scholarly journals Multi-parametric quantitative spinal cord MRI with unified signal readout and image denoising

2019 ◽  
Author(s):  
Francesco Grussu ◽  
Marco Battiston ◽  
Jelle Veraart ◽  
Torben Schneider ◽  
Julien Cohen-Adad ◽  
...  

AbstractMulti-parametric quantitative MRI (qMRI) of the spinal cord is a promising non-invasive tool to probe early microstructural damage in neurological disorders. It is usually performed by combining acquisitions with multiple signal readouts, which exhibit different thermal noise levels, geometrical distortions and susceptibility to physiological noise. This ultimately hinders joint multi-contrast modelling and makes the geometric correspondence of parametric maps challenging. We propose an approach to overcome these limitations, by implementing state-of-the-art microstructural MRI of the spinal cord with a unified signal readout. We base our acquisition on single-shot echo planar imaging with reduced field-of-view, and obtain data from two different vendors (vendor 1: Philips Achieva; vendor 2: Siemens Prisma). Importantly, the unified acquisition allows us to compare signal and noise across contrasts, thus enabling overall quality enhancement via Marchenko-Pastur (MP) Principal Component Analysis (PCA) denoising. MP-PCA is a recent method relying on redundant acquisitions, i.e. such that the number of measurements is much larger than the number of informative principal components. Here we used in vivo and synthetic data to test whether a unified readout enables more efficient denoising of less redundant acquisitions, since these can be denoised jointly with more redundant ones. We demonstrate that a unified readout provides robust multi-parametric maps, including diffusion and kurtosis tensors from diffusion MRI, myelin metrics from two-pool magnetisation transfer, and T1 and T2 from relaxometry. Moreover, we show that MP-PCA improves the quality of our multi-contrast acquisitions, since it reduces the coefficient of variation (i.e. variability) by up to 15% for mean kurtosis, 8% for bound pool fraction (BPF, myelin-sensitive), and 13% for T1, while enabling more efficient denoising of modalities limited in redundancy (e.g. relaxometry). In conclusion, multi-parametric spinal cord qMRI with unified readout is feasible and provides robust microstructural metrics with matched resolution and distortions, whose quality benefits from MP-PCA denoising, a useful pre-processing tool for spinal cord MRI.

NeuroImage ◽  
2009 ◽  
Vol 46 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Wadie Ben Hassen ◽  
Mélina Bégou ◽  
Amidou Traore ◽  
Abdelatif Ben Moussa ◽  
Nelly Boehm ◽  
...  

2020 ◽  
Author(s):  
Mahdi Khajehim ◽  
Thomas Christen ◽  
Fred Tam ◽  
Simon J. Graham

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.


2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Rachael L. Bosma ◽  
Patrick W. Stroman

The aim of this study was to characterizein vivomeasurements of diffusion along the length of the entire healthy spinal cord and to compare DTI indices, including fractional anisotropy (FA) and mean diffusivity (MD), between cord regions. The objective is to determine whether or not there are significant differences in DTI indices along the cord that must be considered for future applications of characterizing the effects of injury or disease. A cardiac gated, single-shot EPI sequence was used to acquire diffusion-weighted images of the cervical, thoracic, and lumbar regions of the spinal cord in nine neurologically intact subjects (19 to 22 years). For each cord section, FA versus MD values were plotted, and a k-means clustering method was applied to partition the data according to tissue properties. FA and MD values from both white matter (averageFA=0.69, averageMD=0.93×10−3 mm2/s) and grey matter (averageFA=0.44, averageMD=1.8×10−3 mm2/s) were relatively consistent along the length of the cord.


2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Bunheang Tay ◽  
Jung Keun Hyun ◽  
Sejong Oh

Diffusion Tensor Imaging (DTI) uses in vivo images that describe extracellular structures by measuring the diffusion of water molecules. These images capture axonal movement and orientation using echo-planar imaging and provide critical information for evaluating lesions and structural damage in the central nervous system. This information can be used for prediction of Spinal Cord Injuries (SCIs) and for assessment of patients who are recovering from such injuries. In this paper, we propose a classification scheme for identifying healthy individuals and patients. In the proposed scheme, a dataset is first constructed from DTI images, after which the constructed dataset undergoes feature selection and classification. The experiment results show that the proposed scheme aids in the diagnosis of SCIs.


2011 ◽  
Vol 32 (5) ◽  
pp. 813-820 ◽  
Author(s):  
G. Zaharchuk ◽  
E.U. Saritas ◽  
J.B. Andre ◽  
C.T. Chin ◽  
J. Rosenberg ◽  
...  

NeuroImage ◽  
2020 ◽  
Vol 217 ◽  
pp. 116884 ◽  
Author(s):  
Francesco Grussu ◽  
Marco Battiston ◽  
Jelle Veraart ◽  
Torben Schneider ◽  
Julien Cohen-Adad ◽  
...  

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