2024
A Flow-based Truncated Denoising Diffusion Model for super-resolution Magnetic Resonance Spectroscopic Imaging
Dong S, Cai Z, Hangel G, Bogner W, Widhalm G, Huang Y, Liang Q, You C, Kumaragamage C, Fulbright R, Mahajan A, Karbasi A, Onofrey J, de Graaf R, Duncan J. A Flow-based Truncated Denoising Diffusion Model for super-resolution Magnetic Resonance Spectroscopic Imaging. Medical Image Analysis 2024, 99: 103358. PMID: 39353335, DOI: 10.1016/j.media.2024.103358.Peer-Reviewed Original ResearchDenoising diffusion modelsDeep learning-based super-resolution methodsLearning-based super-resolution methodsMulti-scale super-resolutionGenerative modelSuper-resolution methodsDeep learning modelsHigh-resolution magnetic resonance spectroscopic imagingHigh-quality imagesPost-processing approachSuper-resolutionFlow-based networksLearning modelsLow resolutionTruncation stepLow-resolution dataSharpness adjustmentNetworkSensitivity restrictionsUncertainty estimationDiffusion modelImagesCapabilitySampling processSpectroscopic imaging
2023
Design and realization of a multi‐coil array for B0 field control in a compact 1.5T head‐only MRI scanner
Theilenberg S, Shang Y, Ghazouani J, Kumaragamage C, Nixon T, McIntyre S, Vaughan J, Parkinson B, Garwood M, de Graaf R, Juchem C. Design and realization of a multi‐coil array for B0 field control in a compact 1.5T head‐only MRI scanner. Magnetic Resonance In Medicine 2023, 90: 1228-1241. PMID: 37145035, PMCID: PMC10330274, DOI: 10.1002/mrm.29692.Peer-Reviewed Original ResearchConceptsMulti-coil arraysMC arraysDuty cycleGeneration capabilityT headNonlinear magnetic fieldHigh duty cycleThermal behaviorBench testingField controlRamp timeResidual imperfectionField generationScanner designMagnetic fieldMC hardwareLinear gradientMRI scannerArrayCapabilityDesignHardwareImage encodingFieldMagnets