Rapid simultaneous acquisition of macromolecular tissue volume, susceptibility, and relaxometry maps

Purpose A major obstacle to the clinical implementation of quantitative MR is the lengthy acquisition time required to derive multi-contrast parametric maps. We sought to reduce the acquisition time for quantitative susceptibility mapping (QSM) and macromolecular tissue volume (MTV) by acquiring both contrasts simultaneously by leveraging their redundancies. The Joint Virtual Coil concept with generalized autocalibrating partially parallel acquisitions (JVC-GRAPPA) was applied to reduce acquisition time further. Methods Three adult volunteers were imaged on a 3T scanner using a multi-echo 3D GRE sequence acquired at three head orientations. MTV, QSM, R2*, T1, and proton density maps were reconstructed. The same sequence (GRAPPA R=4) was performed in subject #1 with a single head orientation for comparison. Fully sampled data was acquired in subject #2, from which retrospective undersampling was performed (R=6 GRAPPA and R=9 JVC-GRAPPA). Prospective undersampling was performed in subject #3 (R=6 GRAPPA and R=9 JVC-GRAPPA) using gradient blips to shift k-space sampling in later echoes. Results Subject #1s multi-orientation and single-orientation MTV maps were not significantly different based on RMSE. For subject #2, the retrospectively undersampled JVC-GRAPPA and GRAPPA generated similar results as fully sampled data. This approach was validated with the prospectively undersampled images in subject #3. Using QSM, R2*, and MTV, the contributions of myelin and iron content to susceptibility was estimated. Conclusion We have developed a novel strategy to simultaneously acquire data for the reconstruction of five intrinsically co-registered 1-mm isotropic resolution multi-parametric maps, with a scan time of 6 minutes using JVC-GRAPPA.

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Navigator-Free Submillimeter Diffusion Imaging Using Multishot-Encoded Simultaneous Multi-Slice (MUSIUM)

10.21203/rs.3.rs-131073/v1 ◽

2020 ◽

Author(s):

Wei-Tang Chang ◽

Khoi Huynh ◽

Pew-Thian Yap ◽

Weili Lin

Keyword(s):

Signal To Noise Ratio ◽

Diffusion Imaging ◽

Brain Structures ◽

Peak Amplitude ◽

Acquisition Time ◽

Rf Power ◽

Isotropic Resolution ◽

Low Snr ◽

Scan Time

Abstract The ability to achieve submillimter isotropic resolution diffusion MR imaging (dMRI) is critically important to study fine-scale brain structures. One of the major challenges in submillimeter dMRI is the inherently low signal-to-noise ratio (SNR). While approaches capable of mitigating the low SNR have been proposed, namely simultaneous multi-slab (SMSlab) and generalized slice dithered enhanced resolution with simultaneous multislice (gSlider-SMS), limitations are associated with these approaches. The SMSlab sequences suffer from the slab boundary artifacts and require additional navigators for phase estimation. On the other hand, gSlider sequences require relatively high RF power and peak amplitude, which increase the SAR and complicate the RF excitation. In this work, we developed a navigator-free multishot-encoded simultaneous multi-slice (MUSIUM) imaging approach, achieving enhanced SNR, low RF power and peak amplitude, and being free from slab boundary artifacts. The dMRI with ultrahigh resolution (0.86 mm isotropic), whole brain coverage and ~12.5 minute acquisition time were achieved, revealing detailed structures at cortical and white matter areas. The simulated and in vivo results also demonstrated that the MUSIUM imaging was minimally affected by the motion. Taken together, the MUSIUM imaging is a promising approach to achieve submillimeter diffusion imaging on 3T scanner within clinically feasible scan time.

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Multidimensional MRI for Characterization of Subtle Axonal Injury Accelerated Using an Adaptive Nonlocal Multispectral Filter

Frontiers in Physics ◽

10.3389/fphy.2021.737374 ◽

2021 ◽

Vol 9 ◽

Author(s):

Dan Benjamini ◽

Mustapha Bouhrara ◽

Michal E. Komlosh ◽

Diego Iacono ◽

Daniel P. Perl ◽

...

