Preliminary Experience with Three Alternative Motion Sensors for 0.55 Tesla MR Imaging.

Due to limitations in current motion tracking technologies and increasing interest in alternative sensors for motion tracking both inside and outside the MRI system, in this study we share our preliminary experience with three alternative sensors utilizing diverse technologies and interactions with tissue to monitor motion of the body surface, respiratory-related motion of major organs, and non-respiratory motion of deep-seated organs. These consist of (1) a Pilot-Tone RF transmitter combined with deep learning algorithms for tracking liver motion, (2) a single-channel ultrasound transducer with deep learning for monitoring bladder motion, and (3) a 3D Time-of-Flight camera for observing the motion of the anterior torso surface. Additionally, we demonstrate the capability of these sensors to simultaneously capture motion data outside the MRI environment, which is particularly relevant for procedures like radiation therapy, where motion status could be related to previously characterized cyclical anatomical data.

Fat suppression using frequency-sweep RF saturation and iterative reconstruction.

Purpose: To introduce an alternative idea for fat suppression that is suited both for low-field applications where conventional fat-suppression approaches become ineffective due to narrow spectral separation and for applications with strong B0 homogeneities.

Methods: Separation of fat and water is achieved by sweeping the frequency of RF saturation pulses during continuous radial acquisition and calculating frequency-resolved images using regularized iterative reconstruction. Voxel-wise signal-response curves are extracted that reflect tissue's response to RF saturation at different frequencies and allow the classification into fat or water.

Sodium signal intensity of CSF using 1H-guided 23Na-MRI as a potential noninvasive biomarker in Alzheimer's disease.

Background And Purpose: Alzheimer's disease (AD) is characterized by cognitive decline and mnestic deficits. The pathophysiology of AD is not fully understood, which renders the development of accurate tools for early diagnosis and effective therapies exceedingly difficult. In this study, we investigated the use of Na-MRI to measure the relative sodium signal intensities (rSSIs) in CSF in patients with AD and healthy controls.

Accelerated MRI reconstructions via variational network and feature domain learning.

We introduce three architecture modifications to enhance the performance of the end-to-end (E2E) variational network (VarNet) for undersampled MRI reconstructions. We first implemented the Feature VarNet, which propagates information throughout the cascades of the network in an N-channel feature-space instead of a 2-channel feature-space. Then, we add an attention layer that utilizes the spatial locations of Cartesian undersampling artifacts to further improve performance.

Deep learning with diffusion MRI as in vivo microscope reveals sex-related differences in human white matter microstructure.

Biological sex is a crucial variable in neuroscience studies where sex differences have been documented across cognitive functions and neuropsychiatric disorders. While gross statistical differences have been previously documented in macroscopic brain structure such as cortical thickness or region size, less is understood about sex-related cellular-level microstructural differences which could provide insight into brain health and disease. Studying these microstructural differences between men and women paves the way for understanding brain disorders and diseases that manifest differently in different sexes.

Early adversity causes sex-specific deficits in perforant pathway connectivity and contextual memory in adolescent mice.

Background: Early life adversity impairs hippocampal development and function across diverse species. While initial evidence indicated potential variations between males and females, further research is required to validate these observations and better understand the underlying mechanisms contributing to these sex differences. Furthermore, most of the preclinical work in rodents was performed in adult males, with only few studies examining sex differences during adolescence when such differences appear more pronounced.

Bias-reduced neural networks for parameter estimation in quantitative MRI.

Purpose: To develop neural network (NN)-based quantitative MRI parameter estimators with minimal bias and a variance close to the Cramér-Rao bound.

Theory And Methods: We generalize the mean squared error loss to control the bias and variance of the NN's estimates, which involves averaging over multiple noise realizations of the same measurements during training. Bias and variance properties of the resulting NNs are studied for two neuroimaging applications.

Sensitivity of unconstrained quantitative magnetization transfer MRI to Amyloid burden in preclinical Alzheimer's disease.

Magnetization transfer MRI is sensitive to semi-solid macromolecules, including amyloid beta, and has previously been used to discriminate Alzheimer's disease (AD) patients from controls. Here, we fit an unconstrained 2-pool quantitative MT (qMT) model, i.e.

Engineered coiled-coil HIF1α protein domain mimic.

The development of targeted anti-cancer therapeutics offers the potential for increased efficacy of drugs and diagnostics. Utilizing modalities agnostic to tumor type, such as the hypoxic tumor microenvironment (TME), may assist in the development of universal tumor targeting agents. The hypoxia-inducible factor (HIF), in particular HIF1, plays a key role in tumor adaptation to hypoxia, and inhibiting its interaction with p300 has been shown to provide therapeutic potential.

Performance of receive head arrays versus ultimate intrinsic SNR at 7 T and 10.5 T.

Purpose: We examined magnetic field dependent SNR gains and ability to capture them with multichannel receive arrays for human head imaging in going from 7 T, the most commonly used ultrahigh magnetic field (UHF) platform at the present, to 10.5 T, which represents the emerging new frontier of >10 T in UHFs.

Methods: Electromagnetic (EM) models of 31-channel and 63-channel multichannel arrays built for 10.

