Improving the robustness of MOLLI T1 maps with a dedicated motion correction algorithm.

Myocardial tissue T1 constitutes a reliable indicator of several heart diseases related to extracellular changes (e.g. edema, fibrosis) as well as fat, iron and amyloid content. Magnetic resonance (MR) T1-mapping is typically achieved by pixel-wise exponential fitting of a series of inversion or saturation recovery measurements. Good anatomical alignment between these measurements is essential for accurate T1 estimation. Motion correction is recommended to improve alignment. However, in the case of inversion recovery sequences, this correction is compromised by the intrinsic contrast variation between frames. A model-based, non-rigid motion correction method for MOLLI series was implemented and validated on a large database of cardiac clinical cases (n = 186). The method relies on a dedicated similarity metric that accounts for the intensity changes caused by T1 magnetization relaxation. The results were compared to uncorrected series and to the standard motion correction included in the scanner. To automate the quantitative analysis of results, a custom data alignment metric was defined. Qualitative evaluation was performed on a subset of cases to confirm the validity of the new metric. Motion correction caused noticeable (i.e. > 5%) performance degradation in 12% of cases with the standard method, compared to 0.3% with the new dedicated method. The average alignment quality was 85% ± 9% with the default correction and 90% ± 7% with the new method. The results of the qualitative evaluation were found to correlate with the quantitative metric. In conclusion, a dedicated motion correction method for T1 mapping MOLLI series has been evaluated on a large database of clinical cardiac MR cases, confirming its increased robustness with respect to the standard method implemented in the scanner.

New insights into CNS development from multiomics approaches.

Our understanding of the central nervous system (CNS) development has been strongly enhanced by the recent progress of single-cell multiomics approaches. Certainly, the multiplex profiling of individual cell epigenomes and transcriptomes together with dynamic lineage tracing systems brings encouraging new perspectives and prompts a paradigm shift in neuroscience developmental research. In this review, we outline the latest multiomics -based findings in CNS development, from the early CNS patterning to the regional specification of the CNS along anterior-posterior axis (forebrain, midbrain, hindbrain and spinal cord). Overall, multiomics development has substantially impacted current knowledge and has challenged our classical models for embryonic CNS development. Integrating all these newly generated -omics databases represents the next step to overcome challenges in understanding developmental diseases.