Unsupervised physiological noise correction of functional magnetic resonance imaging data using phase and magnitude information (PREPAIR).

Of the sources of noise affecting blood oxygen level-dependent functional magnetic resonance imaging (fMRI), respiration and cardiac fluctuations are responsible for the largest part of the variance, particularly at high and ultrahigh field. Existing approaches to removing physiological noise either use external recordings, which can be unwieldy and unreliable, or attempt to identify physiological noise from the magnitude fMRI data. Data-driven approaches are limited by sensitivity, temporal aliasing, and the need for user interaction. In the light of the sensitivity of the phase of the MR signal to local changes in the field stemming from physiological processes, we have developed an unsupervised physiological noise correction method using the information carried in the phase and the magnitude of echo-planar imaging data. Our technique, Physiological Regressor Estimation from Phase and mAgnItude, sub-tR (PREPAIR) derives time series signals sampled at the slice TR from both phase and magnitude images. It allows physiological noise to be captured without aliasing, and efficiently removes other sources of signal fluctuations not related to physiology, prior to regressor estimation. We demonstrate that the physiological signal time courses identified with PREPAIR agree well with those from external devices and retrieve challenging cardiac dynamics. The removal of physiological noise was as effective as that achieved with the most used approach based on external recordings, RETROICOR. In comparison with widely used recording-free physiological noise correction tools-PESTICA and FIX, both performed in unsupervised mode-PREPAIR removed significantly more respiratory and cardiac noise than PESTICA, and achieved a larger increase in temporal signal-to-noise-ratio at both 3 and 7 T.

Reproducibility and Repeatability of MR Fingerprinting Relaxometry in the Human Brain.

Background Only sparse literature investigates the reproducibility and repeatability of relaxometry methods in MRI. However, statistical data on reproducibility and repeatability of any quantitative method is essential for clinical application. Purpose To evaluate the reproducibility and repeatability of two-dimensional fast imaging with steady-state free precession MR fingerprinting in vivo in human brains. Materials and Methods Two-dimensional section-selective MR fingerprinting based on a steady-state free precession sequence with an external radiofrequency transmit field, or B1+, correction was used to generate T1 and T2 maps. This prospective study was conducted between July 2017 and January 2018 with 10 scanners from a single manufacturer, including different models, at four different sites. T1 and T2 relaxation times and their variation across scanners (reproducibility) as well as across repetitions on a scanner (repeatability) were analyzed. The relative deviations of T1 and T2 to the average (95% confidence interval) were calculated for several brain compartments. Results Ten healthy volunteers (mean age ± standard deviation, 28.5 years ± 6.9; eight men, two women) participated in this study. Reproducibility and repeatability of T1 and T2 measures in the human brain varied across brain compartments (1.8%-20.9%) and were higher in solid tissues than in the cerebrospinal fluid. T1 measures in solid tissue brain compartments were more stable compared with T2 measures. The half-widths of the confidence intervals for relative deviations were 3.4% for mean T1 and 8.0% for mean T2 values across scanners. Intrascanner repeatability half-widths of the confidence intervals for relative deviations were in the range of 2.0%-3.1% for T1 and 3.1%-7.9% for T2. Conclusion This study provides values on reproducibility and repeatability of T1 and T2 relaxometry measured with fast imaging with steady-state free precession MR fingerprinting in brain tissues of healthy volunteers. Reproducibility and repeatability are considerably higher in solid brain compartments than in cerebrospinal fluid and are higher for T1 than for T2. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Barkhof and Parker in this issue.

The clinical relevance of distortion correction in presurgical fMRI at 7T.

Presurgical planning with fMRI benefits from increased reliability and the possibility to reduce measurement time introduced by using ultra-high field. Echo-planar imaging suffers, however, from geometric distortions which scale with field strength and potentially give rise to clinically significant displacement of functional activation. We evaluate the effectiveness of a dynamic distortion correction (DDC) method based on unmodified single-echo EPI in the context of simulated presurgical planning fMRI at 7T and compare it with static distortion correction (SDC). The extent of distortion in EPI and activation shifts are investigated in a group of eleven patients with a range of neuropathologies who performed a motor task. The consequences of neglecting to correct images for susceptibility-induced distortions are assessed in a clinical context. It was possible to generate time series of EPI-based field maps which were free of artifacts in the eloquent brain areas relevant to presurgical fMRI, despite the presence of signal dropouts caused by pathologies and post-operative sites. Distortions of up to 5.1mm were observed in the primary motor cortex in raw EPI. These were accurately corrected with DDC and slightly less accurately with SDC. The dynamic nature of distortions in UHF clinical fMRI was demonstrated via investigation of temporal variation in voxel shift maps, confirming the potential inadequacy of SDC based on a single reference field map, particularly in the vicinity of pathologies or in the presence of motion. In two patients, the distortion correction was potentially clinically significant in that it might have affected the localization or interpretation of activation and could thereby have influenced the treatment plan. Distortion correction is shown to be effective and clinically relevant in presurgical planning at 7T.

Robust presurgical functional MRI at 7 T using response consistency.

