scholarly journals Whole-Brain Imaging of Subvoxel T1-Diffusion Correlation Spectra in Human Subjects

2021 ◽  
Vol 15 ◽  
Author(s):  
Alexandru V. Avram ◽  
Joelle E. Sarlls ◽  
Peter J. Basser
Keyword(s):  
Pulse Sequence ◽  
Mean Diffusivity ◽  
Inversion Recovery ◽  
Quantitative Mri ◽  
Single Shot ◽  
Myelinated Fiber ◽  
Whole Brain ◽  
Model Free ◽  
Wide Range ◽  
Biological Specificity

T1 relaxation and water mobility generate eloquent MRI tissue contrasts with great diagnostic value in many neuroradiological applications. However, conventional methods do not adequately quantify the microscopic heterogeneity of these important biophysical properties within a voxel, and therefore have limited biological specificity. We describe a new correlation spectroscopic (CS) MRI method for measuring how T1 and mean diffusivity (MD) co-vary in microscopic tissue environments. We develop a clinical pulse sequence that combines inversion recovery (IR) with single-shot isotropic diffusion encoding (IDE) to efficiently acquire whole-brain MRIs with a wide range of joint T1-MD weightings. Unlike conventional diffusion encoding, the IDE preparation ensures that all subvoxel water pools are weighted by their MDs regardless of the sizes, shapes, and orientations of their corresponding microscopic diffusion tensors. Accordingly, IR-IDE measurements are well-suited for model-free, quantitative spectroscopic analysis of microscopic water pools. Using numerical simulations, phantom experiments, and data from healthy volunteers we demonstrate how IR-IDE MRIs can be processed to reconstruct maps of two-dimensional joint probability density functions, i.e., correlation spectra, of subvoxel T1-MD values. In vivo T1-MD spectra show distinct cerebrospinal fluid and parenchymal tissue components specific to white matter, cortical gray matter, basal ganglia, and myelinated fiber pathways, suggesting the potential for improved biological specificity. The one-dimensional marginal distributions derived from the T1-MD correlation spectra agree well with results from other relaxation spectroscopic and quantitative MRI studies, validating the T1-MD contrast encoding and the spectral reconstruction. Mapping subvoxel T1-diffusion correlations in patient populations may provide a more nuanced, comprehensive, sensitive, and specific neuroradiological assessment of the non-specific changes seen on fluid-attenuated inversion recovery (FLAIR) and diffusion-weighted MRIs (DWIs) in cancer, ischemic stroke, or brain injury.

scholarly journals Streamlined Magnetic Resonance Fingerprinting: Fast Whole-brain Coverage with Deep-learning Based Parameter Estimation

2020 ◽  
Author(s):  
Mahdi Khajehim ◽  
Thomas Christen ◽  
Fred Tam ◽  
Simon J. Graham
Keyword(s):  
Parameter Estimation ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Quantitative Mri ◽  
Parameter Estimates ◽  
Single Shot ◽  
Whole Brain ◽  
Dictionary Matching ◽  
Scan Time

AbstractMagnetic resonance fingerprinting (MRF) is a novel quantitative MRI (qMRI) framework that provides simultaneous estimates of multiple relaxation parameters as well as metrics of field inhomogeneity in a single acquisition. However, current bottlenecks exist in the forms of (1) scan time; (2) need for custom image reconstruction; (3) large dictionary sizes; (4) long dictionary-matching time. The aim of this study is to introduce a novel streamlined magnetic-resonance fingerprinting (sMRF) framework that is based on a single-shot echo-planar imaging (EPI) sequence to simultaneously estimate tissue T1, T2, and T2* with integrated B1+ correction. Encouraged by recent work on EPI-based MRF, we developed a method that combines spin-echo EPI with gradient-echo EPI to achieve T2 in addition to T1 and T2* quantification. To this design, we add simultaneous multi-slice (SMS) acceleration to enable full-brain coverage in a few minutes. Moreover, in the parameter-estimation step, we use deep learning to train a deep neural network (DNN) to accelerate the estimation process by orders of magnitude. Notably, due to the high image quality of the EPI scans, the training process can rely simply on Bloch-simulated data. The DNN also removes the need for storing large dictionaries. Phantom scans along with in-vivo multi-slice scans from seven healthy volunteers were acquired with resolutions of 1.1×1.1×3 mm3 and 1.7×1.7×3 mm3, and the results were validated against ground truth measurements. Excellent correspondence was found between our T1, T2, and T2* estimates and results obtained from standard approaches. In the phantom scan, a strong linear relationship (R=1-1.04, R2>0.96) was found for all parameter estimates, with a particularly high agreement for T2 estimation (R2>0.99). Similar findings are reported for the in-vivo human data for all of our parameter estimates. Incorporation of DNN results in a reduction of parameter estimation time on the order of 1000 x and a reduction in storage requirements on the order of 2500 x while achieving highly similar results as conventional dictionary matching (%differences of 7.4±0.4%, 3.6±0.3% and 6.0±0.4% error in T1, T2, and T2* estimation). Thus, sMRF has the potential to be the method of choice for future MRF studies by providing ease of implementation, fast whole-brain coverage, and ultra-fast T1/T2/T2* estimation.

