Hi,

Would it be possible, moving forward, to implement a way to run APPIAN from the command line in such a way as to use t1 and PET images that were acquired at different timepoints?

Hello, I have run into an error running the example data. Here is the last part of the output with the errors:

`
Running FixHeaderLinkCommand

190212-17:43:20,526 workflow INFO:
Runtime memory and threads stats unavailable
190212-17:43:20,531 workflow INFO:
Executing node pvc_qc_metrics.a1 in dir: /path/to/cimbi/dir/out_cimbi/preproc/_args_run.task.ses02.sid01/pvc_qc_metrics
19 26821106966.0
('PVC MSE:',
array(data=
-0.987869531793,
start=[0 0 0 0], count=[ 38 207 256 256],
separations=[1.0, 1.21875, 1.21875, -1.21875], dimnames=[])
190212-17:43:24,539 workflow INFO:
Runtime memory and threads stats unavailable
190212-17:43:24,543 workflow INFO:
Executing node convertPET.a1 in dir: /path/to/cimbi/dir/out_cimbi/preproc/quantification/_args_run.task.ses02.sid01/convertPET
minctoecat /path/to/cimbi/dir/out_cimbi/preproc/pvc/_args_run.task.ses02.sid01/fixHeaderNode/sub-01_ses-02_pet_center_GTM.mnc temp.v
190212-17:43:46,755 interface INFO:
stdout 2019-02-12T17:43:46.755269:Converting data: ..............................................................
('mv', 'temp.v', '/path/to/cimbi/dir/out_cimbi/preproc/quantification/_args_run.task.ses02.sid01/convertPET/sub-01_ses-02_pet_center_GTM.v')
/path/to/cimbi/dir/out_cimbi/preproc/initialization/_args_run.task.ses02.sid01/petSettings/sub-01_ses-02_pet_center.info
('Start -- Duration:', [28, 33, 38, 43, 48, 53, 58, 73, 88, 103, 118, 133, 148, 163, 178, 193, 208, 238, 268, 298, 328, 448, 568, 688, 808, 928, 1228, 1528, 1828, 2128, 2428, 3028, 3628, 4228, 4828, 5428, 6028, 6628], [5, 5, 5, 5, 5, 5, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 30, 30, 30, 30, 120, 120, 120, 120, 120, 300, 300, 300, 300, 300, 600, 600, 600, 600, 600, 600, 600, 600])
190212-17:44:07,697 interface INFO:
stdout 2019-02-12T17:44:07.696520:/path/to/cimbi/dir/out_cimbi/preproc/quantification/_args_run.task.ses02.sid01/convertPET/sub-01_ses-02_pet_center_GTM.v : unit 'unknown' replaced by 'Bq/cc'
190212-17:44:07,697 interface INFO:
stdout 2019-02-12T17:44:07.696520:Writing image file /path/to/cimbi/dir/out_cimbi/preproc/quantification/_args_run.task.ses02.sid01/convertPET/sub-01_ses-02_pet_center_GTM.v
190212-17:44:09,310 workflow INFO:
Runtime memory and threads stats unavailable
190212-17:44:09,311 workflow INFO:
Executing node referencemask_extract.a1 in dir: /path/to/cimbi/dir/out_cimbi/preproc/quantification/_args_run.task.ses02.sid01/referencemask_extract
190212-17:44:14,954 interface INFO:
stdout 2019-02-12T17:44:14.954082:reading /path/to/cimbi/dir/out_cimbi/preproc/quantification/_args_run.task.ses02.sid01/convertPET/sub-01_ses-02_pet_center_GTM.v
190212-17:44:14,954 interface INFO:
stdout 2019-02-12T17:44:14.954082:reading /path/to/cimbi/dir/out_cimbi/preproc/quantification/_args_run.task.ses02.sid01/minctoecat_reference/sub-01_ses-02_T1w_normalized_brain_labeled_space-pet.v
190212-17:44:14,954 interface INFO:
stdout 2019-02-12T17:44:14.954082:calculating mask VOIs
190212-17:44:14,954 interface INFO:
stdout 2019-02-12T17:44:14.954082:1 regional TACs written in /path/to/cimbi/dir/out_cimbi/preproc/quantification/_args_run.task.ses02.sid01/referencemask_extract/sub-01_ses-02_pet_center_GTM.dft
img2dft /path/to/cimbi/dir/out_cimbi/preproc/quantification/_args_run.task.ses02.sid01/convertPET/sub-01_ses-02_pet_center_GTM.v /path/to/cimbi/dir/out_cimbi/preproc/quantification/_args_run.task.ses02.sid01/minctoecat_reference/sub-01_ses-02_T1w_normalized_brain_labeled_space-pet.v /path/to/cimbi/dir/out_cimbi/preproc/quantification/_args_run.task.ses02.sid01/referencemask_extract/sub-01_ses-02_pet_center_GTM.dft

