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@article{noddi,
  title={NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain},
  author={Zhang, Hui and Schneider, Torben and Wheeler-Kingshott, Claudia A and Alexander, Daniel C},
  journal={Neuroimage},
  volume={61},
  number={4},
  pages={1000--1016},
  year={2012},
  publisher={Elsevier}
}

@article{amico,
  title={Accelerated microstructure imaging via convex optimization (AMICO) from diffusion MRI data},
  author={Daducci, Alessandro and Canales-Rodr{\'\i}guez, Erick J and Zhang, Hui and Dyrby, Tim B and Alexander, Daniel C and Thiran, Jean-Philippe},
  journal={NeuroImage},
  volume={105},
  pages={32--44},
  year={2015},
  publisher={Elsevier}
}

@article{drbuddi,
  title={DR-BUDDI (Diffeomorphic Registration for Blip-Up blip-Down Diffusion Imaging) method for correcting echo planar imaging distortions},
  author={Irfanoglu, M Okan and Modi, Pooja and Nayak, Amritha and Hutchinson, Elizabeth B and Sarlls, Joelle and Pierpaoli, Carlo},
  journal={Neuroimage},
  volume={106},
  pages={284--299},
  year={2015},
  publisher={Elsevier}
}

@inproceedings{tortoisev3,
  title={TORTOISE v3: Improvements and new features of the NIH diffusion MRI processing pipeline},
  author={Irfanoglu, Mustafa Okan and Nayak, Amritha and Jenkins, Jeffrey and Pierpaoli, Carlo},
  booktitle={Program and proceedings of the ISMRM 25th annual meeting and exhibition, Honolulu, HI, USA},
  year={2017}
}

@article{pfgibbs,
  title={Removal of partial Fourier-induced Gibbs (RPG) ringing artifacts in MRI},
  author={Lee, Hong-Hsi and Novikov, Dmitry S and Fieremans, Els},
  journal={Magnetic Resonance in Medicine},
  volume={86},
  number={5},
  pages={2733--2750},
  year={2021},
  publisher={Wiley Online Library}
}

@article{synthstrip,
  title={SynthStrip: skull-stripping for any brain image},
  author={Hoopes, Andrew and Mora, Jocelyn S and Dalca, Adrian V and Fischl, Bruce and Hoffmann, Malte},
  journal={NeuroImage},
  volume={260},
  pages={119474},
  year={2022},
  publisher={Elsevier}
}

@article{synthseg1,
  title={SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining},
  author={Billot, Benjamin and Greve, Douglas N and Puonti, Oula and Thielscher, Axel and Van Leemput, Koen and Fischl, Bruce and Dalca, Adrian V and Iglesias, Juan Eugenio and others},
  journal={Medical image analysis},
  volume={86},
  pages={102789},
  year={2023},
  publisher={Elsevier}
}

@article{synthseg2,
  title={Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets},
  author={Billot, Benjamin and Magdamo, Colin and Cheng, You and Arnold, Steven E and Das, Sudeshna and Iglesias, Juan Eugenio},
  journal={Proceedings of the National Academy of Sciences},
  volume={120},
  number={9},
  pages={e2216399120},
  year={2023},
  publisher={National Acad Sciences}
}

@article{yeh2013deterministic,
  title={Deterministic diffusion fiber tracking improved by quantitative anisotropy},
  author={Yeh, Fang-Cheng and Verstynen, Timothy D and Wang, Yibao and Fern{\'a}ndez-Miranda, Juan C and Tseng, Wen-Yih Isaac},
  journal={PloS one},
  volume={8},
  number={11},
  pages={e80713},
  year={2013},
  publisher={Public Library of Science San Francisco, USA}
}

@article{autotrack,
  title={Shape analysis of the human association pathways},
  author={Yeh, Fang-Cheng},
  journal={Neuroimage},
  volume={223},
  pages={117329},
  year={2020},
  publisher={Elsevier}
}

