A unified model for reconstruction and R2* mapping of accelerated 7T data using the quantitative recurrent inference machine.

Quantitative MRI (qMRI) acquired at the ultra-high field of 7 Tesla has been used in visualizing and analyzing subcortical structures. qMRI relies on the acquisition of multiple images with different scan settings, leading to extended scanning times. Data redundancy and prior information from the relaxometry model can be exploited by deep learning to accelerate the imaging process. We propose the quantitative Recurrent Inference Machine (qRIM), with a unified forward model for joint reconstruction and R2*-mapping from sparse data, embedded in a Recurrent Inference Machine (RIM), an iterative inverse problem-solving network. To study the dependency of the proposed extension of the unified forward model to network architecture, we implemented and compared a quantitative End-to-End Variational Network (qE2EVN). Experiments were performed with high-resolution multi-echo gradient echo data of the brain at 7T of a cohort study covering the entire adult life span. The error in reconstructed R2* from undersampled data relative to reference data significantly decreased for the unified model compared to sequential image reconstruction and parameter fitting using the RIM. With increasing acceleration factor, an increasing reduction in the reconstruction error was observed, pointing to a larger benefit for sparser data. Qualitatively, this was following an observed reduction of image blurriness in R2*-maps. In contrast, when using the U-Net as network architecture, a negative bias in R2* in selected regions of interest was observed. Compressed Sensing rendered accurate, but less precise estimates of R2*. The qE2EVN showed slightly inferior reconstruction quality compared to the qRIM but better quality than the U-Net and Compressed Sensing. Subcortical maturation over age measured by a linearly increasing interquartile range of R2* in the striatum was preserved up to an acceleration factor of 9. With the integrated prior of the unified forward model, the proposed qRIM can exploit the redundancy among repeated measurements and shared information between tasks, facilitating relaxometry in accelerated MRI.

Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction.

Accelerating MRI scans is one of the principal outstanding problems in the MRI research community. Towards this goal, we hosted the second fastMRI competition targeted towards reconstructing MR images with subsampled k-space data. We provided participants with data from 7,299 clinical brain scans (de-identified via a HIPAA-compliant procedure by NYU Langone Health), holding back the fully-sampled data from 894 of these scans for challenge evaluation purposes. In contrast to the 2019 challenge, we focused our radiologist evaluations on pathological assessment in brain images. We also debuted a new Transfer track that required participants to submit models evaluated on MRI scanners from outside the training set. We received 19 submissions from eight different groups. Results showed one team scoring best in both SSIM scores and qualitative radiologist evaluations. We also performed analysis on alternative metrics to mitigate the effects of background noise and collected feedback from the participants to inform future challenges. Lastly, we identify common failure modes across the submissions, highlighting areas of need for future research in the MRI reconstruction community.

APIR4EMC: Autocalibrated parallel imaging reconstruction for extended multi-contrast imaging.

PURPOSE: To improve image quality of multi-contrast imaging with the proposed Autocalibrated Parallel Imaging Reconstruction for Extended Multi-Contrast Imaging (APIR4EMC). METHODS: APIR4EMC reconstructs multi-contrast images in an autocalibrated parallel imaging reconstruction framework by adding contrasts as virtual coils. Compensation of signal evolution along the echo train of different contrasts is performed to improve signal prediction for missing samples. As a proof of concept, we performed prospectively accelerated phantom and in-vivo brain acquisitions with T1, T1-fat saturated (Fatsat), T2, PD, and FLAIR contrasts. The k-space sampling patterns of these acquisitions were jointly optimized. Images were jointly reconstructed with the proposed APIR4EMC method as well as individually with GRAPPA. Root mean square error (RMSE) to fully sampled reference images and g-factor maps were computed for both methods in the phantom experiment. Visual evaluation was performed in the in-vivo experiment. RESULTS: Compared to GRAPPA, APIR4EMC reduced artifacts and improved SNR of the reconstructed images in the phantom acquisitions. Quantitatively, APIR4EMC substantially reduced noise amplification (g-factor) as well as RMSE compared to GRAPPA. Signal evolution compensation reduced artifacts. In the in-vivo experiments, 1 mm3 isotropic 3D images with contrasts of T1, T1-Fatsat, T2, PD, and FLAIR were acquired in as little as 7.5 min with the acceleration factor of 9. Reconstruction quality was consistent with the phantom results. CONCLUSION: Compared to single contrast reconstruction with GRAPPA, APIR4EMC reduces artifacts and noise amplification in accelerated multi-contrast imaging.

A Capacitive Pressure Sensor Interface IC with Wireless Power and Data Transfer.

