Optimal Acquisition Schemes in High Angular Resolution Diffusion Weighted Imaging

Prčkovska, V.; Roebroeck, Alard; Pullens, W.L.P.M.; Vilanova, Anna; Romeny, Bart M. Haar

doi:10.1007/978-3-540-85990-1_2

Cited by 18 publications

(29 citation statements)

References 12 publications

(14 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the DOT, we use R 0 = 12 μm . Although higher values can yield a better resolution accuracy, they result in a little robustness to noise, as it has been pointed out by Prčkovska et al (2008). Hence, R 0 = 12 μm is preferable in real-world scenarios (Özarslan et al, 2006), and it is the trade-off used in all our experiments.…”

Section: Methodsmentioning

confidence: 88%

A new methodology for the estimation of fiber populations in the white matter of the brain with the Funk–Radon transform

2010

View full text Add to dashboard Cite

The Funk-Radon Transform (FRT) is a powerful tool for the estimation of fiber populations with High Angular Resolution Diffusion Imaging (HARDI). It is used in Q-Ball imaging (QBI), and other HARDI techniques such as the recent Orientation Probability Density Transform (OPDT), to estimate fiber populations with very few restrictions on the diffusion model. The FRT consists in the integration of the attenuation signal, sampled by the MRI scanner on the unit sphere, along equators orthogonal to the directions of interest. It is easily proved that this calculation is equivalent to the integration of the diffusion propagator along such directions, although a characteristic blurring with a Bessel kernel is introduced. Under a different point of view, the FRT can be seen as an efficient way to compute the angular part of the integral of the attenuation signal in the plane orthogonal to each direction of the diffusion propagator. In this paper, Stoke’s theorem is used to prove that the FRT can in fact be used to compute accurate estimates of the true integrals defining the functions of interest in HARDI, keeping the diffusion model as little restrictive as possible. Varying the assumptions on the attenuation signal, we derive new estimators of fiber orientations, generalizing both Q-Balls and the OPDT. Extensive experiments with both synthetic and real data have been intended to show that the new techniques improve existing ones in many situations.

show abstract

Section: Methodsmentioning

confidence: 88%

A new methodology for the estimation of fiber populations in the white matter of the brain with the Funk–Radon transform

2010

View full text Add to dashboard Cite

show abstract

“…Noise as measured by SNR had more of an effect on smaller direction sets (ie, 12, 16) sets than larger (ie, 32, 64) sets. The AC represents a relatively new metric in the domain of QBI reconstructions, which has been demonstrated as a necessary metric for QBI-based probabilistic tractography (31,32). AC values showed that the diffusion tensor PEV and the ODF PDV may be subject to an error that biased their orientations by as much as 60 for highly corrupt 32-and 64-direction data cases.…”

Section: Discussionmentioning

confidence: 99%

“…For larger direction sets, as well as for Err-gFA in the case of QBI, the impact of noise on metrics was less apparent. Impacts of noise on DWI measurements have previously been investigated by several authors (23,31).…”

Section: Impact Of Snr Variation: Dti/qbimentioning

confidence: 99%

Impact of outliers on diffusion tensor and Q‐ball imaging: Clinical implications and correction strategies

Sharman¹,

Cohen‐Adad²,

Descoteaux³

et al. 2011

Magnetic Resonance Imaging

View full text Add to dashboard Cite

Purpose: To measure the impact of corrupted images often found to occur in diffusion-weighted magnetic resonance imaging (DW-MRI). To propose a robust method for the correction of outliers, applicable to diffusion tensor imaging (DTI) and q-ball imaging (QBI). Materials and Methods:Monte Carlo simulations were carried out to measure the impact of outliers on DTI and QBI reconstruction in a single voxel. Methods to correct outliers based on q-space interpolation and direction removal were then implemented and validated in real image data.Results: Corruption in a single voxel led to clear variations in DTI and QBI metrics. In real data, the method of q-space interpolation was successful in identifying corrupted voxels and restoring them to values consistent with those of uncorrupted images. Conclusion:For images containing few gradient directions, where outlier removal was either impossible due to limited volumes or resulted in large changes in DTI/QBI metrics, q-space interpolation proved to be the method of choice for image restoration. A simple decision support system is proposed to assist clinicians in the correction of their corrupted DW data.

show abstract

“…A different approach studies DTI (and HARDI) data sets, where several measurements in multiple imaging directions are available. These multiple readings allow one to construct a distribution of possible values, and an estimation of noise related to the choice of b value [13, 46, 12, 17, 38], the registration [41, 23], the model fitting [1, 39, 30, 51, 24], the maximum finding, decomposition, or enhancement [25, 43, 22, 38, 42], or the fiber tracking algorithms [18, 10, 14, 36, 54, 26, 12]. …”

Section: Related Workmentioning

confidence: 99%

Uncertainty visualization in HARDI based on ensembles of ODFs

Jiao

Phillips

Gur

et al. 2012

2012 IEEE Pacific Visualization Symposium

View full text Add to dashboard Cite

In this paper, we propose a new and accurate technique for uncertainty analysis and uncertainty visualization based on fiber orientation distribution function (ODF) glyphs, associated with high angular resolution diffusion imaging (HARDI). Our visualization applies volume rendering techniques to an ensemble of 3D ODF glyphs, which we call SIP functions of diffusion shapes, to capture their variability due to underlying uncertainty. This rendering elucidates the complex heteroscedastic structural variation in these shapes. Furthermore, we quantify the extent of this variation by measuring the fraction of the volume of these shapes, which is consistent across all noise levels, the certain volume ratio. Our uncertainty analysis and visualization framework is then applied to synthetic data, as well as to HARDI human-brain data, to study the impact of various image acquisition parameters and background noise levels on the diffusion shapes.

show abstract

Optimal Acquisition Schemes in High Angular Resolution Diffusion Weighted Imaging

Cited by 18 publications

References 12 publications

A new methodology for the estimation of fiber populations in the white matter of the brain with the Funk–Radon transform

A new methodology for the estimation of fiber populations in the white matter of the brain with the Funk–Radon transform

Impact of outliers on diffusion tensor and Q‐ball imaging: Clinical implications and correction strategies

Uncertainty visualization in HARDI based on ensembles of ODFs

Contact Info

Product

Resources

About