@article{fsl,
	series = {20 {YEARS} {OF} {fMRI}},
	title = {{FSL}},
	volume = {62},
	issn = {1053-8119},
	url = {https://www.sciencedirect.com/science/article/pii/S1053811911010603},
	doi = {10.1016/j.neuroimage.2011.09.015},
	abstract = {FSL (the FMRIB Software Library) is a comprehensive library of analysis tools for functional, structural and diffusion MRI brain imaging data, written mainly by members of the Analysis Group, FMRIB, Oxford. For this NeuroImage special issue on “20 years of fMRI” we have been asked to write about the history, developments and current status of FSL. We also include some descriptions of parts of FSL that are not well covered in the existing literature. We hope that some of this content might be of interest to users of FSL, and also maybe to new research groups considering creating, releasing and supporting new software packages for brain image analysis.},
	number = {2},
	urldate = {2024-08-06},
	journal = {NeuroImage},
	author = {Jenkinson, Mark and Beckmann, Christian F. and Behrens, Timothy E. J. and Woolrich, Mark W. and Smith, Stephen M.},
	month = aug,
	year = {2012},
	keywords = {FSL, Software},
	pages = {782--790},
	file = {ScienceDirect Snapshot:/home/galkepler/Zotero/storage/AKPNE6UP/S1053811911010603.html:text/html;Submitted Version:/home/galkepler/Zotero/storage/WSMH855W/Jenkinson et al. - 2012 - FSL.pdf:application/pdf},
}

@article{eddyqc,
	title = {Automated quality control for within and between studies diffusion {MRI} data using a non-parametric framework for movement and distortion correction},
	volume = {184},
	issn = {1095-9572},
	doi = {10.1016/j.neuroimage.2018.09.073},
	abstract = {Diffusion MRI data can be affected by hardware and subject-related artefacts that can adversely affect downstream analyses. Therefore, automated quality control (QC) is of great importance, especially in large population studies where visual QC is not practical. In this work, we introduce an automated diffusion MRI QC framework for single subject and group studies. The QC is based on a comprehensive, non-parametric approach for movement and distortion correction: FSL EDDY, which allows us to extract a rich set of QC metrics that are both sensitive and specific to different types of artefacts. Two different tools are presented: QUAD (QUality Assessment for DMRI), for single subject QC and SQUAD (Study-wise QUality Assessment for DMRI), which is designed to enable group QC and facilitate cross-studies harmonisation efforts.},
	language = {eng},
	journal = {Neuroimage},
	author = {Bastiani, Matteo and Cottaar, Michiel and Fitzgibbon, Sean P. and Suri, Sana and Alfaro-Almagro, Fidel and Sotiropoulos, Stamatios N. and Jbabdi, Saad and Andersson, Jesper L. R.},
	month = jan,
	year = {2019},
	pmid = {30267859},
	pmcid = {PMC6264528},
	keywords = {Artifacts, Brain, Brain Mapping, Diffusion Magnetic Resonance Imaging, Diffusion MRI, Diffusion Tensor Imaging, Eddy current, Female, Humans, Image Processing, Computer-Assisted, Male, Movement, Quality control, Quality Control, Reproducibility of Results, Signal-To-Noise Ratio, Susceptibility},
	pages = {801--812},
	file = {Full Text:/home/galkepler/Zotero/storage/7CKISWDD/Bastiani et al. - 2019 - Automated quality control for within and between s.pdf:application/pdf},
}

@article{templateflow,
    author = {Ciric, R. and Thompson, William H. and Lorenz, R. and Goncalves, M. and MacNicol, E. and Markiewicz, C. J. and Halchenko, Y. O. and Ghosh, S. S. and Gorgolewski, K. J. and Poldrack, R. A. and Esteban, O.},
    title = {{TemplateFlow}: {FAIR}-sharing of multi-scale, multi-species brain models},
    volume = {19},
    pages = {1568--1571},
    year = {2022},
    doi = {10.1038/s41592-022-01681-2},
    journal = {Nature Methods}
}

