Acquiring high-resolution diagnostic pediatric MR images is challenging due to patient movement during long scan times, resulting in spatial blurring and motion artifacts. Developing rapid acquisition methods is essential to obtaining diagnostic-quality MR images. Recently, an echo planar imaging (EPI)-based Multi-Inversion Spin and Gradient Echo (MI-SAGE) sequence was reported to acquire multiple tissue contrasts from adult brains with a single 1-minute scan. This study demonstrates the utility of a 1-minute MI-SAGE acquisition in pediatric subjects to generate clinically relevant synthetic image contrasts (T1w, T2w, T2*w) from quantitative relaxometry maps (T1, T2, and T2*). Read More

Meeting name: SPR 2024 Annual Meeting & Postgraduate Course , 2024

Authors: Kilpattu Ramaniharan Anandh, Pednekar Amol, Parikh Nehal, Nagaraj Usha, Manhard Mary Kate

Keywords: Pediatric, synthetic MRI, relaxometry

To compare liver shear stiffness estimates obtained by Automatic Liver Elasticity Calculation (ALEC) processing of two-dimensional (2D) gradient-recalled echo (GRE) magnetic resonance elastography (MRE) data to stiffness values obtained from standard-of-care manual processing. Read More

Meeting name: SPR 2020 Annual Meeting & Postgraduate Course , 2020

Authors: Gandhi Deep, Lake David, Dillman Jonathan, Braimah Adebayo, Dudley Jonathan, Tkach Jean, Pednekar Amol, Trout Andrew, Miethke Alexander, Heilman Jeremiah, Dzyubak Bogdan

Keywords: Elastography, MRI, Elasticity

Qualitative scores of parenchymal disease severity from 3D lung MRI predict clinical outcomes such as duration of respiratory support in infants with BPD. However, current methods suffer from low signal-to-noise ratio (SNR) and motion-related blurring, reducing interpretability and diagnostic confidence. The inherently quiet zero echo time (ZTE) MRI with deep learning reconstruction (DLR) may improve diagnostic reliability but has not been evaluated in infants. This study assessed the feasibility of ZTE MRI and image quality improvements with DLR in infants with BPD. Read More

Meeting name: IPR 2026 Congress , 2026

Authors: Munidasa Samal, Bates Alister, Kingma Paul, Hysinger Erik, Woods Jason, Willmering Matthew, Muslu Yavuz, De Arcos Jose, Morin Cara, Kocaoglu Murat, Fleck Robert, Pednekar Amol, Tanimoto Aki, Higano Nara

Keywords: Neonatal, Deep Learning, Pulmonary