tacunit -xconv=min /path/to/cimbi/dir/out_cimbi/preproc/quantification/_args_run.task.ses02.sid01/referencemask_extract/sub-01_ses-02_pet_center_GTM.dft

190212-17:44:16,491 workflow INFO:
Runtime memory and threads stats unavailable
190212-17:44:16,492 workflow INFO:
Executing node lp.a1 in dir: /path/to/cimbi/dir/out_cimbi/preproc/quantification/_args_run.task.ses02.sid01/lp
_parse_inputs
_gen_output
190212-17:44:16,495 workflow INFO:
Running: imgdv /path/to/cimbi/dir/out_cimbi/preproc/quantification/_args_run.task.ses02.sid01/referencemask_extract/sub-01_ses-02_pet_center_GTM.dft /path/to/cimbi/dir/out_cimbi/preproc/quantification/_args_run.task.ses02.sid01/convertPET/sub-01_ses-02_pet_center_GTM.v /path/to/cimbi/dir/out_cimbi/preproc/quantification/_args_run.task.ses02.sid01/lp/sub-01_ses-02_pet_center_GTM_lp.v
_parse_inputs
190212-17:44:16,519 interface INFO:
stderr 2019-02-12T17:44:16.518981:Error: missing result file name.
190212-17:44:17,524 workflow ERROR:
['Node lp.a1 failed to run on host 9dadccbcdd40.']
190212-17:44:17,525 workflow INFO:
Saving crash info to /opt/crash-20190212-174417-root-lp.a1-4886f66e-eb19-423c-a111-3017b2b25b81.pklz
190212-17:44:17,525 workflow INFO:
Traceback (most recent call last):
File "/usr/local/lib/python2.7/dist-packages/nipype/pipeline/plugins/linear.py", line 39, in run
node.run(updatehash=updatehash)
File "/usr/local/lib/python2.7/dist-packages/nipype/pipeline/engine/nodes.py", line 394, in run
self._run_interface()
File "/usr/local/lib/python2.7/dist-packages/nipype/pipeline/engine/nodes.py", line 504, in _run_interface
self._result = self._run_command(execute)
File "/usr/local/lib/python2.7/dist-packages/nipype/pipeline/engine/nodes.py", line 630, in _run_command
result = self._interface.run()
File "/usr/local/lib/python2.7/dist-packages/nipype/interfaces/base.py", line 1043, in run
runtime = self._run_wrapper(runtime)
File "/usr/local/lib/python2.7/dist-packages/nipype/interfaces/base.py", line 1660, in _run_wrapper
runtime = self._run_interface(runtime)
File "/usr/local/lib/python2.7/dist-packages/nipype/interfaces/base.py", line 1694, in _run_interface
self.raise_exception(runtime)
File "/usr/local/lib/python2.7/dist-packages/nipype/interfaces/base.py", line 1618, in raise_exception
raise RuntimeError(message)
RuntimeError: Command:
imgdv /path/to/cimbi/dir/out_cimbi/preproc/quantification/_args_run.task.ses02.sid01/referencemask_extract/sub-01_ses-02_pet_center_GTM.dft /path/to/cimbi/dir/out_cimbi/preproc/quantification/_args_run.task.ses02.sid01/convertPET/sub-01_ses-02_pet_center_GTM.v /path/to/cimbi/dir/out_cimbi/preproc/quantification/_args_run.task.ses02.sid01/lp/sub-01_ses-02_pet_center_GTM_lp.v
Standard output:

Standard error:
Error: missing result file name.
Return code: 1
Interface quantCommand failed to run.

