Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results.

Segmentation is a critical step in analyzing the developing human fetal brain. There have been vast improvements in automatic segmentation methods in the past several years, and the Fetal Brain Tissue Annotation (FeTA) Challenge 2021 helped to establish an excellent standard of fetal brain segmentation. However, FeTA 2021 was a single center study, limiting real-world clinical applicability and acceptance.

7 Tesla magnetic resonance spectroscopic imaging predicting IDH status and glioma grading.

Introduction: With the application of high-resolution 3D 7 Tesla Magnetic Resonance Spectroscopy Imaging (MRSI) in high-grade gliomas, we previously identified intratumoral metabolic heterogeneities. In this study, we evaluated the potential of 3D 7 T-MRSI for the preoperative noninvasive classification of glioma grade and isocitrate dehydrogenase (IDH) status. We demonstrated that IDH mutation and glioma grade are detectable by ultra-high field (UHF) MRI.

Fetal development of functional thalamocortical and cortico-cortical connectivity.

Measuring and understanding functional fetal brain development in utero is critical for the study of the developmental foundations of our cognitive abilities, possible early detection of disorders, and their prevention. Thalamocortical connections are an intricate component of shaping the cortical layout, but so far, only ex-vivo studies provide evidence of how axons enter the sub-plate and cortex during this highly dynamic phase. Evidence for normal in-utero development of the functional thalamocortical connectome in humans is missing.

Longitudinal investigation of a xenograft tumor zebrafish model using polarization-sensitive optical coherence tomography.

Breast cancer is a leading cause of death in female patients worldwide. Further research is needed to get a deeper insight into the mechanisms involved in the development of this devastating disease and to find new therapy strategies. The zebrafish is an established animal model, especially in the field of oncology, which has shown to be a promising candidate for pre-clinical research and precision-based medicine.

Motion correction and volumetric reconstruction for fetal functional magnetic resonance imaging data.

Motion correction is an essential preprocessing step in functional Magnetic Resonance Imaging (fMRI) of the fetal brain with the aim to remove artifacts caused by fetal movement and maternal breathing and consequently to suppress erroneous signal correlations. Current motion correction approaches for fetal fMRI choose a single 3D volume from a specific acquisition timepoint with least motion artefacts as reference volume, and perform interpolation for the reconstruction of the motion corrected time series. The results can suffer, if no low-motion frame is available, and if reconstruction does not exploit any assumptions about the continuity of the fMRI signal.

Reconstruction of visible light optical coherence tomography images retrieved from discontinuous spectral data using a conditional generative adversarial network.

Achieving high resolution in optical coherence tomography typically requires the continuous extension of the spectral bandwidth of the light source. This work demonstrates an alternative approach: combining two discrete spectral windows located in the visible spectrum with a trained conditional generative adversarial network (cGAN) to reconstruct a high-resolution image equivalent to that generated using a continuous spectral band. The cGAN was trained using OCT image pairs acquired with the continuous and discontinuous visible range spectra to learn the relation between low- and high-resolution data.

[Machine learning in radiology : Terminology from individual timepoint to trajectory].

Methodical Issue: Machine learning (ML) algorithms have an increasingly relevant role in radiology tackling tasks such as the automatic detection and segmentation of diagnosis-relevant markers, the quantification of progression and response, and their prediction in individual patients.

Standard Radiological Methods: ML algorithms are relevant for all image acquisition techniques from computed tomography (CT) and magnetic resonance imaging (MRI) to ultrasound. However, different modalities result in different challenges with respect to standardization and variability.