@article{mni152lin,
    title = {A {Probabilistic} {Atlas} of the {Human} {Brain}: {Theory} and {Rationale} for {Its} {Development}: {The} {International} {Consortium} for {Brain} {Mapping} ({ICBM})},
    author = {Mazziotta, John C. and Toga, Arthur W. and Evans, Alan and Fox, Peter and Lancaster, Jack},
    volume = {2},
    issn = {1053-8119},
    shorttitle = {A {Probabilistic} {Atlas} of the {Human} {Brain}},
    doi = {10.1006/nimg.1995.1012},
    number = {2, Part A},
    journal = {NeuroImage},
    year = {1995},
    pages = {89--101}
}

@article{mni152nlin2009casym,
    title = {Unbiased nonlinear average age-appropriate brain templates from birth to adulthood},
    author = {Fonov, VS and Evans, AC and McKinstry, RC and Almli, CR and Collins, DL},
    doi = {10.1016/S1053-8119(09)70884-5},
    journal = {NeuroImage},
    pages = {S102},
    volume = {47, Supplement 1},
    year = 2009
}

@article{mni152nlin6asym,
    author = {Evans, AC and Janke, AL and Collins, DL and Baillet, S},
    title = {Brain templates and atlases},
    doi = {10.1016/j.neuroimage.2012.01.024},
    journal = {NeuroImage},
    volume = {62},
    number = {2},
    pages = {911--922},
    year = 2012
}
@article{fan2016,
	title = {The {Human} {Brainnetome} {Atlas}: {A} {New} {Brain} {Atlas} {Based} on {Connectional} {Architecture}},
	volume = {26},
	issn = {1460-2199},
	shorttitle = {The {Human} {Brainnetome} {Atlas}},
	doi = {10.1093/cercor/bhw157},
	abstract = {The human brain atlases that allow correlating brain anatomy with psychological and cognitive functions are in transition from ex vivo histology-based printed atlases to digital brain maps providing multimodal in vivo information. Many current human brain atlases cover only specific structures, lack fine-grained parcellations, and fail to provide functionally important connectivity information. Using noninvasive multimodal neuroimaging techniques, we designed a connectivity-based parcellation framework that identifies the subdivisions of the entire human brain, revealing the in vivo connectivity architecture. The resulting human Brainnetome Atlas, with 210 cortical and 36 subcortical subregions, provides a fine-grained, cross-validated atlas and contains information on both anatomical and functional connections. Additionally, we further mapped the delineated structures to mental processes by reference to the BrainMap database. It thus provides an objective and stable starting point from which to explore the complex relationships between structure, connectivity, and function, and eventually improves understanding of how the human brain works. The human Brainnetome Atlas will be made freely available for download at http://atlas.brainnetome.org, so that whole brain parcellations, connections, and functional data will be readily available for researchers to use in their investigations into healthy and pathological states.},
	language = {eng},
	number = {8},
	journal = {Cereb Cortex},
	author = {Fan, Lingzhong and Li, Hai and Zhuo, Junjie and Zhang, Yu and Wang, Jiaojian and Chen, Liangfu and Yang, Zhengyi and Chu, Congying and Xie, Sangma and Laird, Angela R. and Fox, Peter T. and Eickhoff, Simon B. and Yu, Chunshui and Jiang, Tianzi},
	month = aug,
	year = {2016},
	pmid = {27230218},
	pmcid = {PMC4961028},
	keywords = {Adolescent, Adult, Atlases as Topic, Brain, brain atlas, connectivity-based parcellation, Connectome, diffusion tensor imaging, Diffusion Tensor Imaging, Female, functional characterization, Humans, Internet, Magnetic Resonance Imaging, Male, Multimodal Imaging, Rest, resting-state functional connectivity, User-Computer Interface, Young Adult},
	pages = {3508--3526},
	file = {Full Text:/home/galkepler/Zotero/storage/WH565Y7H/Fan et al. - 2016 - The Human Brainnetome Atlas A New Brain Atlas Bas.pdf:application/pdf},
}
@article{huang2022,
	title = {An extended {Human} {Connectome} {Project} multimodal parcellation atlas of the human cortex and subcortical areas},
	volume = {227},
	issn = {1863-2661},
	doi = {10.1007/s00429-021-02421-6},
