PURPOSE: Dynamic Contrast-Enhanced (DCE) MRI with 2nd generation pharmacokinetic models provides estimates of plasma flow and permeability surface-area product in contrast to the broadly used 1st generation models (e.g. the Tofts models). However, the use of 2nd generation models requires higher frequency with which the dynamic images are acquired (around 1.5 s per image). Blind deconvolution can decrease the demands on temporal resolution as shown previously for one of the 1st generation models. Here, the temporal-resolution requirements achievable for blind deconvolution with a 2nd generation model are studied. METHODS: The 2nd generation model is formulated as the distributed-capillary adiabatic-tissue-homogeneity (DCATH) model. Blind deconvolution is based on Parker's model of the arterial input function. The accuracy and precision of the estimated arterial input functions and the perfusion parameters is evaluated on synthetic and real clinical datasets with different levels of the temporal resolution. RESULTS: The estimated arterial input functions remained unchanged from their reference high-temporal-resolution estimates (obtained with the sampling interval around 1 s) when increasing the sampling interval up to about 5 s for synthetic data and up to 3.6-4.8 s for real data. Further increasing of the sampling intervals led to systematic distortions, such as lowering and broadening of the 1st pass peak. The resulting perfusion-parameter estimation error was below 10% for the sampling intervals up to 3 s (synthetic data), in line with the real data perfusion-parameter boxplots which remained unchanged up to the sampling interval 3.6 s. CONCLUSION: We show that use of blind deconvolution decreases the demands on temporal resolution in DCE-MRI from about 1.5 s (in case of measured arterial input functions) to 3-4 s. This can be exploited in increased spatial resolution or larger organ coverage.

• Keywords
• 2nd generation pharmacokinetic model, Blind deconvolution, DCE-MRI, Temporal resolution,
• MeSH
• Algorithms MeSH
• Time Factors MeSH
• Contrast Media * pharmacokinetics   MeSH
• Magnetic Resonance Imaging * methods   MeSH
• Perfusion MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• Contrast Media * MeSH

The present trend in dynamic contrast-enhanced MRI is to increase the number of estimated perfusion parameters using complex pharmacokinetic models. However, less attention is given to the precision analysis of the parameter estimates. In this paper, the distributed capillary adiabatic tissue homogeneity pharmacokinetic model is extended by the bolus arrival time formulated as a free continuous parameter. With the continuous formulation of all perfusion parameters, it is possible to use standard gradient-based optimization algorithms in the approximation of the tissue concentration time sequences. This new six-parameter model is investigated by comparing Monte-Carlo simulations with theoretically derived covariance matrices. The covariance-matrix approach is extended from the usual analysis of the primary perfusion parameters of the pharmacokinetic model to the analysis of the perfusion parameters derived from the primary ones. The results indicate that the precision of the estimated perfusion parameters can be described by the covariance matrix for signal-to-noise ratio higher than~20dB. The application of the new analysis model on a real DCE-MRI data set is also presented.

• Keywords
• Bolus arrival time, Dynamic contrast-enhanced MRI (DCE-MRI), Parameter estimation, Perfusion,
• MeSH
• Algorithms MeSH
• Image Interpretation, Computer-Assisted methods   MeSH
• Contrast Media pharmacokinetics   MeSH
• Humans MeSH
• Magnetic Resonance Angiography methods   MeSH
• Models, Cardiovascular * MeSH
• Prostatic Neoplasms diagnosis   physiopathology   MeSH
• Computer Simulation MeSH
• Reproducibility of Results MeSH
• Blood Flow Velocity MeSH
• Sensitivity and Specificity MeSH
• Image Enhancement methods   MeSH
• Check Tag
• Humans MeSH
• Male MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Contrast Media MeSH

