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Functional connectivity (FC) is known to be individually unique and to reflect cognitive variability. Although FC can serve as a valuable correlate and potential predictor of (patho-) physiological nervous function in high-risk constellations, such as preterm birth, templates for individualized FC analysis are lacking, and knowledge about the capacity of the premature brain to develop FC variability is limited. In a cohort of prospectively recruited, preterm-born infants undergoing magnetic resonance imaging close to term-equivalent age, we show that the overall pattern could be reliably detected with a broad range of interindividual FC variability in regions of higher-order cognitive functions (e.g., association cortices) and less interindividual variability in unimodal regions (e.g., visual and motor cortices). However, when comparing the preterm and adult brains, some brain regions showed a marked shift in variability toward adulthood. This shift toward greater variability was strongest in cognitive networks like the attention and frontoparietal networks and could be partially predicted by developmental cortical expansion. Furthermore, FC variability was reflected by brain tissue characteristics indicating cortical maturation. Brain regions with high functional variability (e.g., the inferior frontal gyrus and temporoparietal junction) displayed lower cortical maturation at birth compared with somatosensory cortices. In conclusion, the overall pattern of interindividual variability in FC is already present preterm; however, some brain regions show increased variability toward adulthood, identifying characteristic patterns, such as in cognitive networks. These changes are related to postnatal cortical expansion and maturation, allowing for environmental and developmental factors to translate into marked individual differences in FC.

Fast data acquisition and fast image reconstruction are essential to enable low-latency real-time magnetic resonance (MR) imaging applications with high temporal resolution such as interstitial percutaneous needle interventions or MR-guided radiotherapy. To accelerate the image reconstruction of radially undersampled 2D k-space data, we propose a machine learning (ML) model that consists of a single fully connected linear layer to interpolate radial k-space data to a Cartesian grid, followed by a conventional 2D inverse fast Fourier transform. This k-space-to-image ML model was trained on synthetic data from natural images. It was evaluated with respect to image quality (mean squared error (MSE) compared to ground truth where available) and reconstruction time both on synthetic data with undersampling factors R between 2 and 10 as well as on radial k-space data from MR measurements on two different MRI systems. For comparison, conventional non-iterative zero-filling non-uniform fast Fourier transform (NUFFT) reconstruction and compressed sensing (CS) reconstruction were used. On synthetic data, the ML model achieved better median MSE values than the non-iterative NUFFT reconstruction. The interquartile ranges of the MSE distributions overlapped for the ML and CS reconstructions for all R . Reconstruction times of the ML approach were shorter than for NUFFT and substantially shorter than for CS reconstructions. The generalizability (for real MRI data) of the ML model was demonstrated by reconstructing 0.35-tesla MR-Linac dynamic measurements of three volunteers and phantom data from a diagnostic 1.5-tesla MRI system; the median reconstruction time for the coil-combined images was much shorter than for the conventional approach (ML: < 4 m s ; NUFFT: ≈ 60 − 90 m s ). The proposed ML model reconstructs MR data with reduced streaking artifacts compared to non-iterative NUFFT techniques and with extremely short reconstruction times; thus, it is ideally suited for rapid low-latency real-time MR applications.

Needle artifacts pose a major limitation for MRI-guided interventions, as they impact the visually perceived needle size and needle-to-target-distance. The objective of this agar liver phantom study was to establish an experimental basis to understand and reduce needle artifact formation during MRI-guided abdominal interventions. Using a vendor-specific prototype fluoroscopic T1-weighted gradient echo sequence with real-time multiplanar acquisition at 1.5 T, the influence of 6 parameters (flip angle, bandwidth, matrix, slice thickness, read-out direction, intervention angle relative to B 0 ) on artifact formation of 4 different coaxial MR-compatible coaxial needles (Nitinol, 16G–22G) was investigated. As one parameter was modified, the others remained constant. For each individual parameter variation, 2 independent and blinded readers rated artifact diameters at 2 predefined positions (15 mm distance from the perceived needle tip and at 50% of the needle length). Differences between the experimental subgroups were assessed by Bonferroni-corrected non-parametric tests. Correlations between continuous variables were expressed by the Bravais–Pearson coefficient and interrater reliability was quantified using the intraclass classification coefficient. Needle artifact size increased gradually with increasing flip angles ( p  = 0.002) as well as increasing intervention angles ( p  < 0.001). Artifact diameters differed significantly between the chosen matrix sizes ( p  = 0.002) while modifying bandwidth, readout direction, and slice thickness showed no significant differences. Interrater reliability was high (intraclass correlation coefficient 0.776–0.910). To minimize needle artifacts in MRI-guided abdominal interventions while maintaining optimal visibility of the coaxial needle, we suggest medium-range flip angles and low intervention angles relative to B 0 .