FastMRI Prostate: A public, biparametric MRI dataset to advance machine learning for prostate cancer imaging.

Magnetic resonance imaging (MRI) has experienced remarkable advancements in the integration of artificial intelligence (AI) for image acquisition and reconstruction. The availability of raw k-space data is crucial for training AI models in such tasks, but public MRI datasets are mostly restricted to DICOM images only. To address this limitation, the fastMRI initiative released brain and knee k-space datasets, which have since seen vigorous use.

Coiled-Coil Protein Hydrogels Engineered with Minimized Fiber Diameters for Sustained Release of Doxorubicin in Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) lacks expressed protein targets, making therapy development challenging. Hydrogels offer a promising new route in this regard by improving the chemotherapeutic efficacy through increased solubility and sustained release. Moreover, subcutaneous hydrogel administration reduces patient burden by requiring less therapy and shorter treatment times.

A deep learning approach for fast muscle water T2 mapping with subject specific fat T2 calibration from multi-spin-echo acquisitions.

This work presents a deep learning approach for rapid and accurate muscle water T with subject-specific fat T calibration using multi-spin-echo acquisitions. This method addresses the computational limitations of conventional bi-component Extended Phase Graph fitting methods (nonlinear-least-squares and dictionary-based) by leveraging fully connected neural networks for fast processing with minimal computational resources. We validated the approach through in vivo experiments using two different MRI vendors.

Bias-Reduced Neural Networks for Parameter Estimation in Quantitative MRI.

Purpose: To develop neural network (NN)-based quantitative MRI parameter estimators with minimal bias and a variance close to the Cramér-Rao bound.

Theory And Methods: We generalize the mean squared error loss to control the bias and variance of the NN's estimates, which involves averaging over multiple noise realizations of the same measurements during training. Bias and variance properties of the resulting NNs are studied for two neuroimaging applications.

Assessment of Precision and Accuracy of Brain White Matter Microstructure using Combined Diffusion MRI and Relaxometry.

Joint modeling of diffusion and relaxation has seen growing interest due to its potential to provide complementary information about tissue microstructure. For brain white matter, we designed an optimal diffusion-relaxometry MRI protocol that samples multiple b-values, B-tensor shapes, and echo times (TE). This variable-TE protocol (27 min) has as subsets a fixed-TE protocol (15 min) and a 2-shell dMRI protocol (7 min), both characterizing diffusion only.

A theoretical framework to investigate the effect of high permittivity materials in MRI using anatomy-mimicking cylinders.

Purpose: Recent numerical and empirical results proved that high permittivity materials (HPM) used in pads placed near the subject or directly integrated with coils can increase the SNR and reduce the specific absorption rate (SAR) in MRI. In this paper, we propose an analytical investigation of the effect on the magnetic field distribution of a layer of HPM surrounding an anatomy-mimicking cylindrical sample.

Methods: The study is based on a reformulation of the Mie scattering for cylindrical geometry, following an approach recently introduced for spherical samples.

Open-source versatile 3D-print animal conditioning platform design for in vivo preclinical brain imaging in awake mice and anesthetized mice and rats.

Proper animal conditioning is a key factor in the quality and success of preclinical neuroimaging applications. Here, we introduce an open-source easy-to-modify multimodal 3D printable design for rodent conditioning for magnetic resonance imaging (MRI) or other imaging modalities. Our design can be used for brain imaging in anesthetized or awake mice, and in anesthetized rats.

Triple-tuned birdcage and single-tuned dipole array for quadri-nuclear head MRI at 7 T.

Purpose: The purpose of this work was to design and build a coil for quadri-nuclear MRI of the human brain at 7 T.

Methods: We built a transmit/receive triple-tuned (45.6 MHz for H, 78.

Computational Prediction of Coiled-Coil Protein Gelation Dynamics and Structure.

Protein hydrogels represent an important and growing biomaterial for a multitude of applications, including diagnostics and drug delivery. We have previously explored the ability to engineer the thermoresponsive supramolecular assembly of coiled-coil proteins into hydrogels with varying gelation properties, where we have defined important parameters in the coiled-coil hydrogel design. Using Rosetta energy scores and Poisson-Boltzmann electrostatic energies, we iterate a computational design strategy to predict the gelation of coiled-coil proteins while simultaneously exploring five new coiled-coil protein hydrogel sequences.

Radiomic analysis of the proximal femur in osteoporosis women using 3T MRI.

Introduction: Osteoporosis (OP) results in weak bone and can ultimately lead to fracture. MRI assessment of bone structure and microarchitecture has been proposed as method to assess bone quality and fracture risk . Radiomics provides a framework to analyze the textural information of MR images.

Protein-Engineered Fibers For Drug Encapsulation Traceable via F Magnetic Resonance.

Theranostic materials research is experiencing rapid growth driven by the interest in integrating both therapeutic and diagnostic modalities. These materials offer the unique capability to not only provide treatment but also track the progression of a disease. However, to create an ideal theranostic biomaterial without compromising drug encapsulation, diagnostic imaging must be optimized for improved sensitivity and spatial localization.