A data-driven approach to optimising the encoding for multi-shell diffusion MRI with application to neonatal imaging

AbstractDiffusion MRI has the potential to provide important information about the connectivity and microstructure of the human brain during normal and abnormal development, non-invasively and in vivo. Recent developments in MRI hardware and reconstruction methods now permit the acquisition of large amounts of data within relatively short scan times. This makes it possible to acquire more informative multi-shell data, with diffusion-sensitisation applied along many directions over multiple b-value shells. Such schemes are characterised by the number of shells acquired, and the specific b-value and number of directions sampled for each shell. However, there is currently no clear consensus as to how to optimise these parameters. In this work, we propose a means of optimising multi-shell acquisition schemes by estimating the information content of the diffusion MRI signal, and optimising the acquisition parameters for sensitivity to the observed effects, in a manner agnostic to any particular diffusion analysis method that might subsequently be applied to the data. This method was used to design the acquisition scheme for the neonatal diffusion MRI sequence used in the developing Human Connectome Project, which aims to acquire high quality data and make it freely available to the research community. The final protocol selected by the algorithm, and currently in use within the dHCP, consists of b = 0, 400, 1000, 2600 s/mm2 with 20, 64, 88 & 128 DW directions per shell respectively.HighlightsA data driven method is presented to design multi-shell diffusion MRI acquisition schemes (b-values and no. directions).This method optimises the multi-shell scheme for maximum sensitivity to the information content in the signal.When applied in neonates, the data suggest that a b=0 + 3 shell strategy is appropriate

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Using diffusion MRI data acquired with ultra-high gradients to improve tractography in routine-quality data

10.1101/2021.06.28.450265 ◽

2021 ◽

Author(s):

Chiara Maffei ◽

Christine Lee ◽

Michael Planich ◽

Manisha Ramprasad ◽

Nivedita Ravi ◽

...

Keyword(s):

Diffusion Mri ◽

Region Of Interest ◽

Training Data ◽

Quality Data ◽

High Quality ◽

Scanning Time ◽

Human Connectome Project ◽

High Gradients ◽

Comprehensive Comparison

The development of scanners with ultra-high gradients, spearheaded by the Human Connectome Project, has led to dramatic improvements in the spatial, angular, and diffusion resolution that is feasible for in vivo diffusion MRI acquisitions. The improved quality of the data can be exploited to achieve higher accuracy in the inference of both microstructural and macrostructural anatomy. However, such high-quality data can only be acquired on a handful of Connectom MRI scanners worldwide, while remaining prohibitive in clinical settings because of the constraints imposed by hardware and scanning time. In this study, we first update the classical protocols for tractography-based, manual annotation of major white-matter pathways, to adapt them to the much greater volume and variability of the streamlines that can be produced from today's state-of-the-art diffusion MRI data. We then use these protocols to annotate 42 major pathways manually in data from a Connectom scanner. Finally, we show that, when we use these manually annotated pathways as training data for global probabilistic tractography with anatomical neighborhood priors, we can perform highly accurate, automated reconstruction of the same pathways in much lower-quality, more widely available diffusion MRI data. The outcomes of this work include both a new, comprehensive atlas of WM pathways from Connectom data, and an updated version of our tractography toolbox, TRActs Constrained by UnderLying Anatomy (TRACULA), which is trained on data from this atlas. Both the atlas and TRACULA are distributed publicly as part of FreeSurfer. We present the first comprehensive comparison of TRACULA to the more conventional, multi-region-of-interest approach to automated tractography, and the first demonstration of training TRACULA on high-quality, Connectom data to benefit studies that use more modest acquisition protocols.

