Publications by authors named "Henrike Puchta"

Background: Cardiovascular magnetic resonance T1-mapping is increasingly used for tissue characterization, commonly based on Modified Look-Locker Inversion recovery (MOLLI). However, there are numerous MOLLI variants with differing normal ranges. This lack of standardization presents confusion and difficulty in inter-center comparisons, hindering widespread adoption of T1-mapping.

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Osteoporosis and ischemic heart disease (IHD) represent important public health problems. Existing research suggests an association between the two conditions beyond that attributable to shared risk factors, with a potentially causal relationship. In this study, we tested the association of bone speed of sound (SOS) from quantitative heel ultrasound with (i) measures of arterial compliance from cardiovascular magnetic resonance (aortic distensibility [AD]); (ii) finger photoplethysmography (arterial stiffness index [ASI]); and (iii) incident myocardial infarction and IHD mortality in the UK Biobank cohort.

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Background: Myocardial T1-mapping is increasingly used in multicentre studies and trials. Inconsistent image analysis introduces variability, hinders differentiation of diseases, and results in larger sample sizes. We present a systematic approach to standardize T1-map analysis by human operators to improve accuracy and consistency.

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Article Synopsis
  • - The study developed a fully automated method to detect and localize the ascending (AA) and proximal descending (PDA) aorta in cardiovascular magnetic resonance (CMR) images, overcoming limitations of semi-automated techniques which required manual quality checks and localization.
  • - The algorithm utilized several steps, including foreground segmentation and Random Forest classification, achieving high accuracy, with 99.4% for AA and 99.8% for PDA, and excellent agreement with manual assessments (Dice Similarity Coefficient > 0.9 for most cases).
  • - The automated system proved effective in distinguishing usable scans from those with poor quality and demonstrated 100% accuracy in correctly detecting and localizing AA and PDA in a large dataset of
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