Non-local Robust Detection of DTI White Matter Differences with Small Databases

Commowick, Olivier; Stamm, Aymeric

doi:10.1007/978-3-642-33454-2_59

Cited by 5 publications

(7 citation statements)

References 15 publications

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“…However, the over-estimation of extrema was much smaller for EZ-MAP/DisCo-Z relative to IDS/LOO across all non-normal distributions, suggesting that the correction applied by both algorithms is more effective than not applying any correction at all. Recently, Gaussian error propagation (Gebhard et al 2015; Shaker et al 2017) and a non-local means framework (Commowick and Stamm 2012) have been suggested as other corrective method for approximating SSA across multiple sample sizes and initial distributions.…”

Section: Discussionmentioning

confidence: 99%

An evaluation of Z-transform algorithms for identifying subject-specific abnormalities in neuroimaging data

Mayer

Dodd

Ling

et al. 2017

Brain Imaging and Behavior

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The need for algorithms that capture subject-specific abnormalities (SSA) in neuroimaging data is increasingly recognized across many neuropsychiatric disorders. However, the effects of initial distributional properties (e.g., normal versus non-normally distributed data), sample size, and typical preprocessing steps (spatial normalization, blurring kernel and minimal cluster requirements) on SSA remain poorly understood. The current study evaluated the performance of several commonly used z-transform algorithms [leave-one-out (LOO); independent sample (IDS); Enhanced Z-score Microstructural Assessment of Pathology (EZ-MAP); distribution-corrected z-scores (DisCo-Z); and robust z-scores (ROB-Z)] for identifying SSA using simulated and diffusion tensor imaging data from healthy controls (N = 50). Results indicated that all methods (LOO, IDS, EZ-MAP and DisCo-Z) with the exception of the ROB-Z eliminated spurious differences that are present across artificially created groups following a standard z-transform. However, LOO and IDS consistently overestimated the true number of extrema (i.e., SSA) across all sample sizes and distributions. The EZ-MAP and DisCo-Z algorithms more accurately estimated extrema across most distributions and sample sizes, with the exception of skewed distributions. DTI results indicated that registration algorithm (linear versus non-linear) and blurring kernel size differentially affected the number of extrema in positive versus negative tails. Increasing the blurring kernel size increased the number of extrema, although this effect was much more prominent when a minimum cluster volume was applied to the data. In summary, current results highlight the need to statistically compare the frequency of SSA in control samples or to develop appropriate confidence intervals for patient data.

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Section: Discussionmentioning

confidence: 99%

An evaluation of Z-transform algorithms for identifying subject-specific abnormalities in neuroimaging data

Mayer

Dodd

Ling

et al. 2017

Brain Imaging and Behavior

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“…| S | = 11, including one target noisy image and ten co-denoising images. σ̂ i was estimated based on a cubic volume (radius = 2) centered at x i , similar to the methods described in [2, 29]. …”

Section: Resultsmentioning

confidence: 99%

Denoising magnetic resonance images using collaborative non-local means

Chen

Zhang

et al. 2016

Neurocomputing

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Noise artifacts in magnetic resonance (MR) images increase the complexity of image processing workflows and decrease the reliability of inferences drawn from the images. It is thus often desirable to remove such artifacts beforehand for more robust and effective quantitative analysis. It is important to preserve the integrity of relevant image information while removing noise in MR images. A variety of approaches have been developed for this purpose, and the non-local means (NLM) filter has been shown to be able to achieve state-of-the-art denoising performance. For effective denoising, NLM relies heavily on the existence of repeating structural patterns, which however might not always be present within a single image. This is especially true when one considers the fact that the human brain is complex and contains a lot of unique structures. In this paper we propose to leverage the repeating structures from multiple images to collaboratively denoise an image. The underlying assumption is that it is more likely to find repeating structures from multiple scans than from a single scan. Specifically, to denoise a target image, multiple images, which may be acquired from different subjects, are spatially aligned to the target image, and an NLM-like block matching is performed on these aligned images with the target image as the reference. This will significantly increase the number of matching structures and thus boost the denoising performance. Experiments on both synthetic and real data show that the proposed approach, collaborative non-local means (CNLM), outperforms the classic NLM and yields results with markedly improved structural details.

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“…Coupé et al 27. suggested to set , where is the cardinality of , β is a constant that is set to 1, is an estimate of the standard deviation of the noise at x i , which is spatial-adaptively estimated28. In our case, we set block radius d = 1 voxel and search radius m = 2 voxels.…”

Section: Methodsmentioning

confidence: 99%

Improving Estimation of Fiber Orientations in Diffusion MRI Using Inter-Subject Information Sharing

Chen

Zhang

et al. 2016

Sci Rep

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Diffusion magnetic resonance imaging is widely used to investigate diffusion patterns of water molecules in the human brain. It provides information that is useful for tracing axonal bundles and inferring brain connectivity. Diffusion axonal tracing, namely tractography, relies on local directional information provided by the orientation distribution functions (ODFs) estimated at each voxel. To accurately estimate ODFs, data of good signal-to-noise ratio and sufficient angular samples are desired. This is however not always available in practice. In this paper, we propose to improve ODF estimation by using inter-subject image correlation. Specifically, we demonstrate that diffusion-weighted images acquired from different subjects can be transformed to the space of a target subject to drastically increase the number of angular samples to improve ODF estimation. This is largely due to the incoherence of the angular samples generated when the diffusion signals are reoriented and warped to the target space. To reorient the diffusion signals, we propose a new spatial normalization method that directly acts on diffusion signals using local affine transforms. Experiments on both synthetic data and real data show that our method can reduce noise-induced artifacts, such as spurious ODF peaks, and yield more coherent orientations.

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Non-local Robust Detection of DTI White Matter Differences with Small Databases

Cited by 5 publications

References 15 publications

An evaluation of Z-transform algorithms for identifying subject-specific abnormalities in neuroimaging data

An evaluation of Z-transform algorithms for identifying subject-specific abnormalities in neuroimaging data

Denoising magnetic resonance images using collaborative non-local means

Improving Estimation of Fiber Orientations in Diffusion MRI Using Inter-Subject Information Sharing

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