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Multiple b-values improve discrimination of cortical gray matter regions using diffusion MRI: an experimental validation with a data-driven approach

Magnetic Resonance Materials in Physics Biology and Medicine ◽

10.1007/s10334-021-00914-3 ◽

2021 ◽

Author(s):

Tara Ganepola ◽

Yoojin Lee ◽

Daniel C. Alexander ◽

Martin I. Sereno ◽

Zoltan Nagy

Keyword(s):

Gray Matter ◽

Diffusion Mri ◽

Repeated Measurements ◽

Data Driven ◽

Correct Classification ◽

Classification Rate ◽

Tissue Properties ◽

Cortical Areas ◽

Human Connectome Project ◽

Data Driven Approach

Abstract Objective To investigate whether varied or repeated b-values provide better diffusion MRI data for discriminating cortical areas with a data-driven approach. Methods Data were acquired from three volunteers at 1.5T with b-values of 800, 1400, 2000 s/mm2 along 64 diffusion-encoding directions. The diffusion signal was sampled from gray matter in seven regions of interest (ROIs). Rotational invariants of the local diffusion profile were extracted as features that characterize local tissue properties. Random forest classification experiments assessed whether classification accuracy improved when data with multiple b-values were used over repeated acquisition of the same (1400 s/mm2) b-value to compare all possible pairs of the seven ROIs. Three data sets from the Human Connectome Project were subjected to similar processing and analysis pipelines in eight ROIs. Results Three different b-values showed an average improvement in correct classification rates of 5.6% and 4.6%, respectively, in the local and HCP data over repeated measurements of the same b-value. The improvement in correct classification rate reached as high as 16% for individual binary classification experiments between two ROIs. Often using only two of the available three b-values were adequate to make such an improvement in classification rates. Conclusion Acquisitions with varying b-values are more suitable for discriminating cortical areas.

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Quantifying nerve decussation abnormalities in the optic chiasm

10.1101/633347 ◽

2019 ◽

Author(s):

Robert J. Puzniak ◽

Khazar Ahmadi ◽

Jörn Kaufmann ◽

Andre Gouws ◽

Antony B. Morland ◽

...

Keyword(s):

Diffusion Mri ◽

Diffusion Tensor ◽

Area Under The Curve ◽

Optic Chiasm ◽

List Type ◽

Test Model ◽

Discriminative Power ◽

Significant Group ◽

Structural Abnormalities

AbstractObjectiveThe human optic chiasm comprises partially crossing optic nerve fibres. Here we used diffusion MRI (dMRI) for the in-vivo identification of the abnormally high proportion of crossing fibres found in the optic chiasm of people with albinism.MethodsIn 9 individuals with albinism and 8 controls high-resolution 3T dMRI data was acquired and analyzed with a set of methods for signal modeling [Diffusion Tensor (DT) and Constrained Spherical Deconvolution (CSD)], tractography, and streamline filtering (LiFE, COMMIT, and SIFT2). The number of crossing and non-crossing streamlines and their weights after filtering entered ROC-analyses to compare the discriminative power of the methods based on the area under the curve (AUC). The dMRI results were cross-validated with fMRI estimates of misrouting in a subset of 6 albinotic individuals.ResultsWe detected significant group differences in chiasmal crossing for both unfiltered DT (p=0.014) and CSD tractograms (p=0.0009) also reflected by AUC measures (for DT and CSD: 0.61 and 0.75, respectively), underlining the discriminative power of the approach. Estimates of crossing strengths obtained with dMRI and fMRI were significantly correlated for CSD (R2=0.83, p=0.012). The results show that streamline filtering methods in combination with probabilistic tracking, both optimized for the data at hand, can improve the detection of crossing in the human optic chiasm.ConclusionsEspecially CSD-based tractography provides an efficient approach to detect structural abnormalities in the optic chiasm. The most realistic results were obtained with filtering methods with parameters optimized for the data at hand.SignificanceOur findings demonstrate a novel anatomy-driven approach for the individualized diagnostics of optic chiasm abnormalities.HighlightsDiffusion MRI is capable of detecting structural abnormalities of the optic chiasm.Quantification of crossing strength in optic chiasm is of promise for albinism diagnostics.Optic chiasm is a powerful test model for neuroimaging methods resolving crossing fibers.

