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Sk_documentation_and_organ_s_value #94

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8608d41
add plot_MIP_with_mask_outlines
sarakurkowska Nov 5, 2025
1aaa6d4
improve formatting of variables
sarakurkowska Nov 5, 2025
4623ba7
rename variables for consistency
sarakurkowska Nov 5, 2025
6604d1b
add condition for when there are no lesions
sarakurkowska Nov 5, 2025
aac7403
improve mass scaling for total body
sarakurkowska Nov 13, 2025
e24fc00
Merge branch 'dev' into sk_documentation_and_organ_s_value
sarakurkowska Nov 14, 2025
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153 changes: 73 additions & 80 deletions pytheranostics/dosimetry/base_dosimetry.py
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Original file line number Diff line number Diff line change
Expand Up @@ -123,9 +123,9 @@ def __init__(

# DataFrame storing results
self.results = self.initialize()
self.results_lesions = pandas.DataFrame()
self.results_salivaryglands = pandas.DataFrame()
self.df_ad = pandas.DataFrame()
self.results_dosimetry_lesions = pandas.DataFrame()
self.results_dosimetry_salivaryglands = pandas.DataFrame()
self.results_dosimetry_organs = pandas.DataFrame()

# Sanity Checks:
self.sanity_checks(metric="Volume_CT_mL")
Expand Down Expand Up @@ -460,23 +460,7 @@ def compute_tia(self) -> None:
def smart_fit_selection(
self, region_data: pandas.Series, region: str
) -> lmfit.model.ModelResult:
"""Select the best fit based on Akaike Information Criterion.

If enabled in self.config, iterates through different valid fits orders and select best fit based on Akaike Information Criterion.
If a fit order is specified by the user, then the method will just perform fit following user's selected order and configuration.

Parameters
----------
region_data : pandas.Series
Series containing Time and Activity.
region : str
Region of Interest

Returns
-------
lmfit.model.ModelResult
The best fit model based on Akaike Information Criterion.
"""
"""Select the best fit based on Akaike Information Criterion."""
# If fit_order is defined by user:
if self.config["rois"][region]["fit_order"] is not None:
fit_results, _ = exponential_fit_lmfit(
Expand Down Expand Up @@ -636,8 +620,8 @@ def calculate_bed(self, kinetic: str) -> None:
RADIOBIOLOGY_DATA_FILE = Path(this_dir, "data", "radiobiology.json")
with open(RADIOBIOLOGY_DATA_FILE) as f:
self.radiobiology_dic = json.load(f)
bed_df = self.df_ad[
self.df_ad.index.isin(list(self.radiobiology_dic.keys()))
bed_df = self.results_dosimetry_organs[
self.results_dosimetry_organs.index.isin(list(self.radiobiology_dic.keys()))
] # only organs that we know the radiobiology parameters
organs = numpy.array(bed_df.index.unique())
bed = {}
Expand All @@ -646,7 +630,7 @@ def calculate_bed(self, kinetic: str) -> None:
t_repair = self.radiobiology_dic[organ]["t_repair"]
alpha_beta = self.radiobiology_dic[organ]["alpha_beta"]
AD = (
float(self.df_ad.loc[bed_df.index == organ]["AD[Gy/GBq]"].values[0])
float(bed_df.loc[bed_df.index == organ]["AD_total[Gy/GBq]"].values[0])
* float(self.config["InjectedActivity"])
/ 1000
) # Gy
Expand Down Expand Up @@ -702,7 +686,9 @@ def calculate_bed(self, kinetic: str) -> None:
)
print(f"{organ}", bed[organ])

self.df_ad["BED[Gy]"] = self.df_ad.index.map(bed)
self.results_dosimetry_organs["BED[Gy]"] = (
self.results_dosimetry_organs.index.map(bed)
)

def save_images_and_masks_at(self, time_id: int) -> None:
"""Save CT, NM and masks for a specific time point.
Expand Down Expand Up @@ -775,11 +761,13 @@ def write_json_data(
cycle["InjectedActivity"] = self.config["InjectedActivity"]
cycle["Weight_g"] = self.config["PatientWeight_g"]
cycle["Level"] = self.config["Level"]
cycle["Method"] = self.config["Method"]
cycle["OutputFormat"] = self.config["OutputFormat"]
cycle["ScaleDoseByDensity"] = self.config.get(
"ScaleDoseByDensity", cycle.get("ScaleDoseByDensity", "NA")
)
if cycle["Level"] == "Organ":
cycle["Method"] = self.config["OrganLevel"]
elif cycle["Level"] == "Voxel":
cycle["Method"] = self.config["VoxelLevel"]
cycle["ScaleDoseByDensity"] = self.config.get(
"ScaleDoseByDensity", cycle.get("ScaleDoseByDensity", "NA")
)
cycle["ReferenceTimePoint"] = self.config["ReferenceTimePoint"]
cycle["TimePoints_h"] = self.results["Time_hr"][0]

