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Report the outcomes of pregnant women with type 1 and type 2 diabetes and to identify modifiable and non-modifiable factors associated with poor outcomes.Retrospective analysis of pregnancy preparedness, pregnancy care and outcomes in the Republic of Ireland from 2015 to 2020 and subsequent multivariate analysis.In total 1104 pregnancies were included. Less than one third attended pre-pregnancy care (PPC), mean first trimester haemoglobin A1c was 7.2 ± 3.6% (55.5 ± 15.7 mmol/mol) and 52% received pre-conceptual folic acid. Poor preparation translated into poorer pregnancy outcomes. Livebirth rates (80%) were comparable to the background population however stillbirth rates were 8.7/1000 (four times the national rate). Congenital anomalies occurred in 42.5/1000 births (1.5 times the background rate). More than half of infants were large for gestational age and 47% were admitted to critical care. Multivariate analyses showed strong associations between non-attendance at PPC, poor glycaemic control and critical care admission (adjusted odds ratio of 1.68 (1.48-1.96) and 1.61 (1.43-1.86), p 0.05 respectively) for women with type 1 diabetes. Smoking and teratogenic medications were also associated with critical care admission and hypertensive disorders of pregnancy.Pregnancy outcomes in women with diabetes are suboptimal. Significant effort is needed to optimize the modifiable factors identified in this study.

TAfter nonrigid registration of the image series, a vectorized Levenberg-Marquardt (LM) technique is proposed to improve the robustness of the curve fitting algorithm by allowing spatial regularization of the parametric maps. In addition, a region-based initialization is proposed to improve the initial guess of the TThe vectorized LM fitting showed good agreement with its pixel-wise version but allowed reduced calculation time (60 s against 696 s on average in Matlab with 256 × 256 × 8(11) images). Increasing the spatial regularization parameter led to noise reduction and improved precision of TWe have proposed a vectorized curve fitting algorithm allowing spatial regularization, which could improve the robustness of the curve fitting, especially for myocardial T

To evaluate the impact of a novel postprocessing denoising technique on accuracy and precision in myocardial TThis study introduces a fast and robust denoising method developed for magnetic resonance TSimulations on synthetic phantom showed signal-to-noise ratio and sharpness improvement with the proposed method in comparison with conventional denoising. In vivo results demonstrated that our method preserves accuracy, as no difference in mean TThe proposed denoising method preserves accuracy and improves precision in myocardial T3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1377-1388.

We read with much interest the recent publication by White et al. [(1)][1], assessing the accuracy of the contrast bolus T1 mapping cardiac magnetic resonance technique for measuring myocardial extracellular volume fraction (ECV). The study provides the first validation of the bolus technique

The ability to manipulate image contrast and thus to obtain complementary information is one of the main advantages of MRI. Motion consistency within the whole data set is a key point in the context of multi contrast imaging. In cardiac and abdominal MRI, the acquisition strategy uses multiple breath-holds and often relies on acceleration methods that inherently suffer from a signal to-noise ratio loss. The aim of this work is to propose a free-breathing multi-contrast acquisition and reconstruction workflow to improve image quality and the subsequent data analysis.We extended a previously proposed motion-compensated image reconstruction method for multi-contrast imaging. Shared information throughout the imaging protocol is now exploited by the image reconstruction in the form of an additional constraint based on image gradient sparsity. This constraint helps to minimize the amount of data needed for efficient non-rigid motion correction. T₁and T₂weighted images were reconstructed from free-breathing acquisitions in 4 healthy volunteers and in a phantom. The impact of multi-contrast motion correction was evaluated in a phantom in terms of precision and accuracy of T₁and T₂quantification.In the phantom, the proposed method achieved an accuracy of 97.5 % on the quantified parameters against 88.0 % before motion correction. In volunteers, motion inconsistency in T₁and T₂quantification were noticeably reduced within 5 min of free-breathing acquisition.An efficient, free-breathing, multi-contrast imaging method has been demonstrated that does not require prior assumptions about contrast and that is applicable to a wide range of examinations.