This paper presents a capacitive pressure sensor interface circuit design in 180 nm XH018 CMOS technology for an implantable capacitive pressure sensor, which has a wireless power supply and wireless data transfer function. It integrates full-bridge rectifiers, shorting control switches, low-dropout regulators, bandgap references, analog front end, single slope analog to digital converter (ADC), I2C, and an RC oscillator. The low-dropout regulators regulate the wireless power supply coming from the rectifier and provide a stable and accurate 1.8 V DC voltage to other blocks. The capacitance of the pressure sensor is sampled to a discrete voltage by the analog front end. The single slope ADC converts the discrete voltage into 11 bits of digital data, which is then converted into 1 kbps serial data out by the I2C block. The "1" of serial data is modulated to a 500 kHz digital signal that is used to control the shorting switch for wireless data transfer via inductive back scatter. This capacitive pressure sensor interface IC has a resolution of 0.98 mmHg (1.4 fF), average total power consumption of 7.8 mW, and ±3.2% accuracy at the worst case under a -20 to 80 °C temperature range, which improves to ±0.86% when operated between 20 and 60 °C.

Autocalibrated parallel imaging reconstruction with sampling pattern optimization for GRASE: APIR4GRASE.

PURPOSE: To reduce artifacts and scan time of GRASE imaging by selecting an optimal sampling pattern and jointly reconstructing gradient echo and spin echo images. METHODS: We jointly reconstruct images for the different echo types by considering these as additional virtual coil channels in the novel Autocalibrated Parallel Imaging Reconstruction with Sampling Pattern Optimization for GRASE (APIR4GRASE) method. Besides image reconstruction, we identify optimal sampling patterns for the acquisition. The selected optimal patterns were validated on phantom and in-vivo acquisitions. Comparison to the conventional GRASE without acceleration, and to the GRAPPA reconstruction with a single echo type was also performed. RESULTS: Using identified optimal sampling patterns, APIR4GRASE eliminated modulation artifacts in both phantom and in-vivo experiments; mean square error (MSE) was reduced by 78% and 94%, respectively, compared to the conventional GRASE with similar scan time. Both artifacts and g-factor were reduced compared to the GRAPPA reconstruction with a single echo type. CONCLUSION: APIR4GRASE substantially improves the speed and quality of GRASE imaging over the state-of-the-art, and is able to reconstruct both spin echo and gradient echo images.

Selective iteratively reweighted quantile regression for baseline correction.

Extraction of qualitative and quantitative information from large numbers of analytical signals is difficult with drifted baselines, particularly in multivariate analysis. Baseline drift obscures and "fuzzies" signals, and even deteriorates analytical results. In order to obtain accurate and clear results, some effective methods should be proposed and implemented to perform baseline correction before conducting further data analysis. However, most of the classic methods require user intervention or are prone to variability, especially with low signal-to-noise signals. In this study, a novel baseline correction algorithm based on quantile regression and iteratively reweighting strategy is proposed. This does not require user intervention and prior information, such as peak detection. The iteratively reweighting strategy iteratively changes weights of residuals between fitted baseline and original signals. After a series of tests and comparisons with several other popular methods, using various kinds of analytical signals, the proposed method is found to be fast, flexible, robust, and easy to use both in simulated and real datasets.

[Analysis of tobacco site features using near-infrared spectroscopy and projection model].

In this paper, total of 5170 flue-cured tobacco samples collected from 2003 to 2012 in the domestic and foreign origin by Shanghai Tobacco Group Technical Center were tested by near infrared spectroscopy, including the typical upper leaves 1394, central 2550, the lower part of 1226. Using projection model of based on principal component and Fisher criterion (PPF), follow the projected results to get no statistically significant differences at adjacent principal components, and the number of principal components as little as possible, in this paper, four main components to build projection analysis model, the model results show that: the near-infrared spectral characteristics of the upper and lower leaves have a significant difference that can be achieved almost entirely distinguished; while the middle leaves with upper and lower have a certain degree of overlap, which is consistent to the actual situation of the continuity of tobacco leaf. At the same time, Euclidean distance between the predicted sample projection values and the mean projection values of each class in the model, a description is given for the prediction samples to quantify the extent of the site features, and its first and second close categories. Using the dispersion of projected values in model and the given threshold value, prediction results can be refined into typically upper, upper to central, central to upper, typical central, central to the lower, the lower to central, typically the lower, or super-model range. The model was validated by 34 tobacco samples obtained from the re-drying process in 2012 with different origins and parts. This kind of analysis methods, not only can achieve discriminant analysis, and get richer feature attribute information, can provide guidance on the raw tobacco processing and formulations.

The photochemistry properties on interaction silver with tryptophan.

