Fast high-resolution electric properties tomography using three-dimensional quantitative transient-state imaging-based water fraction estimation.

In this study, we aimed to develop a fast and robust high-resolution technique for clinically feasible electrical properties tomography based on water content maps (wEPT) using Quantitative Transient-state Imaging (QTI), a multiparametric transient state-based method that is similar to MR fingerprinting. Compared with the original wEPT implementation based on standard spin-echo acquisition, QTI provides robust electrical properties quantification towards B1 + inhomogeneities and full quantitative relaxometry data. To validate the proposed approach, 3D QTI data of 12 healthy volunteers were acquired on a 1.5 T scanner. QTI-provided T1 maps were used to compute water content maps of the tissues using an empirical relationship based on literature ex-vivo measurements. Assuming that electrical properties are modulated mainly by tissue water content, the water content maps were used to derive electrical conductivity and relative permittivity maps. The proposed technique was compared with a conventional phase-only Helmholtz EPT (HH-EPT) acquisition both within whole white matter, gray matter, and cerebrospinal fluid masks, and within different white and gray matter subregions. In addition, QTI-based wEPT was retrospectively applied to four multiple sclerosis adolescent and adult patients, compared with conventional contrast-weighted imaging in terms of lesion delineation, and quantitatively assessed by measuring the variation of electrical properties in lesions. Results obtained with the proposed approach agreed well with theoretical predictions and previous in vivo findings in both white and gray matter. The reconstructed maps showed greater anatomical detail and lower variability compared with standard phase-only HH-EPT. The technique can potentially improve delineation of pathology when compared with conventional contrast-weighted imaging and was able to detect significant variations in lesions with respect to normal-appearing tissues. In conclusion, QTI can reliably measure conductivity and relative permittivity of brain tissues within a short scan time, opening the way to the study of electric properties in clinical settings.

Retrospective rigid motion correction of three-dimensional magnetic resonance fingerprinting of the human brain.

PURPOSE: To obtain three-dimensional (3D), quantitative and motion-robust imaging with magnetic resonance fingerprinting (MRF). METHODS: Our acquisition is based on a 3D spiral projection k-space scheme. We compared different orderings of trajectory interleaves in terms of rigid motion-correction robustness. In all tested orderings, we considered the whole dataset as a sum of 56 segments of 7-s duration, acquired sequentially with the same flip angle schedule. We performed a separate image reconstruction for each segment, producing whole-brain navigators that were aligned to the first segment using normalized correlation. The estimated rigid motion was used to correct the k-space data, and the aligned data were matched with the dictionary to obtain motion-corrected maps. RESULTS: A significant improvement on the motion-affected maps after motion correction is evident with the suppression of motion artifacts. Correlation with the motionless baseline improved by 20% on average for both T1 and T2 estimations after motion correction. In addition, the average motion-induced quantification bias of 70 ms for T1 and 18 ms for T2 values was reduced to 12 ms and 6 ms, respectively, improving the reliability of quantitative estimations. CONCLUSION: We established a method that allows correcting 3D rigid motion on a 7-s timescale during the reconstruction of MRF data using self-navigators, improving the image quality and the quantification robustness.

Detection of pathological contrast enhancement with synthetic brain imaging from quantitative multiparametric MRI.

BACKGROUND AND PURPOSE: We aimed to test whether synthetic T1-weighted imaging derived from a post-contrast Quantitative Transient-state Imaging (QTI) acquisition enabled revealing pathological contrast enhancement in intracranial lesions. METHODS: The analysis included 141 patients who underwent a 3 Tesla-MRI brain exam with intravenous contrast media administration, with the post-contrast acquisition protocol comprising a three-dimensional fast spoiled gradient echo (FSPGR) sequence and a QTI acquisition. Synthetic T1-weighted images were generated from QTI-derived quantitative maps of relaxation times and proton density. Two neuroradiologists assessed synthetic and conventional post-contrast T1-weighted images for the presence and pattern of pathological contrast enhancement in intracranial lesions. Enhancement volumes were quantitatively compared. RESULTS: Using conventional imaging as a reference, synthetic T1-weighted imaging was 93% sensitive in revealing the presence of contrast enhancing lesions. The agreement for the presence/absence of contrast enhancement was almost perfect both between readers (k = 1 for both conventional and synthetic imaging) and between sequences (k = 0.98 for both readers). In 91% of lesions, synthetic T1-weighted imaging showed the same pattern of contrast enhancement visible in conventional imaging. Differences in enhancement pattern in the remaining lesions can be due to the lower spatial resolution and the longer acquisition delay from contrast media administration of QTI compared to FSPGR. Overall, enhancement volumes appeared larger in synthetic imaging. CONCLUSIONS: QTI-derived post-contrast synthetic T1-weighted imaging captures pathological contrast enhancement in most intracranial enhancing lesions. Further comparative studies employing quantitative imaging with higher spatial resolution is needed to support our data and explore possible future applications in clinical trials.

Generating Synthetic Radiological Images with PySynthMRI: An Open-Source Cross-Platform Tool.

