MAD-Former: A Traceable Interpretability Model for Alzheimer's Disease Recognition Based on Multi-Patch Attention.

The integration of structural magnetic resonance imaging (sMRI) and deep learning techniques is one of the important research directions for the automatic diagnosis of Alzheimer's disease (AD). Despite the satisfactory performance achieved by existing voxel-based models based on convolutional neural networks (CNNs), such models only handle AD-related brain atrophy at a single spatial scale and lack spatial localization of abnormal brain regions based on model interpretability. To address the above limitations, we propose a traceable interpretability model for AD recognition based on multi-patch attention (MAD-Former). MAD-Former consists of two parts: recognition and interpretability. In the recognition part, we design a 3D brain feature extraction network to extract local features, followed by constructing a dual-branch attention structure with different patch sizes to achieve global feature extraction, forming a multi-scale spatial feature extraction framework. Meanwhile, we propose an important attention similarity position loss function to assist in model decision-making. The interpretability part proposes a traceable method that can obtain a 3D ROI space through attention-based selection and receptive field tracing. This space encompasses key brain tissues that influence model decisions. Experimental results reveal the significant role of brain tissues such as the Fusiform Gyrus (FuG) in AD recognition. MAD-Former achieves outstanding performance in different tasks on ADNI and OASIS datasets, demonstrating reliable model interpretability.

A Combined Synthetic, Magnetic, and Theoretical Study on Enhancing Ligand-Field Axiality for Dy(III) Single-Molecule Magnets Supported by Ferrocene Diamide Ligands.

Molecular design is crucial for improving the performance of single-molecule magnets (SMMs). For dysprosium(III) SMMs, enhancing ligand-field axiality is a well-suited strategy to achieve high-performance SMMs. We synthesized a series of dysprosium(III) complexes, (NNTIPS)DyBr(THF)2 (1, NNTIPS = fc(NSiiPr3)2; fc = 1,1'-ferrocenediyl, THF = tetrahydrofuran), [(NNTIPS)Dy(THF)3][BPh4] (2), (NNTIPS)DyI(THF)2 (3), and [(NNTBS)Dy(THF)3][BPh4] (4, NNTBS = fc(NSitBuMe2)2), supported by ferrocene diamide ligands. X-ray crystallography shows that the rigid ferrocene backbone enforces a nearly axial ligand field with weakly coordinating equatorial ligands. Dysprosium(III) complexes 1-4 all exhibit slow magnetic relaxation under zero fields and possess high effective barriers (Ueff) around 1000 K, comparable to previously reported (NNTBS)DyI(THF)2 (5). We probed the influences of structural variations on SMM behaviors by theoretical calculations and found that the distribution of negative charges defined by rq, i.e., the ratio of the charges on the axial ligands to the charges on the equatorial ligands, plays a decisive role. Moreover, theoretical calculations on a series of model complexes 1'-5' without equatorial ligands unveil that the axial crystal-field parameters B20 are directly proportional to the N-Dy-N angles and support the hypothesis that enhancing the ligand-field axiality could improve SMM performance.

Segmentation of the True Lumen of Aorta Dissection via Morphology-Constrained Stepwise Deep Mesh Regression.

The lumen of aortic dissection (AD) has important clinical value for preoperative diagnosis, interoperative intervention, and post-operative evaluation of AD diseases. AD segmentation is challenging because (i) fitting its irregular profile by using traditional models is difficult, and (ii) the size of the AD image is usually so big that many algorithms have to perform down-sampling to reduce the computational burden, thereby reducing the resolution of the result. In this paper, an automatic AD segmentation algorithm, in which a 3D mesh is gradually moved to the surface of AD based on the offset estimated by a deep mesh deformation module, is presented. AD morphology is used to constrain the initial mesh and guide the deformation, which improves the efficiency of the deep network and avoids down-sampling. Moreover, a stepwise regression strategy is introduced to solve the mesh folding problem and improve the uniformity of the mesh points. On an AD database that involves 35 images, the proposed method obtains the mean Dice of 94.12% and symmetric 95% Hausdorff distance of 2.85 mm, which outperforms five state-of-the-art AD segmentation methods. The average processing time is 16.6 s, and the memory used to train the network is only 0.36 GB, indicating that this method is easy to apply in clinical practice.

