MNI SISCOM: An Open-Source Tool for Subtraction Ictal Single-photon emission CT Coregistered to MRI

Presented During:

Poster No:


Submission Type:

Abstract Submission 


Jeremy Moreau1, Christine Saint-Martin2, Sylvain Baillet3, Roy Dudley2


1Montreal Neurological Institute / Montreal Children's Hospital, McGill University, Montreal, Canada, 2Montreal Children's Hospital, McGill University, Montreal, Canada, 3Montreal Neurological Institute, McGill University, Montreal, Canada

First Author:

Jeremy Moreau  
Montreal Neurological Institute / Montreal Children's Hospital, McGill University
Montreal, Canada


Christine Saint-Martin  
Montreal Children's Hospital, McGill University
Montreal, Canada
Sylvain Baillet  
Montreal Neurological Institute, McGill University
Montreal, Canada
Roy Dudley  
Montreal Children's Hospital, McGill University
Montreal, Canada


Subtraction ictal single-photon emission CT coregistered to MRI (SISCOM) is a well-established technique for quantitative analysis of ictal (during a seizure) vs interictal (between seizures) SPECT images that can contribute to the identification of the epileptogenic zone in patients with drug‐resistant epilepsy (Ahnlide et al., 2007). However, there is presently a lack of user-friendly free and open-source software to compute SISCOM results from raw SPECT and MRI images. Multi-purpose image processing packages (Penny et al., 2011) already provide tools (e.g. coregistration and image calculators) that allow for the computation of SISCOM images, but obtaining these results typically requires several steps and necessitate a certain level of technical expertise. In this project, we developed an open-source graphical and command-line scriptable application to facilitate the process of computing SISCOM images. The goal of this project is to provide a freely available single-purpose and user-friendly tool to implement SISCOM.


MNI SISCOM runs on Windows, Mac, and Linux computers and can be used via the command line or using a desktop graphical interface (Fig. 1). The graphical desktop application is made available as a precompiled binary and can be used without any programming knowledge or familiarity with command line interfaces. The underlying Python library that implements the functionality of MNI SISCOM is also made available via the standard Python package manager (PyPi) and can be used to access these features from user-written Python scripts. The mnisiscom command line tool can be run as follows:

mnisiscom -t1 /path/to/T1.nii -ii /path/to/interictal.nii -i /path/to/ictal.nii -o /path/to/output/folder

MNI SISCOM currently implements the standard SISCOM algorithm [3]. First, interictal and ictal SPECT images are coregistered to the T1 MRI. Then both SPECT images are standardised and the interictal SPECT is subtracted from the ictal SPECT image.
Supporting Image: OHBM_fig1.png
   ·Fig 1. Screenshot of MNI SISCOM desktop graphical user interface


A key feature of MNI SISCOM is its ability to produce convenient SISCOM and interictal/ictal SPECT coregistered to MRI panels for rapid review (Fig. 2). This includes both slides showing interictal, ictal, and SISCOM images side-by-side as well as individual compact slides showing all slices in one plane for a given modality. For every set of interictal/ictal SPECT images, mnisiscom produces NIfTI volumes of unthresholded standardised interictal and ictal SPECT coregistered to the provided T1 MRI. Additionally, thresholded SISCOM images (with a user adjustable threshold) are produced. A normalised image in MNI152 space can also optionally be outputted. This normalised image is also used to produce a schematic glass brain image, using the Nilearn python module (Abraham et al., 2014), if requested.
Supporting Image: OHBM_fig2.png
   ·Fig 2. Example output SISCOM results slide. Bottom L inset shows glass brain image output. Bottom R inset shows a single slice of the output MRI panel showing interictal/ictal SPECT and SISCOM results


MNI SISCOM is a user-friendly free and open-source application for computing SISCOM. The graphical interface allows any user to easily obtain SISCOM images with minimal user interaction. Additionally, MNI SISCOM provides command line and Python interfaces to allow more technically inclined users to integrate these features into their own scripts and pipelines. Future steps will include implementation of newer popular subtraction ictal SPECT algorithms such as STATISCOM (Kazemi et al., 2010).

Disorders of the Nervous System:

Neurodevelopmental/ Early Life (eg. ADHD, autism)

Modeling and Analysis Methods:

Methods Development 2

Neuroinformatics and Data Sharing:

Informatics Other 1


Single Photon Emission Computed Tomography (SPECT)
Other - Software; Open-source; Python

1|2Indicates the priority used for review

My abstract is being submitted as a Software Demonstration.


Please indicate below if your study was a "resting state" or "task-activation” study.


Healthy subjects only or patients (note that patient studies may also involve healthy subjects):


Was any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.


Was any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.

Not applicable

Please indicate which methods were used in your research:

Other, Please specify  -   Single-photon emission computed tomography (SPECT)

Which processing packages did you use for your study?


Provide references using author date format

Abraham A (2014). 'Machine learning for neuroimaging with scikit-learn', Frontiers in Neuroinformatics, vol. 8, pp. 1-14.

Ahnlide J-A (2007). 'Does SISCOM contribute to favorable seizure outcome after epilepsy surgery?', Epilepsia, vol. 48, no. 3, pp. 579–588.

Kazemi NJ (2010). 'Ictal SPECT statistical parametric mapping in temporal lobe epilepsy surgery', Neurology, vol. 74, no. 1, pp. 70–76.

O’Brien TJ (1998). 'Subtraction ictal SPECT co-registered to MRI improves clinical usefulness of SPECT in localizing the surgical seizure focus', Neurology, vol. 50, no. 2, pp. 445–454.

Penny WD (2011). 'Statistical Parametric Mapping: The Analysis of Functional Brain Images', Elsevier.