Probabilistic Presurgical Language Functional MRI Atlas of Brain Tumor Patients.

BACKGROUND AND PURPOSE: Patients with brain tumors have high intersubject variation in putative language regions, which may limit the utility of straightforward application of healthy-subject brain atlases in clinical scenarios. The purpose of this study was to develop a probabilistic functional brain atlas that consolidates language functional activations of sentence completion and silent word generation language paradigms using a large sample of patients with brain tumors. MATERIALS AND METHODS: The atlas was developed using retrospectively collected fMRI data from patients with brain tumors who underwent their first standard-of-care presurgical language fMRI scan at our institution between July 18, 2015, and May 13, 2022. 317 patients (861 fMRI scans) were used to develop the language functional atlas. An independent presurgical language fMRI dataset of 39 patients with brain tumors from a previous study was used to evaluate our atlas. Family-wise error corrected binary functional activation maps from sentence completion, letter fluency, and category fluency presurgical fMRI were used to create probability overlap maps and pooled probabilistic overlap map in Montreal Neurological Institute standard space. Wilcoxon signed-rank test was used to determine significant difference in the maximum Dice coefficient for our atlas compared to a meta-analysis-based template with respect to expert-delineated primary language area activations. RESULTS: Probabilities of activating left anterior primary language area and left posterior primary language area in temporal lobe were 87.9% and 91.5%, respectively, for sentence completion, 88.5% and 74.2%, respectively, for letter fluency, and 83.6% and 67.6%, respectively, for category fluency. Maximum Dice coefficients for templates derived from our language atlas were significantly higher than the meta-analysis-based template in left anterior primary language area (0.351 and 0.326, respectively, P < .05) and left posterior primary language area in temporal lobe (0.274 and 0.244, respectively, P < .005). CONCLUSIONS: Brain tumor patient-and paradigm-specific probabilistic language atlases were developed. These atlases had superior spatial agreement with fMRI activations in individual patients than the meta-analysis-based template. ABBREVIATIONS: SENT = sentence completion, LETT = letter fluency, CAT = category fluency, PLA = primary language area, aPLA = anterior PLA, pPLAT = posterior PLA in the temporal lobe, pPLAP = posterior PLA in the parietal lobe, SMA = supplementary motor area, DLPFC = dorsolateral prefrontal cortex, BTLA = basal temporal language area.

Accelerated 4D-flow MRI with 3-point encoding enabled by machine learning.

PURPOSE: To investigate the acceleration of 4D-flow MRI using a convolutional neural network (CNN) that produces three directional velocities from three flow encodings, without requiring a fourth reference scan measuring background phase. METHODS: A fully 3D CNN using a U-net architecture was trained in a block-wise fashion to take complex images from three flow encodings and to produce three real-valued images for each velocity component. Using neurovascular 4D-flow scans (n = 144), the CNN was trained to predict velocities computed from four flow encodings by standard reconstruction including correction for residual background phase offsets. Methods to optimize loss functions were investigated, including magnitude, complex difference, and uniform velocity weightings. Subsequently, 3-point encoding was evaluated using cross validation of pixelwise correlation, flow measurements in major arteries, and in experiments with data at differing acceleration rates than the training data. RESULTS: The CNN-produced 3-point velocities showed excellent agreements with the 4-point velocities, both qualitatively in velocity images, in flow rate measures, and quantitatively in regression analysis (slope = 0.96, R2  = 0.992). Optimizing the training to focus on vessel velocities rather than the global velocity error and improved the correlation of velocity within vessels themselves. The lowest error was observed when the loss function used uniform velocity weighting, in which the magnitude-weighted inverse of the velocity frequency uniformly distributed weighting across all velocity ranges. When applied to highly accelerated data, the 3-point network maintained a high correlation with ground truth data and demonstrated a denoising effect. CONCLUSION: The 4D-flow MRI can be accelerated using machine learning requiring only three flow encodings to produce three-directional velocity maps with small errors.

Motion-corrected 4D-Flow MRI for neurovascular applications.

Neurovascular 4D-Flow MRI has emerged as a powerful tool for comprehensive cerebrovascular hemodynamic characterization. Clinical studies in at risk populations such as aging adults indicate hemodynamic markers can be confounded by motion-induced bias. This study develops and characterizes a high fidelity 3D self-navigation approach for retrospective rigid motion correction of neurovascular 4D-Flow data. A 3D radial trajectory with pseudorandom ordering was combined with a multi-resolution low rank regularization approach to enable high spatiotemporal resolution self-navigators from extremely undersampled data. Phantom and volunteer experiments were performed at 3.0T to evaluate the ability to correct for different amounts of induced motions. In addition, the approach was applied to clinical-research exams from ongoing aging studies to characterize performance in the clinical setting. Simulations, phantom and volunteer experiments with motion correction produced images with increased vessel conspicuity, reduced image blurring, and decreased variability in quantitative measures. Clinical exams revealed significant changes in hemodynamic parameters including blood flow rates, flow pulsatility index, and lumen areas after motion correction in probed cerebral arteries (Flow: P<0.001 Lt ICA, P=0.002 Rt ICA, P=0.004 Lt MCA, P=0.004 Rt MCA; Area: P<0.001 Lt ICA, P<0.001 Rt ICA, P=0.004 Lt MCA, P=0.004 Rt MCA; flow pulsatility index: P=0.042 Rt ICA, P=0.002 Lt MCA). Motion induced bias can lead to significant overestimation of hemodynamic markers in cerebral arteries. The proposed method reduces measurement bias from rigid motion in neurovascular 4D-Flow MRI in challenging populations such as aging adults.

