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Abstract Background New digital PET scanners with improved time of flight timing and extended axial field of view such as the Siemens Biograph Vision have come on the market and are expected to replace current generation photomultiplier tube (PMT)-based systems such as the Siemens Biograph mCT. These replacements warrant a direct comparison between the systems, so that a smooth transition in clinical practice and research is guaranteed, especially when quantitative values are used for dosimetry-based treatment guidance. The new generation digital PET scanners offer increased sensitivity. This could particularly benefit 90Y imaging, which tends to be very noisy owing to the small positron branching ratio and high random fraction of 90Y. This study aims to determine the ideal reconstruction settings for the digital Vision for quantitative 90Y imaging and to evaluate the image quality and quantification of the digital Vision in comparison with its predecessor, the PMT-based mCT, for 90Y imaging in radioembolisation procedures. Methods The NEMA image quality phantom was scanned to determine the ideal reconstruction settings for the Vision. In addition, an anthropomorphic phantom was scanned with both the Vision and the mCT, mimicking a radioembolisation patient with lung, liver, tumour, and extrahepatic deposition inserts. Image quantification of the anthropomorphic phantom was assessed by the lung shunt fraction, the tumour to non-tumour ratio, the parenchymal dose, and the contrast to noise ratio of extrahepatic depositions. Results For the Vision, a reconstruction with 3 iterations, 5 subsets, and no post-reconstruction filter is recommended for quantitative 90Y imaging, based on the convergence of the recovery coefficient. Comparing both systems showed that the noise level of the Vision is significantly lower than that of the mCT (background variability of 14% for the Vision and 25% for the mCT at 2.5·103 MBq for the 37 mm sphere size). For quantitative 90Y measures, such as needed in radioembolisation, both systems perform similarly. Conclusions We recommend to reconstruct 90Y images acquired on the Vision with 3 iterations, 5 subsets, and no post-reconstruction filter for quantitative imaging. The Vision provides a reduced noise level, but similar quantitative accuracy as compared with its predecessor the mCT.

Abstract Background Monte Carlo-based iterative reconstruction to correct for photon scatter and collimator effects has been proven to be superior over analytical correction schemes in single-photon emission computed tomography (SPECT/CT), but it is currently not commonly used in daily clinical practice due to the long associated reconstruction times. We propose to use a convolutional neural network (CNN) to upgrade fast filtered back projection (FBP) image quality so that reconstructions comparable in quality to the Monte Carlo-based reconstruction can be obtained within seconds. Results A total of 128 technetium-99m macroaggregated albumin pre-treatment SPECT/CT scans used to guide hepatic radioembolization were available. Four reconstruction methods were compared: FBP, clinical reconstruction, Monte Carlo-based reconstruction, and the neural network approach. The CNN generated reconstructions in 5 sec, whereas clinical reconstruction took 5 min and the Monte Carlo-based reconstruction took 19 min. The mean squared error of the neural network approach in the validation set was between that of the Monte Carlo-based and clinical reconstruction, and the lung shunting fraction difference was lower than 2 percent point. A phantom experiment showed that quantitative measures required in radioembolization were accurately retrieved from the CNN-generated reconstructions. Conclusions FBP with an image enhancement neural network provides SPECT reconstructions with quality close to that obtained with Monte Carlo-based reconstruction within seconds.

Spherical deconvolution is a widely used approach to quantify the fiber orientation distribution (FOD) from diffusion MRI data of the brain. The damped Richardson-Lucy (dRL) is an algorithm developed to perform robust spherical deconvolution on single-shell diffusion MRI data while suppressing spurious FOD peaks due to noise or partial volume effects. Due to recent progress in acquisition hardware and scanning protocols, it is becoming increasingly common to acquire multi-shell diffusion MRI data, which allows for the modelling of multiple tissue types beyond white matter. While the dRL algorithm could, in theory, be directly applied to multi-shell data, it is not optimised to exploit its information content to model the signal from multiple tissue types. In this work, we introduce a new framework based on dRL – dubbed generalized Richardson-Lucy (GRL) – that uses multi-shell data in combination with user-chosen tissue models to disentangle partial volume effects and increase the accuracy in FOD estimation. Further, GRL estimates signal fraction maps associated to each user-selected model, which can be used during fiber tractography to dissect and terminate the tracking without need for additional structural data. The optimal weighting of multi-shell data in the fit and the robustness to noise and to partial volume effects of GRL was studied with synthetic data. Subsequently, we investigated the performance of GRL in comparison to dRL and to multi-shell constrained spherical deconvolution (MSCSD) on a high-resolution diffusion MRI dataset from the Human Connectome Project and on an MRI dataset acquired at 3T on a clinical scanner. In line with previous studies, we described the signal of the cerebrospinal-fluid and of the grey matter with isotropic diffusion models, whereas four diffusion models were considered to describe the white matter. With a third dataset including small diffusion weightings, we studied the feasibility of including intra-voxel incoherent motion effects due to blood pseudo-diffusion in the modelling. Further, the reliability of GRL was demonstrated with a test-retest scan of a subject acquired at 3T. Results of simulations show that GRL can robustly disentangle different tissue types at SNR above 20 with respect to the non-weighted image, and that it improves the angular accuracy of the FOD estimation as compared to dRL. On real data, GRL provides signal fraction maps that are physiologically plausible and consistent with those obtained with MSCSD, with correlation coefficients between the two methods up to 0.96. When considering IVIM effects, a high blood pseudo-diffusion fraction is observed in the medial temporal lobe and in the sagittal sinus. In comparison to dRL and MSCSD, GRL provided sharper FODs and less spurious peaks in presence of partial volume effects, but the FOD reconstructions are also highly dependent on the chosen modelling of white matter. When performing fiber tractography, GRL allows to terminate fiber tractography using the signal fraction maps, which results in a better tract termination at the grey-white matter interface or at the outer cortical surface. In terms of inter-scan reliability, GRL performed similarly to or better than compared methods. In conclusion, GRL offers a new modular and flexible framework to perform spherical deconvolution of multi-shell data.