	abstract = {A modified and extended version, HCPex, is provided of the surface-based Human Connectome Project-MultiModal Parcellation atlas of human cortical areas (HCP-MMP v1.0, Glasser et al. 2016). The original atlas with 360 cortical areas has been modified in HCPex for ease of use with volumetric neuroimaging software, such as SPM, FSL, and MRIcroGL. HCPex is also an extended version of the original atlas in which 66 subcortical areas (33 in each hemisphere) have been added, including the amygdala, thalamus, putamen, caudate nucleus, nucleus accumbens, globus pallidus, mammillary bodies, septal nuclei and nucleus basalis. HCPex makes available the excellent parcellation of cortical areas in HCP-MMP v1.0 to users of volumetric software, such as SPM and FSL, as well as adding some subcortical regions, and providing labelled coronal views of the human brain.},
	language = {eng},
	number = {3},
	journal = {Brain Struct Funct},
	author = {Huang, Chu-Chung and Rolls, Edmund T. and Feng, Jianfeng and Lin, Ching-Po},
	month = apr,
	year = {2022},
	pmid = {34791508},
	keywords = {Brain, Connectome, Extended HCP atlas, fMRI, HCPex, Human Connectome Project, Humans, Magnetic Resonance Imaging, Neuroimaging, Neuroimaging atlas, Thalamus},
	pages = {763--778},
}
@article{schaefer2018,
	title = {Local-{Global} {Parcellation} of the {Human} {Cerebral} {Cortex} from {Intrinsic} {Functional} {Connectivity} {MRI}},
	volume = {28},
	issn = {1460-2199},
	doi = {10.1093/cercor/bhx179},
	abstract = {A central goal in systems neuroscience is the parcellation of the cerebral cortex into discrete neurobiological "atoms". Resting-state functional magnetic resonance imaging (rs-fMRI) offers the possibility of in vivo human cortical parcellation. Almost all previous parcellations relied on 1 of 2 approaches. The local gradient approach detects abrupt transitions in functional connectivity patterns. These transitions potentially reflect cortical areal boundaries defined by histology or visuotopic fMRI. By contrast, the global similarity approach clusters similar functional connectivity patterns regardless of spatial proximity, resulting in parcels with homogeneous (similar) rs-fMRI signals. Here, we propose a gradient-weighted Markov Random Field (gwMRF) model integrating local gradient and global similarity approaches. Using task-fMRI and rs-fMRI across diverse acquisition protocols, we found gwMRF parcellations to be more homogeneous than 4 previously published parcellations. Furthermore, gwMRF parcellations agreed with the boundaries of certain cortical areas defined using histology and visuotopic fMRI. Some parcels captured subareal (somatotopic and visuotopic) features that likely reflect distinct computational units within known cortical areas. These results suggest that gwMRF parcellations reveal neurobiologically meaningful features of brain organization and are potentially useful for future applications requiring dimensionality reduction of voxel-wise fMRI data. Multiresolution parcellations generated from 1489 participants are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable\_projects/brain\_parcellation/Schaefer2018\_LocalGlobal).},
	language = {eng},
	number = {9},
	journal = {Cereb Cortex},
	author = {Schaefer, Alexander and Kong, Ru and Gordon, Evan M. and Laumann, Timothy O. and Zuo, Xi-Nian and Holmes, Avram J. and Eickhoff, Simon B. and Yeo, B. T. Thomas},
	month = sep,
	year = {2018},
	pmid = {28981612},
	pmcid = {PMC6095216},
	keywords = {Adolescent, Adult, Brain Mapping, Cerebral Cortex, Female, Humans, Magnetic Resonance Imaging, Male, Nerve Net, Young Adult},
	pages = {3095--3114},
	file = {Full Text:/home/galkepler/Zotero/storage/T57U6SVC/Schaefer et al. - 2018 - Local-Global Parcellation of the Human Cerebral Co.pdf:application/pdf},
}
@article{act2012,
	title = {Anatomically-constrained tractography: improved diffusion {MRI} streamlines tractography through effective use of anatomical information},
	volume = {62},
	issn = {1095-9572},
	shorttitle = {Anatomically-constrained tractography},
	doi = {10.1016/j.neuroimage.2012.06.005},
