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111 changes: 51 additions & 60 deletions
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SIRF_data_preparation/Siemens_mMR_NEMA_IQ/Siemens_mMR_NEMA_VOIs.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,95 +1,86 @@ | ||
| #%% | ||
| %load_ext autoreload | ||
| #%% | ||
| 2 | ||
| # #%% | ||
| import numpy | ||
| import matplotlib.pyplot as plt | ||
| # %% | ||
| # %load_ext autoreload | ||
| # %% | ||
| import os | ||
| import sys | ||
| import sirf.STIR as STIR | ||
| from sirf.Utilities import examples_data_path | ||
| from scipy.ndimage import binary_erosion | ||
| from pathlib import Path | ||
| #%% | ||
| %autoreload 2 | ||
|
|
||
| #%% | ||
| from petric import get_data, SRCDIR | ||
| import data_QC | ||
| #%% | ||
| #%matplotlib ipympl | ||
| #%% | ||
| import matplotlib.pyplot as plt | ||
| import numpy | ||
| from data_QC import VOI_mean, plot_image | ||
| from scipy import ndimage | ||
| from scipy.ndimage import binary_erosion | ||
|
|
||
| import sirf.STIR as STIR | ||
| from petric import SRCDIR | ||
| from SIRF_data_preparation import data_QC | ||
|
|
||
| # %% | ||
| # %autoreload 2 | ||
|
|
||
| # %% | ||
| # %matplotlib ipympl | ||
| # %% | ||
| STIR.AcquisitionData.set_storage_scheme('memory') | ||
| # set-up redirection of STIR messages to files | ||
| #_ = STIR.MessageRedirector('BSREM_info.txt', 'BSREM_warnings.txt', 'BSREM_errors.txt') | ||
| # _ = STIR.MessageRedirector('BSREM_info.txt', 'BSREM_warnings.txt', 'BSREM_errors.txt') | ||
| # fewer messages from STIR and SIRF (set to higher value to diagnose have problems) | ||
| STIR.set_verbosity(0) | ||
|
|
||
| #%% read | ||
| # %% read | ||
| if not SRCDIR.is_dir(): | ||
| PETRICDIR = Path('~/devel/PETRIC').expanduser() | ||
| SRCDIR = PETRICDIR / 'data' | ||
| datadir = str(SRCDIR /'Siemens_mMR_NEMA_IQ/') | ||
| #%% | ||
| datadir = str(SRCDIR / 'Siemens_mMR_NEMA_IQ/') | ||
| # %% | ||
| OSEM_image = STIR.ImageData(datadir + 'OSEM_image.hv') | ||
| im_slice = 72 | ||
| cor_slice = 109 | ||
| sag_slice = OSEM_image.dimensions()[2]//2 | ||
| sag_slice = OSEM_image.dimensions()[2] // 2 | ||
| cmax = OSEM_image.max() | ||
| #%% | ||
| slices = {'transverse_slice':72, 'coronal_slice':109} | ||
| #%% read in original VOIs | ||
| orgVOIs=[] | ||
| for i in range(1,8): | ||
| orgVOIs.append(STIR.ImageData(datadir+f"S{i}.hv")) | ||
| data_QC.plot_image(orgVOIs[i-1], **slices) | ||
| #%% | ||
| reference_image = STIR.ImageData(datadir+'PETRIC/reference_image.hv') | ||
| # %% | ||
| slices = {'transverse_slice': 72, 'coronal_slice': 109} | ||
| # %% read in original VOIs | ||
| orgVOIs = [] | ||
| for i in range(1, 8): | ||
| orgVOIs.append(STIR.ImageData(datadir + f"S{i}.hv")) | ||
| data_QC.plot_image(orgVOIs[i - 1], **slices) | ||
| # %% | ||
| reference_image = STIR.ImageData(datadir + 'PETRIC/reference_image.hv') | ||
| data_QC.plot_image(reference_image, **slices) | ||
| #%% create PETRIC VOIs | ||
| # %% create PETRIC VOIs | ||
| whole_object_mask = reference_image.clone() | ||
| whole_object_mask.fill(binary_erosion(reference_image.as_array()> reference_image.max()*.05, iterations=2)) | ||
| whole_object_mask.fill(binary_erosion(reference_image.as_array() > reference_image.max() * .05, iterations=2)) | ||
| data_QC.plot_image(whole_object_mask, **slices) | ||
| #%% | ||
| # %% | ||
| background_mask = orgVOIs[6] | ||
| VOI1_mask = orgVOIs[4] | ||
| VOI2_mask = orgVOIs[2] | ||
| VOI3_mask = orgVOIs[0] | ||
| #%% write PETRIC VOIs | ||
| whole_object_mask.write(datadir+'PETRIC/VOI_whole_object.hv') | ||
| background_mask.write(datadir+'PETRIC/VOI_background.hv') | ||
| VOI1_mask.write(datadir+'PETRIC/VOI_sphere5.hv') | ||
| VOI2_mask.write(datadir+'PETRIC/VOI_sphere3.hv') | ||
| VOI3_mask.write(datadir+'PETRIC/VOI_sphere1.hv') | ||
| #%% | ||
| # %% write PETRIC VOIs | ||
| whole_object_mask.write(datadir + 'PETRIC/VOI_whole_object.hv') | ||
| background_mask.write(datadir + 'PETRIC/VOI_background.hv') | ||
| VOI1_mask.write(datadir + 'PETRIC/VOI_sphere5.hv') | ||
| VOI2_mask.write(datadir + 'PETRIC/VOI_sphere3.hv') | ||
| VOI3_mask.write(datadir + 'PETRIC/VOI_sphere1.hv') | ||
| # %% | ||
| VOIs = (whole_object_mask, background_mask, VOI1_mask, VOI2_mask, VOI3_mask) | ||
| #%% | ||
| [ data_QC.plot_image(VOI, **slices) for VOI in VOIs] | ||
| #%% | ||
| allVOIs = whole_object_mask.clone()*.2 | ||
| # %% | ||
| [data_QC.plot_image(VOI, **slices) for VOI in VOIs] | ||
| # %% | ||
| allVOIs = whole_object_mask.clone() * .2 | ||
| allVOIs += background_mask | ||
| for VOI in VOIs: | ||
| allVOIs+= VOI | ||
| allVOIs += VOI | ||
| allVOIs /= allVOIs.max() | ||
| #%% | ||
| # %% | ||
| plt.figure() | ||
| data_QC.plot_image(reference_image, **slices) | ||
| plt.figure() | ||
| data_QC.plot_image(reference_image, **slices, alpha=allVOIs) | ||
| #%% | ||
| from data_QC import plot_image_if_exists, VOI_checks | ||
| from data_QC import plot_image | ||
| from data_QC import VOI_mean | ||
|
|
||
| from scipy import ndimage | ||
|
|
||
| #%% | ||
| VOI_checks(['VOI_whole_object', 'VOI_sphere5'], OSEM_image, srcdir=os.path.join(datadir, 'PETRIC'), **slices) | ||
| #%% | ||
| [ data_QC.VOI_mean(OSEM_image, VOI) for VOI in VOIs] | ||
| [ VOI_mean(OSEM_image, VOI) for VOI in VOIs] | ||
| #%% | ||
| [ data_QC.VOI_mean(reference_image, VOI) for VOI in VOIs] | ||
| # %% | ||
| data_QC.main(['--srcdir', datadir]) | ||
| # %% | ||
| [ VOI_mean(reference_image, VOI) for VOI in VOIs] |
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