Background Longitudinal assessments of apparent diffusion coefficients (ADCs) derived from diffusion-weighted imaging (DWI) during intracranial radiotherapy at magnetic resonance imaging-guided linear accelerators (MR-linacs) could enable early response assessment by tracking tumor diffusivity changes. However, DWI pulse sequences are currently unavailable in clinical practice at low-field MR-linacs. Quantifying the in vivo repeatability of ADC measurements is a crucial step towards clinical implementation of DWI sequences but has not yet been reported on for low-field MR-linacs. This study assessed ADC measurement repeatability in a phantom and in vivo at a 0.35 T MR-linac. Methods Eleven volunteers and a diffusion phantom were imaged on a 0.35 T MR-linac. Two echo-planar imaging DWI sequence variants, emphasizing high spatial resolution (“highRes”) and signal-to-noise ratio (“highSNR”), were investigated. A test–retest study with an intermediate outside-scanner-break was performed to assess repeatability in the phantom and volunteers’ brains. Mean ADCs within phantom vials, cerebrospinal fluid (CSF), and four brain tissue regions were compared to literature values. Absolute relative differences of mean ADCs in pre- and post-break scans were calculated for the diffusion phantom, and repeatability coefficients (RC) and relative RC (relRC) with 95% confidence intervals were determined for each region-of-interest (ROI) in volunteers. Results Both DWI sequence variants demonstrated high repeatability, with absolute relative deviations below 1% for water, dimethyl sulfoxide, and polyethylene glycol in the diffusion phantom. RelRCs were 7% [5%, 12%] (CSF; highRes), 12% [9%, 22%] (CSF; highSNR), 9% [8%, 12%] (brain tissue ROIs; highRes), and 6% [5%, 7%] (brain tissue ROIs; highSNR), respectively. ADCs measured with the highSNR variant were consistent with literature values for volunteers, while smaller mean values were measured for the diffusion phantom. Conversely, the highRes variant underestimated ADCs compared to literature values, indicating systematic deviations. Conclusions High repeatability of ADC measurements in a diffusion phantom and volunteers’ brains were measured at a low-field MR-linac. The highSNR variant outperformed the highRes variant in accuracy and repeatability, at the expense of an approximately doubled voxel volume. The observed high in vivo repeatability confirms the potential utility of DWI at low-field MR-linacs for early treatment response assessment.

PurposeThe prediction of an infiltration of the mesorectal fascia (MRF) and malignant lymph nodes is essential for treatment planning and prognosis of patients with rectal cancer. The aim of this study was to assess the additional diagnostic value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for the detection of a malignant involvement of the MRF and of mesorectal lymph nodes in patients with locally advanced rectal cancer.MethodsIn this prospective study, 22 patients with locally advanced rectal cancer were examined with 1.5-T MRI between September 2012 and April 2015. Histopathological assessment of tumor size, tumor infiltration to the MRF, and malignant involvement of locoregional lymph nodes served as standard of reference. Sensitivity and specificity of detecting MRF infiltration and malignant nodes (nodal cut-off size [NCO] ≥ 5 and ≥ 10 mm, respectively) was determined by conventional MRI (cMRI; precontrast and postcontrast T1-weighted, T2-weighted, and diffusion-weighted images) and by additional semi-quantitative DCE-MRI maps (cMRI+DCE-MRI).ResultsCompared to cMRI, additional semi-quantitative DCE-MRI maps significantly increased sensitivity (86 vs. 71% [NCO ≥ 5 mm]/29% [NCO ≥ 10 mm]) and specificity (90 vs. 70% [NCO ≥ 5 mm]) of detecting malignant lymph nodes (p < 0.05). Moreover, DCE-MRI significantly augmented specificity (91 vs. 82%) of discovering a MRF infiltration (p < 0.05), while there was no change in sensitivity (83%; p > 0.05).ConclusionDCE-MRI considerably increases both sensitivity and specificity for the detection of small mesorectal lymph node metastases (≥ 5 mm but < 10 mm) and sufficiently improves specificity of a suspected MRF infiltration in patients with locally advanced rectal cancer.