The complex transverse water proton magnetization subject to diffusion-encoding magnetic field gradient pulses in a heterogeneous medium can be modeled by the multiple compartment Bloch-Torrey partial differential equation. Under the assumption of negligible water exchange between compartments, the time-dependent apparent diffusion coefficient can be directly computed from the solution of a diffusion equation subject to a time-dependent Neumann boundary condition. This paper describes a publicly available MATLAB toolbox called SpinDoctor that can be used 1) to solve the Bloch-Torrey partial differential equation in order to simulate the diffusion magnetic resonance imaging signal; 2) to solve a diffusion partial differential equation to obtain directly the apparent diffusion coefficient; 3) to compare the simulated apparent diffusion coefficient with a short-time approximation formula. The partial differential equations are solved by P1 finite elements combined with built-in MATLAB routines for solving ordinary differential equations. The finite element mesh generation is performed using an external package called Tetgen. SpinDoctor provides built-in options of including 1) spherical cells with a nucleus; 2) cylindrical cells with a myelin layer; 3) an extra-cellular space enclosed either a) in a box or b) in a tight wrapping around the cells; 4) deformation of canonical cells by bending and twisting; 5) permeable membranes; Built-in diffusion-encoding pulse sequences include the Pulsed Gradient Spin Echo and the Oscillating Gradient Spin Echo. We describe in detail how to use the SpinDoctor toolbox. We validate SpinDoctor simulations using reference signals computed by the Matrix Formalism method. We compare the accuracy and computational time of SpinDoctor simulations with Monte-Carlo simulations and show significant speed-up of SpinDoctor over Monte-Carlo simulations in complex geometries. We also illustrate several extensions of SpinDoctor functionalities, including the incorporation of T2 relaxation, the simulation of non-standard diffusion-encoding sequences, as well as the use of externally generated geometrical meshes.

• Keywords
• Apparent diffusion coefficient, Bloch-torrey equation, Diffusion magnetic resonance imaging, Finite elements, Simulation,
• MeSH
• Diffusion Magnetic Resonance Imaging methods   MeSH
• Humans MeSH
• Brain * MeSH
• Neuroimaging methods   MeSH
• Computer Simulation MeSH
• Software * MeSH
• Models, Theoretical * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

PURPOSE: The Tofts and the extended Tofts models are the pharmacokinetic models commonly used in dynamic contrast-enhanced MRI (DCE-MRI) perfusion analysis, although they do not provide two important biological markers, namely, the plasma flow and the permeability-surface area product. Estimates of such markers are possible using advanced pharmacokinetic models describing the vascular distribution phase, such as the tissue homogeneity model. However, the disadvantage of the advanced models lies in biased and uncertain estimates, especially when the estimates are computed voxelwise. The goal of this work is to improve the reliability of the estimates by including information from neighboring voxels. THEORY AND METHODS: Information from the neighboring voxels is incorporated in the estimation process through spatial regularization in the form of total variation. The spatial regularization is applied on five maps of perfusion parameters estimated using the tissue homogeneity model. Since the total variation is not differentiable, two proximal techniques of convex optimization are used to solve the problem numerically. RESULTS: The proposed algorithm helps to reduce noise in the estimated perfusion-parameter maps together with improving accuracy of the estimates. These conclusions are proved using a numerical phantom. In addition, experiments on real data show improved spatial consistency and readability of perfusion maps without considerable lowering of the quality of fit. CONCLUSION: The reliability of the DCE-MRI perfusion analysis using the tissue homogeneity model can be improved by employing spatial regularization. The proposed utilization of modern optimization techniques implies only slightly higher computational costs compared to the standard approach without spatial regularization.