scholarly journals Automatic Hip Detection in Anteroposterior Pelvic Radiographs—A Labelless Practical Framework

2021 ◽  
Vol 11 (6) ◽  
pp. 522
Author(s):  
Feng-Yu Liu ◽  
Chih-Chi Chen ◽  
Chi-Tung Cheng ◽  
Cheng-Ta Wu ◽  
Chih-Po Hsu ◽  
...  
Keyword(s):  
Medical Image ◽  
Image Annotation ◽  
Region Of Interest ◽  
Confidence Score ◽  
Heterogeneous Data ◽  
Single Shot ◽  
Hip Joints ◽  
Pelvic Radiographs ◽  
Wide Range ◽  
Average Confidence

Automated detection of the region of interest (ROI) is a critical step in the two-step classification system in several medical image applications. However, key information such as model parameter selection, image annotation rules, and ROI confidence score are essential but usually not reported. In this study, we proposed a practical framework of ROI detection by analyzing hip joints seen on 7399 anteroposterior pelvic radiographs (PXR) from three diverse sources. We presented a deep learning-based ROI detection framework utilizing a single-shot multi-box detector with a customized head structure based on the characteristics of the obtained datasets. Our method achieved average intersection over union (IoU) = 0.8115, average confidence = 0.9812, and average precision with threshold IoU = 0.5 (AP50) = 0.9901 in the independent testing set, suggesting that the detected hip regions appropriately covered the main features of the hip joints. The proposed approach featured flexible loose-fitting labeling, customized model design, and heterogeneous data testing. We demonstrated the feasibility of training a robust hip region detector for PXRs. This practical framework has a promising potential for a wide range of medical image applications.

Most commonly used sequences and clinical protocols for brain and spine magnetic resonance imaging allowing better identification of pathological changes in dogs

2013 ◽  
Vol 16 (1) ◽  
pp. 157-163 ◽  
Author(s):  
Y. Zhalniarovich ◽  
Z. Adamiak ◽  
A. Pomianowski ◽  
M. Jaskólska
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Imaging Modality ◽  
Spin Echo ◽  
Inversion Recovery ◽  
Fast Spin Echo ◽  
Single Shot ◽  
Turbo Spin Echo ◽  
Resonance Imaging ◽  
Turbo Spin

Abstract Magnetic resonance imaging is the best imaging modality for the brain and spine. Quality of the received images depends on many technical factors. The most significant factors are: positioning the patient, proper coil selection, selection of appropriate sequences and image planes. The present contrast between different tissues provides an opportunity to diagnose various lesions. In many clinics magnetic resonance imaging has replaced myelography because of its noninvasive modality and because it provides excellent anatomic detail. There are many different combinations of sequences possible for spinal and brain MR imaging. Most frequently used are: T2-weighted fast spin echo (FSE), T1- and T2-weighted turbo spin echo, Fluid Attenuation Inversion Recovery (FLAIR), T1-weighted gradient echo (GE) and spin echo (SE), high-resolution three-dimensional (3D) sequences, fat-suppressing short tau inversion recovery (STIR) and half-Fourier acquisition single-shot turbo spin echo (HASTE). Magnetic resonance imaging reveals neurologic lesions which were previously hard to diagnose antemortem.

scholarly journals Mosaic Super-resolution via Sequential Feature Pyramid Networks