Background Interventional magnetic resonance imaging (MRI) can provide a comprehensive setting for microwave ablation of tumors with real-time monitoring of the energy delivery using MRI-based temperature mapping. The purpose of this study was to quantify the accuracy of three-dimensional (3D) real-time MRI temperature mapping during microwave heating in vitro by comparing MRI thermometry data to reference data measured by fiber-optical thermometry. Methods Nine phantom experiments were evaluated in agar-based gel phantoms using an in-room MR-conditional microwave system and MRI thermometry. MRI measurements were performed for 700 s (25 slices; temporal resolution 2 s). The temperature was monitored with two fiber-optical temperature sensors approximately 5 mm and 10 mm distant from the microwave antenna. Temperature curves of the sensors were compared to MRI temperature data of single-voxel regions of interest (ROIs) at the sensor tips; the accuracy of MRI thermometry was assessed as the root-mean-squared (RMS)-averaged temperature difference. Eighteen neighboring voxels around the original ROI were also evaluated and the voxel with the smallest temperature difference was additionally selected for further evaluation. Results The maximum temperature changes measured by the fiber-optical sensors ranged from 7.3 K to 50.7 K. The median RMS-averaged temperature differences in the originally selected voxels ranged from 1.4 K to 3.4 K. When evaluating the minimum-difference voxel from the neighborhood, the temperature differences ranged from 0.5 K to 0.9 K. The microwave antenna and the MRI-conditional in-room microwave generator did not induce relevant radiofrequency artifacts. Conclusion Accurate 3D real-time MRI temperature mapping during microwave heating with very low RMS-averaged temperature errors below 1 K is feasible in gel phantoms. Relevance statement Accurate MRI-based volumetric real-time monitoring of temperature distribution and thermal dose is highly relevant in clinical MRI-based interventions and can be expected to improve local tumor control, as well as procedural safety by extending the limits of thermal ( e.g ., microwave) ablation of tumors in the liver and in other organs. Key Points Interventional MRI can provide a comprehensive setting for the microwave ablation of tumors. MRI can monitor the microwave ablation using real-time MRI-based temperature mapping. 3D real-time MRI temperature mapping during microwave heating is feasible. Measured temperature errors were below 1 °C in gel phantoms. The active in-room microwave generator did not induce any relevant radiofrequency artifacts. Graphical Abstract

Background We assessed immunotherapy response in a murine melanoma model using multiparametric magnetic resonance imaging (mpMRI) features with ex vivo immunohistochemical validation. Methods Murine melanoma cells (B16-F10) were inoculated into the subcutaneous flank of n  = 28 C57BL/6 mice ( n  = 14 therapy; n  = 14 control). Baseline mpMRI was acquired on day 7 at 3 T. The immunotherapy group received three intraperitoneal injections of anti-PD-L1 and anti-CTLA-4 antibodies on days 7, 9, and 11 after inoculation. Controls received a volume equivalent placebo. Follow-up mpMRI was performed on day 12. We assessed tumor volume, diffusion-weighted imaging parameters, including the apparent diffusion coefficient (ADC), and dynamic-contrast-enhanced metrics, including plasma volume and plasma flow. Tumor-infiltrating lymphocytes (TIL; CD8+), cell proliferation (Ki-67), apoptosis (terminal deoxynucleotidyl transferase deoxyuridine triphosphate nick-end labeling, TUNEL), and microvascular density (CD31+) were assessed in a validation cohort of n  = 24 animals for time-matched ex vivo validation. Results An increase in tumor volume was observed in both groups ( p  ≤ 0.004) without difference at follow-up ( p  = 0.630). A lower ADC value was observed in the immunotherapy group at follow-up ( p  = 0.001). Immunohistochemistry revealed higher TUNEL values ( p  < 0.001) and CD8+ TILs ( p  = 0.048) following immunotherapy, as well as lower tumor cell Ki-67 values ( p  < 0.001) and microvascular density/CD31+ ( p  < 0.001). Conclusion Lower tumor ADC, paired with higher intratumoral expression of CD8+ TIL, was observed five days after immunotherapy, suggestive of early immunological response. Ex vivo immunohistochemistry confirmed the antitumoral efficacy of immunotherapy. Relevance statement Compared to tumor size, diffusion-weighted MRI demonstrated potential for early response assessment to immunotherapy in a murine melanoma model, which could reflect changes in the tumor microenvironment and immune cell infiltration. Key Points No difference in tumor volume was observed between groups before and after therapy. Lower ADC values paired with increased CD8+ TILs were observed following immunotherapy. Ex vivo immunohistochemistry confirmed antitumoral efficacy of anti-PD-L1 and anti-CTLA-4 immunotherapy. Graphical Abstract