• Keywords
• DCE-MRI, perfusion parameter estimation, proximal methods, spatial regularization, tissue homogeneity model, total variation,
• MeSH
• Algorithms MeSH
• Phantoms, Imaging MeSH
• Glioblastoma diagnostic imaging   MeSH
• Contrast Media pharmacology   MeSH
• Rats MeSH
• Magnetic Resonance Imaging * MeSH
• Brain diagnostic imaging   MeSH
• Brain Neoplasms diagnostic imaging   MeSH
• Perfusion MeSH
• Permeability MeSH
• Computer Simulation MeSH
• Image Processing, Computer-Assisted MeSH
• Signal-To-Noise Ratio MeSH
• Reproducibility of Results MeSH
• Animals MeSH
• Check Tag
• Rats MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Contrast Media MeSH

A flexible transceiver array based on transmission line resonators (TLRs) combining the advantages of coil arrays with the possibility of form-fitting targeting cardiac MRI at 7 T is presented. The design contains 12 elements which are fabricated on a flexible substrate with rigid PCBs attached on the center of each element to place the interface components, i.e. transmit/receive (T/R) switch, power splitter, pre-amplifier and capacitive tuning/matching circuitry. The mutual coupling between elements is cancelled using a decoupling ring-based technique. The performance of the developed array is evaluated by 3D electromagnetic simulations, bench tests, and MR measurements using phantoms. Efficient inter-element decoupling is demonstrated in flat configuration on a box-shaped phantom (Sij < -19 dB), and bent on a human torso phantom (Sij < -16 dB). Acceleration factors up to 3 can be employed in bent configuration with reasonable g-factors (<1.7) in an ROI at the position of the heart. The array enables geometrical conformity to bodies within a large range of size and shape and is compatible with parallel imaging and parallel transmission techniques.

• Keywords
• Mechanical flexibility, RF coil, Transceiver coil, Transmission line resonators, Ultra high field MRI,
• MeSH
• Electromagnetic Fields MeSH
• Phantoms, Imaging MeSH
• Humans MeSH
• Magnetic Resonance Imaging instrumentation   MeSH
• Computer Simulation MeSH
• Signal-To-Noise Ratio MeSH
• Radio Waves MeSH
• Heart diagnostic imaging   MeSH
• Torso diagnostic imaging   MeSH
• Image Enhancement MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

PURPOSE: One of the main obstacles for reliable quantitative dynamic contrast-enhanced (DCE) MRI is the need for accurate knowledge of the arterial input function (AIF). This is a special challenge for preclinical small animal applications where it is very difficult to measure the AIF without partial volume and flow artifacts. Furthermore, using advanced pharmacokinetic models (allowing estimation of blood flow and permeability-surface area product in addition to the classical perfusion parameters) poses stricter requirements on the accuracy and precision of AIF estimation. This paper addresses small animal DCE-MRI with advanced pharmacokinetic models and presents a method for estimation of the AIF based on blind deconvolution. METHODS: A parametric AIF model designed for small animal physiology and use of advanced pharmacokinetic models is proposed. The parameters of the AIF are estimated using multichannel blind deconvolution. RESULTS: Evaluation on simulated data show that for realistic signal to noise ratios blind deconvolution AIF estimation leads to comparable results as the use of the true AIF. Evaluation on real data based on DCE-MRI with two contrast agents of different molecular weights showed a consistence with the known effects of the molecular weight. CONCLUSION: Multi-channel blind deconvolution using the proposed AIF model specific for small animal DCE-MRI provides reliable perfusion parameter estimates under realistic signal to noise conditions.

• Keywords
• Arterial input function, Blind deconvolution, DCE-MRI,
• MeSH
• Algorithms MeSH
• Arteries diagnostic imaging   MeSH
• Pharmacokinetics MeSH
• Contrast Media pharmacokinetics   MeSH
• Humans MeSH
• Magnetic Resonance Imaging * MeSH
• Mice, Inbred BALB C MeSH
• Mice MeSH
• Necrosis pathology   MeSH
• Perfusion MeSH
• Computer Simulation MeSH
• Image Processing, Computer-Assisted methods   MeSH
• Signal-To-Noise Ratio MeSH
• Regression Analysis MeSH
• Reproducibility of Results MeSH
• Animals MeSH
• Check Tag
• Humans MeSH
• Mice MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Contrast Media MeSH

