Keywords: Data Processing, Data Processing, Denoising, Diffusion Preprocessing

Motivation: Performance of denoising methods and effects on downstream analyses for diffusion-weighted imaging at 7 Tesla are understudied.

Goal(s): Determine which denoising methods provide better performance and whether any skew tractometry outcomes.

Approach: Preliminary data acquired using two different diffusion sequences - 30 or 64 directions/shell multi-shell - were separately denoised using either Patch2Self, oversampled local-PCA, non-local means, or Marchenko-Pastur PCA before QSIPrep pre-processing and DSI Studio AutoTrack GQI.

Results: Contrast-to-noise ratios were best for Patch2Self and oversampled local-PCA, agreeing with visual assessment. Fractional anisotropy distributions were higher and mean diffusivity lower in several major bundles for non-local means than Patch2Self, especially with lower angular resolution.

Impact: The computationally-efficient parallel Patch2Self improves 7T diffusion data quality and produces lower fractional anisotropy values in tractometry of common bundles for biomarker searches than non-local means. Denoising methods should be considered in literature comparisons and image preprocessing in clinical trials.

Keywords: System Imperfections, Precision & Accuracy, Phantoms, brain, quantitative imaging, system imperfections: Measurement & correction

Motivation: A quantitative MRI (qMRI) protocol was developed for a clinical study aimed at identifying regions of tumour hypoxia in glioblastoma patients. Technical validation of qMRI biomarkers requires thorough testing of the protocol against reference standards.

Goal(s): To assess and report accuracy, repeatability, and reproducibility of the qMRI protocol.

Approach: Test-retest scans of the NIST systems phantom were acquired on two 3T MAGNETOM VIDA scanners. Accuracy, repeatability and reproducibility of T1, T2 maps, and calibrated T1 maps from dynamic oxygen enhanced imaging were assessed.

Results: qMRI parameters acquired with the study protocol showed accuracy, repeatability and reproducibility comparable to published literature findings.

Impact: Accuracy and precision (repeatability and reproducibility) of a qMRI protocol for glioblastoma hypoxia imaging were quantified. T1, including calibrated dynamic values, showed high accuracy and precision. T2 showed low accuracy compared to published findings. T2 and T2* showed moderate precision.

Keywords: Segmentation, Susceptibility, QSM template, QSM-T1w, Segmentation, Quantitative, QSM, Normalization

Motivation: Previously, automated delineation of deep gray matter (DGM) regions predominantly relied on T1-weighted (T1w) brain images with limited iron-rich DGM contrast. Multi-contrast atlases incorporating quantitative susceptibility mapping (QSM) have been introduced to overcome this issue and are increasingly being used in multi-atlas segmentation methods.

Goal(s): To determine a generalizable minimum number of subjects to be used for generating high quality QSM-T1w templates. 

Approach: We quantitatively investigated the effect of increasing (factor=2) the number of subjects (N=10-160) used for template construction on resulting template quality.

Results: In highly heterogeneous cohorts, more than 40 subjects result in a diminishing return for QSM-T1w template generation. 

Impact: Using a small number of subjects for template generation ensures economic use of resources and facilitates the creation of more sub-group templates from the same cohort, to be used in advanced multi-atlas techniques. 

Keywords: Data Processing, Perfusion, denoising

Motivation: NORDIC PCA denoising is a recent denoising technique that promises mitigation of thermal noise in MRI data, and its application in high-resolution ASL remains unexplored.

Goal(s): Assess the effectiveness of NORDIC denoising for improving the quality of high-resolution ASL perfusion signal.

Approach: NORDIC denoising was applied to ASL data in two different approaches. NORDIC1, on control and label separately, and NORDIC2 on the original ASL time course (control and label combined).

Results: NORDIC denoising results in a twofold increase in tSNR. The denoising effect is most effective when NORDIC is applied to ASL timeseries and in low SNR voxels.

Impact: ASL is often not utilized in clinical and cognitive neuroscience studies due to its low SNR. Thus, the improvement in tSNR afforded by NORDIC denoising paves the way for the implementation of high-resolution ASL in cutting-edge brain studies. 

Keywords: Data Processing, Artifacts

Motivation: The  implementation of QC in T1w MRI scans remains unstandardized and still involves human specialists, without consensus on a systematic approach to identify the presence of artifacts

Goal(s): Our goal was to compare the relative performance of three algorithms with visual QC, and compare their capabilities in detecting simulated blurring, ghosting, and noise artifacts on a new unseen dataset.

Approach: Synthetic artifacts were introduced into MRI scans that passed visual quality control, and thresholds were determined for CAT12 and LONIQC, and a classifier for MRIQC was trained.

Results: MRIQC outperformed CAT12 and LONIQC in detecting both real artifacts as well as simulated artifacts.

Impact: Substantial differences in the performance of different automatic quality control algorithms were shown when compared to visual QC and on simulated data. This suggests that better evaluation of the relation between artifact type, input features and classification methods is needed.