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Predicting toxins found in toxin–antitoxin systems with a role in host-induced Burkholderia pseudomallei persistence

Scientific Reports ◽

10.1038/s41598-020-73887-3 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Brittany N. Ross ◽

Joseph D. Thiriot ◽

Shane M. Wilson ◽

Alfredo G. Torres

Keyword(s):

Burkholderia Pseudomallei ◽

Bacterial Pathogen ◽

Transcriptional Response ◽

Data Driven ◽

Environmental Cues ◽

In Vitro Experiments ◽

Host Defenses ◽

Data Driven Approach

Abstract Burkholderia pseudomallei (Bpm) is a bacterial pathogen that causes Melioidosis, a disease with up to 40% mortality and an infection relapse of 15–23% despite antibiotic treatment. Ineffective clearance of Bpm by antibiotics is believed to be due to persistence, a hibernation-like survival mechanism modulated, in part, by toxin–antitoxin systems (TAS). Several organisms possess a repertoire of TASs but defining environmental cues eliciting their activity is hindered by laborious in vitro experiments, especially when there are many toxins with redundant function. Here, we identified which of 103 proteins in Bpm that share features found in toxins of the TAS and repurposed transcriptional data to identify which ones play a role in surviving intracellular host defenses. Putative toxins with the strongest transcriptional response were found to have low conservation between Bpm strains, while toxins that were constitutively expressed were highly conserved. Further examination of highly conserved toxins BPSS0899, BPSS1321, and BPSL1494 showed that they were functional, and their mutation led to reduce survival within macrophages and reduced in vivo persistence-associated pathology (abscesses) during treatment, but did not affect macrophages persistence. These findings highlight the utility of a data-driven approach to select putative toxins and suggests a selective role for some TAS in host survival.

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Unbiased construction of a temporally consistent morphological atlas of neonatal brain development

10.1101/251512 ◽

2018 ◽

Cited By ~ 9

Author(s):

Andreas Schuh ◽

Antonios Makropoulos ◽

Emma C. Robinson ◽

Lucilio Cordero-Grande ◽

Emer Hughes ◽

...

Keyword(s):

Brain Development ◽

Longitudinal Change ◽

Abnormal Development ◽

Cross Sectional ◽

Brain Images ◽

Human Connectome Project ◽

Mean Shape ◽

Spatio Temporal ◽

And Function

AbstractPremature birth increases the risk of developing neurocognitive and neurobe-havioural disorders. The mechanisms of altered brain development causing these disorders are yet unknown. Studying the morphology and function of the brain during maturation provides us not only with a better understanding of normal development, but may help us to identify causes of abnormal development and their consequences. A particular difficulty is to distinguish abnormal patterns of neurodevelopment from normal variation. The Developing Human Connectome Project (dHCP) seeks to create a detailed four-dimensional (4D) connectome of early life. This connectome may provide insights into normal as well as abnormal patterns of brain development. As part of this project, more than a thousand healthy fetal and neonatal brains will be scanned in vivo. This requires computational methods which scale well to larger data sets. We propose a novel groupwise method for the construction of a spatio-temporal model of mean morphology from cross-sectional brain scans at different gestational ages. This model scales linearly with the number of images and thus improves upon methods used to build existing public neonatal atlases, which derive correspondence between all pairs of images. By jointly estimating mean shape and longitudinal change, the atlas created with our method overcomes temporal inconsistencies, which are encountered when mean shape and intensity images are constructed separately for each time point. Using this approach, we have constructed a spatio-temporal atlas from 275 healthy neonates between 35 and 44 weeks post-menstrual age (PMA). The resulting atlas qualitatively preserves cortical details significantly better than publicly available atlases. This is moreover confirmed by a number of quantitative measures of the quality of the spatial normalisation and sharpness of the resulting template brain images.

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Ventralis intermedius nucleus anatomical variability assessment by MRI structural connectivity

10.1101/2020.08.05.236679 ◽

2020 ◽

Author(s):

Francisca Ferreira ◽

Harith Akram ◽

John Ashburner ◽

Ludvic Zrinzo ◽

Hui Zhang ◽

...

