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\begin{document}

\title{Automating quality control for image processing pipelines}



\author[1]{Thomas Funck}%
\affil[1]{McGill University}%


\vspace{-1em}



  
  \date{May 05, 2024}


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\section*{Objectives}

{\label{126927}}

The increasing availability of large brain imaging datasets makes
automated analysis essential but requires rigorous QC to ensure that
each processing step has been performed as expected.~We have implemented
a novel technique for automated groupwise QC (agQC) and evaluate this
approach for the automated QC of PET analysis using APPIAN (Automated
Pipeline for PET Image ANalysis), an open-source package available
through~\href{http://www.github.com/APPIAN-PET/APPIAN}{www.github.com/APPIAN-PET/APPIAN}~or
Docker Hub:~tffunck/appian.~The overall approach of the agQC is to
define a metric that quantifies the performance of a given processing
step and perform outlier detection on the~empirical distribution of the
QC metrics. We assess the sensitivity and specificity of our automated
QC algorithm in the case of detecting errors in co-registration and
tracer kinetic analysis (TKA).

\section*{Methods}

{\label{613978}}

26{[}18-F{]}-flumazenil PET images were acquired on the Siemens HRRT
scanner and reconstructed with 3D-FBP. Corresponding T1 MRI were
acquired on a Siemens Magnetom TrioTim Syngo MR using an MPRAGE sequence
(repetition time (TR) 2300 ms, echo time (TE) 2.98 ms, TI 9 ms and flip
angle = 9\selectlanguage{ngerman}° matrix size = 160 × 256 × 256).

To test the performance of agQC for coregistration, a set of correctly
co-registered PET and MRI were created by running APPIAN on each of the
three sets of PET images. For each of the correctly co-registered PET
images, a series of transformations were applied to the correctly
registered image to systematically misalign it relative to the T1 MRI.
The misalignments were produced by rotations of 2, 4, 8, 12, 16, 20
degrees in the axial plane. Three QC metrics were used to describe the
success of the co-registration processing stage: cross-correlation (CC),
mutual information (MI), and feature-space entropy (FSE), as well as the
combination of all three measures (All).~

The performance of agQC for TKA by introducing error into the
calculation of BPnd for each of the images using the Logan plot method
(CITATION). This was done by simulating the misspecification of the
reference region for the Logan plot, such that the reference region was
progressively contaminated with radiotracer concentration from regions
with specific radiotracer binding. That is, a pseudo-reference time
activity curve TAC was created : pseudo-reference TAC = true-reference
TAC x (alpha) + ROI TAC x (1-alpha), where alpha = 0, 0.25, 0.5, 0.75,
1. The reference region was defined as average TAC of the white matter
and the~ ROI TAC from the cortical GM.~

Outliers measures were detected using the local outlier factor
(LOF)(CITATION), kernel density estimation (KDE), isolation forest
(CITATION), and median average deviation (MAD). The area under the curve
(AUC) of the ROC curves for each outlier measure was calculated to
compare the performance of agQC at various levels of error.

\par\null

\section*{Results}

{\label{609722}}

AgQC~was able to detect moderate misregistrations in the PET and MRI
images. Overall, the best combination of QC~metrics and outlier measures
for coregistration appeared to be the use of all metrics with KDE
(AUC=0.8 for an 8degree rotation error). In the case of TKA, all outlier
measures performed similarly for detecting outliers in the distribution
of BPnd (AUC=\textasciitilde{}0.75 for a 40\% contamination of the
reference TAC).~\selectlanguage{english}
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{\label{650979}}%
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\par\null

\section*{Conclusions}

{\label{482632}}

AgQC is a general framework for automated QC that is able to detect
errors in common processing steps used in PET image analysis,
specifically PET-MRI coregistration and TKA. APPIAN implements agQC to
facilitate the reliable automation of research with PET imaging. Future
work will involve extending the agQC to more processing steps, e.g.,
partial-volume correction, evaluating its performance with more
radiotracers, and testing additional outlier measures.~

\par\null

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