Search

Type 1 diabetes mellitus occurs in one in every 275 pregnancies and can result in increased morbidity and mortality for both mother and baby. Several pregnancy complications can be reduced or prevented by attendance at pre-pregnancy care (PPC). Despite this, less than 40% of pregnant women with pre-gestational diabetes receive formal PPC. The aim of this scoping review is to identify the barriers to PPC attendance among women with type 1 diabetes.We conducted a scoping review by searching five databases (Ebsco, Embase, Ovid and PubMed for literature and the ProQuest for any grey/unpublished literature) for studies in English between 2000 and 2022. Studies that evaluated attendance at PPC for women with type 1 diabetes were included.There are multiple barriers to PPC attendance, and many of these barriers have been unchanged since the 1990s. Identified barriers can be grouped under patient-centered and clinician-centered headings. Patient factors include knowledge and awareness, unplanned pregnancies, negative perceptions of healthcare and communication issues, unclear attendance pathways and logistical issues including time off work and childcare. Clinician factors include physician knowledge, time constraints and lack of comfort discussing pregnancy/contraception.This review highlights the ongoing problem of poor attendance at PPC and identifies key barriers to be addressed when developing and implementing PPC programs for women with type 1 diabetes.

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.

Purpose To evaluate the impact of a novel postprocessing denoising technique on accuracy and precision in myocardial T1 mapping. Materials and Methods This study introduces a fast and robust denoising method developed for magnetic resonance T1 mapping. The technique imposes edge-preserving regularity and exploits the co-occurence of spatial gradients in the acquired T1-weighted images. The proposed approach was assessed in simulations, ex vivo data and in vivo imaging on a cohort of 16 healthy volunteers (12 males, average age 39 ± 8 years, 62 ± 9 bpm) both in pre- and postcontrast injection. The method was evaluated in myocardial T1 mapping at 3T with a saturation-recovery technique that is accurate but sensitive to noise. ROIs in the myocardium and left-ventricle blood pool were analyzed by an experienced reader. Mean T1 values and standard deviation were extracted and compared in all studies. Results Simulations 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 T1 values was observed in the myocardium (precontrast: 1433/1426 msec, 95%CI: [−40.7, 55.9], p = 0.75, postcontrast: 766/759 msec, 95%CI: [−60.7, 77.2], p = 0.8). Meanwhile, precision was improved with standard deviations of T1 values being significantly decreased (precontrast: 223/151 msec, 95%CI: [27.3, 116.5], p = 0.003, postcontrast: 176/135 msec, 95%CI: [5.5, 77.1], p = 0.03). Conclusion The proposed denoising method preserves accuracy and improves precision in myocardial T1 mapping, with the potential to offer better map visualization and analysis. Level of Evidence: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1377–1388.

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

International audience; OBJECT: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.MATERIALS AND METHODS: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.RESULTS: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.CONCLUSION: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.

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.