Keyword(s):

Axonal Injury ◽

Noise Removal ◽

Imaging Biomarkers ◽

Joint Analysis ◽

Acquisition Time ◽

Full Dataset ◽

Scan Time ◽

Time Required ◽

And Diffusion

Multidimensional MRI is an emerging approach that simultaneously encodes water relaxation (T1 and T2) and mobility (diffusion) and replaces voxel-averaged values with subvoxel distributions of those MR properties. While conventional (i.e., voxel-averaged) MRI methods cannot adequately quantify the microscopic heterogeneity of biological tissue, using subvoxel information allows to selectively map a specific T1-T2-diffusion spectral range that corresponds to a group of tissue elements. The major obstacle to the adoption of rich, multidimensional MRI protocols for diagnostic or monitoring purposes is the prolonged scan time. Our main goal in the present study is to evaluate the performance of a nonlocal estimation of multispectral magnitudes (NESMA) filter on reduced datasets to limit the total acquisition time required for reliable multidimensional MRI characterization of the brain. Here we focused and reprocessed results from a recent study that identified potential imaging biomarkers of axonal injury pathology from the joint analysis of multidimensional MRI, in particular voxelwise T1-T2 and diffusion-T2 spectra in human Corpus Callosum, and histopathological data. We tested the performance of NESMA and its effect on the accuracy of the injury biomarker maps, relative to the co-registered histological reference. Noise reduction improved the accuracy of the resulting injury biomarker maps, while permitting data reduction of 35.7 and 59.6% from the full dataset for T1-T2 and diffusion-T2 cases, respectively. As successful clinical proof-of-concept applications of multidimensional MRI are continuously being introduced, reliable and robust noise removal and consequent acquisition acceleration would advance the field towards clinically-feasible diagnostic multidimensional MRI protocols.

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Multidimensional MRI for characterization of subtle axonal injury accelerated using an adaptive nonlocal multispectral filter

10.1101/2021.07.06.451291 ◽

2021 ◽

Author(s):

Dan Benjamini ◽

Mustapha Bouhrara ◽

Michal E Komlosh ◽

Diego Iacono ◽

Daniel P Perl ◽

...

Keyword(s):

Axonal Injury ◽

Noise Removal ◽

Imaging Biomarkers ◽

Joint Analysis ◽

Acquisition Time ◽

Full Dataset ◽

Scan Time ◽

Time Required ◽

The Brain

Multidimensional MRI is an emerging approach that simultaneously encodes water relaxation (T1 and T2) and mobility (diffusion) and replaces voxel-averaged values with subvoxel distributions of those MR properties. While conventional (i.e., voxel-averaged) MRI methods cannot adequately quantify the microscopic heterogeneity of biological tissue, using subvoxel information allows to selectively map a specific T1-T2-diffusion spectral range that corresponds to a group of tissue elements. The major obstacle to the adoption of rich, multidimensional MRI protocols for diagnostic or monitoring purposes is the prolonged scan time. Our main goal in the present study is to evaluate the performance of a nonlocal estimation of multispectral magnitudes (NESMA) filter on reduced datasets to limit the total acquisition time required for reliable multidimensional MRI characterization of the brain. Here we focused and reprocessed results from a recent study that identified potential imaging biomarkers of axonal injury pathology from the joint analysis of multidimensional MRI, in particular voxelwise T1-T2 and diffusion-T2 spectra in human Corpus Callosum, and histopathological data. We tested the performance of NESMA and its effect on the accuracy of the injury biomarker maps, relative to the co-registered histological reference. Noise reduction improved the accuracy of the resulting injury biomarker maps, while permitting data reduction of 35.7% and 59.6% from the full dataset for T1-T2 and MD-T2 cases, respectively. As successful clinical proof-of-concept applications of multidimensional MRI are continuously being introduced, reliable and robust noise removal and consequent acquisition acceleration would advance the field towards clinically-feasible diagnostic multidimensional MRI protocols.

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Simple low dose radiography allows precise lung volume assessment in mice

Scientific Reports ◽

10.1038/s41598-021-83319-5 ◽

2021 ◽

Vol 11 (1) ◽

Cited By ~ 1

Author(s):

Amara Khan ◽

Andrea Markus ◽

Thomas Rittmann ◽

Jonas Albers ◽

Frauke Alves ◽

...

