Kidney and visualization changes

pytheranostics/dicomtools/dicomtools.py

@@ -46,16 +46,22 @@ def make_bqml_suv(
         if "siemens" in self.ds.Manufacturer.lower():
             self.ds.SeriesTime = self.ds.AcquisitionTime
             self.ds.ContentTime = self.ds.AcquisitionTime
+        elif "ge" in self.ds.Manufacturer.lower():  # i think it applies to ge as well
+            self.ds.SeriesTime = self.ds.AcquisitionTime
+            self.ds.ContentTime = self.ds.AcquisitionTime
 
         # Get the frame duration in seconds
         frame_duration = (
             self.ds.RotationInformationSequence[0].ActualFrameDuration / 1000
         )
         # get number of projections because manufacturers scale by this in the dicomfile
-        n_proj = (
-            self.ds.RotationInformationSequence[0].NumberOfFramesInRotation
-            * n_detectors
-        )
+        if "siemens" in self.ds.Manufacturer.lower():
+            n_proj = (
+                self.ds.RotationInformationSequence[0].NumberOfFramesInRotation
+                * n_detectors
+            )
+        elif "ge" in self.ds.Manufacturer.lower():
+            n_proj = self.ds.RotationInformationSequence[0].NumberOfFramesInRotation
         # get voxel volume in ml
         vox_vol = np.append(
             np.asarray(self.ds.PixelSpacing), float(self.ds.SliceThickness)

pytheranostics/dosimetry/base_dosimetry.py

@@ -645,14 +645,20 @@ def calculate_bed(self, kinetic: str) -> None:
             )  # Gy
 
             if kinetic == "monoexp":
-                t_eff = numpy.log(2) / (
-                    (
-                        self.results.loc["Kidney_Left"]["Fit_params"][1]
-                        + self.results.loc["Kidney_Right"]["Fit_params"][1]
-                    )
-                    / 2
-                )
+                # gather existing kidneys dynamically
+                kidney_labels = [
+                    s for s in self.results.index if s.startswith("Kidney_")
+                ]
+
+                # extract alpha parameters for those that exist
+                alphas = [self.results.loc[k]["Fit_params"][1] for k in kidney_labels]
+
+                # compute effective half-time using the mean alpha
+                alpha_mean = numpy.mean(alphas)
+                t_eff = numpy.log(2) / alpha_mean
+
                 bed[organ] = AD + 1 / alpha_beta * t_repair / (t_repair + t_eff) * AD**2
+
             elif kinetic == "biexp":
                 mean_lambda_washout = (
                     self.results.loc["Kidney_Left"]["Fit_params"][1]

pytheranostics/dosimetry/organ_s_dosimetry.py

@@ -54,7 +54,7 @@ def check_mandatory_fields_organ(self) -> None:
 
         return None
 
-    @staticmethod
+    # @staticmethod
     def _load_human_mass_target_organs_table(self) -> pandas.DataFrame:
         """Load the reference human phantom masses."""
         with resource_path(
@@ -180,7 +180,9 @@ def prepare_data(self) -> None:
             ].apply(lambda x: numpy.mean(x))
 
             # Combine Kidneys.
-            kidneys = ["Kidney_Left", "Kidney_Right"]
+            kidneys = [
+                s for s in self.results_fitting.index if s.startswith("Kidney_")
+            ]  # e.g., Kidney_Left, Kidney_Right, or one kidney only
             self.results_fitting.loc["Kidneys"] = self.results_fitting.loc[
                 kidneys
             ].sum()
@@ -231,7 +233,7 @@ def prepare_data(self) -> None:
 
             self.results_fitting.loc["Red Marrow"][
                 "Volume_CT_mL"
-            ] = 1170  # TODO volume hardcoded, think about alternatives
+            ] = 1170  # TODO volume hardcoded, think about alternatives #this one works for blood based method only; for imaging method it should be scaled # EANM Dosimetry Committee guidelines for bone marro and whole-body dosimetry
 
             self.results_fitting.loc["RemainderOfBody"]["Volume_CT_mL"] = (
                 self.config["PatientWeight_g"]