Fix windowing accuracy and optimize MRI volume memory usage#54
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Ashutosh0x wants to merge 2 commits intoGoogle-Health:mainfrom
Open
Fix windowing accuracy and optimize MRI volume memory usage#54Ashutosh0x wants to merge 2 commits intoGoogle-Health:mainfrom
Ashutosh0x wants to merge 2 commits intoGoogle-Health:mainfrom
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- Added window_accurate to image_utils.py with proper range and rounding. - Updated generic_dicom_handler.py to use centralized window_accurate. - Optimized _process_buffered_mri_volume in generic_dicom_handler.py to reduce RAM footprint by processing slices one-by-one and calculating statistics per-slice.
- Added conditional check for os.register_at_fork (Unix-only). - Fixed typing.Self import for Python < 3.11 using typing_extensions. - Added python/data_processing/window_accurate_test.py.
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This PR addresses inaccuracies in medical image windowing and resolves Out-of-Memory (OOM) issues for large MRI volumes.
window_accuratetoimage_utils.pywhich implements the correct window range (center +/- half width) and uses rounding to minimize precision loss. This addresses known bugs in the legacywindowimplementation._process_buffered_mri_volumeingeneric_dicom_handler.pyto calculate statistics per-slice and process slices one-by-one usingpop(). This allows the garbage collector to free raw slice memory progressively, significantly reducing peak RAM usage for large 3D acquisitions.