	abstract = {Diffusion MRI streamlines tractography suffers from a number of inherent limitations, one of which is the accurate determination of when streamlines should be terminated. Use of an accurate streamlines propagation mask from segmentation of an anatomical image confines the streamlines to the volume of the brain white matter, but does not take full advantage of all of the information available from such an image. We present a modular addition to streamlines tractography, which makes more effective use of the information available from anatomical image segmentation, and the known properties of the neuronal axons being reconstructed, to apply biologically realistic priors to the streamlines generated; we refer to this as "Anatomically-Constrained Tractography". Results indicate that some of the known false positives associated with tractography algorithms are prevented, such that the biological accuracy of the reconstructions should be improved, provided that state-of-the-art streamlines tractography methods are used.},
	language = {eng},
	number = {3},
	journal = {Neuroimage},
	author = {Smith, Robert E. and Tournier, Jacques-Donald and Calamante, Fernando and Connelly, Alan},
	month = sep,
	year = {2012},
	pmid = {22705374},
	keywords = {Brain, Brain Mapping, Diffusion Tensor Imaging, Humans, Image Interpretation, Computer-Assisted},
	pages = {1924--1938},
}
@article{fsl_flirt,
	title = {A {Bayesian} model of shape and appearance for subcortical brain segmentation},
	volume = {56},
	issn = {1095-9572},
	doi = {10.1016/j.neuroimage.2011.02.046},
	abstract = {Automatic segmentation of subcortical structures in human brain MR images is an important but difficult task due to poor and variable intensity contrast. Clear, well-defined intensity features are absent in many places along typical structure boundaries and so extra information is required to achieve successful segmentation. A method is proposed here that uses manually labelled image data to provide anatomical training information. It utilises the principles of the Active Shape and Appearance Models but places them within a Bayesian framework, allowing probabilistic relationships between shape and intensity to be fully exploited. The model is trained for 15 different subcortical structures using 336 manually-labelled T1-weighted MR images. Using the Bayesian approach, conditional probabilities can be calculated easily and efficiently, avoiding technical problems of ill-conditioned covariance matrices, even with weak priors, and eliminating the need for fitting extra empirical scaling parameters, as is required in standard Active Appearance Models. Furthermore, differences in boundary vertex locations provide a direct, purely local measure of geometric change in structure between groups that, unlike voxel-based morphometry, is not dependent on tissue classification methods or arbitrary smoothing. In this paper the fully-automated segmentation method is presented and assessed both quantitatively, using Leave-One-Out testing on the 336 training images, and qualitatively, using an independent clinical dataset involving Alzheimer's disease. Median Dice overlaps between 0.7 and 0.9 are obtained with this method, which is comparable or better than other automated methods. An implementation of this method, called FIRST, is currently distributed with the freely-available FSL package.},
	language = {eng},
	number = {3},
	journal = {Neuroimage},
	author = {Patenaude, Brian and Smith, Stephen M. and Kennedy, David N. and Jenkinson, Mark},
	month = jun,
	year = {2011},
	pmid = {21352927},
	pmcid = {PMC3417233},
	keywords = {Adolescent, Adult, Aged, Algorithms, Artificial Intelligence, Bayes Theorem, Brain, Computer-Assisted, Female, Humans, Image Processing, Linear Models, Magnetic Resonance Imaging, Male, Middle Aged, Models, Neurological, Reproducibility of Results, Statistical, Thalamus, Young Adult},
	pages = {907--922},
	file = {Accepted Version:/home/galkepler/Zotero/storage/G9M2W6PG/Patenaude et al. - 2011 - A Bayesian model of shape and appearance for subco.pdf:application/pdf},
}