Keywords: Data Processing, Data Analysis, Quality Analysis

Motivation: Qualitative assessment of magnetic resonance spectroscopy (MRS) data is the standard for data quality assessment (DQA), however it's inefficient with high interobservational variability. 

Goal(s): The goal was to compare qualitative DQA with quantitative DQA (signal to noise ratio (SNR), linewidth (FWHM), and Cramer-Rao lower bounds (CRLB)), determining if quantitative measures alone are sufficient.  

Approach: 7,155 spectra were classified on a 5-measure scale with ratings 1 (acceptable) to 5 (rejected). 

Results: SNR was negatively correlated and FWHM and CRLB were positively correlated with qualitative DQA. Multiple cases showed spectra quantitatively acceptable but qualitatively rejected, demonstrating additional measures are needed for comprehensive DQA.

Impact: Providing quantitative analysis for qualitative MR spectroscopy ratings when comparing SNR, FWHM, and CRLB variables will direct the process of quality analysis to demonstrate that additional quantitative DQA measures are needed to provide comprehensive DQA.

Keywords: Data Processing, Data Processing

Motivation: A quantitative evaluation of image quality is crucial in various aspects of MRI, such as developing and validating new image reconstruction and artifact correction techniques. Currently, no image quality metric covers all possible artifacts, making it difficult to choose the right quality measure.  

Goal(s): Evaluate consistency and reliability of image quality metrics in relation to image pre-processing and radiologists assessment.

Approach: We studied the correlation of ten commonly used quality metrics with radiological evaluations in datasets with and without motion.

Results: SSIM and PSNR had the strongest correlation with observer scores. Among reference-free metrics,  Image Entropy and AES consistently showed strong correlations.

Impact: Automatically evaluating the quality of MR images is crucial. Our results show variability in the correlation between image-quality metrics and radiologists scores across datasets, highlighting the need for preprocessing optimization especially when no reference image is available.

Keywords: Segmentation, COVID-19, olfactory atlas, tractography

Motivation: Several pathologies, including COVID-19, affect the sense of smell, causing anosmia.

Goal(s): The goal is to create an atlas that includes grey matter regions and white matter tracts involved in the olfactory circuit. 

Approach: Grey matter regions involved in the olfactory circuit were selected and an atlas of tracts connecting pairs of regions was extracted using tractography. The atlas was used on subjects with anosmia caused by COVID-19 to investigate changes in grey matter density (voxel-based analysis) and quantitative microstructural maps (mean diffusivity and fractional anisotropy). 

Results: Structural alterations were found in subjects with anosmia, mainly in the cerebellum.

Impact: The created olfactory circuit atlas, which includes grey matter regions and axonal bundles, can be crucial for studying pathologies involving alterations in the olfactory circuit. The atlas was useful to detect structural alterations in subjects with anosmia caused by COVID-19.

Keywords: Visualization, Breast, Cancer

Motivation: Tumor-infiltration lymphocytes (TILs) express variably in different molecular subtypes. 

Goal(s): To compare the rate of high vs. low TILs and MRI features in three subtypes: Hormonal-Receptor positive, HER2 negative (HR+/HER2-), HER2+, and TN, and compare imaging features in each subtype. 

Approach: The percentage of TILs of 457 breast cancers was assessed. Three radiologists reviewed MRI features.

Results: HER2+ cancers were more likely to present as non-mass enhancement (NME). In HR+, high TILs cases were more likely to present peritumoral edema. In TN, high TILs cases were more likely to present regular shapes and circumscribed margins.

Impact: TILs expression increases from HR+ to HER2+ to TN. MRI features in different molecular subtypes show substantial variations. Different models should be built for different subtypes when building MR radiomics models to predict TILs.

Keywords: Software Tools, Software Tools, Aneurysm Wall Enhancement

Motivation: Aneurysm wall enhancement  visualized on high-resolution magnetic resonance imaging is considered as an indicator of inflammation.

Goal(s): Recently, there has been increased attention on 3D AWE mapping, which is seen as an objective tool for examining rupture risk of aneurysms.However, the roughly estimated vessel wall location and thickness result in some measurement errors.

Approach: In this study, we propose a fast measurement method that can automatically access wall thickness and generate the 3D spatial distribution of wall enhancement ratio.

Results: The automatic method simplifies and accelerates the workflow of aneurysm wall identification and analysis.

Impact: We propose a fast measurement method that can automatically access wall thickness and generate the 3D spatial distribution of wall enhancement ratio. The automatic method simplifies and accelerates the workflow of aneurysm wall identification and analysis.

Keywords: Segmentation, Brain

Motivation: Whole-brain vasculature modeling remains challenging due to background variations and diverse vessel sizes.

Goal(s): We pursued an integrated framework for the accurate segmentation and tracking of vasculature in the whole brain.

