Converting MAT Files to HDR Format Files

Converting MAT files to HDR format files is a common requirement in medical image processing, particularly when using tools like SPM (Statistical Parametric Mapping) for data analysis. MAT files typically store multidimensional data (such as 3D or 4D imaging information), while the HDR format serves as the header file for medical imaging formats like Analyze or NIfTI, describing data dimensions, data types, orientation, and other metadata.

Here is a general implementation approach: Reading MAT files: First, use tools that support MAT file parsing (such as MATLAB or Python's `scipy.io` module) to load the data. MAT files may contain multiple variables, so it's crucial to specifically extract the target image data matrix. In Python, this can be done using `scipy.io.loadmat()` followed by identifying the correct variable key. Parsing data attributes: Confirm data dimensions (such as voxel size and time series length) and data types (e.g., int16, float32), which need to be written to the HDR file. If metadata isn't directly stored in the MAT file, manual supplementation may be necessary. This involves using array shape inspection and dtype conversion functions. Generating HDR header files: HDR files can be text or binary files that must follow standard formatting, including key fields like data dimensions, voxel sizes, data types, and coordinate system orientation. For example, SPM typically expects HDR files to work with companion IMG files (which store actual voxel values). Python's `nibabel` library provides built-in functions like `nib.Nifti1Image()` to create header structures programmatically. Saving as HDR/IMG pairs: Export the image data as a binary IMG file while generating the corresponding HDR file. Some toolkits (like Python's `nibabel` or MATLAB's `spm_vol` function series) can automate this process through methods such as `nib.save()` which handles both header and image data simultaneously.

Important considerations: SPM has specific requirements for HDR file versions (such as Analyze 7.5 or NIfTI), so compatibility must be ensured through proper format selection. If MAT files contain dynamic images (like fMRI time series), the time axis parameters must be correctly set in the HDR file using appropriate dimension fields.

Extension recommendations: For batch processing, scripts can be written to automate the workflow, or built-in SPM tools like `mat2nii` can be used for indirect conversion through command-line or script integration.