Introduction: Previous studies have predominantly explored the relationship of the glymphatic system with motor symptoms in Parkinson's disease (PD); however, research on non-motor symptoms remains limited. Therefore, this study investigated the association between glymphatic function and non-motor symptoms, including cognitive impairment and sleep disorders, in PD patients. Methods: This study recruited 49 PD patients and 38 healthy controls (HC). Glymphatic function was evaluated using enlarged perivascular spaces (EPVS) in the basal ganglia (BG) region and diffusion tensor image analysis along the perivascular space (DTIALPS) index. Cognition, sleep, anxiety, and depression scales were assessed in all participants. According to the scale scores, PD patients were further divided into several groups to identify the presence of non-motor symptoms. Differences in EPVS numbers and ALPS index between PD subgroups and HC group were compared. Spearman correlation analysis was performed to investigate the association between the PD non-motor symptoms and ALPS index. Additionally, receiver operating characteristic (ROC) curves analysis was conducted for ALPS index to predict cognitive impairment and insomnia in PD patients. Results: PD patients with and without non-motor symptoms all showed more EPVS numbers than the controls, and the EPVS numbers in PD patients with cognitive impairment were also greater than those without. Notably, except for the depression subgroup, PD patients with non-motor symptoms showed significantly lower ALPS index than the controls. The Montreal Cognitive Assessment (MoCA) scores were positively correlated, whereas the Parkinson's Disease Sleep Scale (PDSS)-2 and REM Sleep Behavior Disorder Screening Questionnaire (RBDSQ) scores were negatively correlated with the ALPS index in PD patients (r=0.3618, P=0.0053; r=-0.4146, P=0.0015; r=-0.2655, P=0.0326, respectively). The ALPS index proved to be predictive of cognitive impairment and insomnia in PD Conclusion: Glymphatic function is closely associated with cognition and sleep of PD patients.

To evaluate the intracranial structures and brain parenchyma radiomics surrounding the occipital horn of the lateral ventricle in normal fetuses (NFs) and fetuses with ventriculomegaly (FVs), as well as to predict postnatally enlarged lateral ventricle alterations in FVs. Between January 2014 and August 2023, 141 NFs and 101 FVs underwent 1.5 T balanced steady-state free precession (BSSFP), including 68 FVs with resolved lateral ventricles (FVM-resolved) and 33 FVs with stable lateral ventricles (FVM-stable). Demographic data and intracranial structures were analyzed. To predict the enlarged ventricle alterations of FVs postnatally, logistic regression models with 5-fold cross-validation were developed based on lateral ventricle morphology, blended-cortical or/and subcortical radiomics characteristics. Validation of the models’ performance was conducted using the receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). Significant alterations in cerebral structures were observed between NFs and FVs (p < 0.05), excluding the maximum frontal horn diameter (FD). However, there was no notable distinction between the FVM-resolved and FVM-stable groups (all p > 0.05). Based on subcortical-radiomics on the aberrant sides of FVs, this approach exhibited high efficacy in distinguishing NFs from FVs in the training/validation set, yielding an impressive AUC of 1/0.992. With an AUC value of 0.822/0.743 in the training/validation set, the Subcortical-radiomics model demonstrated its ability to predict lateral ventricle alterations in FVs, which had the greatest predictive advantages indicated by DCA. Microstructural alterations in subcortical parenchyma associated with ventriculomegaly can serve as predictive indicators for postnatal lateral ventricle variations in FVs. It is critical to gain pertinent information from a solitary fetal MRI to anticipate postnatal lateral ventricle alterations in fetuses with ventriculomegaly. This approach holds the potential to diminish the necessity for recurrent prenatal ultrasound or MRI examinations.

Extraction of prostate contour on transrectal ultrasound (TRUS) is significant in prostate cancer diagnosis. With the low-contrast images of TRUS and the existence of imaging artifacts, detecting the prostate accurately from TRUS images becomes more and more difficult. In this paper, we present four strategies to achieve improved segmentation precision on difficult images. First, to achieve the data sequence, we adopt a variant of the principal curve-based algorithm, where a small part of prior point information is utilized as the approximate initialization. Secondly, we devise an evolution neural network to assist in finding the optimal neural network. Thirdly, we train the fractional-order-based neural network using data sequence as input, thereby decreasing the model error and increasing the precision of results during training. Finally, a smooth and explainable mathematical function of organ boundary is represented by the parameters of a fractional-order-based neural network. The performance of our method was evaluated on two clinical datasets with 1,338 TRUS prostate images using Accuracy (ACC), Dice similarity coefficient (DSC), and Jaccard similarity coefficient (OMG). Results show that our model 1) achieves better performance than the state-of-the-art segmentation models; the corresponding ACC, DSC, and OMG of our method are 95.3 & PLUSMN; 2.2 %, 95.9 & PLUSMN; 2.3 %, and 94.9 & PLUSMN; 2.4 %, respectively, and 2) detects unseen or vague regions satisfactorily.

