# Comparing Longitudinal Preprocessing Pipelines for Brain Volume Consistency in T1-Weighted MRI Test-Retest Scans

Matthieu Joulot, Olivier Colliot, Ninon Burgos

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## To cite this version:

Matthieu Joulot, Olivier Colliot, Ninon Burgos. Comparing Longitudinal Preprocessing Pipelines for Brain Volume Consistency in T1-Weighted MRI Test-Retest Scans. SPIE Medical Imaging 2026, Feb 2026, Vancouver, Canada. hal-05349492

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# Comparing Longitudinal Preprocessing Pipelines for Brain Volume Consistency in T1-Weighted MRI Test-Retest Scans

Matthieu Joulot<sup>a</sup>, Olivier Colliot<sup>a</sup>, and Ninon Burgos<sup>a</sup>

<sup>a</sup>Sorbonne Université, Paris Brain Institute, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France

## ABSTRACT

Neurodegenerative diseases require longitudinal assessment to track disease progression, with brain volume change from T1-weighted MRI serving as a key biomarker that demands robust and precise processing methods. Although several longitudinal preprocessing pipelines exist, there is no consensus on which offers the highest reliability. In this study, we evaluate six widely used open-source tools for cross-sectional and longitudinal preprocessing of T1-weighted MRI: FreeSurfer, SAMSEG, ANTs, ANTsPyNet, SPM12, and CAT12. We assess their robustness using test-retest data from the MIRIAD cohort, in which no meaningful anatomical change is expected between repeated scans. Our results show that, overall, longitudinal preprocessing methods demonstrate greater robustness than their cross-sectional counterparts. However, this pattern is not consistent across all tools: some longitudinal implementations do not outperform their cross-sectional versions, and the magnitude of improvement varies by method and brain region. We conclude that while the existing longitudinal preprocessing approaches can improve consistency in brain volume estimation, these benefits are method-dependent.

**Keywords:** Benchmark, Open-source, Longitudinal preprocessing, T1w MRI, Brain, Test-retest

## 1. INTRODUCTION

When studying neurodegenerative diseases, which progress over time, imaging, biological, and neuropsychological test data are acquired at multiple time points, since a single assessment may not sufficiently characterise the patient's condition. These longitudinal data allow the estimation of key biomarkers such as annualised atrophy rates. Given that annualised atrophy rates in early stages of neurodegenerative diseases are around 2%,¹ detecting such subtle changes requires image processing methods with sufficient precision. While longitudinal methods have been developed and partially compared, there remains no consensus on which approach is the most precise.

In this study, we propose a first step in the evaluation of several widely used open-source tools for cross-sectional and longitudinal preprocessing of T1-weighted MRI data, using test-retest scans for assessment. Our goal is to assess which method is the most robust and to provide practical guidance for users, researchers and clinicians alike, who must choose among multiple available tools.

## 2. MATERIAL & METHODS

### 2.1 Longitudinal Methods

The principle of longitudinal preprocessing in neuroimaging is that, given a series of scans from a subject acquired at different timepoints, the preprocessing can leverage their temporal dependency, ensuring that the structural images maintain anatomical consistency. This contrasts with processing images in a cross-sectional manner, where scans from multiple timepoints are treated as independent images. A longitudinal preprocessing workflow involves using all timepoints from a subject to create a subject-specific template—essentially an average of all the timepoints. To create this template, all the timepoints are coregistered, and averaged together. Once the template is created, it is processed using a cross-sectional pipeline, and the results serve as initialisation for subsequent longitudinal processing steps.² While the core principle remains consistent across different methods, each implements its own variation of this general framework.

Send correspondence to Matthieu Joulot (matthieu.joulot.pro@gmail.com) and Ninon Burgos (ninon.burgos@cnrs.fr).
We now briefly present the longitudinal version of the methods to be evaluated. They all have a cross-sectional counterpart. From a practical standpoint, we tested each method using default parameters whenever possible, relying on resources readily available to users: articles, documentation, and code when necessary. Where information was incomplete, we made informed assumptions to complete the pipelines.

**FreeSurfer** FreeSurfer's³ longitudinal pipeline is structured into three main stages. Initially, each timepoint undergoes preprocessing using the standard cross-sectional *recon-all* pipeline. Then, a subject-specific template is generated using *mri_robust_template*.² This template is created by mapping all timepoints to a common space defined iteratively. The process begins by aligning all images to a randomly selected reference scan, followed by the computation of a midspace using the transformations. All images are then resampled into this common space. A median image is computed, and each timepoint is iteratively registered to it until convergence. Once the template is built, it is itself processed with *recon-all*, and the results serve as initialisation for subsequent longitudinal analyses. Each timepoint is then reprocessed with *recon-all*, now aligned to the template space. The outputs include a longitudinal template, and for each timepoint, the T1-weighted scans registered to this template, transformations between native and template space, white and grey matter segmentations and cortical thickness maps.