Expand Down Expand Up @@ -886,24 +874,24 @@ def write_json_data(
if "Lesion" in organ or "TTB" in organ:
cycle["rois"][organ]["density_gml"]["different_tps"] = "NA"
cycle["rois"][organ]["density_gml"]["uncertainty"] = "NA"
cycle["rois"][organ]["density_gml"]["mean"] = self.results_lesions.loc[
organ, "Density_g_per_mL"
]
cycle["rois"][organ]["density_gml"]["mean"] = (
self.results_dosimetry_lesions.loc[organ, "Density_g_per_mL"]
)
cycle["rois"][organ]["density_gml"]["mean_uncertainty"] = "NA"
cycle["rois"][organ]["mass_g"]["different_tps"] = "NA"
cycle["rois"][organ]["mass_g"]["uncertainty"] = "NA"
cycle["rois"][organ]["mass_g"]["mean"] = self.results_lesions.loc[
organ, "Mass_g"
]
cycle["rois"][organ]["mass_g"]["mean"] = (
self.results_dosimetry_lesions.loc[organ, "Mass_g"]
)
cycle["rois"][organ]["mass_g"]["mean_uncertainty"] = "NA"
cycle["rois"][organ]["composition"] = self.results_lesions.loc[
organ, "Composition"
]
cycle["rois"][organ]["total_s_value"] = self.results_lesions.loc[
organ, "Total_S_Value"
]
cycle["rois"][organ]["composition"] = (
self.results_dosimetry_lesions.loc[organ, "Composition"]
)
cycle["rois"][organ]["total_s_value"] = (
self.results_dosimetry_lesions.loc[organ, "Total_S_Value"]
)
cycle["rois"][organ]["total_s_value_uncertainty"] = "NA"
cycle["rois"][organ]["mean_AD_Gy"] = self.results_lesions.loc[
cycle["rois"][organ]["mean_AD_Gy"] = self.results_dosimetry_lesions.loc[
organ, "AD_Gy"
]
cycle["rois"][organ]["mean_AD_Gy_uncertainty"] = "NA"
Expand All @@ -917,30 +905,30 @@ def write_json_data(
cycle["rois"][organ]["density_gml"]["different_tps"] = "NA"
cycle["rois"][organ]["density_gml"]["uncertainty"] = "NA"
cycle["rois"][organ]["density_gml"]["mean"] = (
self.results_salivaryglands.loc[organ, "Density_g_per_mL"]
self.results_dosimetry_salivaryglands.loc[organ, "Density_g_per_mL"]
)
cycle["rois"][organ]["density_gml"]["mean_uncertainty"] = "NA"
cycle["rois"][organ]["mass_g"]["different_tps"] = "NA"
cycle["rois"][organ]["mass_g"]["uncertainty"] = "NA"
cycle["rois"][organ]["mass_g"]["mean"] = (
self.results_salivaryglands.loc[organ, "Mass_g"]
self.results_dosimetry_salivaryglands.loc[organ, "Mass_g"]
)
cycle["rois"][organ]["mass_g"]["mean_uncertainty"] = "NA"
cycle["rois"][organ]["composition"] = self.results_salivaryglands.loc[
organ, "Composition"
]
cycle["rois"][organ]["total_s_value"] = self.results_salivaryglands.loc[
organ, "Total_S_Value"
]
cycle["rois"][organ]["composition"] = (
self.results_dosimetry_salivaryglands.loc[organ, "Composition"]
)
cycle["rois"][organ]["total_s_value"] = (
self.results_dosimetry_salivaryglands.loc[organ, "Total_S_Value"]
)
cycle["rois"][organ]["total_s_value_uncertainty"] = "NA"
cycle["rois"][organ]["mean_AD_Gy"] = self.results_salivaryglands.loc[
organ, "AD_Gy"
]
cycle["rois"][organ]["mean_AD_Gy"] = (
self.results_dosimetry_salivaryglands.loc[organ, "AD_Gy"]
)
cycle["rois"][organ]["mean_AD_Gy_uncertainty"] = "NA"

