Fluid suppression in amide proton transfer-weighted (APTw) CEST imaging: New theoretical insights and clinical benefits.

PURPOSE: Amide proton transfer-weighted (APTw) MRI at 3T provides a unique contrast for brain tumor imaging. However, APTw imaging suffers from hyperintensities in liquid compartments such as cystic or necrotic structures and provides a distorted APTw signal intensity. Recently, it has been shown that heuristically motivated fluid suppression can remove such artifacts and significantly improve the readability of APTw imaging. THEORY AND METHODS: In this work, we show that the fluid suppression can actually be understood by the known concept of spillover dilution, which itself can be derived from the Bloch-McConnell equations in comparison to the heuristic approach. Therefore, we derive a novel post-processing formula that efficiently removes fluid artifact, and explains previous approaches. We demonstrate the utility of this APTw assessment in silico, in vitro, and in vivo in brain tumor patients acquired at MR scanners from different vendors. RESULTS: Our results show a reduction of the CEST signals from fluid environments while keeping the APTw-CEST signal intensity almost unchanged for semi-solid tissue structures such as the contralateral normal appearing white matter. This further allows us to use the same color bar settings as for conventional APTw imaging. CONCLUSION: Fluid suppression has considerable value in improving the readability of APTw maps in the neuro-oncological field. In this work, we derive a novel post-processing formula from the underlying Bloch-McConnell equations that efficiently removes fluid artifact, and explains previous approaches which justify the derivation of this metric from a theoretical point of view, to reassure the scientific and medical field about its use.

Virtual MEG sensors based on beamformer and independent component analysis can reconstruct epileptic activity as measured on simultaneous intracerebral recordings.

The prevailing gold standard for presurgical determination of epileptogenic brain networks is intracerebral EEG, a potent yet invasive approach. Magnetoencephalography (MEG) is a state-of-the art non-invasive method for investigating epileptiform discharges. However, it is not clear at what level the precision offered by MEG can reach that of SEEG. Here, we present a strategy for non-invasively retrieving the constituents of the interictal network, with high spatial and temporal precision. Our method is based on MEG and a combination of spatial filtering and independent component analysis (ICA). We validated this approach in twelve patients with drug-resistant focal epilepsy, thanks to the unprecedented ground truth provided by simultaneous recordings of MEG and SEEG. A minimum variance adaptive beamformer estimated the source time series and ICA was used to further decompose these time series into network constituents (MEG-ICs), each having a time series (virtual electrode) and a topography (spatial distribution of amplitudes in the brain). We show that MEG has a considerable sensitivity of 0.80 and 0.84 and a specificity of 0.93 and 0.91 for reconstructing deep and superficial sources, respectively, when compared to the ground truth (SEEG). For each epileptic MEG-IC (n = 131), we found at least one significantly correlating SEEG contact close to zero lag after correcting for multiple comparisons. All the patients except one had at least one epileptic component that was highly correlated (Spearman rho>0.3) with that of SEEG traces. MEG-ICs correlated well with SEEG traces. The strength of correlation coefficients did not depend on the depth of the SEEG contacts or the clinical outcome of the patient. A significant proportion of the MEG-ICs (n = 83/131) were localized in proximity with their maximally correlating SEEG, within a mean distance of 20±12.18mm. Our research is the first to validate the MEG-retrieved beamformer IC sources against SEEG-derived ground truth in a simultaneous MEG-SEEG framework. Observations from the present study suggest that non-invasive MEG source components may potentially provide additional information, comparable to SEEG in a number of instances.

CEST 2022 - Differences in APT-weighted signal in T1 weighted isointense lesions, black holes and normal-appearing white matter in people with relapsing-remitting multiple sclerosis.

