Assessment of cerebral oxygenation response to hemodialysis using near-infrared spectroscopy (NIRS):Challenges and solutions

To date,the clinical use of functional near-infrared spectroscopy(NIRS)to detect cerebral ischemia has been largely limited to surgical settings,where motion artifacts are minimal.In this study,we present novel techniques to address the challenges of using NIRS to monitor ambu-latory patients with kidney disease during approximately eight hours of hemodialysis(HD)treatment.People with end-stage kidney disease who require HD are at higher risk for cognitive impairment and dementia than age-matched controls.Recent studies have suggested that HD-related declines in cerebral blood flow might explain some of the adverse outcomes of HD treatment.However,there are currently no established paradigms for monitoring cerebral per-fusion in real-time during HD treatment.In this study,we used NIRS to assess cerebral hemo-dynamic responses among 95 prevalent HD patients during two consecutive HD treatments.We observed substantial signal attenuation in our predominantly Black patient cohort that required probe modifications.We also observed consistent motion artifacts that we addressed by devel-oping a novel NIRS methodology,called the HD cerebral oxygen demand algorithm(HD-CODA),to identify episodes when cerebral oxygen demand might be outpacing supply during HD treatment.We then examined the association between a summary measure of time spent in cerebral deoxygenation,derived using the HD-CODA,and hemodynamic and treatment-related variables.We found that this summary measure was associated with intradialytic mean arterial pressure,heart rate,and volume removal.Future studies should use the HD-CODA to implement studies of real-time NIRS monitoring for incident dialysis patients,over longer time frames,and in other dialysis modalities.

EEG processing and its application in brain-computer interface

Electroencephalogram (EEG) is an efficient tool in exploring human brains. It plays a very important role in diagnosis of disorders related to epilepsy and development of new interaction techniques between machines and human beings,namely,brain-computer interface (BCI). The purpose of this review is to illustrate the recent researches in EEG processing and EEG-based BCI. First,we outline several methods in removing artifacts from EEGs,and classical algorithms for fatigue detection are discussed. Then,two BCI paradigms including motor imagery and steady-state motion visual evoked potentials (SSMVEP) produced by oscillating Newton's rings are introduced. Finally,BCI systems including wheelchair controlling and electronic car navigation are elaborated. As a new technique to control equipments,BCI has promising potential in rehabilitation of disorders in central nervous system,such as stroke and spinal cord injury,treatment of attention deficit hyperactivity disorder (ADHD) in children and development of novel games such as brain-controlled auto racings.

Automatic Removal of Multiple Artifacts for Single-Channel Electroencephalography

Removing different types of artifacts from the electroencephalography(EEG)recordings is a critical step in performing EEG signal analysis and diagnosis.Most of the existing algorithms aim for removing single type of artifacts,leading to a complex system if an EEG recording contains different types of artifacts.With the advancement in wearable technologies,it is necessary to develop an energy-efficient algorithm to deal with different types of artifacts for single-channel wearable EEG devices.In this paper,an automatic EEG artifact removal algorithm is proposed that effectively reduces three types of artifacts,i.e.,ocular artifact(OA),transmission-line/harmonic-wave artifact(TA/HA),and muscle artifact(MA),from a single-channel EEG recording.The effectiveness of the proposed algorithm is verified on both simulated noisy EEG signals and real EEG from CHB-MIT dataset.The experimental results show that the proposed algorithm effectively suppresses OA,MA and TA/HA from a single-channel EEG recording as well as physical movement artifact.

Quantitative analysis of retinal intermediate and deep capillary plexus in patients with retinal deep vascular complex ischemia

AIM: To quantitatively analyze the retinal intermediate and deep capillary plexus(ICP and DCP) in patients with retinal deep vascular complex ischemia(RDVCI), using 3D projection artifacts removal(3D PAR) optical coherence tomography angiography(OCTA).METHODS: RDVCI patients and gender-and agematched healthy controls were assessed and underwent OCTA examinations. The parafoveal vessel density(PFVD) of retinal deep vascular complex(DVC), ICP, and DCP were analyzed, and the percentage of reduction(PR) of PFVD was calculated.RESULTS: Twenty-four eyes in 22 RDVCI patients(20 in acute phase and 4 in chronic phase) and 24 eyes of 22 healthy subjects were enrolled as the control group. Significant reduction of PFVD in DVC, ICP, and DCP was observed in comparison with the controls(DVC: acute: 43.59%±6.58% vs 49.92%±5.49%, PR=12.69%;chronic: 43.50%±3.33% vs 51.20%±3.80%, PR=15.04%. ICP: acute: 40.28%±7.91% vs 46.97%±7.14%, PR=14.23%;chronic: 41.48%±2.87% vs 46.43%±3.29%, PR=10.66%. DCP: acute: 45.44%±8.27% vs 51.51%±9.97%, PR=11.79%;chronic: 37.78%±3.48% vs 51.73%±5.17%, PR=26.97%;all P<0.05). No significant PR difference was found among DVC, ICP, and DCP of RDVCI in acute phase(P=0.812), but significant difference in chronic phase(P=0.006, DVC vs DCP, ICP vs DCP). No significant difference in PR between acute and chronic phases in the DVC(P=0.735) or ICP(P=0.681) was found, but significant difference in the DCP(P=0.041).CONCLUSION: The PFVD of DVC, ICP, and DCP in RDVCI is significantly decreased in both acute and chronic phases. ICP impairment is stabilized from acute to chronic phase in RDVCI, whereas subsequent DCP impairment is uncovered and can be explained by ischemia-reperfusion damage.