1,720,976 research outputs found
Quantitative Magnetic Resonance Imaging of the NIST Phantom
No full textMagnetic resonance imaging dataset of the T1 layer of the NIST Phantom Model 106 (https://doi.org/10.1002/mrm.28779) acquired with inversion-recovery balanced steady-state free-precession (IR-bSSFP), inversion-recovery fast low angle shot (IR-FLASH) sequences, inversion-recovery single-echo spin echo, single-echo spin echo, gradient echo and FLASH-based B1 mapping sequence with preconditioning RF pulse.
The files are intended to be used with the Berkeley Advanced Reconstruction Toolbox (https://doi.org/10.5281/zenodo.592960). Each dataset consists of a ASCII Text-encoded header (.hdr) and a binary-encoded complex float file (.cfl).
data_GSM_t1
Type: Gold-Standard T1 measurement
Object: Single-slice of the T1 spheres of the NIST phantom (Model 106)
Sequence: IR Single-Echo Spin-Echo
TR|TE [ms]: 10000|12
T_INV [ms]: 35 75 100 125 150 250 1000 1500 2000 3000
Inversion: Slice Selective
Dimensions: 2
FOV [mm]: 192
Matrix size: 256x256
Receiver Bandwidth: 130 Hz/Px
data_GSM_t2
Type: Gold-Standard T2 measurement
Object: Single-slice of the T1 spheres of the NIST phantom (Model 106)
Sequence: Single-Echo Spin-Echo
TR|TE [ms]: 10000|(12 20 30 40 70 100 150 200 250 350)
Dimensions: 2
FOV [mm]: 192
Matrix size: 256x256
Receiver Bandwidth: 130Hz/Px
data_b0_gre
Type: B0 Map
Object: Single-slice of the T1 spheres of the NIST phantom (Model 106)
Sequence: Two GRE Acquisitions with different TE, "gre_field_mapping" Sequence
TR|TE1|TE2 [ms]: 400|4.92|7.38
Flipangle [deg]: 60
Dimensions: 2
FOV [mm]: 220
Matrix size: 256x256
Receiver Bandwidth: 849 Hz/Px
data_b1_precond
Type: B1 Map
Object: Single-slice of the T1 spheres of the NIST phantom (Model 106)
Sequence: Preconditioned RF pulse with TurboFLASH Readout
TR|TE [ms]: 6830|2.25
FA [deg]: 8
FA preconditioning pulse [deg]: 80
Dimensions: 2
FOV [mm]: 220
Matrix size: 256x256
Receiver Bandwidth: 490 Hz/Px
data_irflash
Type: Radial Single-Shot Inversion-Recovery FLASH
Object: Single-slice of the T1 spheres of the NIST phantom (Model 106)
Sequence: IR FLASH
TR|TE [ms]: 3.8|2.26
FA [deg]: 8
T_RF [ms]: 1
BWTP: 4
Inversion Block Length [ms]: 15
#Tiny GA: 7
Number of Radial Spokes: 1200
Inversion: Non-Selective
Spoiling: Random RF
Dimensions: 2
FOV [mm]: 220
Matrix size: 256x256
Receiver Bandwidth: 720 Hz/Px
data_irbssfp
Type: Radial Single-Shot Inversion-Recovery bSSFP
Object: Single-slice of the T1 spheres of the NIST phantom (Model 106)
Sequence: IR bSSFP
TR|TE [ms]: 4.5|2.25
FA [deg]: 45
T_RF [ms]: 1
Inversion Block Length [ms]: 15
BWTP: 4
#Tiny GA: 7
Number of Radial Spokes: 1000
Inversion: Non-Selective
Dimensions: 2
FOV [mm]: 220
Matrix size: 256x256
Receiver Bandwidth: 720 Hz/Px
</dl
Bayesian MRI reconstruction with joint uncertainty estimation using diffusion models
No full textPurpose
We introduce a framework that enables efficient sampling from learned probability distributions for MRI reconstruction.
Method
Samples are drawn from the posterior distribution given the measured k-space using the Markov chain Monte Carlo (MCMC) method, different from conventional deep learning-based MRI reconstruction techniques. In addition to the maximum a posteriori estimate for the image, which can be obtained by maximizing the log-likelihood indirectly or directly, the minimum mean square error estimate and uncertainty maps can also be computed from those drawn samples. The data-driven Markov chains are constructed with the score-based generative model learned from a given image database and are independent of the forward operator that is used to model the k-space measurement.
Results
We numerically investigate the framework from these perspectives: (1) the interpretation of the uncertainty of the image reconstructed from undersampled k-space; (2) the effect of the number of noise scales used to train the generative models; (3) using a burn-in phase in MCMC sampling to reduce computation; (4) the comparison to conventional
-wavelet regularized reconstruction; (5) the transferability of learned information; and (6) the comparison to fastMRI challenge.
Conclusion
A framework is described that connects the diffusion process and advanced generative models with Markov chains. We demonstrate its flexibility in terms of contrasts and sampling patterns using advanced generative priors and the benefits of also quantifying the uncertainty for every pixel
Bayesian MRI Reconstruction with Joint Uncertainty Estimation using Diffusion Models
No full textWe introduce a framework that enables efficient sampling from learned probability distributions for MRI reconstruction. Different from conventional deep learning-based MRI reconstruction techniques, samples are drawn from the posterior distribution given the measured k-space using the Markov chain Monte Carlo (MCMC) method. In addition to the maximum a posteriori (MAP) estimate for the image, which can be obtained with conventional methods, the minimum mean square error (MMSE) estimate and uncertainty maps can also be computed. The data-driven Markov chains are constructed from the generative model learned from a given image database and are independent of the forward operator that is used to model the k-space measurement. This provides flexibility because the method can be applied to k-space acquired with different sampling schemes or receive coils using the same pre-trained models. Furthermore, we use a framework based on a reverse diffusion process to be able to utilize advanced generative models. The performance of the method is evaluated on an open dataset using 10-fold undersampling in k-space
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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