DSMRI: Domain Shift Analyzer for Multi-Center MRI Datasets

Abstract

In medical research and clinical applications, the utilization of MRI datasets from multiple centers has become increasingly prevalent. However, inherent variability between these centers presents challenges due to domain shift, which can impact the quality and reliability of the analysis. Regrettably, the absence of adequate tools for domain shift analysis hinders the development and validation of domain adaptation and harmonization techniques. To address this issue, this paper presents a novel Domain Shift analyzer for MRI (DSMRI) framework designed explicitly for domain shift analysis in multi-center MRI datasets. The proposed model assesses the degree of domain shift within an MRI dataset by leveraging various MRI-quality-related metrics derived from the spatial domain. DSMRI also incorporates features from the frequency domain to capture low- and high-frequency information about the image. It further includes the wavelet domain features by effectively measuring the sparsity and energy present in the wavelet coefficients. Furthermore, DSMRI introduces several texture features, thereby enhancing the robustness of the domain shift analysis process. The proposed framework includes visualization techniques such as t-SNE and UMAP to demonstrate that similar data are grouped closely while dissimilar data are in separate clusters. Additionally, quantitative analysis is used to measure the domain shift distance, domain classification accuracy, and the ranking of significant features. The effectiveness of the proposed approach is demonstrated using experimental evaluations on seven large-scale multi-site neuroimaging datasets.

Keywords: MRI; UMAP; domain shift; quality control; t-SNE; texture analysis.

Figure 1

An overview of the proposed DSMRI framework. The different colours in the brain icon show that MRI data originated from different sites or may be acquired with distinct image acquisition protocols. Twenty-two significant features are extracted from 2D MRI slices of each subject. Utilizing these feature maps, t-SNE and UMAP methods are used to visualize the position of each scan in a reduced two-dimensional plot. The results are also interpreted in quantitative analysis, where the domain shift distance can be obtained with the maximum mean discrepancy distance (MMD) and the ranking of 22 features to show which features play a more significant role in classifying different domains. Best viewed in colour.

Figure 2

t-SNE plots illustrating data distributions across various datasets: CALSNIC1, CALSNIC2, ADNI2, ADNI1, PPMI, and ABIDE. Each data point in the graph corresponds to an individual MRI scan, using three distinct colours to distinguish scans acquired from different scanner manufacturers.

Figure 3

t-SNE plots illustrating the domain shift effects resulting from different scanner models of the same manufacturer, observed in the ADNI1 and AIBL datasets.

Figure 4

t-SNE and UMAP plots depicting the domain shift effects arising from varying resolutions within the CALSNIC2 dataset.

Figure 5

t-SNE and UMAP plots illustrating the domain shift effects observed within the CALSNIC2 dataset due to the utilization of T2-weighted and FLAIR images.

Figure 6

t-SNE plots for the CALSNIC1 and CALSNIC2 datasets showing the effects of data after performing skull stripping and registration to MNI-152 template.

Figure 7

Feature importance ranking across various datasets and data types, assessing domain shift presence through prioritizing the 22 proposed features.

Figure 8

Comparison of the proposed framework with two prior approaches visualizing data distribution through t-SNE plots for the challenging ADNI1, PPMI, and ABIDE datasets.