Functional MRI is valuable in presurgical planning due to its non-invasive nature, repeatability, and broad availability. Using ultra-high field MRI increases the specificity and sensitivity, increasing the localization reliability and reducing scan time. Ideally, fMRI analysis for this application should identify unreliable runs and work even if the patient deviates from the prescribed task timing or if there are changes to the hemodynamic response due to pathology. In this study, a model-free analysis method-UNBIASED-based on the consistency of fMRI responses over runs was applied, to ultra-high field fMRI localizations of the hand area. Ten patients with brain tumors and epilepsy underwent 7 Tesla fMRI with multiple runs of a hand motor task in a block design. FMRI data were analyzed with the proposed approach (UNBIASED) and the conventional General Linear Model (GLM) approach. UNBIASED correctly identified and excluded fMRI runs that contained little or no activation. Generally, less motion artifact contamination was present in UNBIASED than in GLM results. Some cortical regions were identified as activated in UNBIASED but not GLM results. These were confirmed to show reproducible delayed or transient activation, which was time-locked to the task. UNBIASED is a robust approach to generating activation maps without the need for assumptions about response timing or shape. In presurgical planning, UNBIASED can complement model-based methods to aid surgeons in making prudent choices about optimal surgical access and resection margins for each patient, even if the hemodynamic response is modified by pathology. Hum Brain Mapp 38:3163-3174, 2017. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

A Comparison of 7 Tesla MR Spectroscopic Imaging and 3 Tesla MR Fingerprinting for Tumor Localization in Glioma Patients.

This paper investigated the correlation between magnetic resonance spectroscopic imaging (MRSI) and magnetic resonance fingerprinting (MRF) in glioma patients by comparing neuro-oncological markers obtained from MRSI to T1/T2 maps from MRF. Data from 12 consenting patients with gliomas were analyzed by defining hotspots for T1, T2, and various metabolic ratios, and comparing them using Sørensen-Dice similarity coefficients (DSCs) and the distances between their centers of intensity (COIDs). The median DSCs between MRF and the tumor segmentation were 0.73 (T1) and 0.79 (T2). The DSCs between MRSI and MRF were the highest for Gln/tNAA (T1: 0.75, T2: 0.80, tumor: 0.78), followed by Gly/tNAA (T1: 0.57, T2: 0.62, tumor: 0.54) and tCho/tNAA (T1: 0.61, T2: 0.58, tumor: 0.45). The median values in the tumor hotspot were T1 = 1724 ms, T2 = 86 ms, Gln/tNAA = 0.61, Gly/tNAA = 0.28, Ins/tNAA = 1.15, and tCho/tNAA = 0.48, and, in the peritumoral region, were T1 = 1756 ms, T2 = 102 ms, Gln/tNAA = 0.38, Gly/tNAA = 0.20, Ins/tNAA = 1.06, and tCho/tNAA = 0.38, and, in the NAWM, were T1 = 950 ms, T2 = 43 ms, Gln/tNAA = 0.16, Gly/tNAA = 0.07, Ins/tNAA = 0.54, and tCho/tNAA = 0.20. The results of this study constitute the first comparison of 7T MRSI and 3T MRF, showing a good correspondence between these methods.

The Impact of Echo Time Shifts and Temporal Signal Fluctuations on BOLD Sensitivity in Presurgical Planning at 7 T.

OBJECTIVES: Gradients in the static magnetic field caused by tissues with differing magnetic susceptibilities lead to regional variations in the effective echo time, which modifies both image signal and BOLD sensitivity. Local echo time changes are not considered in the most commonly used metric for BOLD sensitivity, temporal signal-to-noise ratio (tSNR), but may be significant, particularly at ultrahigh field close to air cavities (such as the sinuses and ear canals) and near gross brain pathologies and postoperative sites. MATERIALS AND METHODS: We have studied the effect of local variations in echo time and tSNR on BOLD sensitivity in 3 healthy volunteers and 11 patients with tumors, postoperative cavities, and venous malformations at 7 T. Temporal signal-to-noise ratio was estimated from a 5-minute run of resting state echo planar imaging with a nominal echo time of 22 milliseconds. Maps of local echo time were derived from the phase of a multiecho GE scan. One healthy volunteer performed 10 runs of a breath-hold task. The t-map from this experiment served as a criterion standard BOLD sensitivity measure. Two runs of a less demanding breath-hold paradigm were used for patients. RESULTS: In all subjects, a strong reduction in the echo time (from 22 milliseconds to around 11 milliseconds) was found close to the ear canals and sinuses. These regions were characterized by high tSNR but low t-values in breath-hold t-maps. In some patients, regions of particular interest in presurgical planning were affected by reductions in the echo time to approximately 13-15 milliseconds. These included the primary motor cortex, Broca's area, and auditory cortex. These regions were characterized by high tSNR values (70 and above). Breath-hold results were corrupted by strong motion artifacts in all patients. CONCLUSIONS: Criterion standard BOLD sensitivity estimation using hypercapnic experiments is challenging, especially in patient populations. Taking into consideration the tSNR, commonly used for BOLD sensitivity estimation, but ignoring local reductions in the echo time (eg, from 22 to 11 milliseconds), would erroneously suggest functional sensitivity sufficient to map BOLD signal changes. It is therefore important to consider both local variations in the echo time and temporal variations in signal, using the product metric of these two indices for instance. This should ensure a reliable estimation of BOLD sensitivity and to facilitate the identification of potential false-negative results. This is particularly true at high fields, such as 7 T and in patients with large pathologies and postoperative cavities.