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from datetime import datetime |
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import numpy |
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from niprov.basefile import BaseFile |
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from niprov.libraries import Libraries |
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class ParrecFile(BaseFile): |
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def __init__(self, location, **kwargs): |
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super(ParrecFile, self).__init__(location, **kwargs) |
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self.libs = self.dependencies.getLibraries() |
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self.camera = self.dependencies.getCamera() |
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def inspect(self): |
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provenance = super(ParrecFile, self).inspect() |
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img = self.libs.nibabel.load(self.path) |
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info = img.header.general_info |
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provenance['dimensions'] = list(img.shape) |
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dateformat = '%Y.%m.%d / %H:%M:%S' |
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acqstring = info['exam_date'] |
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provenance['acquired'] = datetime.strptime(acqstring, dateformat) |
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provenance['subject'] = info['patient_name'] |
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provenance['subject-position'] = info['patient_position'] |
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provenance['protocol'] = info['protocol_name'] |
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provenance['technique'] = info['tech'] |
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tr = info['repetition_time'] |
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if isinstance(tr, numpy.ndarray): |
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tr = tr.tolist() |
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provenance['repetition-time'] = tr |
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provenance['field-of-view'] = info['fov'].tolist() |
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provenance['epi-factor'] = info['epi_factor'] |
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provenance['magnetization-transfer-contrast'] = bool(info['mtc']) |
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provenance['diffusion'] = bool(info['diffusion']) |
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provenance['duration'] = info['scan_duration'] |
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provenance['water-fat-shift'] = info['water_fat_shift'] |
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# per-image |
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img0info = img.header.image_defs[0] |
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provenance['slice-thickness'] = img0info['slice thickness'] |
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provenance['slice-orientation'] = img0info['slice orientation'] |
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provenance['echo-time'] = img0info['echo_time'] |
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provenance['flip-angle'] = img0info['image_flip_angle'] |
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provenance['inversion-time'] = img0info['Inversion delay'] |
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if provenance['diffusion']: |
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provenance['modality'] = 'DWI' |
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else: |
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provenance['modality'] = 'MRI' |
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self.camera.saveSnapshot(img.get_data(), for_=self) |
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return provenance |
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def getProtocolFields(self): |
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return ['repetition-time', 'echo-time', 'flip-angle', 'epi-factor', |
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'water-fat-shift', 'subject-position'] |
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54
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ilogue /
niprov
