![]() However, for 3D MRI images, the slope of slice can be corrected by free rotation (linear interpolation) of the slice plane, implemented, for example, in MRIcron or ImageJ free software.Ī more serious problem is that postmortem sections undergo distortion during tissue processing which may include shrinkage, tears, and folds. The slope of histological sections cannot be changed once they have been obtained. First, MRI and histological sections should have the same spatial location, which is determined by the coordinate perpendicular to the slice plane (for example, along the anteroposterior axis for coronal slices), as well as the slope of the slice relative to this axis (sagittal plane in the case of coronal slices). The reliability of the correlations between in vivo and ex vivo data is determined by the similar anatomy in the ROIs on MRI and histology images. ![]() Typically, the validation of new MRI techniques includes an evaluation of the relationship between MRI and histological measurements in anatomically similar areas or regions of interest (ROI). Cell transplantation studies and gene reporter imaging also require histological validation. Histological validation of MRI findings is an important component of animal models of cerebrovascular and neurodegenerative pathologies, animal tumor models, human post-MRI studies when the treatment includes resections, and human post-MRI post-mortem histopathology. The gold standards for confirming the accuracy of MRI myelin estimates are Luxol Fast Blue (LFB) histology staining, immunohistochemistry for myelin basic protein (MBP), or proteolipid protein (PLP). Quantitative MRI techniques with an improved specificity to myelin have been rapidly developed in recent decades, such as methods based on single- or multi-component relaxation, magnetization transfer, anisotropic diffusion, and magnetic susceptibility. Many of these methods are positioned as quantitative therefore, they must be histologically validated in experimental animal studies to provide the foundation for further clinical applications. Novel MRI methods have been developed for the evaluation of tissue composition (e.g., conducting tracts, myelin, collagen) or specific pathological conditions (ischemia, demyelination, inflammation). If it is, it creates an ROI including both particles and searches for next particle in the range of 0.5 mm.īut here macro at first calculates differences between two serial particles, but I need to include all particles in range of 0.5 mm.Magnetic resonance imaging (MRI) provides important information about anatomy and pathology, allowing a non-invasive assessment of an organ’s structure and function. Second step is fun: for each particle, it check if there is a particle around in a range of 0.5 mm. What I need is something like that: With Analyze particles function program recognize all vessels (objects) on a thresholded image. But those rings could be extremely variable. I was trying to make this task using some plugins: Template Matching, Feature Finder and Visual grap. in which program will recognize each ring and mark it as ROI. ![]() To see a photo I want to work with, you can check it from my dropbox: ![]() ![]() I am facing a challange in my field and I would need some advices. ![]()
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