@article{fsl_bet,
	title = {Fast robust automated brain extraction},
	volume = {17},
	issn = {1065-9471},
	doi = {10.1002/hbm.10062},
	abstract = {An automated method for segmenting magnetic resonance head images into brain and non-brain has been developed. It is very robust and accurate and has been tested on thousands of data sets from a wide variety of scanners and taken with a wide variety of MR sequences. The method, Brain Extraction Tool (BET), uses a deformable model that evolves to fit the brain's surface by the application of a set of locally adaptive model forces. The method is very fast and requires no preregistration or other pre-processing before being applied. We describe the new method and give examples of results and the results of extensive quantitative testing against "gold-standard" hand segmentations, and two other popular automated methods.},
	language = {eng},
	number = {3},
	journal = {Hum Brain Mapp},
	author = {Smith, Stephen M.},
	month = nov,
	year = {2002},
	pmid = {12391568},
	pmcid = {PMC6871816},
	keywords = {Algorithms, Animals, Brain, Humans, Magnetic Resonance Imaging},
	pages = {143--155},
	file = {Full Text:/home/galkepler/Zotero/storage/TU674HXC/Smith - 2002 - Fast robust automated brain extraction.pdf:application/pdf},
}
@inproceedings{hsvs_2020,
	title = {Hybrid {Surface}-{Volume} {Segmentation} for improved {Anatomically}-{Constrained} {Tractography}},
	abstract = {The "Anatomically-Constrained Tractography (ACT)" framework enhances the biological accuracy of diffusion MRI streamlines tractography reconstruction, by constraining the propagation and termination of streamlines based on tissue segmentation from a high-resolution, high-contrast anatomical image (e.g. T1-weighted).