**SAMSEG** The longitudinal extension of SAMSEG⁴,⁵ also begins with the creation of a subject-specific template using FreeSurfer's *mri_robust_template*. Once this template is constructed, it is segmented using the standard cross-sectional SAMSEG pipeline. This segmentation informs the estimation of the segmentation priors and the likelihood model in template space. These estimates are then transferred to each timepoint to initialise their individual segmentations. The final segmentations of each timepoint are computed based on the adapted priors and likelihoods. The outputs include the T1-weighted image in the template space, the whole-brain segmentation, and the posteriors of each region.

**ANTs** The ANTs longitudinal cortical thickness pipeline⁶ follows a multistep process that integrates both longitudinal registration and segmentation. It starts by creating a group-level template along with a brain mask and tissue probability priors. From this, a subject-specific template is generated, and the cortical thickness estimation algorithm, *antsCorticalThickness*, is applied in two phases. First, the algorithm is applied to the subject template using group priors. This produces refined priors that are specific to the subject. These are then used in a second round where each timepoint is processed individually, using the subject template and its derived priors to guide the cortical thickness estimation. A label fusion step is finally employed to enhance segmentation consistency. The pipeline produces a subject-specific template, timepoint images aligned to this template, forward and inverse transformations between native and template space, grey and white matter segmentations, and cortical thickness maps.

**ANTsPyNet** The ANTsPyNet⁷ longitudinal cortical thickness pipeline follows the same framework as the ANTs longitudinal cortical thickness pipeline, but replaces the brain extraction, tissue segmentation, and joint label fusion steps with convolutional neural networks.

**SPM12** SPM12⁸,⁹ provides a dedicated longitudinal registration pipeline that operates through iterative refinement. It begins by estimating a common template space based on the spatial configuration of the input timepoints. A group-wise rigid registration is performed to align the timepoints, and a mean image is computed. This is followed by inhomogeneity correction, and a refined mean image is generated. A group-wise diffeomorphic registration step is then applied, further enhancing the alignment of anatomical structures across timepoints. These steps are repeated iteratively until the mean image no longer changes appreciably. The final outputs include the longitudinal template, transformations from native space to template space, as well as deformation fields such as divergence maps and Jacobian determinant images. To transform each timepoint to template space, the *Normalise: write* module is used. The resulting images are then passed through SPM12's segmentation pipeline to obtain grey and white matter segmentations.

**CAT12** CAT12¹⁰ extends SPM12's capabilities by offering a longitudinal framework that integrates both registration and segmentation. The pipeline begins by creating a subject-specific template and aligning each timepoint to it. Segmentation is then carried out in template space using priors estimated across timepoints. The final outputs include the subject-specific template, T1w images aligned to it, transformation fields, and gray and white matter segmentations from the longitudinal segmentation module.
<figure><img src="image_3.png" /><figcaption>Figure 1. ASPC for grey matter (left) and white matter (right) for all methods. Outliers beyond the axis limits are represented by count and value range.</figcaption></figure>

## 2.2 Experiments

To evaluate the reliability of cross-sectional and longitudinal T1-weighted MRI preprocessing methods, we designed an experiment based on test-retest data. The objective is to assess how consistently each method processes anatomically identical scans acquired within a short time interval. A reliable method should yield highly similar results between the test and retest sessions for the same subject. We use the absolute symmetric percentage change (ASPC) as a reproducibility metric, defined as $ASPC = 100 \times \left| \frac{x_2 - x_1}{(x_1 + x_2)/2} \right|$, where $x_1$ and $x_2$ are the measurements (e.g., regional volume) obtained from the test and retest scans, respectively. This metric reflects the relative change between two measurements. Lower ASPC values indicate higher reproducibility. We compared the methods at two levels: tissue and regional levels. For the tissue level, we compared the volumes of grey and white matter, which are provided by all the methods. Regional analysis required two criteria: availability across methods and relevance to neurodegeneration. Since no atlas covered all methods, we used the Desikan-Killiany-Tourville (DKT)¹¹ atlas for FreeSurfer, SAMSEG, ANTs, and ANTsPyNet, and the Neuromorphometrics atlas¹² for SPM12 and CAT12. Selected regions, common to both atlases, were either typically affected by neurodegeneration or expected to remain stable: lateral ventricles, thalamus, hippocampus, putamen, caudate, pallidum, amygdala, and brainstem. At both tissue and regional levels, we assessed if cross-sectional and longitudinal ASPC differed significantly for each method using a Welch t-test with Benjamini-Hochberg correction.