Synthetic MR Imaging allows for the reconstruction of different image contrasts from a single acquisition, reducing scan times. Commercial products that implement synthetic MRI are used in research. They rely on vendor-specific acquisitions and do not include the possibility of using custom multiparametric imaging techniques. We introduce PySynthMRI, an open-source tool with a user-friendly interface that uses a set of input images to generate synthetic images with diverse radiological contrasts by varying representative parameters of the desired target sequence, including the echo time, repetition time and inversion time(s). PySynthMRI is written in Python 3.6, and it can be executed under Linux, Windows, or MacOS as a python script or an executable. The tool is free and open source and is developed while taking into consideration the possibility of software customization by the end user. PySynthMRI generates synthetic images by calculating the pixelwise signal intensity as a function of a set of input images (e.g., T1 and T2 maps) and simulated scanner parameters chosen by the user via a graphical interface. The distribution provides a set of default synthetic contrasts, including T1w gradient echo, T2w spin echo, FLAIR and Double Inversion Recovery. The synthetic images can be exported in DICOM or NiFTI format. PySynthMRI allows for the fast synthetization of differently weighted MR images based on quantitative maps. Specialists can use the provided signal models to retrospectively generate contrasts and add custom ones. The modular architecture of the tool can be exploited to add new features without impacting the codebase.

Quantitative T1 mapping detects blood-brain barrier breakdown in apparently non-enhancing multiple sclerosis lesions.

OBJECTIVES: The disruption of the blood-brain barrier (BBB) is a key and early feature in the pathogenesis of demyelinating multiple sclerosis (MS) lesions and has been neuropathologically demonstrated in both active and chronic plaques. The local overt BBB disruption in acute demyelinating lesions is captured as signal hyperintensity in post-contrast T1-weighted images because of the contrast-related shortening of the T1 relaxation time. On the contrary, the subtle BBB disruption in chronic lesions is not visible at conventional radiological evaluation but it might be of clinical relevance. Indeed, persistent, subtle BBB leakage might be linked to low-grade inflammation and plaque evolution. Here we hypothesised that 3D Quantitative Transient-state Imaging (QTI) was able to reveal and measure T1 shortening (ΔT1) reflecting small amounts of contrast media leakage in apparently non-enhancing lesions (ANELs). MATERIALS AND METHODS: Thirty-four patients with relapsing remitting MS were included in the study. All patients underwent a 3 T MRI exam of the brain including conventional sequences and QTI acquisitions (1.1 mm isotropic voxel) performed both before and after contrast media administration. For each patient, a ΔT1 map was obtained via voxel-wise subtraction of pre- and post- contrast QTI-derived T1 maps. ΔT1 values measured in ANELs were compared with those recorded in enhancing lesions and in the normal appearing white matter. A reference distribution of ΔT1 in the white matter was obtained from datasets acquired in 10 non-MS patients with unrevealing MR imaging. RESULTS: Mean ΔT1 in ANELs (57.45 ± 48.27 ms) was significantly lower than in enhancing lesions (297.71 ± 177.52 ms; p < 0. 0001) and higher than in the normal appearing white matter (36.57 ± 10.53 ms; p < 0.005). Fifty-two percent of ANELs exhibited ΔT1 higher than those observed in the white matter of non-MS patients. CONCLUSIONS: QTI-derived quantitative ΔT1 mapping enabled to measure contrast-related T1 shortening in ANELs. ANELs exhibiting ΔT1 values that deviate from the reference distribution in non-MS patients may indicate persistent, subtle, BBB disruption. Access to this information may be proved useful to better characterise pathology and objectively monitor disease activity and response to therapy.

Learning residual motion correction for fast and robust 3D multiparametric MRI.

Voluntary and involuntary patient motion is a major problem for data quality in clinical routine of Magnetic Resonance Imaging (MRI). It has been thoroughly investigated and, yet it still remains unresolved. In quantitative MRI, motion artifacts impair the entire temporal evolution of the magnetization and cause errors in parameter estimation. Here, we present a novel strategy based on residual learning for retrospective motion correction in fast 3D whole-brain multiparametric MRI. We propose a 3D multiscale convolutional neural network (CNN) that learns the non-linear relationship between the motion-affected quantitative parameter maps and the residual error to their motion-free reference. For supervised model training, despite limited data availability, we propose a physics-informed simulation to generate self-contained paired datasets from a priori motion-free data. We evaluate motion-correction performance of the proposed method for the example of 3D Quantitative Transient-state Imaging at 1.5T and 3T. We show the robustness of the motion correction for various motion regimes and demonstrate the generalization capabilities of the residual CNN in terms of real-motion in vivo data of healthy volunteers and clinical patient cases, including pediatric and adult patients with large brain lesions. Our study demonstrates that the proposed motion correction outperforms current state of the art, reliably providing a high, clinically relevant image quality for mild to pronounced patient movements. This has important implications in clinical setups where large amounts of motion affected data must be discarded as they are rendered diagnostically unusable.

Rapid three-dimensional multiparametric MRI with quantitative transient-state imaging.

Novel methods for quantitative, transient-state multiparametric imaging are increasingly being demonstrated for assessment of disease and treatment efficacy. Here, we build on these by assessing the most common Non-Cartesian readout trajectories (2D/3D radials and spirals), demonstrating efficient anti-aliasing with a k-space view-sharing technique, and proposing novel methods for parameter inference with neural networks that incorporate the estimation of proton density. Our results show good agreement with gold standard and phantom references for all readout trajectories at 1.5 T and 3 T. Parameters inferred with the neural network were within 6.58% difference from the parameters inferred with a high-resolution dictionary. Concordance correlation coefficients were above 0.92 and the normalized root mean squared error ranged between 4.2 and 12.7% with respect to gold-standard phantom references for T1 and T2. In vivo acquisitions demonstrate sub-millimetric isotropic resolution in under five minutes with reconstruction and inference times < 7 min. Our 3D quantitative transient-state imaging approach could enable high-resolution multiparametric tissue quantification within clinically acceptable acquisition and reconstruction times.