Approach: Small-patch CNN (SP-CNN) and centerline-Dice nnU-Net (CDNN) were utilized for multiscale segmenting of vessels. A random forest and soft-skeletonization were applied for tracking. A novel rotation algorithm was used to calculate diameters accurately.

Results:

We accurately reconstructed the arteries from compressed sensing time-of-flight MR angiography (CS-TOF-MRA) and geometric parameters of full-size arteries were obtained.

Impact: Our work enables the segmentation of full-size-arteries in CS-TOF-MRA. As this method integrates algorithms for higher accuracy and robustness, it will be beneficial for clinical diagnosis.

Keywords: Data Processing, Brain, High-Field MRI, Data Analysis, Analysis/Processing, Neuroscience, Multi-Contrast, Data processing, Neuro, Signal Representations

Motivation: MPRAGE is the standard high-resolution T_1w sequence used for anatomical MRI. Very few sequences propose such a high Gray-White matter contrast with high-resolution.

Goal(s): Propose a novel technique that computes MPRAGElike images if spoiled GRE images with different T1w (and more) contrasts are available.

Approach: MPRAGE and Multi-Parameter Mapping (MPM) images were acquired on 16 subjects across three 7T sites. MPM images were used to produce three variations of MPRAGElike and one synMPRAGE images. SNR and CNR were evaluated against the MPRAGE.

Results: Our proposed MPRAGElike technique gave larger SNR than MPRAGE in most ROIs while also having superior CNR compared to synMPRAGE.

Impact: Neuroscientists with Multi-Parameter Mapping sequences in their protocols can compute MPRAGElike images which exhibit highly similar image quality as a typical MPRAGE and better than one previously reported technique producing synthetic MPRAGE.

Keywords: Data Processing, Data Processing

Motivation: MP2RAGE provides enhanced T1-weighted images but contains high-amplitude noise in areas of low signal.  This can cause processing pipelines developed for traditional T1 images to fail. A “denoising” algorithm exists, but requires complex-valued image data which are not available retrospectively.

Goal(s): Provide an algorithm and easy-to-use interface for eliminating MP2RAGE background noise using only available scanner outputs.

Approach: We have developed a 3D Slicer extension for performing noise suppression with only the available MP2RAGE and inversion magnitude images.

Results: Our method generates background-suppressed and artifact-free images. The program was tested and optimized to be used with the HCP structural pipeline.

Impact: Here, we present a fast easy-to-use 3D Slicer extension for suppressing background noise in MP2RAGE images. It requires no extra phase data, enables users to reprocess already acquired images, and encourages the adoption of MP2RAGE as a primary T1-weighted acquisition.

Keywords: Phantoms, Software Tools, Quality assurance, ACR Phantom, Low contrast detectability

Motivation: The low-contrast object detectability test in the ACR phantom is conventionally performed manually, which results in low reproducibility of measurements due to intra- and inter-rater variability.

Goal(s): To automate the test in MRI systems and verify its reliability against the manual procedure.

Approach: The algorithm creates 1-dimensional image profiles and compares it with the known structure of the low-contrast objects using the general linear method test.

Results: Raters demonstrated substantial to almost perfect intra-rater agreement (0.786 and 0.841), and the algorithm showed perfect intra-rater agreement (1). Raters exhibited substantial inter-rater agreement (0.807), while raters and the algorithm averaged moderate inter-rater agreement (0.583).

Impact: We implemented an automated method for low-contrast object detectability of the ACR MRI phantom. The manual and automated methods showed strong intra- and inter-rater agreement, supporting its potential clinical use.

Keywords: Data Processing, Quantitative Imaging

Motivation: Quantitative MRI at clinical strengths is well-established, yet RF inhomogeneity at ultrahigh-field strength, such as 7T, complicates tissue-specific measurement.

Goal(s): To extend quantitative water content mapping from clinical to ultrahigh-field MRI.

Approach: We utilize a modified variable flip angle method to acquire high-resolution parametric maps at 7T within 7.5 minutes.

Results: Method validity is affirmed by comparing water content and relaxometry data from a healthy cohort against 3T benchmarks and reported literature.

Impact: This technique paves the way for advanced brain tissue analysis at ultrahigh-field MRI (7T), crucial for accurate neurological assessment.

Keywords: Visualization, Visualization, Virtual Reality, VR, Software

Motivation: Complex medical procedures often require clinicians to construct a (3D) three-dimensional mental model of a patient's anatomy from 2D medical imaging data.

Goal(s): Our goal was to develop a set of tools which convert 2D imaging data into 3D objects to view in virtual reality (VR). 

Approach: Two pipelines were created, one for brain imaging data and another for label mask images, which automatically segment the images, convert them to objects, and merge them into a VR viewable model.

Results: Our software has been successfully used to transform a variety of medical imaging data into 3D files which are viewable on VR platforms.

Impact: Some of the challenges with mentally visualizing two-dimensional medical imaging data should be alleviated by using our software to automatically make the data viewable in a three-dimensional format.