## 2.3 Dataset

For this study, we selected the Minimal Interval Resonance Imaging in Alzheimer's Disease (MIRIAD) dataset¹³ as our test-retest resource. MIRIAD includes MRI data from 23 healthy control subjects (48% female, 69.7 years ± 7.2) and 46 individuals diagnosed with Alzheimer's disease (59% female, 69.4 years ± 7.1), each scanned at multiple time points. Three sessions included scan-rescan, and we use only the first one. Regarding the scan-rescan specificities, both scans were acquired during the same session, and the participants did not get out of the MRI between the scan and the rescan.

# 3. RESULTS

At the tissue segmentation level, where we evaluate the volume change for grey and white matter, no single method consistently outperforms the others across all comparisons as shown in Fig. 1. For grey matter, FreeSurfer's and ANTsPyNet's longitudinal versions significantly outperform their cross-sectional counterpart while for white matter, SAMSEG's and ANTsPyNet's longitudinal versions significantly outperform their cross-sectional counterpart. Overall, ANTsPyNet⁷ generally demonstrates the most favourable performance.

At the regional level, longitudinal methods tend to outperform their cross-sectional counterparts in subcortical grey matter and brain stem regions, significantly for FreeSurfer and SAMSEG, as shown in Fig. 2. Exceptions include ANTs and CAT12, where the longitudinal variants show no clear advantage over the cross-sectional ones. In the lateral ventricles, only FreeSurfer's longitudinal method outperforms its cross-sectional version. Among the longitudinal pipelines, Fig. 3 shows that SAMSEG and SPM12 exhibit the highest robustness and reliability across regional assessments. FreeSurfer, ANTs and ANTsPyNet perform similarly, and CAT12 performs worse.
<figure><img src="image_4.png" /><figcaption>Figure 2. Comparison of ASPC for regional volumes between cross-sectional and longitudinal methods. The ASPC of the thalamus, hippocampus, putamen, caudate, pallidum and amygdala have been averaged per subject and grouped into the subcortical grey matter category. Outliers beyond the axis limits are represented by count and value range.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Figure 3. Comparison of ASPC of regional volumes between longitudinal methods. Outliers beyond the axis limits are represented by count and value range.</figcaption></figure>

# 4. DISCUSSION & CONCLUSION

In this study, we have shown that while longitudinal preprocessing methods often exhibit greater robustness than their cross-sectional counterparts on test-retest T1-weighted MRI data, this does not hold uniformly across all methods, regions, and scales. Moreover, even for longitudinal methods, the volume change observed is not always inferior to the annualised atrophy rate of early stages of neurodegenerative diseases, which is approximately 2%.$^{1}$

This work represents the beginning of a larger study, and as such, it has several limitations and various aspects could be improved. First, the dataset used, while relevant, is relatively small and limited to 1.5 T acquisitions from a single scanner and population type. As a result, our findings may not fully generalise to broader contexts involving different acquisition protocols or demographic characteristics. Additionally, the test-retest design adopted here reflects only one mode of repeated scanning. Therefore, we plan to extend our evaluation to additional publicly available test-retest datasets, such as KIRBY21 and the Human Connectome Project. Regarding the metrics used, while ASPC effectively measures volume changes and provides insight into the robustness of pipeline results, additional metrics such as the DICE could evaluate segmentation overlap, providing complementary spatial information beyond volume-based measurements alone. Another limitation is the reliance on two different atlases due to method-specific constraints, which may introduce bias in the comparison. Most importantly, our current experiment evaluates robustness only in a no-change condition, where the expected variation between scans is ideally zero. While this allows us to assess reproducibility, it does not address the accuracy of each method in detecting meaningful changes. A method may appear highly robust simply because it consistently produces the same (but potentially incorrect) results. To overcome this, future work will incorporate controlled-change scenarios.
ACKNOWLEDGMENTS

The research leading to these results has received funding from the French government under management of
Agence Nationale de la Recherche as part of the “Investissements d'avenir” program (references ANR-19-P3IA-
0001 - PRAIRIE - and reference ANR-10IAIHU-06 - IHU ICM) and the “France 2030” program (reference
ANR-23-IACL-0008, PRAIRIE-PSAI), and from the European Union's Horizon Europe Framework Program
(grant number 101136607, project CLARA). This research was also funded by the Agence Nationale de la
Recherche under the project “ANR-23-CE45-0005-01”.

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