Displacement Encoding with Stimulated Echoes (DENSE) has recently shown potential for measuring cardiac-induced cerebral volumetric strain in the human brain. As such, it may provide a powerful tool for investigating the cerebral small vessels. However, further development and validation are necessary. This study aims, first, to validate a retrospectively-gated implementation of the DENSE method for assessing brain tissue pulsations as a physiological marker, and second, to use the acquired measurements to explore intracranial volume dynamics. We acquired repeated measurements of cerebral volumetric strain in 8 healthy subjects, and internally validated these measurements by comparing them to spinal CSF stroke volumes obtained in the same scan session.Peak volumetric strain was found to be highly repeatable between scan sessions. First/second measured peak volumetric strains were: (6.4 ​± ​1.7)x10−4/(6.7 ​± ​1.6)x10−4 for whole brain, (9.5 ​± ​2.5)x10−4/(9.6 ​± ​2.4)x10−4 for grey matter, and (4.4 ​± ​1.7)x10−4/(4.1 ​± ​0.8)x10−4 for white matter. Grey matter showed significantly higher peak strain (p ​

Nonlinearities of gradient magnetic fields in diffusion MRI (dMRI) can introduce systematic errors in estimates of diffusion measures. While there are correction methods that can compensate for these errors, as presented in the Human Connectome Project, such nonlinear effects are often assumed to be negligible for typical applications, and as a result, gradient nonlinearities are mostly left uncorrected. In this work, we perform a systematic analysis to investigate the effect of gradient nonlinearities on dMRI studies, from voxel-wise estimates to group study outcomes. We present a novel framework to quantify and visualize these effects by decomposing them into their magnitude and angle components. Mean magnitude deviation and fractional gradient anisotropy are introduced to quantify the distortions in the size and shape of gradient vector distributions. By means of Monte-Carlo simulations and real data from the Human Connectome Project, the errors on dMRI measures derived from the diffusion tensor imaging and diffusional kurtosis imaging are highlighted. We perform a group study to showcase the alteration in the significance and effect size due to ignoring gradient nonlinearity correction. Our results indicate that the effect of gradient field nonlinearities on dMRI studies can be significant and may complicate the interpretation of the results and conclusions.

Abstract Background Generation of a SPECT scan during procedure may aid in the optimization of treatments as liver radioembolization by offering image-guided dosimetry. This, however, requires both shortened acquisition times and fast quantitative reconstruction. Focusing collimators increase sensitivity and thus may speed up imaging. Monte Carlo-based iterative reconstruction has shown to provide quantitative results for parallel hole collimators but may be slow. The purpose of this work is to develop fast Monte Carlo-based reconstruction for focusing collimators and to evaluate the impact of reconstruction and collimator choice on quantitative accuracy of liver dosimetry by means of simulations. Results The developed fast Monte Carlo simulator was found to accurately generate projections compared to a full Monte Carlo simulation, providing projections in several seconds instead of several days. Monte Carlo-based scatter correction was superior to other scatter correction methods in describing recovered activity and reached similar noise levels as dual-energy window scatter correction. Although truncation artifacts were present in the cone beam collimator (50 cm), the region inside the field of view (FOV) could be reconstructed without loss of accuracy. Provided the object to image is inside the FOV, the focusing collimator with 50 cm focal distance could retrieve the same noise levels as a parallel hole collimator in 68% of the total scanning time, the multifocal collimator in 73% of the time, and the 100-cm focal distance collimator in 84% of the time. Conclusion Focusing collimators combined with Monte Carlo-based reconstruction have the ability to enable quantitative imaging of the FOV in a significantly shorter timeframe. The proposed approach to the forward projector will additionally make it possible to reconstruct within minutes. These are crucial steps in moving toward real-time dosimetry during interventions.