190212-17:44:17,528 workflow INFO:
***********************************
190212-17:44:17,528 workflow ERROR:
could not run node: preproc.quantification.lp.a0
190212-17:44:17,529 workflow INFO:
crashfile: /opt/crash-20190212-174231-root-lp.a0-ab68a46a-c829-4ede-89ba-1ed0497c4e41.pklz
190212-17:44:17,529 workflow ERROR:
could not run node: preproc.quantification.lp.a1
190212-17:44:17,529 workflow INFO:
crashfile: /opt/crash-20190212-174417-root-lp.a1-4886f66e-eb19-423c-a111-3017b2b25b81.pklz
190212-17:44:17,529 workflow INFO:
***********************************
Traceback (most recent call last):
File "/opt/APPIAN/Launcher.py", line 43, in
run_scan_level(opts,args)
File "/opt/APPIAN/scanLevel.py", line 31, in run_scan_level
scan_level_workflow.workflow.run()
File "/usr/local/lib/python2.7/dist-packages/nipype/pipeline/engine/workflows.py", line 597, in run
runner.run(execgraph, updatehash=updatehash, config=self.config)
File "/usr/local/lib/python2.7/dist-packages/nipype/pipeline/plugins/linear.py", line 57, in run
report_nodes_not_run(notrun)
File "/usr/local/lib/python2.7/dist-packages/nipype/pipeline/plugins/base.py", line 95, in report_nodes_not_run
raise RuntimeError(('Workflow did not execute cleanly. '
RuntimeError: Workflow did not execute cleanly. Check log for details
`

Hi,

When I try to run the example in the User Guide Default: Coregistration + MRI Preprocessing + Results Report, after some processing using the default settings, an error occurs: could not run node: preproc.initialization.petVolume.a0

Any tips on how to solve it?

We need continual testing of APPIAN whenever changes are made and to ensure that Docker releases are bug-free!

https://docs.travis-ci.com/user/tutorial/

Hello,

I'm using APPIAN to run an SUVR pipeline with T1, freesurfer segmentation and static florbetapir PET image that is one single frame of 50-60mins post-injection. I'm using the latest release downloaded from dockerhub on 19th May 2022, running it through singularity on a HPC cluster (as I can't use docker on the HPC).

This is the command that is being run:
python3 /opt/APPIAN/Launcher.py -s /appian_test/data -t /appian_test/cerebellum_suvr_10015124_baseline --sub 10015124 --ses baseline --no-group-level --quant-method suvr --start-time 3000 --end-time 3600 --quant-label-img fs-segm --quant-label 7 8 46 47 --quant-labels-ones-only --quant-label-space t1 --no-mri-mask --normalization-type nl --results-label-img fs-segm --results-label-space t1 --threads 4

It fails consistently on the 'brain_mask_space_pet' step after completing normalisation step. I've attached the full log file. Any help working out what is going wrong would be much appreciated!
Many thanks - Will

appian_log.txt

Hi!
I am experiencing some issues with running a quantitative analysis, and I hope you can provide some insights for me.

My command is as follows:

python3 /opt/APPIAN/Launcher.py --sub 000101 --ses baseline --user-ants-command /opt/APPIAN/src/ants_command_quick.txt -s INPUT -t OUTPUT --start-time 0.5 --filter --quant-method srtm --quant-label-space pet --quant-label-img reference_mask.nii.gz --quant-labels-ones-only

I get the following error:

Traceback (most recent call last):
File "/opt/APPIAN/Launcher.py", line 26, in
run_scan_level(opts,args)
File "/opt/APPIAN/src/scanLevel.py", line 34, in run_scan_level
scan_level_workflow.workflow.run()
File "/usr/local/lib/python3.8/dist-packages/nipype/pipeline/engine/workflows.py", line 638, in run
runner.run(execgraph, updatehash=updatehash, config=self.config)
File "/usr/local/lib/python3.8/dist-packages/nipype/pipeline/plugins/linear.py", line 82, in run
raise error from cause
File "/usr/local/lib/python3.8/dist-packages/nipype/pipeline/plugins/linear.py", line 47, in run
node.run(updatehash=updatehash)
File "/usr/local/lib/python3.8/dist-packages/nipype/pipeline/engine/nodes.py", line 521, in run
result = self._run_interface(execute=True)
File "/usr/local/lib/python3.8/dist-packages/nipype/pipeline/engine/nodes.py", line 639, in _run_interface
return self._run_command(execute)
File "/usr/local/lib/python3.8/dist-packages/nipype/pipeline/engine/nodes.py", line 750, in _run_command
raise NodeExecutionError(
nipype.pipeline.engine.nodes.NodeExecutionError: Exception raised while executing Node quant-srtm.

Traceback (most recent call last):
File "/opt/APPIAN/src/quantification.py", line 178, in srtm
isotope=header["Info"]["Tracer"]["Isotope"][0]
KeyError: 'Info'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "/usr/local/lib/python3.8/dist-packages/nipype/interfaces/base/core.py", line 398, in run
runtime = self._run_interface(runtime)
File "/opt/APPIAN/src/quantification.py", line 586, in _run_interface
quant_vol = model(pet_vol, int_vol, ref_tac_rsl, int_ref, time_frames, opts=opts, header=header)
File "/opt/APPIAN/src/quantification.py", line 184, in srtm
half_life = opts.quant_half_life
AttributeError: 'TraitDictObject' object has no attribute 'quant_half_life'

For some context, I am using APPIAN through Docker (tffunck/appian:latest).

I have tried to investigate why this error is occurring and I believe there is a discrepancy between the source code pulled in Docker and the latest source code on the GitHub website (where line 178 of quanitifcation.py is updated to reflect the actual JSON key name).

So, I decided to enter the container and from there checkout the latest commit to the master branch, dated Mar 8 2023. When I run my command again, I get "ModuleNotFoundError: No module named 'src.ants_nibabel'".

I have 3 main questions that I would ask for your input with:

1. Why doesn't the Docker tffunck/appian:latest appear to pull the most up to date source code from the git repository?
2. In the latest source code, is there a missing file ants_nibabel.py? Do you have any plans to add this file so the imports work correctly?
3. I see that I could get around (1) by instead directly specifying the additional flag "--half-life 20.362". But this does not work either, because I get the error "AttributeError: 'TraitDictObject' object has no attribute 'quant_half_life'". Am I using this flag incorrectly?

I would very much appreciate your time in helping to resolve these issues.

Hi again,

When I try to run the default Launcher.py, it gives an error in the mincreshape call:

Standard error:
Unknown image dimension "time"
Return code: 1
Interface ReshapeCommand failed to run.
Interface pet3DVolume failed to run.

It probably happens because of the -dimrange 'time=0,1' parameter.

Could it be optional?

Hi,

When I installed the software, I met the error in the attached screenshot. It seems that the package is missing "ModuleNotFoundError: No module named 'sklearn.neighbors.kde". Please see the screenshot for details.

Hi,

I'd like to be able to view the output of my preprocessing in the dashboard GUI mentioned briefly in your documentation and the paper but I can't work out how? I assumed it was the index.html page in preproc/ however opening that just gave this:

Is this possible yet? If so, how do I get to it.

Many thanks.

Thanks for developing and sharing the nice tool for PET quantification.

I am currently working with multi-modal data with both MRI and FDG images.

Since the preprocessing steps of MRI take a lot of time, I wonder if it is possible to use the preprocessed MRI images (e.g., preprocessed by fMRIprep that also used ANTs and the Brain Imaging Data Structure (BIDS)) as input for APPIAN?

Thanks a lot!

I am a postdoc fellow at University of Genoa starting to work on pediatric PET images. First of all, I was looking for an automated image processing software for PET and I came into APPIAN. I have read both the original paper and the relative software documentation, and had a couple of questions about its implementation for my specific case.

