def test_t1_calculation_shape(self):
tr = 1
alphas = np.array(range(1, 6))
dwi = np.zeros((1, 2, 3, 5))
t1 = scalar_analysis.calculate_t1(dwi, alphas, tr)
expected = np.zeros((1, 2, 3))
npt.assert_array_equal(expected, t1)
def test_t1_calculation(self):
tr = 3.7220 # repetition time taken from sample t1 weighted dicom files
# test with 1.1 log weight
alphas = np.array([
np.deg2rad(flip_angle_degrees)
for flip_angle_degrees in [43, 22, 15, 8, 5]
])
dwi = np.array([[[[155, 290, 405, 600, 800]]]])
t1 = scalar_analysis.calculate_t1(dwi, alphas, tr)
npt.assert_approx_equal(t1[0][0][0], 1572.275, significant=6)
def test_t1_calculation_slice(self):
demo_file_dir = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
"test_data/breast_single_t1_slice/")
dcm_dir = dicom_util.read_dicomdir(demo_file_dir)
t1_array = scalar_analysis.calculate_t1(dcm_dir["dwi"],
dcm_dir["alphas"],
dcm_dir["rep_time_seconds"])
expected_t1 = np.fromfile(os.path.join(demo_file_dir,
't1_test_slice.txt'),
sep=' ').reshape(1, 256, 256)
npt.assert_allclose(t1_array, expected_t1, rtol=1e-07, atol=1e-07)
def baseline_format(expected, dir_output, voi_output, phantom_definition):
baseline = defaultdict(lambda: defaultdict) # 2 keys deep
center_distances = []
volume_differences = []
scalar_median_differences = []
scalar_mean_differences = []
total_clamped_pixels = 0
# Calculate scalar maps and VOI stats
scalar_type = dir_output["scalar_type"]
label_map = voi_output["label_map"]
if scalar_type == "ADC":
scalar_map = scalar_analysis.calculate_adc(dir_output["dwi"],
dir_output["bvalues"]) * 1e6
voi_stats = scalar_analysis.voi_stats(
label_map, scalar_map, dir_output["image_coordinate_system"],
phantom_definition)
elif scalar_type == "T1":
scalar_map = scalar_analysis.calculate_t1(
dir_output["dwi"],
dir_output["alphas"],
dir_output["rep_time_seconds"],
use_pool=not WINDOWS,
clamp=CLAMP,
threshold=5)
voi_stats = scalar_analysis.voi_stats(
label_map,
scalar_map,
dir_output["image_coordinate_system"],
phantom_definition,
clamp=CLAMP)
else:
# Shouldn't ever get here
return baseline
# Analyze stats for each VOI
vois = voi_output["found_vois"]
for voi in vois:
expected_voi_dict = expected[voi]
baseline[voi] = {}
# Compare center coordinates and variation in center
if "center" in vois[voi]:
center_info = {
"center_left_cm": vois[voi]["center"][0],
"center_posterior_cm": vois[voi]["center"][1],
"center_superior_cm": vois[voi]["center"][2],
"std_dev_of_coronal_center": {
"coronal x": vois[voi]["coronal_center_std_dev"][0],
"coronal y": vois[voi]["coronal_center_std_dev"][1]
}
}
baseline[voi].update(center_info)
# Compare found center to expected (if known)
if "center_left_cm" in expected_voi_dict:
center_distance_cm = np.linalg.norm(
np.array([
expected_voi_dict["center_left_cm"],
expected_voi_dict["center_posterior_cm"],
expected_voi_dict["center_superior_cm"]
]) - vois[voi]["center"])
center_distances.append(center_distance_cm)
baseline[voi].update(
{"center_distance_cm": center_distance_cm})
# Compare scalar stats to expected values for this dataset (ADC/T1)
scalar_type = scalar_type.lower()
if voi in voi_stats:
median_diff = expected_voi_dict["{}_median".format(
scalar_type)] - voi_stats[voi]["median"]
mean_diff = expected_voi_dict["{}_mean".format(
