The high soft-tissue contrast of MRI and the absence of ionizing radiation make it a valuable tool for assessment of body pathology in children. Infants and young children are often unable to ...cooperate with awake MRI so sedation or general anesthesia might be required. However, given recent data on the costs and potential risks of anesthesia in young children, there is a need to try to decrease or avoid sedation in this population when possible. Child life specialists in radiology frequently use behavioral techniques and audiovisual support devices, and they practice with children and families using mock scanners to improve child compliance with MRI. Optimization of the MR scanner environment is also important to create a child-friendly space. If the child can remain inside the MRI scanner, a variety of emerging techniques can reduce the effect of involuntary motion. Using sequences with short acquisition times such as single-shot fast spin echo and volumetric gradient echo can decrease artifacts and improve image quality. Breath-holding, respiratory triggering and signal averaging all reduce respiratory motion. Emerging techniques such as radial and multislice k-space acquisition, navigator motion correction, as well as parallel imaging and compressed sensing reconstruction methods can further accelerate acquisition and decrease motion. Collaboration among radiologists, anesthesiologists, technologists, child life specialists and families is crucial for successful performance of MRI in young children.
The interpretation and analysis of Magnetic resonance imaging (MRI) benefit from high spatial resolution. Unfortunately, direct acquisition of high spatial resolution MRI is time-consuming and ...costly, which increases the potential for motion artifact, and suffers from reduced signal-to-noise ratio (SNR). Super-resolution reconstruction (SRR) is one of the most widely used methods in MRI since it allows for the trade-off between high spatial resolution, high SNR, and reduced scan times. Deep learning has emerged for improved SRR as compared to conventional methods. However, current deep learning-based SRR methods require large-scale training datasets of high-resolution images, which are practically difficult to obtain at a suitable SNR. We sought to develop a methodology that allows for dataset-free deep learning-based SRR, through which to construct images with higher spatial resolution and of higher SNR than can be practically obtained by direct Fourier encoding. We developed a dataset-free learning method that leverages a generative neural network trained for each specific scan or set of scans, which in turn, allows for SRR tailored to the individual patient. With the SRR from three short duration scans, we achieved high quality brain MRI at an isotropic spatial resolution of 0.125 cubic mm with six minutes of imaging time for T2 contrast and an average increase of 7.2 dB (34.2%) in SNR to these short duration scans. Motion compensation was achieved by aligning the three short duration scans together. We assessed our technique on simulated MRI data and clinical data acquired from 15 subjects. Extensive experimental results demonstrate that our approach achieved superior results to state-of-the-art methods, while in parallel, performed at reduced cost as scans delivered with direct high-resolution acquisition.
•We propose a method to generate realistic data and parameter maps for fetal DW-MRI.•Our method uses data from fetuses and preterm newborns in a synergistic manner.•We use the generated data to train ...a deep learning model to predict color-FA.•On fetal test data, our method achieves significantly more accurate predictions.•Predictions of the deep learning model obtain higher scores from neuroanatomists.
Diffusion-weighted magnetic resonance imaging (DW-MRI) of fetal brain is challenged by frequent fetal motion and signal to noise ratio that is much lower than non-fetal imaging. As a result, accurate and robust parameter estimation in fetal DW-MRI remains an open problem. Recently, deep learning techniques have been successfully used for DW-MRI parameter estimation in non-fetal subjects. However, none of those prior works has addressed the fetal brain because obtaining reliable fetal training data is challenging. To address this problem, in this work we propose a novel methodology that utilizes fetal scans as well as scans from prematurely-born infants. High-quality newborn scans are used to estimate accurate maps of the parameter of interest. These parameter maps are then used to generate DW-MRI data that match the measurement scheme and noise distribution that are characteristic of fetal data. In order to demonstrate the effectiveness and reliability of the proposed data generation pipeline, we used the generated data to train a convolutional neural network (CNN) to estimate color fractional anisotropy (CFA). We evaluated the trained CNN on independent sets of fetal data in terms of reconstruction accuracy, precision, and expert assessment of reconstruction quality. Results showed significantly lower reconstruction error (n=100,p<0.001) and higher reconstruction precision (n=20,p<0.001) for the proposed machine learning pipeline compared with standard estimation methods. Expert assessments on 20 fetal test scans showed significantly better overall reconstruction quality (p<0.001) and more accurate reconstruction of 11 regions of interest (p<0.001) with the proposed method.
Fetal MRI is the modality of choice to study supratentorial brain malformations. To accurately interpret the MRI, the radiologist needs to understand the normal sequence of events that occurs during ...prenatal brain development; this includes familiarity with the processes of hemispheric cleavage, formation of interhemispheric commissures, neuro-glial proliferation and migration, and cortical folding. Disruption of these processes results in malformations observed on fetal MRI including holoprosencephaly, callosal agenesis, heterotopic gray matter, lissencephaly and other malformations of cortical development (focal cortical dysplasia, polymicrogyria). The radiologist should also be familiar with findings that have high association with specific conditions affecting the central nervous system or other organ systems. This review summarizes and illustrates common patterns of supratentorial brain malformations and emphasizes aspects that are important to patient care.
•We propose to use machine learning to estimate fiber orientation distribution (fODF).•Our model maps the resampled diffusion signal to fODF using a multilayer perceptron.•We develop methods for ...synthesizing reliable training data in-silico.•We show that the proposed method has important merits compared with existing methods.
