[ENH] Add recon workflow for hbcd beta (#735)

add recon workflow for hbcd beta

qsiprep/data/pipelines/hbcd_scalar_maps.json

@@ -0,0 +1,122 @@
+{
+  "name": "hbcd_postproc_beta",
+  "space" : "T1w",
+  "atlases": [ ],
+  "anatomical": [ ],
+  "nodes": [
+    {
+      "name": "dipy_dki",
+      "software": "Dipy",
+      "action": "DKI_reconstruction",
+      "input": "qsiprep",
+      "qsirecon_suffix": "DIPYDKI",
+      "parameters": {
+        "write_mif": false,
+        "write_fibgz": false
+      }
+    },
+    {
+      "name": "tortoise_dtmapmri",
+      "software": "TORTOISE",
+      "action": "estimate",
+      "qsirecon_suffix": "TORTOISE_model-MAPMRI",
+      "input": "qsiprep",
+      "parameters": {
+        "estimate_tensor_separately": true,
+        "big_delta": null,
+        "small_delta": null,
+        "estimate_tensor": {
+          "bval_cutoff": 1200,
+          "write_cs": true},
+        "estimate_mapmri": {
+          "map_order": 4}
+      }
+    },
+    {
+      "name": "tortoise_fullshell_tensor",
+      "software": "TORTOISE",
+      "action": "estimate",
+      "qsirecon_suffix": "TORTOISE_model-tensor",
+      "input": "qsiprep",
+      "parameters": {
+        "estimate_tensor_separately": true,
+        "big_delta": null,
+        "small_delta": null,
+        "estimate_tensor": {
+          "bval_cutoff": 4000,
+          "write_cs": true}
+      }
+    },
+    {
+      "name": "fit_noddi",
+      "action": "fit_noddi",
+      "software": "AMICO",
+      "input": "qsiprep",
+      "qsirecon_suffix": "NODDI",
+      "parameters": {
+        "isExvivo": false,
+        "dPar": 1.7E-3,
+        "dIso": 3.0E-3
+      }
+    },
+    {
+      "name": "dsistudio_gqi",
+      "software": "DSI Studio",
+      "action": "reconstruction",
+      "input": "qsiprep",
+      "qsirecon_suffix": "DSIStudio",
+      "parameters": {
+        "method": "gqi"
+      }
+    },
+    {
+      "name": "autotrackgqi",
+      "software": "DSI Studio",
+      "action": "autotrack",
+      "input": "dsistudio_gqi",
+      "qsirecon_suffix": "DSIStudio",
+      "parameters": {
+        "track_id": "Fasciculus,Cingulum,Aslant,Corticos,Thalamic_R,Reticular,Optic,Fornix,Corpus",
+        "tolerance": "22,26,30",
+        "track_voxel_ratio": 2.0,
+        "yield_rate": 0.000001
+      }
+    },
+    {
+      "name": "gqi_scalars",
+      "software": "DSI Studio",
+      "action": "export",
+      "input": "dsistudio_gqi",
+      "qsirecon_suffix": "DSIStudio"
+    },
+    {
+      "name": "bundle_means",
+      "software": "qsiprep",
+      "action": "bundle_map",
+      "input": "autotrackgqi",
+      "scalars_from": [
+        "gqi_scalars", 
+        "dipy_dki", 
+        "fit_noddi", 
+        "tortoise_fullshell_tensor",
+        "tortoise_dtmapmri"
+      ]
+    },
+    {
+      "name": "template_map",
+      "software": "qsiprep",
+      "action": "template_map",
+      "input": "qsiprep",
+      "scalars_from": [
+        "gqi_scalars", 
+        "dipy_dki", 
+        "fit_noddi", 
+        "tortoise_fullshell_tensor",
+        "tortoise_dtmapmri"
+      ],
+      "parameters": {
+        "interpolation": "NearestNeighbor"
+      }
+    }
+  ]
+}

qsiprep/interfaces/scalar_mapping.py

@@ -2,6 +2,7 @@
 # -*- coding: utf-8 -*-
 # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
 # vi: set ft=python sts=4 ts=4 sw=4 et:
+import logging
 import os.path as op
 import subprocess
 
@@ -22,6 +23,7 @@
 )
 from nipype.utils.filemanip import fname_presuffix
 
+LOGGER = logging.getLogger("nipype.interface")
 from .bids import get_bids_params
 
 
@@ -198,9 +200,15 @@ def calculate_mask_stats(
     if weighting_vector is not None:
         results["weighted_mean"] = np.sum(voxel_data * weighting_vector)
         nz_weighting_vector = weighting_vector.copy()
-        nz_weighting_vector[np.isnan(nz_voxel_data)] = np.nan
-        nz_weighting_vector = nz_weighting_vector / np.nansum(nz_weighting_vector)
-        results["masked_weighted_mean"] = np.nansum(nz_voxel_data * nz_weighting_vector)
+        try:
+            nz_weighting_vector[np.isnan(nz_voxel_data)] = np.nan
+            nz_weighting_vector = nz_weighting_vector / np.nansum(nz_weighting_vector)
+            results["masked_weighted_mean"] = np.nansum(nz_voxel_data * nz_weighting_vector)
+        except Exception as exc:
+            LOGGER.warn(
+                f"Error calculating weighted mean of {variable_name} in {mask_name}\n{exc}"
+            )
+            results["masked_weighted_mean"] = np.nan
 
     return results