A dynamic susceptibility contrast MRI digital reference object for testing software with leakage correction: Effect of background simulation.

PURPOSE: Dynamic susceptibility contrast (DSC)-MRI is a perfusion imaging technique from which useful quantitative imaging biomarkers can be derived. Relative cerebral blood volume (rCBV) is such a biomarker commonly used for evaluating brain tumors. To account for the extravasation of contrast agents in tumors, post-processing leakage correction is often applied to improve rCBV accuracy. Digital reference objects (DRO) are ideal for testing the post-processing software, because the biophysical model used to generate the DRO can be matched to the one that the software uses. This study aims to develop DROs to validate the leakage correction of software using Weisskoff model and to examine the effect of background signal time curves that are required by the model. METHODS: Three DROs were generated using the Weisskoff model, each composed of nine foreground lesion objects with combinations of different levels of rCBV and contrast leakage parameter (K2). Three types of background were implemented for these DROs: (1) a multi-compartment brain-like background, (2) a sphere background with a constant signal time curve, and (3) a sphere background with signal time curve identical to that of the brain-like DRO's white matter (WM). The DROs were then analyzed with an FDA-cleared software with and without leakage correction. Leakage correction was tested with and without brain segmentation. RESULTS: Accuracy of leakage correction was able to be verified using the brain-like phantom and the sphere phantom with WM background. The sphere with constant background did not perform well with leakage correction with or without brain segmentation. The DROs were able to verify that for the particular software tested, leakage correction with brain segmentation achieved the lowest error. CONCLUSIONS: DSC-MRI DROs with biophysical model matched to that of the post-processing software can be well used for the software's validation, provided that the background signals are also properly simulated for generating the reference time curve required by the model. Care needs to be taken to consider the interaction of the design of the DRO with the software's implementation of brain segmentation to extract the reference time curve.

Spatial dependency and the role of local susceptibility for velocity selective arterial spin labeling (VS-ASL) relative tagging efficiency using accelerated 3D radial sampling with a BIR-8 preparation.

PURPOSE: Velocity selective arterial spin labeling (VS-ASL) is a promising approach for non-contrast perfusion imaging that provides robustness to vascular geometry and transit times; however, VS-ASL assumes spatially uniform tagging efficiency. This work presents a mapping approach to investigate VS-ASL relative tagging efficiency including the impact of local susceptibility effects on a BIR-8 preparation. METHODS: Numerical simulations of tagging efficiency were performed to evaluate sensitivity to regionally varying local susceptibility gradients and blood velocity. Tagging efficiency mapping was performed in susceptibility phantoms and healthy human subjects (N = 7) using a VS-ASL preparation module followed by a short, high spatial resolution 3D radial-based image acquisition. Tagging efficiency maps were compared to 4D-flow, B1 , and B0 maps acquired in the same imaging session for six of the seven subjects. RESULTS: Numerical simulations were found to predict reduced tagging efficiency with the combination of high blood velocity and local gradient fields. Phantom experiments corroborated numerical results. Relative efficiency mapping in normal volunteers showed unique efficiency patterns depending on individual subject anatomy and physiology. Uniform tagging efficiency was generally observed in vivo, but reduced efficiency was noted in regions of high blood velocity and local susceptibility gradients. CONCLUSION: We demonstrate an approach to map the relative tagging efficiency and show application of this methodology to a novel BIR-8 preparation recently proposed in the literature. We present results showing rapid flow in the presence of local susceptibility gradients can lead to complicated signal modulations in both tag and control images and reduced tagging efficiency.

Comparison of functional localization accuracy with different co-registration strategies in presurgical fMRI for brain tumor patients.

PURPOSE: Presurgical fMRI is an important tool for surgery navigation in achieving maximum resection of a brain tumor. However, the functional localization accuracy may be compromised by spatial transformation from echo-planar images to high-resolution structural images. We evaluated functional localization errors associated with the spatial transformation process using three algorithms commonly applied to the presurgical fMRI in the clinic. METHODS: MR images of 20 brain tumor patients for presurgical evaluation of eloquent areas near motor cortices were analyzed. All fMRI data were spatially transferred to 3D T1-weighted images using three algorithms: (a) coordinate matching (CM), (b) automated registration (AR), and (c) AR plus manual adjustment (ARadj ). Activation clusters overlaid on original echo-planar images were manually delineated on slice-matched 2D T1- weighted images and then transferred to the 3D T1-weighted image volume, and served as the reference localization. Functional localization errors were estimated by measuring the distance between the reference localization and the activation cluster after spatial transformation and then compared for the three algorithms. RESULTS: The 3D Euclidean distance for AR (10.2 ± 4.9 mm) was found to be significantly larger (P < 0.05) than those for CM (5.6 ± 2.6 mm) and ARadj (5.8 ± 3.0 mm) algorithms. The difference between the localization errors in CM and ARadj was not statistically significant. CONCLUSIONS: A procedure was proposed to evaluate functional localization errors associated with spatial transformation in presurgical fMRI. Our results highlighted the necessity of routine quality control for the AR processing in the clinic.