With the exacerbation of aging population in China, the number of patients with Alzheimer's disease (AD) is increasing rapidly. AD is a chronic but irreversible neurodegenerative disease, which cannot be cured radically at present. In recent years, in order to intervene in the course of AD in advance, many researchers have explored how to detect AD as early as possible, which may be helpful for effective treatment of AD. Imaging genomics is a kind of diagnosis method developed in recent years, which combines the medical imaging and high-throughput genetic omics together. It studies changes in cognitive function in patients with AD by extracting effective information from high-throughput medical imaging data and genomic data, providing effective guidance for early detection and treatment of AD patients. In this paper, the association analysis of magnetic resonance image (MRI) with genetic variation are summarized, as well as the research progress on AD with this method. According to complexity, the objects in the association analysis are classified as candidate brain phenotype, candidate genetic variation, genome-wide genetic variation and whole brain voxel. Then we briefly describe the specific methods corresponding to phenotypic of the brain and genetic variation respectively. Finally, some unsolved problems such as phenotype selection and limited polymorphism of candidate genes are put forward.
Oral teeth image segmentation plays an important role in teeth orthodontic surgery and implant surgery. As the tooth roots are often surrounded by the alveolar, the molar’s structure is complex and the inner pulp chamber usually exists in tooth, it is easy to over-segment or lead to inner edges in teeth segmentation process. In order to further improve the segmentation accuracy, a segmentation algorithm based on local Gaussian distribution fitting and edge detection is proposed to solve the above problems. This algorithm combines the local pixels’ variance and mean values, which improves the algorithm’s robustness by incorporating the gradient information. In the experiment, the root is segmented precisely in cone beam computed tomography (CBCT) teeth images. Segmentation results by the proposed algorithm are then compared with the classical algorithms’ results. The comparison results show that the proposed method can distinguish the root and alveolar around the root. In addition, the split molars can be segmented accurately and there are no inner contours around the pulp chamber.
Constrained spherical deconvolution can quantify white matter fiber orientation distribution information from diffusion magnetic resonance imaging data. But this method is only applicable to single shell diffusion magnetic resonance imaging data and will provide wrong fiber orientation information in white matter tissue which contains isotropic diffusion signals. To solve these problems, this paper proposes a constrained spherical deconvolution method based on multi-model response function. Multi-shell data can improve the stability of fiber orientation, and multi-model response function can attenuate isotropic diffusion signals in white matter, providing more accurate fiber orientation information. Synthetic data and real brain data from public database were used to verify the effectiveness of this algorithm. The results demonstrate that the proposed algorithm can attenuate isotropic diffusion signals in white matter and overcome the influence of partial volume effect on fiber direction estimation, thus estimate fiber direction more accurately. The reconstructed fiber direction distribution is stable, the false peaks are less, and the recognition ability of cross fiber is stronger, which lays a foundation for the further research of fiber bundle tracking technology.
Present study used diffusion tensor image and tractography to construct brain white matter networks of 15 cerebral palsy infants and 30 healthy infants that matched for age and gender. After white matter network analysis, we found that both cerebral palsy and healthy infants had a small-world topology in white matter network, but cerebral palsy infants exhibited abnormal topological organization: increased shortest path length but decreased normalize clustering coefficient, global efficiency and local efficiency. Furthermore, we also found that white matter network hub regions were located in the left cuneus, precuneus, and left posterior cingulate gyrus. However, some abnormal nodes existed in the frontal, temporal, occipital and parietal lobes of cerebral palsy infants. These results indicated that the white matter networks for cerebral palsy infants were disrupted, which was consistent with previous studies about the abnormal brain white matter areas. This work could help us further study the pathogenesis of cerebral palsy infants.
Effective medical image enhancement method can not only highlight the interested target and region, but also suppress the background and noise, thus improving the quality of the image and reducing the noise while keeping the original geometric structure, which contributes to easier diagnosis in disease based on the image enhanced. This article carries out research on strengthening methods of subtle structure in medical image nowadays, including images sharpening enhancement, rough sets and fuzzy sets, multi-scale geometrical analysis and differential operator. Finally, some commonly used quantitative evaluation criteria of image detail enhancement are given, and further research directions of fine structure enhancement of medical images are discussed.
Mild cognitive impairment (MCI) is a clinical transition state between age-related cognitive decline and dementia. Researchers can use neuroimaging and neurophysiological techniques to obtain structural and functional information about the human brain. Using this information researchers can construct the brain network based on complex network theory. The literature on graph theory shows that the large-scale brain network of MCI patient exhibits small-world property, which ranges intermediately between Alzheimer's disease and that in the normal control group. But brain connectivity of MCI patients presents topologically structural disorder. The disorder is significantly correlated to the cognitive functions. This article reviews the recent findings on brain connectivity of MCI patients from the perspective of multimodal data. Specifically, the article focuses on the graph theory evidences of the whole brain structural and functional and the joint covariance network disorders. At last, the article shows the limitations and future research directions in this field.
Objective To analyze the correlation between the morphology of tibial intercondylar eminence and non-contact anterior cruciate ligament (ACL) injury, and provide a theoretical basis for the prevention and risk identification of ACL injury. Methods A retrospective analysis was conducted on the knee radiographs of 401 patients admitted to the Chengdu Second People’s Hospital between January 2017 and October 2021, including 219 males and 182 females. Non-contact rupture of ACL was observed in 180 patients and confirmed by arthroscopy or surgery, while the remained 221 patients were confirmed to have normal ACL by physical examination and MRI. The heights of medial and lateral tibial intercondylar eminence and the width of tibial intercondylar eminence of the 401 patients were measured, and the risk factors of ACL injury were analyzed. Results The height of medial tibial intercondylar eminence was lower and the width of tibial intercondylar eminence was smaller in male patients with ACL fracture than those in the male control group with statistical significance (P<0.05). Logistic regression analysis showed that a narrow width of tibial intercondylar eminence was a risk factor of ACL injury in males (P<0.05). The receiver operating characteristic (ROC) curve showed that the diagnostic threshold was 11.40 mm, the area under the curve (AUC) was 0.851 [95% confidence interval (CI) (0.797, 0.896)], the sensitivity was 72.81%, and the specificity was 84.76%. The height of medial tibial intercondylar eminence was lower and the width of tibial intercondylar eminence was smaller in female patients than those in the female control group with statistical significance (P<0.05). Logistic regression analysis showed that both a low height of medial tibial intercondylar eminence and a narrow width of tibial intercondylar eminence were risk factors of ACL injury in females (P<0.05). For the width of medial tibial intercondylar eminence, the ROC curve showed that the diagnostic threshold was 8.30 mm, and the AUC was 0.684 [95%CI (0.611, 0.751)], the sensitivity and specificity were 63.64% and 72.41%, respectively; for the height of medial tibial intercondylar eminence, the diagnostic threshold was 11.30 mm, and the AUC was 0.699 [95%CI (0.627, 0.756)], the sensitivity was 89.39%, and the specificity was 47.41%. Conclusions The reduced width of tibial intercondylar eminence is a risk factor and effective predictor of non-contact ACL injury in males. Both the reduced height of the medial tibial intercondylar eminence and the reduced width of tibial intercondylar eminence are risk factors and may be predictors for non-contact ACL injury in females.