Keyword(s):

Structural Connectivity ◽

List Type ◽

Superior Cerebellar Peduncle ◽

Probabilistic Tractography ◽

Thalamic Nuclei ◽

Individual Subject ◽

Neurosurgical Planning ◽

Human Connectome Project ◽

The Right

AbstractThe ventralis intermedius nucleus (Vim) is centrally placed in the dentato-thalamo-cortical pathway (DTCp) and is a key surgical target in the treatment of severe medically refractory tremor. It is not visible on conventional MRI sequences; consequently, stereotactic targeting currently relies on atlas-based coordinates. This fails to capture individual anatomical variability, which may lead to poor long-term clinical efficacy. Probabilistic tractography, combined with known anatomical connectivity, enables localisation of thalamic nuclei at an individual subject level. There are, however, a number of confounds associated with this technique that may influence results.Here we focused on an established method, using probabilistic tractography to reconstruct the DTCp, to identify the connectivity-defined Vim (cd-Vim) in vivo. Using 100 healthy individuals from the Human Connectome Project, our aim was to quantify cd-Vim variability across this population, measure the discrepancy with atlas-defined Vim (ad-Vim), and assess the influence of potential methodological confounds.We found no significant effect of any of the confounds. The mean cd-Vim coordinate was located within 1.9 mm (left) and 2.1 mm (right) of the average midpoint and 4.9 mm (left) and 5.4 mm (right) from the ad-Vim coordinates. cd-Vim location was more variable on the right, which reflects hemispheric asymmetries in the probabilistic DTCp reconstructed. The superior cerebellar peduncle was identified as a potential source of artificial variance.This work demonstrates significant individual anatomical variability of the cd-Vim that atlas-based approaches fail to capture. This variability was not related to any methodological confound tested. Lateralisation of cerebellar functions, such as speech, may contribute to the observed asymmetry. Tractography-based methods seem sensitive to individual anatomical variability that is missed by conventional neurosurgical targeting; These findings may form the basis for translational tools to improve efficacy and reduce side-effects of thalamic surgery for tremor.HighlightsConnectivity-based Vim position varied markedly between subjects and from atlas-defined coordinates.This positional variability was not related to any methodological confound tested.Hemispheric asymmetry was observed in connectivity-based Vim position.We hypothesise lateralization of cerebellar functions, such as language, may contribute to asymmetry.Knowledge of Vim position variability could help inform neurosurgical planning in the management of tremor.

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Automated characterization of noise distributions in diffusion MRI data

10.1101/686436 ◽

2019 ◽

Cited By ~ 1

Author(s):

Samuel St-Jean ◽

Alberto De Luca ◽

Chantal M. W. Tax ◽

Max A. Viergever ◽

Alexander Leemans

Keyword(s):