2019 ◽  
Author(s):  
Mehrdad Shoeiby ◽  
Mohammad Ali Armin ◽  
Sadegh Aliakbarian ◽  
Saeed Anwar ◽  
Lars petersson
Keyword(s):  
State Of The Art ◽  
Super Resolution ◽  
Autonomous Driving ◽  
Single Shot ◽  
Current State ◽  
Wide Range ◽  
Feature Pyramid ◽  
Novel Method ◽  
Convolutional Lstm ◽  
Mosaic Images

<div>Advances in the design of multi-spectral cameras have</div><div>led to great interests in a wide range of applications, from</div><div>astronomy to autonomous driving. However, such cameras</div><div>inherently suffer from a trade-off between the spatial and</div><div>spectral resolution. In this paper, we propose to address</div><div>this limitation by introducing a novel method to carry out</div><div>super-resolution on raw mosaic images, multi-spectral or</div><div>RGB Bayer, captured by modern real-time single-shot mo-</div><div>saic sensors. To this end, we design a deep super-resolution</div><div>architecture that benefits from a sequential feature pyramid</div><div>along the depth of the network. This, in fact, is achieved</div><div>by utilizing a convolutional LSTM (ConvLSTM) to learn the</div><div>inter-dependencies between features at different receptive</div><div>fields. Additionally, by investigating the effect of different</div><div>attention mechanisms in our framework, we show that a</div><div>ConvLSTM inspired module is able to provide superior at-</div><div>tention in our context. Our extensive experiments and anal-</div><div>yses evidence that our approach yields significant super-</div><div>resolution quality, outperforming current state-of-the-art</div><div>mosaic super-resolution methods on both Bayer and multi-</div><div>spectral images. Additionally, to the best of our knowledge,</div><div>our method is the first specialized method to super-resolve</div><div>mosaic images, whether it be multi-spectral or Bayer.</div><div><br></div>

A Review of Gunshot Noise as Factor in Hearing Disorders

2019 ◽  
Vol 105 (6) ◽  
pp. 904-911 ◽  
Author(s):  
Ewa Skrodzka ◽  
Andrzej Wicher ◽  
Roman Gołe¸biewski
Keyword(s):  
Hearing Loss ◽  
Hair Cells ◽  
Impulse Noise ◽  
Noise Exposure ◽  
Single Shot ◽  
Negative Consequences ◽  
Mean Values ◽  
Sport Training ◽  
Wide Range ◽  
Auditory Systems

The impulse noise produced by personal weapons (guns, rifles, shotguns) during military activity, and while people engage in sport, training and hunting, is a threat to the auditory systems of soldiers, civilians, policemen, hunters, forest officers, sportspeople and bystanders not actively engaged in professional or recreational firing. An overview of noise levels generated by different types of weapon is provided, and potential short-term and long-term consequences for the human auditory system are described. The mean values of LC, peak sound pressure level during the shot, at the shooter's ears, for various types of weapons are approximately 160 dB SPL. These are levels that can cause permanent, irreversible negative effects on hearing (hearing loss, tinnitus, etc.) even as a result of a single shot being fired. One of the largest groups of weapon users in Poland (about 120 thousand) are hunters and field masters. They are not obligated by any regulations to protect their auditory systems from impulse noise. This means that this group of firearm users is at particularly high risk of hearing damage. On the basis of the literature review, it is shown that hearing exposure to high-level impulse noise such as a gunshot can result in such consequences as damage to the middle ear and destruction of the outer/inner hair cells in the cochlea. Especially difficult to diagnose is 'hidden hearing loss', i.e. damage to the synaptic connections between the hair cells of the inner ear and the auditory nerve fibres, which is not reflected in the results of basic audiometric testing and can cause hearing problems many years after impulse noise exposure. The wide range of negative consequences of gunfire noise clearly indicates the need for the hearing of the shooters to be protected.

scholarly journals Rapid whole-brain quantitative magnetization transfer imaging using 3D selective inversion recovery sequences

2020 ◽  
Vol 68 ◽  
pp. 66-74 ◽  
Author(s):  
Matthew J. Cronin ◽  
Junzhong Xu ◽  
Francesca Bagnato ◽  
Daniel F. Gochberg ◽  
John C. Gore ◽  
...  
Keyword(s):  
Magnetization Transfer ◽  
Inversion Recovery ◽  
Magnetization Transfer Imaging ◽  
Whole Brain ◽  
Selective Inversion
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