Journal: Imaging Neuroscience

Article Title: Automated quality control of T1-weighted brain MRI scans for clinical research datasets: methods comparison and design of a quality prediction classifier

doi: 10.1162/IMAG.a.4

Figure Lengend Snippet: Balanced accuracy of proposed classifiers, MRIQC and CAT12 on combined and site-wise test data. The model’s performance on the test data is assessed across different (ranked) feature sizes (refer ), with the displayed plot here representing only the best performance selected across these feature sizes. Number of samples in the test data are provided in brackets for each dataset (x-axis). Note that three sites (ADNI GE 3T, ADNI Philips 1.5T, and ADNI Siemens 2.9T) are not included in the figure due to the absence of samples in the reject class resulting in NaN values for balanced accuracies.

Article Snippet: However, there is no clustering within the sites of ADNI dataset irrespective of the difference in the scanner manufacturer (ADNI Philips 3T and ADNI Siemens 3T, panel d) and field strength (ADNI Siemens 1.5T and ADNI Siemens 3T, panel e).

Techniques:

Journal: Imaging Neuroscience

Article Title: Automated quality control of T1-weighted brain MRI scans for clinical research datasets: methods comparison and design of a quality prediction classifier

doi: 10.1162/IMAG.a.4

Figure Lengend Snippet: Kernel density plots showing the distribution of top 10 ranked features (panels a-l) in the final combined data model for sites within the ADNI dataset. For a description of the features, refer to and .

Article Snippet: However, there is no clustering within the sites of ADNI dataset irrespective of the difference in the scanner manufacturer (ADNI Philips 3T and ADNI Siemens 3T, panel d) and field strength (ADNI Siemens 1.5T and ADNI Siemens 3T, panel e).

Techniques:

Journal: Imaging Neuroscience

Article Title: Automated quality control of T1-weighted brain MRI scans for clinical research datasets: methods comparison and design of a quality prediction classifier

doi: 10.1162/IMAG.a.4

Figure Lengend Snippet: Scatter plots for two features snr-total from MRIQC on x-axis and noiseNCR-rps from CAT12 on y-axis showing different levels of overlap for different combinations of dataset, field strength and manufacturer: a) same dataset (Whitehall2), manufacturer (Siemens) and field strength (3T) but different scanner models; b) same scanner model (Siemens 3T Prisma) but different datasets; c) same manufacturer and field strength (Siemens 3T) but different datasets; d) same dataset (ADNI) and field strength (3T) but different manufacturers; e) same dataset (ADNI) and manufacturer (Siemens) but different field strengths; f) different datasets, manufacturers and field strength.

Article Snippet: However, there is no clustering within the sites of ADNI dataset irrespective of the difference in the scanner manufacturer (ADNI Philips 3T and ADNI Siemens 3T, panel d) and field strength (ADNI Siemens 1.5T and ADNI Siemens 3T, panel e).

Techniques:

Journal: Imaging Neuroscience

Article Title: Automated quality control of T1-weighted brain MRI scans for clinical research datasets: methods comparison and design of a quality prediction classifier

doi: 10.1162/IMAG.a.4

Figure Lengend Snippet: Balanced accuracy of MRIQC, CAT12, and the proposed RUS classifier for leave-one-site-out models. The model’s performance on the test data is assessed across different (ranked) feature sizes (refer in the Supplementary Document), with the displayed plot here representing only the best performance selected across these feature sizes. The total number of samples for each test site is provided in brackets (x-axis). For RUS classifier, each site was kept as test data and classifier was trained on remaining sites using the hyperparameters and feature ranking derived from combined data model (best model with 80 feature size). For reference, we also provide the balanced accuracy of RUS classifier for each site within the test data of the combined data model to see how well our classifier generalises to test data from different sites (diamond marker with grey). Note that balanced accuracies for the combined data model are not included for three sites (ADNI GE 3T, ADNI Philips 1.5T, and ADNI Siemens 2.9T) due to the absence of samples in the reject class of the test data (resulting in NaN values for balanced accuracies).

Article Snippet: However, there is no clustering within the sites of ADNI dataset irrespective of the difference in the scanner manufacturer (ADNI Philips 3T and ADNI Siemens 3T, panel d) and field strength (ADNI Siemens 1.5T and ADNI Siemens 3T, panel e).

Techniques: Derivative Assay, Marker