ESR Journals

❤️ Photon-counting detector CT acquires spectral data at every scan, enabling iodine map reconstruction without dedicated dual-energy protocols. This scoping review mapped the extent characteristics, and findings of early clinical studies. ❤️

buff.ly/9M3CegA (Ana M. Rendón-Garavito et al.)

Automated, AI-based, organ segmentation has the potential to streamline preclinical imaging workflows, but its suitability must be evaluated not only by geometric accuracy, but also by impact on downstream quantitative analyses. The authors validated a commercially available AI-based organ segmentation workflow for whole-body mmicro-CT data regarding segmentation accuracy, reproducibility, processing time, and effects on fluorescence tomography quantification

buff.ly/hucW2MD (Elena Rama et al.)

Deep learning-based noise reduction enhances image quality, overcoming the tradeoff among acquisition time, spatial resolution, and signal-to-noise ratio (SNR). The authors implemented deep learning reconstruction into a 1.5-T musculoskeletal MRI protocol to improve image quality without compromising SNR

buff.ly/6IOQ3Wf (Marco Porta et al.)

🩷 This exploratory study aimed to characterise high-resolution in vivo optaacoustic signal characteristics of the different breast cancer molecular subtypes using second-generation handheld multispectral optoacoustic tomography. 🩷

buff.ly/hhqYif6 (Georg C. Feuerriegel et al.)

The volume and diversity of large MR imaging datasets require efficient automated labelling tools for cataloguing MR series, as manual annotation is impractical and costly. However, relying on DICOM header fields alone is unreliable because sequence descriptors are heterogeneous and locally defined, frequently missing or incorrect, and may be altered or removed during anonymisation

buff.ly/yiEoeWz (Mariusz J. Kujawa et al.)