if self.config["Level"] == "Organ":
for organ in self.df_ad.index:
if organ in self.df_ad.index:
for organ in self.results_dosimetry_organs.index:
if organ in self.results_dosimetry_organs.index:
cycle["Organ-level_AD"][organ] = {
"AD[Gy/GBq]": {},
"AD[Gy/GBq]_uncertianty": {},
Expand All @@ -949,27 +937,31 @@ def write_json_data(
"BED[Gy]": {},
"BED[Gy]_uncertianty": {},
}
cycle["Organ-level_AD"][organ]["AD[Gy/GBq]"] = self.df_ad.loc[
organ, "AD[Gy/GBq]"
]
cycle["Organ-level_AD"][organ]["AD[Gy/GBq]"] = (
self.results_dosimetry_organs.loc[organ, "AD_total[Gy/GBq]"]
)
cycle["Organ-level_AD"][organ]["AD[Gy/GBq]_uncertainty"] = "NA"
cycle["Organ-level_AD"][organ]["AD[Gy]"] = self.df_ad.loc[
organ, "AD[Gy]"
]
cycle["Organ-level_AD"][organ]["AD[Gy]"] = (
self.results_dosimetry_organs.loc[organ, "AD_total[Gy]"]
)
cycle["Organ-level_AD"][organ]["AD[Gy]_uncertainty"] = "NA"

if "BED[Gy]" in self.df_ad.columns:
if "BED[Gy]" in self.results_dosimetry_organs.columns:
cycle["Organ-level_AD"][organ]["BED[Gy]"] = (
self.df_ad.loc[organ, "BED[Gy]"]
if pandas.notna(self.df_ad.loc[organ, "BED[Gy]"])
self.results_dosimetry_organs.loc[organ, "BED[Gy]"]
if pandas.notna(
self.results_dosimetry_organs.loc[organ, "BED[Gy]"]
)
else "NA"
)
else:
cycle["Organ-level_AD"][organ]["BED[Gy]"] = "NA"

cycle["Organ-level_AD"][organ]["BED[Gy]_uncertianty"] = "NA"

if "Yes" in self.config["LesionDosimetry"]:
if "Yes" in self.config["OrganLevel"]["AdditionalOptions"].get(
"LesionDosimetry"
):
cycle["Organ-level_AD"]["TTB"] = {
"mass_g": {},
"volumes_mL": {},
Expand All @@ -979,22 +971,23 @@ def write_json_data(
"AD[Gy/GBq]": {},
"AD[Gy/GBq]_uncertianty": {},
}
cycle["Organ-level_AD"]["TTB"]["mass_g"] = self.results_lesions.loc[
"TTB", "Mass_g"
]
cycle["Organ-level_AD"]["TTB"]["volumes_mL"] = self.results_lesions.loc[
"TTB", "Volume_CT_mL"
]
cycle["Organ-level_AD"]["TTB"]["TIA_h"] = self.results_lesions.loc[
"TTB", "TIA_h"
]
cycle["Organ-level_AD"]["TTB"]["AD[Gy]"] = self.results_lesions.loc[
"TTB", "AD_Gy"
]
cycle["Organ-level_AD"]["TTB"]["mass_g"] = (
self.results_dosimetry_lesions.loc["TTB", "Mass_g"]
)
cycle["Organ-level_AD"]["TTB"]["volumes_mL"] = (
self.results_dosimetry_lesions.loc["TTB", "Volume_CT_mL"]
)
cycle["Organ-level_AD"]["TTB"]["TIA_h"] = (
self.results_dosimetry_lesions.loc["TTB", "TIA_h"]
)
cycle["Organ-level_AD"]["TTB"]["AD[Gy]"] = (
self.results_dosimetry_lesions.loc["TTB", "AD_Gy"]
)
cycle["Organ-level_AD"]["TTB"]["AD[Gy]_uncertainty"] = "NA"
cycle["Organ-level_AD"]["TTB"]["AD[Gy/GBq]"] = self.results_lesions.loc[
"TTB", "AD_Gy"
] / (float(self.config["InjectedActivity"]) / 1000)
cycle["Organ-level_AD"]["TTB"]["AD[Gy/GBq]"] = (
self.results_dosimetry_lesions.loc["TTB", "AD_Gy"]
/ (float(self.config["InjectedActivity"]) / 1000)
)
cycle["Organ-level_AD"]["TTB"]["AD[Gy/GBq]_uncertianty"] = "NA"

with open(file_path, "w") as file:
Expand Down
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