I would like to try and automatically reproduce some image processing step conducted semi-quantitatively by a group of radiologists on a bunch of pediatric PET/CT images on subjects affected by pontine gliomas. Specifically,

• PET scanner in use is PET/CT Discovery ST system (GE Healthcare, Milwaukee, WI, USA).
• MRI examinations were performed on 1.5T and 3T (5 patients; Ingenia Cx, Philips, Best, the Netherlands) scanners
• Co-registration and fusion of 18F-DOPA PET and MR images to ensure precise anatomical comparability.
• Volumetric tumor analysis on axial 18F-DOPA PET and MRI FLAIR images. In detail, the anatomic tumor extent was delineated on MRI FLAIR images using a perimeter technique with user-assisted semi-automated software; 18F-DOPA PET tumor volume was delineated based on 18F-DOPA uptake avidity (tumoral areas with increased uptake compared to normal background reference region).
• For each patient, 18F-DOPA uniformity, defined as the percentage of the MRI tumor volume at admission (as delineated on FLAIR images) demonstrating increased 18F-DOPA PET uptake, was also calculated
• PET tumor volume was delineated on 18F-DOPA PET studies by including all voxels with standardized uptake value (SUV) above the maximum (max) SUV of the normal background reference tissue. For the normal (N) background reference tissue, a VOI (diameter 20 mm) was drawn in the normal cerebral hemisphere at the level of the left centrum semiovale, including cortical and white matter. For each case, the radiotracer concentration in the tumoral VOI was normalized to the injected dose per patient body weight, and the SUV max was obtained for each lesion [maximum pixel value (kBq/mL) within the VOI/ injected dose (kBq)/patient weight (g)]. An additional VOI was drawn over the left striatum including the entire putamen (S). Ratios of tumor to normal tissue uptake were also generated by dividing the tumor SUVmax by the SUVmax of the striatum (T/S)
• In case of absence of increased 18F-DOPA uptake, a VOI including the tumor on MRI FLAIR images was delineated on co-registered 18F-DOPA PET images to generate T/S and T/N ratios.

Is it possible to automatically perform all aforementioned steps through your platflorm?
thank you so much in advance,
Rosella Trò

Hi,

I'm having an issue with APPIAN finding the image I want to use to define the stereotaxic space. I have tried defining the image path with both absolute and relative paths to the same result; failing with the error:

'Status : FAIL 1 All_Command_lines_OK

Using single precision for computations.

file OASIS/T_template0.nii.gz does not exist .'

I am wondering, does the container have some expectation that template images will be stored in a particular directory? I have tried using a sub-directory contained within the input directory defined by the -s option.

Hi,
I was wondering whether via Docker it is possible to segment a specific ROI corresponding to tumor volume on PET scans. How can manual segmentation be performed via Docker if interactive dashboard is not feasible?

thank you,

Rosella

Hey!

I was bouncing around to try and remember the status of the Boutiques descriptor for this project, and realized that there's no DOI for citing the actual APPIAN codebase! These are super easy to obtain through Zenodo as you probably know, and make it easy to differentiate between version of software when citing (esp. useful for before/after bug fixes), and giving new devs co-authorship automatically w/out a new in-press publication.

There's the official guide for how to do it, here: https://guides.github.com/activities/citable-code/

I'd recommend taking the 5-10 min to set it up sooner than later, so that it'll be there moving forward :) Happy to help/chat/or even do it if you wanted to give me temporary access to the APPIAN org that I'd give back when finished the integration.

Once that's done, I'll circle back on the Boutiques stuff ;)

cc: @tfunck @llevitis @pjtoussaint

The FixHeaderCommand function used by ecattominc2 updates the header of the final MINC file to match the dimensions of the native-space PET input image. If analysis-space is set to t1, this results in the final lp or other tka image not being in the same space as the native T1 image, result labels in T1 space, and the PET co-registered to the T1.

Python 2.7 will be deprecated and lose support as of January 2020, so we should begin gradually transitioning to Python 3.