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Background: One major challenge in cardiovascular magnetic resonance is reducing scan times to be more compatible with clinical workflows. In 3D magnetic resonance imaging (MRI), strategies to shorten scan times mostly rely on ECG-triggering or self-navigation for motion management, but are affected by heart rate variabilities or respiratory drifts. A similarity-driven multi-dimensional binning algorithm (SIMBA) was introduced for 3D whole-heart angiography from ferumoxytol-enhanced free-running MRI. This study explores acceleration limits using SIMBA, and its compressed-sensing extension extra-dimensional motion-compensation (XD-MC)-SIMBA, while preserving image quality., Methods: Data from 6-min free-running acquisitions of 30 congenital heart disease (CHD) patients were retrospectively undersampled to simulate 5-, 4-, 3-, 2-, and 1-min datasets. SIMBA and XD-MC-SIMBA reconstructions were applied. and the consistency of the data selection together with sharpness metrics were computed as a function of undersampling. Image quality was rated on a 5-point Likert scale. Shorter 3-minute acquisitions were prospectively acquired in nine CHD patients., Results: SIMBA's motion state selection was consistent across undersampling levels, with only 2 of 30 cases showing completely different selections. Image quality metrics decreased with increased undersampling, with SIMBA scoring lower compared to XD-MC-SIMBA. The diagnostic quality was good, with lower scores for 2- and 1-min datasets. Using XD-MC-SIMBA, 43% (31/72) of cases showed improved scores compared to SIMBA and 58% (7/12) of 1-min datasets improved to good or excellent quality., Conclusions: This study demonstrates that ferumoxytol-enhanced free-running MRI can be highly accelerated for 3D angiography in CHD.With the aid of compressed sensing, XD-MC-SIMBA supports the acceleration down to 3 minutes or less., Competing Interests: Declaration of competing interests L.R.’s PhD studies are financially supported by Siemens Healthcare (Erlangen, Germany). At the time of the study, D.P. was an employee of Siemens Healthineers International AG (Lausanne, Switzerland) and is now an employee of Siemens Healthcare Srl (Italy). M.S. receives non-monetary research support from Siemens Healthcare (Erlangen, Germany). M.S. is a senior advisor of JCMR. The other authors have no competing financial interests or personal relationships., (Copyright © 2025 The Author(s). Published by Elsevier Inc. All rights reserved.)

Leaves of tomato plants contain various glandular trichomes that produce a wide range of metabolic products including acylsugars, which may serve as a defense mechanism against various insect pests. Acylsugars exhibit significant structural diversity, differing in their sugar cores, acylated positions, and type of acyl chains. This work demonstrated a comprehensive approach using multidimensional separation techniques, specifically liquid chromatography-ion mobility-tandem mass spectrometry (LC-IM-MS/MS), for structural characterization, and the discrimination of different tomato plants (one cultivar and five accessions) was demonstrated using tomato leaf extracts; six genotypes from five species of Solanum were represented. As a result, we identified 16 acylsugars through their molecular formulas and annotations using LC and MS analyses. The incorporation of ion mobility (IM) analysis revealed an additional 9 isomeric forms, resulting in a comprehensive total of 25 isomeric acylsugars identified. Furthermore, the experimental collision cross section (CCS exp ) values agreed reasonably well with the corresponding predicted values (CCS pred ), with an overall estimated error of less than 2%. These findings pave the way for research into how the different structural isomers of acylsugars might influence the self-defense mechanism in plants. Moreover, this work demonstrated that the investigated cultivar and accessions of tomatoes can be distinguished from each other based on their metabolite profile, e.g., acylsugars, with principal component analysis (PCA) and linear discriminant analysis (LDA) statistical models, yielding a prediction rate of 98.3%.