Keyword(s):

Lung Function ◽

Whole Body ◽

Acquisition Time ◽

Micro Ct ◽

Lung Volumes ◽

X Ray ◽

Promising Tool ◽

Volume Assessment ◽

Set Up ◽

Time Required

AbstractX-ray based lung function (XLF) as a planar method uses dramatically less X-ray dose than computed tomography (CT) but so far lacked the ability to relate its parameters to pulmonary air volume. The purpose of this study was to calibrate the functional constituents of XLF that are biomedically decipherable and directly comparable to that of micro-CT and whole-body plethysmography (WBP). Here, we developed a unique set-up for simultaneous assessment of lung function and volume using XLF, micro-CT and WBP on healthy mice. Our results reveal a strong correlation of lung volumes obtained from radiographic XLF and micro-CT and demonstrate that XLF is superior to WBP in sensitivity and precision to assess lung volumes. Importantly, XLF measurement uses only a fraction of the radiation dose and acquisition time required for CT. Therefore, the redefined XLF approach is a promising tool for preclinical longitudinal studies with a substantial potential of clinical translation.

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MRI Image Reconstruction using Compressive Sensing

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1056.078219 ◽

2019 ◽

Vol 8 (2) ◽

pp. 5256-5260

Keyword(s):

Compressive Sensing ◽

Communication Systems ◽

Optimization Algorithms ◽

Image Data ◽

Acquisition Time ◽

Huge Amount ◽

Scan Time ◽

Small Set ◽

Picture Archiving And Communication ◽

Made In

A large number of diagnostic images which also include the MRIs are generated by the imaging departments of the hospitals for medical and legal reasons. This results in the creation of a huge amount of data in the form of images which are required to be stored for a long period. The primary challenge for the picture archiving and communication systems (PACS) allowing to store the image data and the display and reconstruction of the image for recalling at various sites. Image compression and reconstruction are necessary to cope up with these tasks. Significant efforts have been made in the recent towards the application of compressive sensing techniques for acquiring the data in MRI process. The primary aim of the theory of Compressive Sensing (CS) in signal processing is reducing the quantity of data that is acquired for successfully reconstructing the signals. Decreasing the number of coefficients of the acquired images will result in reduced acquisition time i.e. nothing but the duration for which the images are exposed to the MRI apparatus. This paper aims at using optimization algorithms in designing the scanner of the MR integrated with the CS, which results in the reduction of the scan time of the MRI. From a small set of acquired samples, images of satisfactory quality can be obtained. Various Compressive Sensing based optimization algorithms for reconstructing the MRI images are assessed, and a relative comparison is done for further research in this paper.

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Improving the Quantification of the Lateral Geniculate Nucleus in Magnetic Resonance Imaging Using a Novel 3D-Edge Enhancement Technique

Frontiers in Computational Neuroscience ◽

10.3389/fncom.2021.708866 ◽

2021 ◽

Vol 15 ◽

Author(s):

Mikhail Lipin ◽

Jean Bennett ◽

Gui-Shuang Ying ◽

Yinxi Yu ◽

Manzar Ashtari

Keyword(s):

Lateral Geniculate Nucleus ◽

Brain Structure ◽

High Reliability ◽

Acquisition Time ◽

Proton Density ◽

Normal Brain ◽

Edge Enhancement ◽

Mri Imaging ◽

Lateral Geniculate ◽

Noise Ratio

The lateral geniculate nucleus (LGN) is a small, inhomogeneous structure that relays major sensory inputs from the retina to the visual cortex. LGN morphology has been intensively studied due to various retinal diseases, as well as in the context of normal brain development. However, many of the methods used for LGN structural evaluations have not adequately addressed the challenges presented by the suboptimal routine MRI imaging of this structure. Here, we propose a novel method of edge enhancement that allows for high reliability and accuracy with regard to LGN morphometry, using routine 3D-MRI imaging protocols. This new algorithm is based on modeling a small brain structure as a polyhedron with its faces, edges, and vertices fitted with one plane, the intersection of two planes, and the intersection of three planes, respectively. This algorithm dramatically increases the contrast-to-noise ratio between the LGN and its surrounding structures as well as doubling the original spatial resolution. To show the algorithm efficacy, two raters (MA and ML) measured LGN volumes bilaterally in 19 subjects using the edge-enhanced LGN extracted areas from the 3D-T1 weighted images. The averages of the left and right LGN volumes from the two raters were 175 ± 8 and 174 ± 9 mm3, respectively. The intra-class correlations between raters were 0.74 for the left and 0.81 for the right LGN volumes. The high contrast edge-enhanced LGN images presented here, from a 7-min routine 3T-MRI acquisition, is qualitatively comparable to previously reported LGN images that were acquired using a proton density sequence with 30–40 averages and 1.5-h of acquisition time. The proposed edge-enhancement algorithm is not limited only to the LGN, but can significantly improve the contrast-to-noise ratio of any small deep-seated gray matter brain structure that is prone to high-levels of noise and partial volume effects, and can also increase their morphometric accuracy and reliability. An immensely useful feature of the proposed algorithm is that it can be used retrospectively on noisy and low contrast 3D brain images previously acquired as part of any routine clinical MRI visit.