PURPOSE: Dual velocity encoding PC-MRI can produce spurious artifacts when using high ratios of velocity encoding values (VENCs), limiting its ability to generate high-quality images across a wide range of encoding velocities. This study aims to propose and compare dual-VENC correction methods for such artifacts. THEORY AND METHODS: Two denoising approaches based on spatiotemporal regularization are proposed and compared with a state-of-the-art method based on sign correction. Accuracy is assessed using simulated data from an aorta and brain aneurysm, as well as 8 two-dimensional (2D) PC-MRI ascending aorta datasets. Two temporal resolutions (30,60) ms and noise levels (9,12) dB are considered, with noise added to the complex magnetization. The error is evaluated with respect to the noise-free measurement in the synthetic case and to the unwrapped image without additional noise in the volunteer datasets. RESULTS: In all studied cases, the proposed methods are more accurate than the Sign Correction technique. Using simulated 2D+T data from the aorta (60 ms, 9 dB), the Dual-VENC (DV) error 0 . 82 ± 0 . 07 $$ 0.82\pm 0.07 $$ is reduced to: 0 . 66 ± 0 . 04 $$ 0.66\pm 0.04 $$ (Sign Correction); 0 . 34 ± 0 . 04 $$ 0.34\pm 0.04 $$ and 0 . 32 ± 0 . 04 $$ 0.32\pm 0.04 $$ (proposed techniques). The methods are found to be significantly different (p-value < 0 . 05 $$ <0.05 $$ ). Importantly, brain aneurysm data revealed that the Sign Correction method is not suitable, as it increases error when the flow is not unidirectional. All three methods improve the accuracy of in vivo data. CONCLUSION: The newly proposed methods outperform the Sign Correction method in improving dual-VENC PC-MRI images. Among them, the approach based on temporal differences has shown the highest accuracy.

• Keywords
• denoising, dual‐VENC, phase‐contrast MRI,
• MeSH
• Algorithms * MeSH
• Aorta * diagnostic imaging   MeSH
• Artifacts * MeSH
• Phantoms, Imaging MeSH
• Image Interpretation, Computer-Assisted methods   MeSH
• Intracranial Aneurysm diagnostic imaging   MeSH
• Humans MeSH
• Magnetic Resonance Imaging * methods   MeSH
• Brain diagnostic imaging   MeSH
• Computer Simulation MeSH
• Image Processing, Computer-Assisted * methods   MeSH
• Signal-To-Noise Ratio * MeSH
• Reproducibility of Results MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH

Deep learning (DL) methods have in recent years yielded impressive results in medical imaging, with the potential to function as clinical aid to radiologists. However, DL models in medical imaging are often trained on public research cohorts with images acquired with a single scanner or with strict protocol harmonization, which is not representative of a clinical setting. The aim of this study was to investigate how well a DL model performs in unseen clinical datasets-collected with different scanners, protocols and disease populations-and whether more heterogeneous training data improves generalization. In total, 3117 MRI scans of brains from multiple dementia research cohorts and memory clinics, that had been visually rated by a neuroradiologist according to Scheltens' scale of medial temporal atrophy (MTA), were included in this study. By training multiple versions of a convolutional neural network on different subsets of this data to predict MTA ratings, we assessed the impact of including images from a wider distribution during training had on performance in external memory clinic data. Our results showed that our model generalized well to datasets acquired with similar protocols as the training data, but substantially worse in clinical cohorts with visibly different tissue contrasts in the images. This implies that future DL studies investigating performance in out-of-distribution (OOD) MRI data need to assess multiple external cohorts for reliable results. Further, by including data from a wider range of scanners and protocols the performance improved in OOD data, which suggests that more heterogeneous training data makes the model generalize better. To conclude, this is the most comprehensive study to date investigating the domain shift in deep learning on MRI data, and we advocate rigorous evaluation of DL models on clinical data prior to being certified for deployment.