Bias Correction ◽

Diffusion Mri ◽

Parallel Imaging ◽

Accurate Estimation ◽

Single Subject ◽

Noise Distribution ◽

Acquisition Process ◽

Reconstruction Methods ◽

Imaging Results

AbstractKnowledge of the noise distribution in magnitude diffusion MRI images is the centerpiece to quantify uncertainties arising from the acquisition process. The use of parallel imaging methods, the number of receiver coils and imaging filters applied by the scanner, amongst other factors, dictate the resulting signal distribution. Accurate estimation beyond textbook Rician or noncentral chi distributions often requires information about the acquisition process (e.g., coils sensitivity maps or reconstruction coeffcients), which is usually not available. We introduce two new automated methods using the moments and maximum likelihood equations of the Gamma distribution to estimate noise distributions as they explicitly depend on the number of coils, making it possible to estimate all unknown parameters using only the magnitude data. A rejection step is used to make the framework automatic and robust to artifacts. Simulations using stationary and spatially varying noncentral chi noise distributions were created for two diffusion weightings with SENSE or GRAPPA reconstruction and 8, 12 or 32 receiver coils. Furthermore, MRI data of a water phantom with different combinations of parallel imaging were acquired on a 3T Philips scanner along with noise-only measurements. Finally, experiments on freely available datasets from a single subject acquired on a 3T GE scanner are used to assess reproducibility when limited information about the acquisition protocol is available. Additionally, we demonstrated the applicability of the proposed methods for a bias correction and denoising task on an in vivo dataset acquired on a 3T Siemens scanner. A generalized version of the bias correction framework for non integer degrees of freedom is also introduced. The proposed framework is compared with three other algorithms with datasets from three vendors, employing different reconstruction methods. Simulations showed that assuming a Rician distribution can lead to misestimation of the noise distribution in parallel imaging. Results on the acquired datasets showed that signal leakage in multiband can also lead to a misestimation of the noise distribution. Repeated acquisitions of in vivo datasets show that the estimated parameters are stable and have lower variability than compared methods. Results for the bias correction and denoising task show that the proposed methods reduce the appearance of noise at high b-value. The proposed algorithms herein can estimate both parameters of the noise distribution automatically, are robust to signal leakage artifacts and perform best when used on acquired noise maps.

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A data‐driven approach to optimising the encoding for multi‐shell diffusion MRI with application to neonatal imaging

NMR in Biomedicine ◽

10.1002/nbm.4348 ◽

2020 ◽

Vol 33 (9) ◽

Cited By ~ 2

Author(s):

Jacques‐Donald Tournier ◽

Daan Christiaens ◽

Jana Hutter ◽

Anthony N. Price ◽

Lucilio Cordero‐Grande ◽

...

Keyword(s):

Diffusion Mri ◽

Data Driven ◽

Data Driven Approach ◽

Neonatal Imaging

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Pushing the limits of in vivo diffusion MRI for the Human Connectome Project

NeuroImage ◽

10.1016/j.neuroimage.2013.05.078 ◽

2013 ◽

Vol 80 ◽

pp. 220-233 ◽

Cited By ~ 279

Author(s):

K. Setsompop ◽

R. Kimmlingen ◽

E. Eberlein ◽

T. Witzel ◽

J. Cohen-Adad ◽

...

Keyword(s):

Diffusion Mri ◽

Human Connectome Project

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MODELLING ERRORS IN X-RAY FLUOROSCOPIC IMAGING SYSTEMS USING PHOTOGRAMMETRIC BUNDLE ADJUSTMENT WITH A DATA-DRIVEN SELF-CALIBRATION APPROACH

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-1-101-2018 ◽

2018 ◽

Vol XLII-1 ◽

pp. 101-106

Author(s):

J. C. K. Chow ◽

D. D. Lichti ◽

K. D. Ang ◽

K. Al-Durgham ◽

G. Kuntze ◽

...

Keyword(s):

Bundle Adjustment ◽

Imaging Systems ◽

Data Driven ◽

Video Frame ◽

X Ray ◽

Target Field ◽

X Ray Imaging ◽

Data Driven Approach ◽

Fluoroscopic Imaging

Abstract. X-ray imaging is a fundamental tool of routine clinical diagnosis. Fluoroscopic imaging can further acquire X-ray images at video frame rates, thus enabling non-invasive in-vivo motion studies of joints, gastrointestinal tract, etc. For both the qualitative and quantitative analysis of static and dynamic X-ray images, the data should be free of systematic biases. Besides precise fabrication of hardware, software-based calibration solutions are commonly used for modelling the distortions. In this primary research study, a robust photogrammetric bundle adjustment was used to model the projective geometry of two fluoroscopic X-ray imaging systems. However, instead of relying on an expert photogrammetrist’s knowledge and judgement to decide on a parametric model for describing the systematic errors, a self-tuning data-driven approach is used to model the complex non-linear distortion profile of the sensors. Quality control from the experiment showed that 0.06mm to 0.09mm 3D reconstruction accuracy was achievable post-calibration using merely 15 X-ray images. As part of the bundle adjustment, the location of the virtual fluoroscopic system relative to the target field can also be spatially resected with an RMSE between 3.10mm and 3.31mm.

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