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2½-minute 3D 7T 31P-MRSI of the human heart using concentric rings (CRT)

10.1101/2021.12.10.472120 ◽

2021 ◽

Author(s):

William T Clarke ◽

Lukas Hingerl ◽

Bernhard Strasser ◽

Wolfgang Bogner ◽

Ladislav Valkovic ◽

...

Keyword(s):

Signal To Noise Ratio ◽

Variable Number ◽

Low Rank ◽

Acquisition Time ◽

Temporal Data ◽

Scan Time ◽

Spread Function ◽

3D Acquisition ◽

Spatio Temporal ◽

31P Mrsi

A 3D density-weighted concentric ring trajectory (CRT) MRSI sequence is implemented for cardiac 31P-MRS at 7T. The point-by-point k-space sampling of traditional phase-encoded CSI sequences severely restricts the minimum scan time at higher spatial resolutions. Our proposed CRT sequence implements a stack of concentric rings trajectory, with a variable number of rings and planes spaced to optimise the density of k-space weighting. This creates flexibility in acquisition time, allowing acquisitions substantially faster than traditional phase-encoded CSI sequences, while retaining high SNR. We first characterise the signal-to-noise ratio and point spread function of the CRT sequence in phantoms. We then evaluate it at five different acquisition times and spatial resolutions in the hearts of five healthy participants at 7T. These different sequence durations are compared with existing published 3D acquisition-weighted CSI sequences with matched acquisition times and spatial resolutions. To minimise the effect of noise on the short acquisitions, low-rank denoising of the spatio-temporal data was also performed after acquisition. The proposed sequence measures 3D localised PCr/ATP ratios of the human myocardium in 2.5 minutes, 2.6 times faster than the minimum scan time for the acquisition-weighted phase-encoded CSI. Alternatively, in the same scan time a 1.7-times smaller nominal voxel volume can be achieved. Low-rank denoising reduced the variance of measured PCr/ATP ratios by 11% across all protocols. The faster acquisitions permitted by 7T CRT 31P-MRSI could make cardiac stress protocols or creatine kinase rate measurements (which involve repeated scans) more tolerable for patients without sacrificing spatial resolution.

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Multiparametric mapping in post-mortem perinatal MRI: a feasibility study

British Journal of Radiology ◽

10.1259/bjr.20190952 ◽

2020 ◽

Vol 93 (1111) ◽

pp. 20190952

Author(s):

Amy R McDowell ◽

Susan C Shelmerdine ◽

Sara Lorio ◽

Wendy Norman ◽

Rod Jones ◽

...

Keyword(s):

Flip Angle ◽

Whole Body ◽

Proton Density ◽

Single Shot ◽

Post Mortem ◽

Variable Flip Angle ◽

High Quality ◽

Isotropic Resolution ◽

Tissue Contrast ◽

Magnetic Transfer

Objectives: To demonstrate feasibility of a 3 T multiparametric mapping (MPM) quantitative pipeline for perinatal post-mortem MR (PMMR) imaging. Methods: Whole body quantitative PMMR imaging was acquired in four cases, mean gestational age 34 weeks, range (29–38 weeks) on a 3 T Siemens Prisma scanner. A multicontrast protocol yielded proton density, T1 and magnetic transfer (MT) weighted multi-echo images obtained from variable flip angle (FA) 3D fast low angle single-shot (FLASH) acquisitions, radiofrequency transmit ﬁeld map and one B0 ﬁeld map alongside four MT weighted acquisitions with saturation pulses of 180, 220, 260 and 300 degrees were acquired, all at 1 mm isotropic resolution. Results: Whole body MPM was achievable in all four foetuses, with R1, R2*, PD and MT maps reconstructed from a single protocol. Multiparametric maps were of high quality and show good tissue contrast, especially the MT maps. Conclusion: MPM is a feasible technique in a perinatal post-mortem setting, which may allow quantification of post-mortem change, prior to being evaluated in a clinical setting. Advances in knowledge: We have shown that the MPM sequence is feasible in PMMR imaging and shown the potential of MT imaging in this setting.