PURPOSE: To evaluate amide proton transfer weighted (APTw) signal differences between multiple sclerosis (MS) lesions and contralateral normal-appearing white matter (cNAWM). Cellular changes during the demyelination process were also assessed by comparing APTw signal intensity in T1weighted isointense (ISO) and hypointense (black hole -BH) MS lesions in relation to cNAWM. METHODS: Twenty-four people with relapsing-remitting MS (pw-RRMS) on stable therapy were recruited. MRI/APTw acquisitions were undertaken on a 3 T MRI scanner. The pre and post-processing, analysis, co-registration with structural MRI maps, and identification of regions of interest (ROIs) were all performed with Olea Sphere 3.0 software. Generalized linear model (GLM) univariate ANOVA was undertaken to test the hypotheses that differences in mean APTw were entered as dependent variables. ROIs were entered as random effect variables, which allowed all data to be included. Regions (lesions and cNAWM) and/or structure (ISO and BH) were the main factor variables. The models also included age, sex, disease duration, EDSS, and ROI volumes as covariates. Receiver operating characteristic (ROC) curve analyses were performed to evaluate the diagnostic performance of these comparisons. RESULTS: A total of 502 MS lesions manually identified on T2-FLAIR from twenty-four pw-RRMS were subcategorized as 359 ISO and 143 BH with reference to the T1-MPRAGE cerebral cortex signal. Also, 490 ROIs of cNAWM were manually delineated to match the MS lesion positions. A two-tailed t-test showed that mean APTw values were higher in females than in males (t = 3.52, p < 0.001). Additionally, the mean APTw values of MS lesions were higher than those of cNAWM after accounting for covariates (mean lesion = 0.44, mean cNAWM = 0.13, F = 44.12, p < 0.001).The mean APTw values of ISO lesions were higher than those of cNAWM after accounting for covariates (mean ISO lesions = 0.42, mean cNAWM = 0.21, F = 12.12, p < 0.001). The mean APTw values of BH were also higher than those of cNAWM (mean BH lesions = 0.47, mean cNAWM = 0.033, F = 40.3, p < 0.001). The effect size (i.e., difference between lesion and cNAWM) for BH was found to be higher than for ISO (14 vs. 2). Diagnostic performance showed that APT was able to discriminate between all lesions and cNAWM with an accuracy of >75% (AUC = 0.79, SE = 0.014). Discrimination between ISO lesions and cNAWM was accomplished with an accuracy of >69% (AUC = 0.74, SE = 0.018), while discrimination between BH lesions and cNAWM was achieved at an accuracy of >80% (AUC = 0.87, SE = 0.021). CONCLUSIONS: Our results highlight the potential of APTw imaging for use as a non-invasive technique that is able to provide essential molecular information to clinicians and researchers so that the stages of inflammation and degeneration in MS lesions can be better characterized.

Fast WASABI post-processing: Access to rapid B0 and B1 correction in clinical routine for CEST MRI.

CEST MRI methods, such as APT and NOE imaging reveal biomarkers with significant diagnostic potential due to their ability to access molecular tissue information. Regardless of the technique used, CEST MRI data are affected by static magnetic B0 and radiofrequency B1 field inhomogeneities that degrade their contrast. For this reason, the correction of B0 field-induced artefacts is essential, whereas accounting for B1 field inhomogeneities have shown significant improvements in image readability. In a previous work, an MRI protocol called WASABI was presented, which can map simultaneously B0 and B1 field inhomogeneities, while maintaining the same sequence and readout types as used for CEST MRI. Despite the highly satisfactory quality of B0 and B1 maps computed from the WASABI data, the post-processing method is based on an exhaustive search of a four-parameter space and an additional four-parameter non-linear model fitting step. This leads to long post-processing times that are prohibitive in clinical practice. This work provides a new method for fast post-processing of WASABI data with outstanding acceleration of the parameter estimation procedure and without compromising its stability. The resulting computational acceleration makes the WASABI technique suitable for clinical use. The stability of the method is demonstrated on phantom data and clinical 3 Tesla in vivo data.