Estimation of white matter fiber orientation distribution function (fODF) is the essential first step for reliable brain tractography and connectivity analysis. Most of the existing fODF estimation methods rely on sub-optimal physical models of the diffusion signal or mathematical simplifications, which can impact the estimation accuracy. In this paper, we propose a data-driven method that avoids some of these pitfalls. Our proposed method is based on a multilayer perceptron that learns to map the diffusion-weighted measurements, interpolated onto a fixed spherical grid in the q space, to the target fODF. Importantly, we also propose methods for synthesizing reliable simulated training data. We show that the model can be effectively trained with simulated or real training data. Our phantom experiments show that the proposed method results in more accurate fODF estimation and tractography than several competing methods including the multi-tensor model, Bayesian estimation, spherical deconvolution, and two other machine learning techniques. On real data, we compare our method with other techniques in terms of accuracy of estimating the ground-truth fODF. The results show that our method is more accurate than other methods, and that it performs better than the competing methods when applied to under-sampled diffusion measurements. We also compare our method with the Sparse Fascicle Model in terms of expert ratings of the accuracy of reconstruction of several commissural, projection, association, and cerebellar tracts. The results show that the tracts reconstructed with the proposed method are rated significantly higher by three independent experts. Our study demonstrates the potential of data-driven methods for improving the accuracy and robustness of fODF estimation.
Given the increasing use of MRI in the pediatric population, the need for sedation in MRI performed in young children is a topic of growing importance. Although sedation is generally tolerated well ...by children, the financial and operational impacts of anesthesia on MRI workflow, as well as potential adverse effects of anesthetic medications, highlight the need to perform MRI in children without sedation whenever possible. This review focuses on current techniques to facilitate non-sedation MRI in children, including exam preparation with MRI simulation; asleep but not sedated techniques; awake and relaxed techniques using certified child life specialists, animal-assisted therapy, a child-friendly environment and in-scan entertainment; and non-sedated MRI protocol modifications such as shorter scan time, prioritizing sequences, reducing motion artifact, noise reduction, limiting use of gadolinium, employing an open MRI and modifying protocols.
Currently, most CNS tumors require tissue sampling to discern their molecular/genomic landscape. However, growing research has shown the powerful role imaging can play in non-invasively and ...accurately detecting the molecular signature of these tumors. The overarching theme of this review article is to provide neuroradiologists and neurooncologists with a framework of several important molecular markers, their associated imaging features and the accuracy of those features. A particular emphasis is placed on those tumors and mutations that have specific or promising imaging correlates as well as their respective therapeutic potentials.
Fetal MRI allows for earlier and better detection of complex congenital anomalies. However, its diagnostic utility is often limited by technical barriers that introduce artifacts and reduce image ...quality. The main determinants of fetal MR image quality are speed of acquisition, spatial resolution and signal-to-noise ratio (SNR). Imaging optimization is a challenge because a change to improve one scan parameter often leads to worsening of another. Moreover, the recent introduction of fetal MRI on 3-tesla (T) scanners to achieve better SNR can amplify some technical issues. Motion, banding artifacts and aliasing artifacts impact the quality of fetal acquisitions at any field strength. High specific absorption rate (SAR) and artifacts from inhomogeneities in the radiofrequency field are important limitations of high-field-strength imaging. We discuss technical barriers that impact image quality and are important limitations to prenatal MRI diagnosis, and propose solutions to improve image quality and reduce artifacts.
Background
Faster and motion robust magnetic resonance imaging (MRI) sequences are desirable in pediatric brain MRI as they can help reduce the need for monitored anesthesia care, which is a costly ...and limited resource that carries medical risks.
Objective
To evaluate the diagnostic equivalency of commercially available accelerated motion robust MR sequences relative to standard sequences.
Materials and methods
This was an institutional review board-approved prospective study. Subjects underwent a clinical brain MRI using conventional multiplanar images at 3 Tesla followed by fast axial T2 and FLAIR (fluid-attenuated inversion recovery) sequences optimized for an approximately 50% reduction in acquisition time. Conventional and fast images from each subject were reviewed by two blinded pediatric neuroradiologists. The readers evaluated the presence of 12 findings. Intra-observer agreement was estimated for fast versus conventional sequences. For each set of sequences, interobserver agreement calculations and chi-square tests were used to evaluate differences between fast and conventional acquisitions. An independent third reader reviewed the intra-observer discrepancies and adjudicated them as being more conspicuous on fast sequence, conventional sequence or the equivalent. The readers also were asked to rate motion artifacts with a previously validated score.
Results
Images from 77 children (mean age: 11.3 years) were analyzed. Intra-observer agreement (fast versus conventional) ranged between 89.2% and 92.3%. Interobserver agreement ranged between 86.1% and 88.4%. Interobserver agreement was significantly higher for conventional FLAIR relative to fast FLAIR for small (<5 mm) foci of T2 in the white matter. Otherwise, interobserver agreement was not different between the fast and conventional sequences. For awake subjects, fast sequences had significantly fewer artifacts (
P
<0.05).
Conclusion
Conventional T2 and FLAIR sequences can be optimized to shorten acquisition while maintaining diagnostic equivalency. These faster sequences were also less susceptible to motion artifacts.
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