UV-vis and fluorescence spectra for interaction silver(I) ion with tryptophan (Trp) have been studied. The influence of pH of media, multicomponent concentration, including amino acids, silver(I) ion, formaldehyde, sodium dodecyl sulfate (SDS), and trihydroxymethylaminomethane (Tris) as well as temperature, illumination, time, etc. on reaction were investigated, and the mechanism of reaction has been explored. The results shown that the optimum condition for the interaction between tryptophan and silver(I) ion were found.

Pilot-scale experiment on anaerobic bioreactor landfills in China.

Developing countries have begun to investigate bioreactor landfills for municipal solid waste management. This paper describes the impacts of leachate recirculation and recirculation loadings on waste stabilization, landfill gas (LFG) generation and leachate characteristics. Four simulated anaerobic columns, R1-R4, were each filled with about 30 tons of waste and recirculated weekly with 1.6, 0.8 and 0.2m(3) leachate and 0.1m(3) tap water. The results indicated that the chemical oxygen demand (COD) half-time of leachate from R1 was about 180 days, which was 8-14 weeks shorter than that of R2-R4. A large amount of LFG was first produced in R1, and its generation rate was positively correlated to the COD or volatile fatty acid concentrations of influent leachates after the 30th week. By the 50th week of recirculation, the waste in R1 was more stabilized, with 931.2 kg COD or 175.6 kg total organic carbon released and with the highest landfill gas production. However, this contributed mainly to washout by leachate, which also resulted in the reduction of LFG generation potential and accumulation of ammonia and/or phosphorus in the early stage. Therefore, the regimes of leachate recirculation should be adjusted to the phases of waste stabilization to enhance efficiency of energy recovery. Integrated with the strategy of in situ leachate management, extra pre-treatment or post-treatment methods to remove the nutrients are recommended.

Preparation of Cu-Ni alloy nanocrystallites in water-in-oil microemulsions.

The well-mixed copper-nickel nanoparticles were prepared when the molar ratio of Cu2+ to Ni2+ was 1:1 by simultaneous reduction of CuSO4 and NiCl2 with hydrazine in the microemulsion of SDS/n-butanol/n-heptane/water at 70 degrees C, and was characterized by TEM, ED, TGA, EDS, and XRD. ED analysis and XRD patterns suggest the formation of the homogeneous alloy structure in the bimetallic nanoparticles. Average size of the sample calculated from the full width at medium height of peak 111 in the XRD patterns using Scherrer formula is 5.53 nm. TEM photographs show a narrow distribution of Cu-Ni nanoparticles that essentially are monodispersed and the mean diameter is 12 nm. The results indicate that the composition and size of alloy nanoparticles depend on the mole ratio of H2O to SDS, the method of addition of Cu2+ and Ni2+, and the mole ratio of Cu2+ and Ni2+ in the initial precursor solution.

[The preparation of NiFe2O4 composite nanoparticles and spectral properties].

The composite nanoparticles of Ni-ferrite with coated gelatin were prepared with the gel-microemulsion chemical tailoring method. The gelatin FeCl2 and NiCl2 were used to prepare gel, which was tailored to be particles in micellar of microemulsion, and the particles were reduced, compounded and nucleated. Surveying by XRD, TEM, IR and EDS showed that the particles formed were coated by gelatin protein and their mean sizes are in the range of 10-100 nm, and particle sizes are 3.3-4.6 nm. There are about 3-22 NiFe2O4 particles on each spheroid. The measurement of magnetic parameters indicated that the specific saturation magnetization was sigma(s) = 36.31 x 10(3)/4pi(A x m(-1) x g(-1)), coercivity was H(c) = 6750 A x m(-1), and residual magnetism B(r) = 4.39 x 10(3)/4pi(A x m(-1) x g(-1)).

[Preparation and characterization of polymeric ultrafine particles on Hb-SDS-Ag hydrogeles (I)].

Preparation and characterization of polymeric particles ultrafine on Hb-SDS-Ag have been reported. (Taking fixed quantity of Hb, SDS to be putting in a beaker, then Ag(NH3)2NO3 solution, pH--12 buffer, n-heptane and iso-amyl-alcohol were added. And fixed quantity formaldehyde, n-heptane and iso-amyl-alcohol were added in another beaker. They were stirred emulsification respectively, both of mixture were mixed in a three-well flask and which were reacted for 1.5 h at 60 +/- 0.5 degrees C. After cooling, the precipitation was separated and washed once and again, it was dried) The structure of surface have been investigated by XRD, TEM and FT-IR for this polymeric ultrafine particles, and size of diameter is about 120 nm. It is shown that Ag+ ion first formed chemical binding with Hb, then reduced to Ag particles and finally collected to a network polymers of coated by Hb.