Automatic Aortic Dissection Centerline Extraction Via Morphology-Guided CRN Tracker.

Aortic dissection (AD) centerline extraction has important clinical value in the quantitative diagnosis and treatment of AD disease. However, AD centerline extraction is a difficult task and quantitative evaluation is rarely studied. In this work, we propose a fully automatic algorithm to extract AD centerline based on a convolutional regression network (CRN) and the morphological properties of AD. To this end, we first design a topological model to describe the complex topology of AD. With this model, CRNs are trained to estimate the position, tangential vector, and scale of the centerline. The tracking accuracy is further improved by centerline continuity and a gradient-based penalty function. In addition, seed points are extracted on the basis of random regression and line clustering to ensure automated vessel tracking. The proposed method has been evaluated on an AD database and a public aortic database, and achieved high overlapping ratios of 0.9610 and 1.0000, respectively. The tracked centerline is very close to the ground truth and shows good stability, with low average distance errors of 1.4720 mm and 1.8748 mm, respectively.

Deep feature regression (DFR) for 3D vessel segmentation.

The structural information of coronary arteries has important clinical value for quantitative diagnosis and treatment of coronary artery disease. In this study, a deep feature regression (DFR) method based on a convolutional regression network (CRN) and a stable point clustering mechanism for 3D vessel segmentation is proposed. First, the vessel model is constructed by a vessel section generator and a series of deviation parameter estimators. The generator provides 2D images for the training and prediction processes, while the estimators calculate pose parameters of an input vessel section. Second, estimators are trained by a series of CRNs, in which deep vessel features are automatically learned from 600 000 sample images. Third, we propose a stable point clustering mechanism that evaluates the reliability of the CRN estimation through iterative regression of vessel parameters. This mechanism eliminates the outliers, thereby increasing tracking robustness. Finally, we present a vessel segmentation algorithm using trained deviation parameter estimators. And, the termination criteria are designed based on both the number of stable points and an intensity constraint. The proposed method is evaluated on a public coronary artery data set. The average overlapping ratio and error are 97.5% and 0.27 mm, respectively. A quantitative test on a public cerebral artery data set demonstrates that the proposed DFR method tracks the vessel centerline with high accuracy, for which the average error is less than 0.33 mm.

Clinical features and treatment options for Chinese patients with severe primary erectile dysfunction.

OBJECTIVES: To investigate clinical features of Chinese patients with severe primary erectile dysfunction (S-PED) and to identify the ideal treatment options for this population. METHODS: Patients with PED were screened for enrollment in our study. Sexual history, marital status, and erectile function were evaluated by inquiry including International Index of Erectile Function-5. Individuals with severe PED (defined as refractory to management with phosphodiesterase type 5 inhibitor [PDE5i]) underwent serum hormone analysis, penile color duplex Doppler ultrasound, neuroelectromyogram, and cavernosography as appropriate. Long-term treatment results were determined. RESULTS: Among 220 PED patients, 72 (32.7%) suffered from severe PED (PDE5i nonresponse). Mean age was 31.5 +/- 4.5 years and mean duration of attempts at sexual activity was 2.4 +/- 3.2 years, Sixty-eight men (94.5%) had organic etiologies for erectile dysfunction, including arteriogenic (n = 13), venogenic (n = 35), endocrinologic (n = 6), neurologic (n = 9), and cavernosal fibrosis (n = 5). Sixteen men (22.2%) had been divorced. Mean erectile function and quality-of-life were significantly improved (P <.001) in the 25 men (34.7%) who were treated by penile prosthesis implantation, at a mean follow-up of 5.6 years. Satisfaction with penile prosthesis for patients and partner was 93.4% and 92.3%, respectively. CONCLUSIONS: Severe PED has a major impact on young couple's life quality. Venous leak is the most common cause of severe PED. Penile prosthesis implantation is safe and effective for severe PED.