Abstract This study investigates the predictive ability of automatic quantitative brain MRI descriptors for the identification of infants with low cognitive and/or motor outcome at 2–3 years chronological age. MR brain images of 173 patients were acquired at 30 weeks postmenstrual age (PMA) (n = 86) and 40 weeks PMA (n = 153) between 2008 and 2013. Eight tissue volumes and measures of cortical morphology were automatically computed. A support vector machine classifier was employed to identify infants who exhibit low cognitive and/or motor outcome (

A fully automatic method for detection and quantification of ischemic lesions in diffusion-weighted MR images of neonatal hypoxic ischemic encephalopathy (HIE) is presented. Ischemic lesions are manually segmented by two independent observers in 1.5 T data from 20 subjects and an automatic algorithm using a random forest classifier is developed and trained on the annotations of observer 1. The algorithm obtains a median sensitivity and specificity of 0.72 and 0.99 respectively. F1-scores are calculated per subject for algorithm performance (median = 0.52) and observer 2 performance (median = 0.56). A paired t-test on the F1-scores shows no statistical difference between the algorithm and observer 2 performances. The method is applied to a larger dataset including 54 additional subjects scanned at both 1.5 T and 3.0 T. The algorithm findings are shown to correspond well with the injury pattern noted by clinicians in both 1.5 T and 3.0 T data and to have a strong relationship with outcome. The results of the automatic method are condensed to a single score for each subject which has significant correlation with an MR score assigned by experienced clinicians (p < 0.0001). This work represents a quantitative method of evaluating diffusion-weighted MR images in neonatal HIE and a first step in the development of an automatic system for more in-depth analysis and prognostication.

Fiber tractography (FT) using diffusion magnetic resonance imaging (dMRI) is widely used for investigating microstructural properties of white matter (WM) fiber-bundles and for mapping structural connections of the human brain. While studying the architectural configuration of the brain’s circuitry with FT is not without controversy, recent progress in acquisition, processing, modeling, analysis, and visualization of dMRI data pushes forward the reliability in reconstructing WM pathways. Despite being aware of the well-known pitfalls in analyzing dMRI data and several other limitations of FT discussed in recent literature, we present the superoanterior fasciculus (SAF), a novel bilateral fiber tract in the frontal region of the human brain that—to the best of our knowledge—has not been documented. The SAF has a similar shape to the anterior part of the cingulum bundle, but it is located more frontally. To minimize the possibility that these FT findings are based on acquisition or processing artifacts, different dMRI data sets and processing pipelines have been used to describe the SAF. Furthermore, we evaluated the configuration of the SAF with complementary methods, such as polarized light imaging (PLI) and human brain dissections. The FT results of the SAF demonstrate a long pathway, consistent across individuals, while the human dissections indicate fiber pathways connecting the postero-dorsal with the antero-dorsal cortices of the frontal lobe.

Food choices constitute a classic self-control dilemma involving the trade-off between immediate eating enjoyment and the long term goal of being slim and healthy, especially for weight-concerned women. For them, decision-making concerning high (HE) and low energy (LE) snacks differs when it comes to the need for self-control. In line, our first study aim was to investigate which brain regions are activated during food choices during HE compared to LE energy snacks in weight-concerned women. Since it is particularly difficult to resist HE snacks when they are very tasty, our second aim was to investigate in which brain regions choice-related activation varies with the food’s tastiness. Our third aim was to assess in which brain regions choice-related activation varies with individual differences in self-regulatory success. To this end, 20 weight-concerned women indicated for 100 HE or LE snacks whether they wanted to eat them or not, while their brains were scanned using fMRI. HE snacks were refused more often than equally-liked LE snacks. HE snack choice elicited stronger activation in reward-related brain regions (medial to middle orbitofrontal cortex (OFC), caudate). Highly tasty HE snacks were more difficult to resist and, accordingly, activation in inhibitory areas (inferior frontal gyrus, lateral OFC) was negatively associated with tastiness. More successful self-controllers showed increased activation in the supplementary motor area during HE food choices. In sum, the results suggest that HE snacks constitute a higher reward for weight-concerned women compared to (equally-liked) LE snacks, and that activation during food choice in brain regions involved in response inhibition varied with tastiness and individual differences in self-regulatory success. These findings advance our understanding of the neural correlates of food choice and point to new avenues for investigating explanations for self-regulatory failure.

Infiltrated monocytes play a crucial role in the demyelination process during multiple sclerosis (MS), an inflammatory disease of the central nervous system (CNS). Still, methods to monitor their infiltration pattern over time are lacking. In this study, magnetoelectroporation (MEP) was used to label rat monocytes with the superparamagnetic iron oxide particles Sinerem, Endorem, and Supravist. Supravist-labeled monocytes were injected in rats that we induced with experimental autoimmune encephalomyelitis, a model for MS. Imaging at 4.7 and 9.4 T revealed multiple foci of decreased signal intensity predominantly located in the cerebellum. Immunohistochemical evaluation confirmed the presence of intracellular iron in infiltrated cells, indicating the suitability of MEP to specifically follow labeled monocytes in vivo in this disease model. This technique may be further optimized and potentially used in MS patients to assess monocyte migration into the brain and to monitor the efficacy of therapeutic agents aimed at blocking cellular migration into the CNS.