Background A variety of animal models has been developed for research on atherosclerosis and neointimal hyperplasia. While small animal models contain limits for translational research, we aimed to develop an atherosclerosis model with lumen-narrowing plaques to foster basic research in vascular biology, the development of new angioplasty devices, and vessel wall imaging approaches. Methods Endothelial denudation was performed via a minimally invasive approach through the auricular artery, followed by stent-retriever mediated endothelial injury in New Zealand White rabbits ( n  = 10). Along with a high-fat diet, the rabbits developed lumen-narrowing atherosclerosis and neointimal hyperplasia of the iliac arteries within a 6-week period after mechanical injury. The stent-retriever method was compared with a conventional rabbit model ( n  = 10) using balloon denudation via surgical access, and both models were analyzed with a particular focus on animal welfare. Fisher’s exact, Mann–Whitney U , and unpaired t -tests were used. Results The average time for the entire procedure was 62 min for the balloon group and 31 min for the stent-retriever group ( p  < 0.001). The stent-retriever model resulted in less periprocedural morbidity (including expenditure, intubation time, anesthetics, and end-tidal CO 2 level) and mortality (40% mortality in the conventional group compared to 0% in the stent-retriever model, p  = 0.011), while generating lumen-narrowing atherosclerotic lesions with key features as compared to humans as revealed by time-of-flight magnetic resonance imaging and histology. Conclusion We developed a minimally invasive model of iliac atherosclerosis with high reproducibility and improved animal welfare for translational research. Relevance statement This advanced rabbit model could allow for translational research in atherosclerosis, including pharmacological investigations as well as research on interventional angioplasty procedures. Key Points Rabbit models show similar lipid metabolism as humans. Stent-retriever mediated endothelial denudation causes neointimal hyperplasia and lumen narrowing. This minimal invasive model allows for clinical translation, including pharmacological investigations and vessel wall imaging. Graphical Abstract

(1) Background: CT perfusion (CTP) is used to quantify cerebral hypoperfusion in acute ischemic stroke. Conventional attenuation curve analysis is not standardized and might require input from expert users, hampering clinical application. This study aims to bypass conventional tracer-kinetic analysis with an end-to-end deep learning model to directly categorize patients by stroke core volume from raw, slice-reduced CTP data. (2) Methods: In this retrospective analysis, we included patients with acute ischemic stroke due to proximal occlusion of the anterior circulation who underwent CTP imaging. A novel convolutional neural network was implemented to extract spatial and temporal features from time-resolved imaging data. In a classification task, the network categorized patients into small or large core. In ten-fold cross-validation, the network was repeatedly trained, evaluated, and tested, using the area under the receiver operating characteristic curve (ROC-AUC). A final model was created in an ensemble approach and independently validated on an external dataset. (3) Results: 217 patients were included in the training cohort and 23 patients in the independent test cohort. Median core volume was 32.4 mL and was used as threshold value for the binary classification task. Model performance yielded a mean (SD) ROC-AUC of 0.72 (0.10) for the test folds. External independent validation resulted in an ensembled mean ROC-AUC of 0.61. (4) Conclusions: In this proof-of-concept study, the proposed end-to-end deep learning approach bypasses conventional perfusion analysis and allows to predict dichotomized infarction core volume solely from slice-reduced CTP images without underlying tracer kinetic assumptions. Further studies can easily extend to additional clinically relevant endpoints.

Objectives We aimed to develop and evaluate a new method that reliably differentiates between cerebral arteries and veins using voxel-wise CT-perfusion-derived parameters. Materials and Methods Fourteen consecutive patients with suspected stroke but without pathological findings were examined on a multi-detector CT system: 32 dynamic phases (∆t = 1.5 s) during application of 35 mL iomeprol-350 were acquired at 80 kV/200mAs. Three hemodynamic parameters were calculated for 18 arterial and venous vessel segments: A (maximum of the time-density-curve), T (time-to-peak), and W (full-width-at-half-maximum). Using receiver operator characteristic (ROC) curve analysis and Fisher’s linear discriminant analysis (FLDA), the performance of every classifier ( A , T , W ) and of all linear combinations for the differentiation of arterial and venous vessels was determined. Results A maximum area under the ROC-curve (AUC) of 0.945 (accuracy = 86.8 %) was obtained using the FLDA combination of A&T or the triplet FLDA of A&T&W for the classification of venous and arterial vessels. The best single parameter was T with an AUC of 0.871 (accuracy = 79.0 %), which performed significantly worse than the combination A&T ( p  < 0.001). Conclusions Arteries and veins can be accurately differentiated based on dynamic CT perfusion data using the maximum of the time-density curve, its time-to-peak, its width, and FLDA combinations of these parameters, which yield accuracies up to 87 %. Key points • For classification of cerebral vasculature, time-to-peak has the best single-parameter accuracy. • Fisher’s linear discriminant analysis improves the performance of the individual classifiers. • Combining signal maximum and time-to-peak parameters significantly increased the classifying potential. • Pre-processing of time-density-curves by Gaussian filtering or fitting can improve diagnostic accuracy.