Voxel-based morphometry (VBM) is a widely-used image analysis technique for brain morphometry using structural MR images. VBM is advantageous in that it can evaluate the whole brain objectively, without laborious and time-consuming manual intervention. It has become a major analysis technique since it was firstly applied to psychological and neurological diseases accompanying cerebral atrophy, and is now also applied to cognitive sciences fields. In this paper, VBM is summarized focusing on two essential algorithms, segmentation and normalization, including cautions and applications.
Brain perfusion imaging using SPECT or arterial spin labeling by MRI is useful for early and differential diagnosis or prediction of prognosis of dementia as a supplementary objective biomarker. Although visual inspection is a basis for image evaluation, statistical evaluation after anatomic standardization of images is quite useful for accurate detection of subtle perfusion changes with good reproducibility. Statistical parametric mapping (SPM) that is the most common method for this statistical approach has been used for both research and clinical aspects. Moreover graph analysis of anatomically standardized brain perfusion images has been applied to evaluation of brain networks. Graph analysis can demonstrate small-world-ness and robustness of brain networks in dementia which cannot be revealed by SPM. This information about brain networks may be valuable for comprehension of pathophysiology and evaluation of therapeutic effects.
Resting-state functional connectivity MRI (rsfcMRI) is an emerging MRI technique that allows for assessment of functional connectivity between brain regions through the detection of intrinsic oscillations occurring in the resting-state networks. The default mode network (DMN) is one of the resting-state networks and consists of the medial prefrontal cortex, posterior cingulate cortex-precuneus, temporo-parietal junction and medial temporal cortex including the hippocampus. DMN is characterized as a set of brain regions that show higher brain activity during rest than during demanding cognitive tasks, and is suggested for roles in higher cognitive functions such as the self and awareness. Moreover, rsfcMRI studies have shown that DMN is abnormal in patients with senile dementia of Alzheimer type and mild cognitive impairment and also in cognitively normal elderly subjects with amyloid deposition. As such rsfcMRI is suitable as a tool for measuring biomarkers for the diagnosis and monitoring of neuro-psychiatric disorders including dementia. Many measurement and analytic methods are available for rsfcMRI. For the clinical application, however, it is important to develop standardized measurement and analytic techniques, which can be easily applicable to clinical settings.
Structural connectivity analysis of the neural network of brain based on diffusion MR tractography has been available in research fields. Graph theory analysis of connectivity matrix can evaluate a whole and regional network of the brain. The neural network analysis of the diffusion MR may contribute as biomarker of a diagnosis, the progression of Alzheimer's disease that presents in part a disconnection syndrome.
On the clinical management of neurodegenerative dementia including Alzheimer's disease (AD), molecular imaging using positron emission topography (PET), which makes it possible to detect in vivo pathological modification in the brain, have been developed. While amyloid PET imaging implies an important role for diagnosis of AD, tau PET imaging also would be useful as a biomarker to diagnose tauopathies including non-AD dementia as well as to evaluate disease severity.
The use of compressive sensing (CS) in applications with rapid spatial phase variations is difficult, since not only the magnitude but also phase regularization is required in the CS framework. In this article we propose a novel image reconstruction scheme for MR phase varied images in which phase regularizer is not required in the rather simple CS reconstruction scheme. In our work, to improve the incoherence between the sampling matrix and the basis of the sparsifying transform, successive thresholding in eFREBAS transform domain using the higher feasibility in the choice of eFREBAS scaling parameter, i.e. multi-scale eFREBAS transform domain thresholding were used. Proposed method has an advantage over phase and magnitude regularization method in that the reconstruction time is almost the same as that for real-valued images and there is no need for estimating the phase variation in the iterative algorithm. Reconstruction experiments showed that proposed method using 8-scale eFREBAS transform can restore the magnitude and phase of images much better than the conventional method, especially at the region where phase changes rapidly.
Computer-Aided-Diagnosis (CAD) research and development is being conducted in diverse fields. However, these activities are hindered by the difficulty in acquiring case images. To deal with this, efforts are currently underway to artificially create case images by embedding tumors into lesion-free images. This approach adopts as its criteria the ability to produce artificial case images that are indistinguishable to a physician's eye from actual case images. Its usefulness in actual application to CAD, however, remains an unknown quantity. In this study, we developed liver tumor-extraction CAD and used artificial case images in clinical testing employing machine learning to investigate their usefulness. Also investigated was the impact on CAD performance when artificial and actual cases images were mixed. Leave-one-out cross-validation was used for investigation, by which assessment was conducted by switching between actual and artificial images at various fixed ratios. Artificial images bore comparison with actual images at proportions of 50% or less, suggesting the feasibility of artificial case images when used at fixed ratios.
In the U.S., the public service of the lung cancer CT Screening was started for the specific person. As for the recommended screening population and radiation dose level, it was determined according to evidence. The task of the Accreditation Council for Lung Cancer CT Screening contributed to radiation dose reduction of the screening greatly. Institution authorizing under plan also requires the image quality management which is a precondition of a radiation dose level. By an information technology according to the International Standard, and a communalized quality control, it is efficient and the comparison verification of the data collected is achieved. This task will serve as an opportunity of new information dissemination.
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