diff --git a/run_dipy_cpu.py b/run_dipy_cpu.py
index 9b32d98..63d5dd0 100644
--- a/run_dipy_cpu.py
+++ b/run_dipy_cpu.py
@@ -49,6 +49,9 @@
 import nibabel as nib
 from nibabel.orientations import aff2axcodes
 
+import numpy.linalg as npl
+from dipy.tracking.streamline import transform_streamlines
+
 # Import custom module
 import cuslines.cuslines as cuslines
 
@@ -88,6 +91,15 @@ def get_img(ep2_seq):
 roi_data = roi.get_data()
 mask = mask.get_data()
 
+img_affine = img.affine
+
+fa_threshold = 0.1
+min_relative_peak = 0.25
+min_peak_spacing = 0.7
+min_peak_deg = 45
+sm_lambda = 0
+seed_density = 5
+
 tenmodel = dti.TensorModel(gtab, fit_method='WLS')
 print('Fitting Tensor')
 tenfit = tenmodel.fit(data, mask)
@@ -96,26 +108,26 @@ def get_img(ep2_seq):
 FA[np.isnan(FA)] = 0
 
 # Setup tissue_classifier args
-tissue_classifier = ThresholdStoppingCriterion(FA, 0.1)
+tissue_classifier = ThresholdStoppingCriterion(FA, fa_threshold)
 metric_map = np.asarray(FA, 'float64')
 
 # Create seeds for ROI
-seed_mask = utils.seeds_from_mask(roi_data, density=3, affine=np.eye(4))
+seed_mask = utils.seeds_from_mask(roi_data, density=seed_density, affine=img_affine)
 
 # Setup model
 print('slowadcodf')
-sh_order = 6
-model = OpdtModel(gtab, sh_order=sh_order, min_signal=1)
+sh_order = 4
+model = OpdtModel(gtab, sh_order=sh_order, smooth=sm_lambda, min_signal=1)
 
 # Setup direction getter args
 print('Bootstrap direction getter')
-boot_dg = BootDirectionGetter.from_data(data, model, max_angle=60., sphere=small_sphere)
+boot_dg = BootDirectionGetter.from_data(data, model, max_angle=60., sphere=small_sphere, sh_order=sh_order, relative_peak_threshold=min_relative_peak, min_separation_angle=min_peak_deg)
 
 print('streamline gen')
 global_chunk_size = args.chunk_size
 nchunks = (seed_mask.shape[0] + global_chunk_size - 1) // global_chunk_size
 
-streamline_generator = LocalTracking(boot_dg, tissue_classifier, seed_mask, affine=np.eye(4), step_size=.5)
+streamline_generator = LocalTracking(boot_dg, tissue_classifier, seed_mask, affine=img_affine, step_size=.5)
 
 t1 = time.time()
 streamline_time = 0
@@ -130,12 +142,18 @@ def get_img(ep2_seq):
                                                                     seed_mask[idx*global_chunk_size:(idx+1)*global_chunk_size].shape[0],
                                                                     te-ts))
 
+  # Invert streamline affine
+  inv_affine = npl.inv(img_affine)
+  streamlines = transform_streamlines(streamlines,inv_affine)
+  
+  
   # Save tracklines file
   if args.output_prefix:
     fname = "{}.{}_{}.trk".format(args.output_prefix, idx+1, nchunks)
     ts = time.time()
     #save_tractogram(fname, streamlines, img.affine, vox_size=roi.header.get_zooms(), shape=roi_data.shape)
     sft = StatefulTractogram(streamlines, args.nifti_file, Space.VOX)
+    sft.to_rasmm()
     save_tractogram(sft, fname)
     te = time.time()
     print("Saved streamlines to {}, time {} s".format(fname, te-ts))