Janis Stiegeler^1,2 and Sina Straub^1
1Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany, ²Department of Physics and Astronomy, University of Heidelberg, Heidelberg, Germany

In this work noise is assumed to be a random vector and the method of unbiased predictive risk estimator (UPRE) is used to select suitable data/regularization parameters to solve the local phase to susceptibility deconvolution problem of quantitative susceptibility mapping (QSM). The proposed algorithm is tested on the simulated multi-echo data provided at the 2019 QSM Reconstruction Challenge. This work is a further development of the algorithm presented at the ISMRM Meeting 2021 and its purpose is to show that the method of UPRE can be applied advantageously to a shearlet /TGV based susceptibility reconstruction. 

Seong Dae Yun¹ and N. Jon Shah^1,2,3,4
1Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Juelich, Juelich, Germany, ²Institute of Neuroscience and Medicine 11, INM-11, JARA, Forschungszentrum Juelich, Juelich, Germany, ³JARA - BRAIN - Translational Medicine, Aachen, Germany, ⁴Department of Neurology, RWTH Aachen University, Aachen, Germany

EPI is of great importance in the MR community due to its wide applicability in various dynamic MR applications. Recent advances in EPI techniques extend its use for the depiction of neuronal activation with a cortical depth-dependence. Therefore, enhanced performance with reliable image quality is highly desirable in EPI. This work presents an in-house designed EPI sequence and its corresponding image reconstruction software at 7T. Results show that our EPI sequence outperforms two widely used sequences: Siemens-EPI and CMRR-EPI. In addition, our image reconstruction routine significantly improves the reconstructed image quality of Siemens and CMRR data.

Jost M. Kollmeier¹ and Jens Frahm^1
1Biomed NMR, Max-Planck-Institut für biophysikalische Chemie, Göttingen, Germany

In undersampled radial phase-contrast imaging, Maxwell terms can vary for individual radial projections (spokes). Integrated into a model-based image reconstruction, a computationally expensive spoke-by-spoke correction has been proposed, for which the reconstruction times scale with the number of spokes per frame. To make this approach practical for large numbers of spokes, this work proposes to use k-means clustering of Maxwell terms in the spoke dimension and thereby reduce the computational costs of the model-based image reconstruction for phase-contrast MRI with radial Maxwell correction.

Anh Tu Van¹, Sean McTavish¹, Johannes K. J. Raspe¹, Felix Harder¹, Johannes M. Peeters², Kilian Weiss³, Marcus R Makowski¹, Rickmer F. Braren¹, and Dimitrios C Karampinos^1
1Department of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany, ²Philips Healthcare, Best, Netherlands, ³Philips GmbH Market DACH, Hamburg, Germany

Motion-induced signal loss and phase maps were generated and used in a SENSE-like reconstruction routine to solve for a single DWI from a multi-average liver diffusion-weighted imaging experiment. The proposed reconstruction yields homogeneous liver signal and possibly improves diagnostic values.

Luis Miguel Lacerda¹, Alki Liasis^2,3, Sian Handley³, Martin Tisdall⁴, Helen Cross⁵, Faraneh Vargha-khadem⁵, and Chris Clark^1
1Developmental Imaging and Biophysics Section, UCL Great Ormond Street Institute of Child Health, LONDON, United Kingdom, ²Children’s Hospital of Pittsburgh, University of Pittsburgh Medical Centre, Pittsburgh, KS, United States, ³Clinical and Academic Department of Ophthalmology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, United Kingdom, ⁴Neurosurgery, Great Ormond Street Hospital for Children NHS Foundation Trust, London, United Kingdom, ⁵Clinical Neurosciences, UCL Great Ormond Street Institute of Child Health, LONDON, United Kingdom

There is a need to map microstructural changes over long time periods and develop/apply methods that work with legacy data. In this study, we applied the novel single shell 3-Tissue method to data from a cohort of 4 patients who were scanned 20-years following childhood hemidisconnection surgery and presented degeneration of the visual system. We believe this study suggests that diffusion MRI can be used to monitor the integrity of the visual system following hemispherectomy and if extended to larger cohorts and a greater number of time-points, provide a clearer picture of the natural history of visual system degeneration.

Tobias Spronk^1,2,3, Oliver Kraff¹, Gregor Schaefers^3,4, and Harald H. Quick^1,2
1Erwin L. Hahn Institute for MR Imaging, University of Duisburg-Essen, Essen, Germany, ²High-Field and Hybrid MR Imaging, University Hospital Essen, University Duisburg-Essen, Essen, Germany, ³MRI-STaR Magnetic Resonance Institute for Safety, Technology and Research GmbH, Gelsenkirchen, Germany, ⁴MR:comp GmbH, Testing Services for MR Safety & Compatibility, Gelsenkirchen, Germany

This study evaluates a numerical approach to simulate artifacts due to presence of orthopedic metallic implants in the MR environment. Further to previously published studies, the numerical approach is validated by comparing simulations and measurements of orthopedic implants at three different field strengths (1.5T, 3T, and 7T). Artifact simulations and measurements of the implants show high correlation at all field strengths. The method potentially can be applied to improve the artifacts testing procedure for medical implants according to ASTM F2119. Additionally, the influence of different imaging parameters (echo time and bandwidth) on the artifact size is quantified via numerical simulations.

Dimitra Flouri^1,2, Jack RT Darby³, Stacey L Holman³, Sunthara R Perumal⁴, Sebastien Ourselin¹, Anna L David^5,6, Andrew Melbourne^1,2, and Janna L Morrison^3
1School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom, ²Department of Medical Physics & Biomedical Engineering, University College London, London, United Kingdom, ³Early Origins of Adult Health Research Group, University of South Australia, Adelaide, Australia, ⁴Preclinical Imaging and Research Laboratories, South Australian Health and Medical Research Institute, Adelaide, Australia, ⁵Institute for Women's Health, University College London, London, United Kingdom, ⁶NIHR Biomedical Research Center, University College London Hospitals, London, United Kingdom

Abnormal placental development is postulated as one of the leading causes of fetal growth restriction (FGR). Advances in MRI technology enable non-invasive measurement of fetal oxygen saturation, but not all have yet been validated. Due to the invasiveness of tests required, validation in human subjects is not possible. Preclinical models such as pregnant sheep allow invasive methods to validate MRI measurements. Here we show that multi-compartment modelling of non-invasive placental MRI can be used to estimate the oxygen saturation of feto-placental oxygenation in normal sheep pregnancy and pregnancies affected by early gestation onstet FGR.

Marcelo Victor Wust Zibetti¹, Florian Knoll², and Ravinder Regatte^1
1Radiology, NYU Langone Health, New York, NY, United States, ²Department of Artificial Intelligence in Biomedical Engineering, FAU Erlangen-Nuremberg, Erlangen, Germany

We propose a new alternating learning approach to jointly learn the sampling pattern (SP) and the parameters of a variational network (VN) for acquisition and reconstruction on 3D Cartesian parallel MRI problems. This approach is composed of alternating short training with BASS algorithm to learn the SP, and ADAM algorithm to learn the parameters of the VN, both with forced monotonicity. The results illustrate that this approach provides reduced error when compared to other joint learning approaches, and surpasses VN trained with recently developed fixed SPs.