Skip to main content
100%

Gist b2dba9d3a950202a27aaf70eb500b55b

✓ Published0🌍 Public
Aakeshavan
Last edited Nov 2, 2016
Created on Nov 2, 2016

This example visualizes the relationships between multiple MRI scan sessions by computing and plotting the affine and warp transformations needed to align them to a common template. The notebook uses Python with pandas, numpy, and matplotlib, along with custom functions built on nipype’s file management utilities, to load alignment status JSONs and construct transformation dictionaries. It shows how each session’s T1-weighted image can be mapped to any other session through a chain of affine and warp operations, with the resulting paths displayed as a network of connections. The rendering relies on matplotlib’s inline plotting to present the computed transformation graph.

AI-generated description

Similar vizzes

Loading thumbnail…

Gist 61f729d5fdcda29e57ebc8ec0b06652f

This example generates image tiles from 3D neuroimaging data, converting MRI volumes into 2D slice images along a specified axis. It loads NIfTI files with nibabel, reorients the data to IPL coordinates, and renders each slice as a grayscale PNG using matplotlib. The code also creates JSON manifests that record slice indices, mask information, and file paths, enabling tile-based visualization of brain scans.

Aakeshavan
73% match
Loading thumbnail…

Gist 156e9d479d1f72e7656463843af8fd6d

This notebook demonstrates how to convert Mindboggle neuroimaging outputs into a format compatible with Mindcontrol, a web-based visualization tool. It shows the process of generating structured entries that link brain segmentation results, T1-weighted MRI images, and associated metrics for each subject. The code uses Python libraries including pandas for handling demographic data, nipype for JSON file management, and custom functions to extract file paths and create metadata entries. The visualization approach relies on organizing subject-specific data into JSON-ready dictionaries that Mindcontrol can consume for interactive display.

Aakeshavan
72% match
Loading thumbnail…

Gist 9963650

This visualization demonstrates how linear calibration transforms MRI brain volume measurements from two scanner sites, comparing simulated models that combine variables, add site regressors, or calibrate data to evaluate equivalence between scanners. It uses Python with numpy, matplotlib, seaborn, statsmodels, scipy, and rpy2 to run ordinary least squares and robust linear models, while rendering plots inline via IPython Notebook.

Aakeshavan
72% match
Loading thumbnail…

Gist 5c36e7318dd5c85aa743

This visualization displays a 16-slice mosaic of brain MRI scans across three orthogonal planes (x, y, z), with yellow contour lines outlining anatomical labels from a segmentation. The code loads NIfTI brain images and masks via nibabel, masks background data with NumPy masked arrays, and uses nipy’s `plot_anat` and `contour_map` to render slices with matplotlib subplots. It computes bounding boxes from the affine transformation to select meaningful cut coordinates, then arranges the slices in 4×4 grids per plane. The function supports optional contouring and saving each mosaic as a PNG image.

Aakeshavan
72% match
Loading thumbnail…

Gist 59ae1a59826b5b7e09a35c922852cb2b

This example prepares and summarizes a dataset of brain MRI slice images. The notebook loads a metadata CSV into a pandas DataFrame and verifies that all referenced image files exist. It then groups the data by subject and slice direction, counting the number of slices for each binary label (0 or 1) with `groupby` and `apply`, before restructuring the result with `unstack`. The output is a cross-tabulated summary showing slice counts per subject, direction, and label.

Aakeshavan
71% match
Loading thumbnail…

Gist 51ee3c0f179008ff4a15

This visualization demonstrates white-stripe normalization of a T1-weighted brain MRI, overlaying a colored white-stripe mask on the normalized anatomical image. The notebook imports nipy’s `plot_map` for rendering, and uses scikit-image’s `threshold_otsu` to mask background voxels. It processes a control subject’s N4-corrected, rigidly aligned image via an R script (WhiteStripe package) called through rpy2, then visualizes the mask at the stripe’s centroid coordinates derived from the affine transform. The rendering uses a HSV colormap for the stripe overlay.

Aakeshavan
71% match
Loading thumbnail…

Gist 4bbeb39fd0c727966cbbac7d8b41ebc1

This notebook demonstrates a neuroimaging workflow that processes anatomical T1 brain images from five subjects in the ABIDE dataset. It uses FSL’s BET to create brain masks, FAST to segment gray matter, white matter, and CSF, and nilearn’s plotting tools to overlay masks and probability maps on the original scans. The code builds a nipype workflow with DataGrabber, DataSink, IdentityInterface, and Function nodes, then runs it iteratively over all subjects and visualizes the workflow graphs.

Aakeshavan
70% match
Loading thumbnail…

Gist 0c28ed2b6d7235146517

This example visualizes intraclass correlations for within- and between-site reliability of brain volume estimates from Freesurfer and SIENAX across multicenter MS study scans. It presents a pandas DataFrame table of scanning parameters and reliability metrics for nine scanners at six sites. The code uses IPython’s `%pylab inline` for interactive plotting, pandas for tabular data handling, and nipype’s `loadpkl` to load the analysis results. The visualization is built in a Jupyter notebook with Vue integration, showing how heterogeneous acquisition protocols affect measurement consistency.

Aakeshavan
69% match