• Keywords
• Clinical application, Deep learning, Domain shift, Neuroimaging,
• MeSH
• Deep Learning * MeSH
• Humans MeSH
• Magnetic Resonance Imaging MeSH
• Brain diagnostic imaging   MeSH
• Neural Networks, Computer MeSH
• Reproducibility of Results MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Research Support, N.I.H., Extramural MeSH

BACKGROUND AND PURPOSE: One of the hallmarks of schizophrenia is altered brain structure, potentially due to antipsychotic treatment, the disorder itself or both. It was proposed that functional changes may precede the structural ones. In order to understand and potentially prevent this unwanted process, brain function assessment should be validated as a diagnostic tool. METHODS: We used Arterial Spin Labelling MRI technique for the evaluation of brain perfusion in several brain regions in a neurodevelopmental poly(I:C) model of schizophrenia (8mg/kg on a gestational day 15) in rats taking into account sex-dependent effects and chronic treatment with aripiprazole (30days), an atypical antipsychotic acting as a partial agonist on dopaminergic receptors. RESULTS: We found the sex of the animal to have a highly significant effect in all regions of interest, with females showing lower blood perfusion than males. However, both males and females treated prenatally with poly(I:C) showed enlargement of the lateral ventricles. Furthermore, we detected increased perfusion in the circle of Willis, hippocampus, and sensorimotor cortex, which was not influenced by chronic atypical antipsychotic aripiprazole treatment in male poly(I:C) rats. CONCLUSION: We hypothesize that perfusion alterations may be caused by the hyperdopaminergic activity in the poly(I:C) model, and the absence of aripiprazole effect on perfusion in brain regions related to schizophrenia may be due to its partial agonistic mechanism.

• Keywords
• Aripiprazole *, Arterial Spin Labelling *, MRI *, Schizophrenia *, Sex *, Wistar rats *,
• MeSH
• Antipsychotic Agents pharmacology   MeSH
• Aripiprazole pharmacology   MeSH
• Magnetic Resonance Imaging MeSH
• Disease Models, Animal MeSH
• Brain diagnostic imaging   drug effects   physiopathology   MeSH
• Cerebrovascular Circulation drug effects   physiology   MeSH
• Random Allocation MeSH
• Sex Characteristics * MeSH
• Poly I-C MeSH
• Rats, Wistar MeSH
• Schizophrenia diagnostic imaging   drug therapy   physiopathology   MeSH
• Pregnancy MeSH
• Prenatal Exposure Delayed Effects MeSH
• Animals MeSH
• Check Tag
• Male MeSH
• Pregnancy MeSH
• Female MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Antipsychotic Agents MeSH
• Aripiprazole MeSH
• Poly I-C MeSH

Image registration methods play a crucial role in computational neuroanatomy. This paper mainly contributes to the field of image registration with the use of nonlinear spatial transformations. Particularly, problems connected to matching magnetic resonance imaging (MRI) brain image data obtained from various subjects and with various imaging conditions are solved here. Registration is driven by local forces derived from multimodal point similarity measures which are estimated with the use of joint intensity histogram and tissue probability maps. A spatial deformation model imitating principles of continuum mechanics is used. Five similarity measures are tested in an experiment with image data obtained from the Simulated Brain Database and a quantitative evaluation of the algorithm is presented. Results of application of the method in automated spatial detection of anatomical abnormalities in first-episode schizophrenia are presented.

• MeSH
• Algorithms * MeSH
• Image Interpretation, Computer-Assisted methods   MeSH
• Humans MeSH
• Magnetic Resonance Imaging methods   MeSH
• Models, Neurological MeSH
• Brain anatomy & histology   physiology   MeSH
• Neuroanatomy methods   MeSH
• Neurology methods   MeSH
• Computer Simulation MeSH
• Elasticity MeSH
• Psychiatry methods   MeSH
• Reproducibility of Results MeSH
• Pattern Recognition, Automated methods   MeSH
• Sensitivity and Specificity MeSH
• Subtraction Technique MeSH
• Artificial Intelligence * MeSH
• Image Enhancement methods   MeSH
• Imaging, Three-Dimensional methods   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Evaluation Study MeSH
• Research Support, Non-U.S. Gov't MeSH