scalar_type)] - voi_stats[voi]["mean"]
scalar_median_differences.append(np.abs(median_diff))
scalar_mean_differences.append(np.abs(mean_diff))
total_clamped_pixels += voi_stats[voi]["clamped_pixels"]
volume_difference_percent = (
voi_stats[voi]["volume"] - expected_voi_dict["volume_cm3"]
) / expected_voi_dict["volume_cm3"] * 100
volume_differences.append(volume_difference_percent)
baseline[voi].update({
"{}_median".format(scalar_type):
voi_stats[voi]["median"],
"{}_median_difference".format(scalar_type):
median_diff,
"{}_mean".format(scalar_type):
voi_stats[voi]["mean"],
"{}_mean_difference".format(scalar_type):
mean_diff,
"{}_max".format(scalar_type):
voi_stats[voi]["max"],
"{}_min".format(scalar_type):
voi_stats[voi]["min"],
"{}_std_dev".format(scalar_type):
voi_stats[voi]["std_dev"],
"number_clamped_pixels":
voi_stats[voi]["clamped_pixels"],
"volume_cm3":
voi_stats[voi]["volume"],
"volume_difference_percent":
volume_difference_percent
})
# Gather information about entire dataset
missing_voi_count = len(expected.keys()) - len(voi_stats.keys())
baseline.update({
"center_max_distance_cm":
max(center_distances) if center_distances else 0.0,
"center_average_distance_cm":
np.mean(center_distances) if center_distances else 0.0,
"volume_max_difference_percent":
max(volume_differences) if volume_differences else 0.0,
"volume_average_difference_percent":
np.mean(volume_differences) if volume_differences else 0.0,
"missing_voi_count":
missing_voi_count,
"{}_median_max_difference".format(scalar_type):
max(scalar_median_differences) if scalar_median_differences else 0.0,
"{}_median_average_difference".format(scalar_type):
np.mean(scalar_median_differences)
if scalar_median_differences else 0.0,
"{}_mean_max_difference".format(scalar_type):
max(scalar_mean_differences) if scalar_mean_differences else 0.0,
"{}_mean_average_difference".format(scalar_type):
np.mean(scalar_mean_differences) if scalar_mean_differences else 0.0,
"total_clamped_pixels":
total_clamped_pixels,
"error_in_voi_finding_for_dataset":
voi_output["total_error"]
})
return baseline
"../phantom_data/4493056_4493_T1_NIST_PHANTOM_NoPE/E7217/7")
#demo_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "test_data/voi/adc/breast_131_spheres_adc_20190124_19")
dcm_dir = dicom_util.read_dicomdir(demo_dir)
cs = dcm_dir["image_coordinate_system"]
logging.info("generating vois")
coronal_dwi = np.transpose(deepcopy(dcm_dir["dwi"]), (3, 1, 0, 2))
voi_list = voi_analysis.get_vois(dcm_dir["dwi"], cs,
phantom_definitions.BREAST_131_CYLS_T1)
#for v in voi_list["found_vois"]:
#print("voi {} has height of {} cm".format(v, voi_list["found_vois"][v]["height"]))
scalar_map = scalar_analysis.calculate_t1(dcm_dir["dwi"],
dcm_dir["alphas"],
dcm_dir["rep_time_seconds"],
use_pool=not WINDOWS,
clamp=CLAMP,
threshold=5)
#scalar_map = scalar_analysis.calculate_adc(dcm_dir["dwi"], dcm_dir["bvalues"]) * 1e6
stats = scalar_analysis.voi_stats(voi_list["label_map"],
scalar_map,
cs,
phantom_definitions.BREAST_131_CYLS_T1,
clamp=CLAMP)
for v in stats:
#print("voi: {}, num clamped pixels: {}".format(v, stats[v]["clamped_pixels"]))
#print("voi: {}, mean scalar value: {}, expected value: {}".format(v, stats[v]["mean"], phantom_definitions.NEW_ADC["vois"][v]["expected_value"]))
print(stats[v])