Summary of what happened:
I am running mrtrix_multishell_msmt_pyafq_tractometry in qsirecon and want to access all of the “built-in” white matter tracts listed on the pyAFQ documentation: Major Fiber Tracts — AFQ 2.1 documentation
Does anyone know if the optic nerve/tract bundles are available using built-in functions like reco_bd() or default18_bd()? When I pass either of these functions in the bundle_info section of my yaml file (see below) I do not appear to get those segmentations. I do get the optic radiations when running reco_bd(80)
Thanks!
Command used (and if a helper script was used, a link to the helper script or the command generated):
PASTE COMMAND HERE
Updated recon spec:
description: Use pyAFQ to perform the Tractometry pipeline, with tractography from
qsirecon
name: mrtrix_multishell_msmt_pyafq_tractometry
nodes:
- action: csd
input: qsirecon
name: msmt_csd
parameters:
fod:
algorithm: msmt_csd
max_sh:
- 8
- 8
- 8
mtnormalize: true
response:
algorithm: dhollander
qsirecon_suffix: MRtrix3
software: MRTrix3
- action: tractography
input: msmt_csd
name: track_ifod2
parameters:
sift2: {}
tckgen:
algorithm: iFOD2
max_length: 250
min_length: 10
power: 0.33
select: 50000.0
use_5tt: false
use_sift2: true
qsirecon_suffix: MRtrix3
software: MRTrix3
- action: pyafq_tractometry
input: track_ifod2
name: pyafq_tractometry
parameters:
bundle_info: 'reco_bd(80)'
b0_threshold: 50
brain_mask_definition: ''
clean_rounds: 5
clip_edges: false
csd_lambda_: 1
csd_response: ''
csd_sh_order: ''
csd_tau: 0.1
directions: prob
dist_to_atlas: 4
dist_to_waypoint: ''
distance_threshold: 3
export: all
filter_b: true
filter_by_endpoints: true
greater_than: 20
gtol: 0.01
import_tract: ''
length_threshold: 4
mapping_definition: ''
max_angle: 30.0
max_bval: ''
max_length: 250
min_bval: ''
min_length: 10
min_sl: 20
model_clust_thr: 1.25
n_points: 100
n_points_bundles: 40
n_points_indiv: 40
n_seeds: 1
nb_points: false
nb_streamlines: false
odf_model: CSD
parallel_segmentation: '{''n_jobs'': -1, ''engine'': ''joblib'', ''backend'':
''loky''}'
presegment_bundle_dict: null
presegment_kwargs: '{}'
prob_threshold: 0
profile_weights: gauss
progressive: true
pruning_thr: 12
random_seeds: false
reduction_thr: 25
refine: false
reg_algo: ''
reg_subject_spec: power_map
reg_template_spec: mni_T1
return_idx: false
rm_small_clusters: 50
rng: ''
rng_seed: ''
robust_tensor_fitting: false
roi_dist_tie_break: false
save_intermediates: ''
sbv_lims_bundles: '[None, None]'
sbv_lims_indiv: '[None, None]'
scalars: '[''dti_fa'', ''dti_md'']'
seed_mask: ''
seed_threshold: 0
seg_algo: AFQ
sphere: ''
stat: mean
step_size: 0.5
stop_mask: ''
stop_threshold: 0
tracker: local
use_external_tracking: true
virtual_frame_buffer: false
viz_backend_spec: plotly_no_gif
volume_opacity_bundles: 0.3
volume_opacity_indiv: 0.3
qsirecon_suffix: PYAFQ
software: pyAFQ
space: T1w
Version:
PUT VERSION HERE
Environment (Docker, Singularity / Apptainer, custom installation):
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Data formatted according to a validatable standard? Please provide the output of the validator:
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Relevant log outputs (up to 20 lines):
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