BackgroundThe glymphatic system is reportedly involved in Parkinson's disease (PD). Based on previous studies, we aimed to confirm the correlation between the glymphatic system and PD progression by combining two imaging parameters, diffusion tensor image analysis along the perivascular space (DTI-ALPS), and enlarged perivascular spaces (EPVS).MethodsFifty-one PD patients and fifty healthy control (HC) were included. Based on the Hoehn-Yahr scale, the PD group was divided into early-stage and medium-to late-stage. All PD patients were scored using the Unified PD Rating Scale (UPDRS). We assessed the DTI-ALPS indices in the bilateral hemispheres and EPVS numbers in bilateral centrum semiovale (CSO), basal ganglia (BG), and midbrain.ResultsThe DTI-ALPS indices were significantly lower bilaterally in PD patients than in the HC group, and EPVS numbers in any of the bilateral CSO, BG, and midbrain were significantly higher, especially for the medium- to late-stage group and the BG region. In PD patients, the DTI-ALPS index was significantly negatively correlated with age, while the BG-EPVS numbers were significantly positively correlated with age. Furthermore, the DTI-ALPS index was negatively correlated with UPDRS II and III scores, while the BG-EPVS numbers were positively correlated with UPDRS II and III scores. Similarly, the correlation was more pronounced in the medium- to late-stage group.ConclusionThe DTI-ALPS index and EPVS numbers (especially in the BG region) are closely related to age and PD progression and can serve as non-invasive assessments for glymphatic dysfunction and its interventions in clinical studies.

Automatic segmentation of the prostate in transrectal ultrasound (TRUS) images provides useful information for prostate cancer diagnosis and treatment. However, boundaries between the prostate and other tissues are often absent or ill defined in TRUS images, which means that automatic segmentation of the prostate in TRUS images is highly challenging. In this study, we attempted to overcome these challenges by developing a novel method we termed “automatic prostate segmentation” (Auto-ProSeg) that is capable of effectively segmenting the prostate in TRUS images. Auto-ProSeg comprises two steps: the first step is a preprocessing step that uses attention U-Net to extract approximate prostate contours automatically; then, in the second step, the approximate prostate contours are optimized via a modified principal curve-based method linked to an evolutionary neural network, whereby a mathematical mapping formula based on the parameters of an enhanced evolutionary neural network is used to generate smooth prostate contours. Our results illustrate that Auto-ProSeg exhibits better prostate segmentation performance than other recently developed methods: the average Dice similarity coefficient and Jaccard similarity coefficient (Ω) of Auto-ProSeg-generated prostate contours against ground truths were 94.2

Introduction: Accurate contour extraction in ultrasound images is of great interest for image-guided organ interventions and disease diagnosis. Nevertheless, it remains a problematic issue owing to the missing or ambiguous outline between organs (i.e., prostate and kidney) and surrounding tissues, the appearance of shadow artifacts, and the large variability in the shape of organs.Methods: To address these issues, we devised a method that includes four stages. In the first stage, the data sequence is acquired using an improved adaptive selection principal curve method, in which a limited number of radiologist defined data points are adopted as the prior. The second stage then uses an enhanced quantum evolution network to help acquire the optimal neural network. The third stage involves increasing the precision of the experimental outcomes after training the neural network, while using the data sequence as the input. In the final stage, the contour is smoothed using an explicable mathematical formula explained by the model parameters of the neural network.Results: Our experiments showed that our approach outperformed other current methods, including hybrid and Transformer-based deep-learning methods, achieving an average Dice similarity coefficient, Jaccard similarity coefficient, and accuracy of 95.7 ± 2.4%, 94.6 ± 2.6%, and 95.3 ± 2.6%, respectively.Discussion: This work develops an intelligent contour extraction approach on ultrasound images. Our approach obtained more satisfactory outcome compared with recent state-of-the-art approaches . The knowledge of precise boundaries of the organ is significant for the conservation of risk structures. Our developed approach has the potential to enhance disease diagnosis and therapeutic outcomes.

The precise extraction of the contour of prostate on transrectal ultrasound (TRUS) is crucial for the diagnosis and treatment of prostate tumor. Due to the relatively low signal-to-noise ratio (SNR) of TRUS images and the potential of imaging artifacts, accurate contouring of the prostate from TRUS images has been a challenging task. This paper proposes four strategies to achieve higher precision of segmentation on TRUS images. Firstly, a modified principal curve-based algorithm is used to obtain the data sequence, with a small amount of prior point information adopted for coarse initialization. Secondly, an evolution neural network is devised to find an optimal network. Thirdly, a fractional-order-based network is trained with the data sequence as input, resulting in a decreased model error and increased precision. Finally, the parameters of a fractional-order-based neural network were utilized to construct an interpretable and smooth mathematical equation of the organ border. The Dice similarity coefficient (DSC), Jaccard similarity coefficient (OMG), and accuracy (ACC) of model outputs against ground-truths were 95.9 ± 2.3%, 94.9 ± 2.4%, and 95.3 ± 2.2%, respectively. The results of our method outperform several popular state-of-the-art segmentation methods.