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Development of an Additive Manufactured Artifact to Characterize Unfused Powder Using Computed Tomography

International Journal of Automation Technology ◽

10.20965/ijat.2020.p0439 ◽

2020 ◽

Vol 14 (3) ◽

pp. 439-446 ◽

Cited By ~ 2

Author(s):

Ahmed Tawfik ◽

◽

Paul Bills ◽

Liam Blunt ◽

Radu Racasan

Keyword(s):

Computed Tomography ◽

Data Analysis ◽

Additive Manufacturing ◽

Voxel Size ◽

Core Technology ◽

Scan Time ◽

Time Required ◽

Porosity Analysis ◽

The Impact ◽

Subtractive Machining

Additive manufacturing (AM) is recognized as a core technology for producing high-value components. The production of complex and individually modified components, as well as prototypes, gives additive manufacturing a substantial advantage over conventional subtractive machining. For most industries, some of the current barriers to implementing AM include the lack of build repeatability and a deficit of quality assurance standards. The mechanical properties of the components depend critically on the density achieved. Therefore, defect/porosity analysis must be carried out to verify the components’ integrity and viability. In parts produced using AM, the detection of unfused powder using computed tomography is challenging because the detection relies on differences in density. This study presents an optimized methodology for differentiating between unfused powder and voids in additive manufactured components, using computed tomography. Detecting the unfused powder requires detecting the cavities between particles. Previous studies have found that the detection of unfused powder requires a voxel size that is as small as 4 μm3. For most applications, scanning using a small voxel size is not reasonable because of the part size, long scan time, and data analysis. In this study, different voxel sizes are used to compare the time required for scanning, and the data analysis showing the impact of voxel size on the detection of micro defects. The powder used was Ti6Al4V, which has a grain size of 45–100 μm, and is typically employed by Arcam electron beam melting (EBM) machines. The artifact consisted of a 6 mm round bar with designed internal features ranging from 50 μm to 1400 μm and containing a mixture of voids and unfused powder. The diameter and depth of the defects were characterized using a focus variation microscope, after which they were scanned using a Nikon XTH225 industrial CT to measure the artifacts and characterize the internal features for defects/pores. To reduce the number of the process variables, the measurement parameters, such as filament current, acceleration voltage, and X-ray filtering material and thickness were kept constant. The VGStudio MAX 3.0 (Volume Graphics, Germany) software package was used for data processing, surface determination, and defects/porosity analysis. The main focus of this study is to explore the optimal methods for enhancing the detection of pores/defects while minimizing the time taken for scanning, data analysis, and determining the effects of noise on the analysis.

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Whole-Brain High-Resolution Metabolite Mapping with 3D Compressed-Sensing-SENSE-LowRank 1H FID-MRSI

10.1101/2020.05.18.101618 ◽

2020 ◽

Author(s):

Antoine Klauser ◽

Paul Klauser ◽

Frédéric Grouiller ◽

Sebastien Courvoisier ◽

Francois Lazeyras

Keyword(s):

High Resolution ◽

Brain Regions ◽

Low Rank ◽

Acquisition Time ◽

Resonance Spectroscopy ◽

Brain Anatomy ◽

Reconstruction Technique ◽

Whole Brain ◽

Isotropic Resolution

There is a growing interest of the neuroscience community to map the distribution of brain metabolites in vivo. Magnetic resonance spectroscopy imaging (MRSI) is often limited by either a poor spatial resolution and/or a long acquisition time which severely limits its applications for clinical or research purposes. We developed a novel acquisition-reconstruction technique combining fast 1H-FID-MRSI sequence accelerated by random k-space undersampling and a low-rank and total-generalized variation (TGV) constrained model. This framework was applied to the brain of four healthy volunteers. Following 20 min acquisition, reconstruction and quantification, the resulting metabolic maps with a 5 mm isotropic resolution reflected the detailed neurochemical composition of all brain regions and revealed part of the underlying brain anatomy. Contrasts and features from the 3D metabolite distributions were in agreement with the literature and consistent across the four subjects. The successful combination of the 3D 1H-FID-MRSI with a constrained reconstruction enables the detailed mapping of metabolite concentrations at high-resolution in the whole brain and with an acquisition time that is compatible with clinical or research settings.

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