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Register a new userA Portable Brain MRI Scanner for Underserved Settings and Point-Of-Care Imaging
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Access to and availability of MRI scanners is typically limited by their cost, siting and infrastructure requirements. This precludes MRI diagnostics, the reference standard for neurological assessment, in patients who cannot be transported to specialized scanner suites. This includes patients who are critically ill and unstable, and patients located in low-resource settings. The scanner design presented here aims to extend the reach of MRI by substantially reducing these limitations. Our goal is to shift the cost-benefit calculation for MRI toward more frequent and varied use, including improved accessibility worldwide and point of care operation. Here, we describe a portable brain MRI scanner using a compact, lightweight permanent magnet, with a built-in readout field gradient. Our low-field (80 mT) Halbach cylinder design of rare-earth permanent magnets results in a 122 kg magnet with minimal stray-field, requiring neither cryogenics nor external power. The built-in magnetic field gradient reduces reliance on high-power gradient drivers, which not only lowers overall system power and cooling requirements, but also reduces acoustic noise. Imperfections in the encoding fields are mitigated with a generalized iterative image reconstruction technique, that uses prior characterization of the field patterns. Our system was validated using T1, T2 and proton density weighted in vivo brain images with a spatial resolution of 2.2 x 1.3 x 6.8 mm$^3$.
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Multi-shot echo planar imaging (msEPI) is a promising approach to achieve high in-plane resolution with high sampling efficiency and low T2* blurring. However, due to the geometric distortion, shot-to-shot phase variations and potential subject motion, msEPI continues to be a challenge in MRI. In this work, we introduce acquisition and reconstruction strategies for robust, high-quality msEPI without phase navigators. We propose Blip Up-Down Acquisition (BUDA) using interleaved blip-up and -down phase encoding, and incorporate B0 forward-modeling into Hankel structured low-rank model to enable distortion- and navigator-free msEPI. We improve the acquisition efficiency and reconstruction quality by incorporating simultaneous multi-slice acquisition and virtual-coil reconstruction into the BUDA technique. We further combine BUDA with the novel RF-encoded gSlider acquisition, dubbed BUDA-gSlider, to achieve rapid high isotropic-resolution MRI. Deploying BUDA-gSlider with model-based reconstruction allows for distortion-free whole-brain 1mm isotropic T2 mapping in about 1 minute. It also provides whole-brain 1mm isotropic diffusion imaging with high geometric fidelity and SNR efficiency. We finally incorporate sinusoidal wave gradients during the EPI readout to better use coil sensitivity encoding with controlled aliasing.
A to-date unsolved and highly limiting safety concern for Ultra High-Field (UHF) magnetic resonance imaging (MRI) is the deposition of radiofrequency (RF) power in the body, quantified by the specific absorption rate (SAR), leading to dangerous tissue heating/damage in the form of local SAR hotspots that cannot currently be measured/monitored, thereby severely limiting the applicability of the technology for clinical practice and in regulatory approval. The goal of this study has been to show proof of concept of an artificial intelligence (AI) based exam-integrated real-time MRI safety prediction software (MRSaiFE) facilitating the safe generation of 3T and 7T images by means of accurate local SAR-monitoring at sub-W/kg levels. We trained the software with a small database of image as a feasibility study and achieved successful proof of concept for both field strengths. SAR patterns were predicted with a residual root mean squared error (RSME) of <11% along with a structural similarity (SSIM) level of >84% for both field strengths (3T and 7T).
In recent years, many papers have reported state-of-the-art performance on Alzheimers Disease classification with MRI scans from the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset using convolutional neural networks. However, we discover that when we split that data into training and testing sets at the subject level, we are not able to obtain similar performance, bringing the validity of many of the previous studies into question. Furthermore, we point out that previous works use different subsets of the ADNI data, making comparison across similar works tricky. In this study, we present the results of three splitting methods, discuss the motivations behind their validity, and report our results using all of the available subjects.
The design, construction, and preliminary testing of a second generation proton CT scanner is presented. All current treatment planning systems at proton therapy centers use X-ray CT as the primary imaging modality for treatment planning to calculate doses to tumor and healthy tissues. One of the limitations of X-ray CT is in the conversion of X-ray attenuation coefficients to relative (proton) stopping powers, or RSP. This results in more proton range uncertainty, larger target volumes and therefore, more dose to healthy tissues. To help improve this, we present a novel scanner capable of high dose rates, up to 2~MHz, and large area coverage, 20~x~24~cm$^2$, for imaging an adult head phantom and reconstructing more accurate RSP values.
Brain MRI segmentation results should always undergo a quality control (QC) process, since automatic segmentation tools can be prone to errors. In this work, we propose two deep learning-based architectures for performing QC automatically. First, we used generative adversarial networks for creating error maps that highlight the locations of segmentation errors. Subsequently, a 3D convolutional neural network was implemented to predict segmentation quality. The present pipeline was shown to achieve promising results and, in particular, high sensitivity in both tasks.
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