Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userSimultaneous imaging of hard and soft biological tissues in a low-field dental MRI scanner
106
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Magnetic Resonance Imaging (MRI) of hard biological tissues is challenging due to the fleeting lifetime and low strength of their response to resonant stimuli, especially at low magnetic fields. Consequently, the impact of MRI on some medical applications, such as dentistry, continues to be limited. Here, we present three-dimensional reconstructions of ex-vivo human teeth, as well as a rabbit head and part of a cow femur, all obtained at a field strength of only 260 mT. These images are the first featuring soft and hard tissues simultaneously at sub-Tesla fields, and they have been acquired in a home-made, special-purpose, pre-medical MRI scanner designed with the goal of demonstrating dental imaging at low field settings. We encode spatial information with two variations of zero-echo time (ZTE) pulse sequences: Pointwise-Encoding Time reduction with Radial Acquisition (PETRA) and a new sequence we have called Double Radial Non-Stop Spin Echo (DRaNSSE), which we find to perform better than the former. For image reconstruction we employ Algebraic Reconstruction Techniques (ART) as well as standard Fourier methods. A noise analysis of the resulting images shows that ART reconstructions exhibit a higher signal to noise ratio with a more homogeneous noise distribution.
rate research
Read More
Purpose: This study demonstrated an MR signal multitask learning method for 3D simultaneous segmentation and relaxometry of human brain tissues. Materials and Methods: A 3D inversion-prepared balanced steady-state free precession sequence was used for acquiring in vivo multi-contrast brain images. The deep neural network contained 3 residual blocks, and each block had 8 fully connected layers with sigmoid activation, layer norm, and 256 neurons in each layer. Online synthesized MR signal evolutions and labels were used to train the neural network batch-by-batch. Empirically defined ranges of T1 and T2 values for the normal gray matter, white matter and cerebrospinal fluid (CSF) were used as the prior knowledge. MRI brain experiments were performed on 3 healthy volunteers as well as animal (N=6) and prostate patient (N=1) experiments. Results: In animal validation experiment, the differences/errors (mean difference $pm$ standard deviation of difference) between the T1 and T2 values estimated from the proposed method and the ground truth were 113 $pm$ 486 and 154 $pm$ 512 ms for T1, and 5 $pm$ 33 and 7 $pm$ 41 ms for T2, respectively. In healthy volunteer experiments (N=3), whole brain segmentation and relaxometry were finished within ~5 seconds. The estimated apparent T1 and T2 maps were in accordance with known brain anatomy, and not affected by coil sensitivity variation. Gray matter, white matter, and CSF were successfully segmented. The deep neural network can also generate synthetic T1 and T2 weighted images. Conclusion: The proposed multitask learning method can directly generate brain apparent T1 and T2 maps, as well as synthetic T1 and T2 weighted images, in conjunction with segmentation of gray matter, white matter and CSF.
Purpose: To demonstrate an ultrashort echo time magnetic resonance fingerprinting (UTE-MRF) method that can simultaneously quantify tissue relaxometries for muscle and bone in musculoskeletal systems and tissue components in brain and therefore can synthesize pseudo-CT images. Methods: A FISP-MRF sequence with half pulse excitation and half spoke radial acquisition was designed to sample fast T2 decay signals. Sinusoidal echo time (TE) pattern was applied to enhance MRF sensitivity for tissues with short and ultrashort T2 values. The performance of UTE-MRF was evaluated via simulations, phantoms, and in vivo experiments. Results: A minimal TE of 0.05 ms was achieved in UTE-MRF. Simulations indicated that extension of TE sampling increased T2 quantification accuracy in cortical bone and tendon, and had little impact on long T2 muscle quantifications. For a rubber phantom, an average T1/T2 of 162/1.07 ms from UTE-MRF were compared well with gold standard T2 of 190 ms from IR-UTE and T2* of 1.03 ms from UTE sequence. For a long T2 agarose phantom, the linear regression slope between UTE-MRF and gold standard was 1.07 (R2=0.991) for T1 and 1.04 (R2=0.994) for T2. In vivo experiments showed the detection of cortical bone and Achilles tendon, where the averaged T2 was respectively 1.0 ms and 15 ms. Scalp images were in good agreement with CT. Conclusion: UTE-MRF with sinusoidal TE variations shows its capability to produce pseudo-CT images and simultaneously output T1, T2, proton density, and B0 maps for tissues with long T2 and short/ultrashort T2 in the brain and musculoskeletal system.
The speckle statistics of optical coherence tomography images of biological tissue have been studied using several historical probability density functions. A recent hypothesis implies that underlying power-law distributions in the medium structure, such as the fractal branching vasculature, will contribute to power-law probability distributions of speckle statistics. Specifically, these are the Burr type XII distribution for speckle amplitude, the Lomax distribution for intensity, and the generalized logistic distribution for log amplitude. In this study, these three distributions are fitted to histogram data from nine optical coherence tomography scans of various biological tissues and samples. The distributions are also compared with conventional distributions such as the Rayleigh, K, and gamma distributions. The results indicate that these newer distributions based on power laws are, in general, more appropriate models and support the plausibility of their use for characterizing biological tissue. Potentially, the governing power-law parameter of these distributions could be used as a biomarker for tissue disease or pathology.
Multi-contrast images are commonly acquired together to maximize complementary diagnostic information, albeit at the expense of longer scan times. A time-efficient strategy to acquire high-quality multi-contrast images is to accelerate individual sequences and then reconstruct undersampled data with joint regularization terms that leverage common information across contrasts. However, these terms can cause features that are unique to a subset of contrasts to leak into the other contrasts. Such leakage-of-features may appear as artificial tissues, thereby misleading diagnosis. The goal of this study is to develop a compressive sensing method for multi-channel multi-contrast magnetic resonance imaging (MRI) that optimally utilizes shared information while preventing feature leakage. Joint regularization terms group sparsity and colour total variation are used to exploit common features across images while individual sparsity and total variation are also used to prevent leakage of distinct features across contrasts. The multi-channel multi-contrast reconstruction problem is solved via a fast algorithm based on Alternating Direction Method of Multipliers. The proposed method is compared against using only individual and only joint regularization terms in reconstruction. Comparisons were performed on single-channel simulated and multi-channel in-vivo datasets in terms of reconstruction quality and neuroradiologist reader scores. The proposed method demonstrates rapid convergence and improved image quality for both simulated and in-vivo datasets. Furthermore, while reconstructions that solely use joint regularization terms are prone to leakage-of-features, the proposed method reliably avoids leakage via simultaneous use of joint and individual terms, thereby holding great promise for clinical use.
Novel methods for quantitative, transient-state multiparametric imaging are increasingly being demonstrated for assessment of disease and treatment efficacy. Here, we build on these by assessing the most common Non-Cartesian readout trajectories (2D/3D radials and spirals), demonstrating efficient anti-aliasing with a k-space view-sharing technique, and proposing novel methods for parameter inference with neural networks that incorporate the estimation of proton density. Our results show good agreement with gold standard and phantom references for all readout trajectories at 1.5T and 3T. Parameters inferred with the neural network were within 6.58% difference from the parameters inferred with a high-resolution dictionary. Concordance correlation coefficients were above 0.92 and the normalized root mean squared error ranged between 4.2% - 12.7% with respect to gold-standard phantom references for T1 and T2. In vivo acquisitions demonstrate sub-millimetric isotropic resolution in under five minutes with reconstruction and inference times < 7 minutes. Our 3D quantitative transient-state imaging approach could enable high-resolution multiparametric tissue quantification within clinically acceptable acquisition and reconstruction times.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