The tissue segmentation image to be used in this context has historically been derived using a sequence of processing tools provided within the FSL software package - in particular the intensity-based segmentation of the "fast" command - as automated in the MRtrix3 software command "5ttgen fsl". This can however lead to erroneous segmentations in the presence of image noise and/or poor tissue contrast, with ACT concomitantly applying inappropriate anatomical priors in such locations. The ACT framework itself is however independent of the particular approach used to derive this image, and thus may benefit from the use of more advanced tissue segmentation algorithms.

Here we demonstrate a novel processing pipeline for the derivation of a tissue segmentation image for ACT. It combines information from both surface-based reconstructions of various structures, and volume-based tissue segmentations for structures for which the former is not performed explicitly; we thus name this algorithm "Hybrid Surface-Volume Segmentation (HSVS)". Because these data are mapped back to a common image space, its utilisation does not require modification to the ACT software framework, nor does it incur any substantial computational performance penalty during streamlines propagation.},
	author = {Smith, Robert and Skoch, Antonin and Bajada, Claude and Caspers, Svenja and Connelly, Alan},
	month = jun,
	year = {2020},
	file = {Full Text PDF:/home/galkepler/Zotero/storage/4LAK2G8A/Smith et al. - 2020 - Hybrid Surface-Volume Segmentation for improved An.pdf:application/pdf},
}
@article{hitchhiker_dti_2006,
	title = {A hitchhiker's guide to diffusion tensor imaging},
	volume = {7},
	issn = {1662-4548},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3594764/},
	doi = {10.3389/fnins.2013.00031},
	abstract = {Diffusion Tensor Imaging (DTI) studies are increasingly popular among clinicians and researchers as they provide unique insights into brain network connectivity. However, in order to optimize the use of DTI, several technical and methodological aspects must be factored in. These include decisions on: acquisition protocol, artifact handling, data quality control, reconstruction algorithm, and visualization approaches, and quantitative analysis methodology. Furthermore, the researcher and/or clinician also needs to take into account and decide on the most suited software tool(s) for each stage of the DTI analysis pipeline. Herein, we provide a straightforward hitchhiker's guide, covering all of the workflow's major stages. Ultimately, this guide will help newcomers navigate the most critical roadblocks in the analysis and further encourage the use of DTI.},
	urldate = {2024-08-13},
	journal = {Front Neurosci},
	author = {Soares, José M. and Marques, Paulo and Alves, Victor and Sousa, Nuno},
	month = mar,
	year = {2013},
	pmid = {23486659},
	pmcid = {PMC3594764},
	pages = {31},
}
@article{dti_1994,
	title = {{MR} diffusion tensor spectroscopy and imaging},
	volume = {66},
	issn = {0006-3495},
	doi = {10.1016/S0006-3495(94)80775-1},
	abstract = {This paper describes a new NMR imaging modality--MR diffusion tensor imaging. It consists of estimating an effective diffusion tensor, Deff, within a voxel, and then displaying useful quantities derived from it. We show how the phenomenon of anisotropic diffusion of water (or metabolites) in anisotropic tissues, measured noninvasively by these NMR methods, is exploited to determine fiber tract orientation and mean particle displacements. Once Deff is estimated from a series of NMR pulsed-gradient, spin-echo experiments, a tissue's three orthotropic axes can be determined. They coincide with the eigenvectors of Deff, while the effective diffusivities along these orthotropic directions are the eigenvalues of Deff. Diffusion ellipsoids, constructed in each voxel from Deff, depict both these orthotropic axes and the mean diffusion distances in these directions. Moreover, the three scalar invariants of Deff, which are independent of the tissue's orientation in the laboratory frame of reference, reveal useful information about molecular mobility reflective of local microstructure and anatomy. Inherently tensors (like Deff) describing transport processes in anisotropic media contain new information within a macroscopic voxel that scalars (such as the apparent diffusivity, proton density, T1, and T2) do not.},
	language = {eng},
	number = {1},
	journal = {Biophys J},
	author = {Basser, P. J. and Mattiello, J. and LeBihan, D.},
	month = jan,
	year = {1994},
	pmid = {8130344},
	pmcid = {PMC1275686},
	keywords = {Animals, Brain, Cats, Magnetic Resonance Spectroscopy, Mathematics},
	pages = {259--267},
	file = {Full Text:/home/galkepler/Zotero/storage/VA7SBQ9K/Basser et al. - 1994 - MR diffusion tensor spectroscopy and imaging.pdf:application/pdf},
}
@article{westin_2002,
	title = {Processing and visualization for diffusion tensor {MRI}},
	volume = {6},
	issn = {1361-8415},
	doi = {10.1016/s1361-8415(02)00053-1},
	abstract = {This paper presents processing and visualization techniques for Diffusion Tensor Magnetic Resonance Imaging (DT-MRI). In DT-MRI, each voxel is assigned a tensor that describes local water diffusion. The geometric nature of diffusion tensors enables us to quantitatively characterize the local structure in tissues such as bone, muscle, and white matter of the brain. This makes DT-MRI an interesting modality for image analysis. In this paper we present a novel analytical solution to the Stejskal-Tanner diffusion equation system whereby a dual tensor basis, derived from the diffusion sensitizing gradient configuration, eliminates the need to solve this equation for each voxel. We further describe decomposition of the diffusion tensor based on its symmetrical properties, which in turn describe the geometry of the diffusion ellipsoid. A simple anisotropy measure follows naturally from this analysis. We describe how the geometry or shape of the tensor can be visualized using a coloring scheme based on the derived shape measures. In addition, we demonstrate that human brain tensor data when filtered can effectively describe macrostructural diffusion, which is important in the assessment of fiber-tract organization. We also describe how white matter pathways can be monitored with the methods introduced in this paper. DT-MRI tractography is useful for demonstrating neural connectivity (in vivo) in healthy and diseased brain tissue.},
	language = {eng},
	number = {2},
	journal = {Med Image Anal},
	author = {Westin, C.-F. and Maier, S. E. and Mamata, H. and Nabavi, A. and Jolesz, F. A. and Kikinis, R.},
	month = jun,
	year = {2002},
	pmid = {12044998},
	keywords = {Brain, Data Display, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Models, Theoretical, Sensitivity and Specificity},
	pages = {93--108},
}
@inproceedings{dhollander_2016,
	title = {Unsupervised 3-tissue response function estimation from single-shell or multi-shell diffusion {MR} data without a co-registered {T1} image},
	abstract = {To overcome the fact that the fibre orientation distribution (FOD) from constrained spherical deconvolution (CSD) assumes a single-fibre white matter (WM) response function—and is thus inappropriate and distorted in voxels containing grey matter (GM) or cerebrospinal fluid (CSF)—multi-shell multi-tissue CSD (MSMT-CSD) was proposed. MSMT-CSD can resolve WM, GM and CSF signal contributions, but requires multi-shell data. Very recently, we proposed a novel method that can achieve the same results using just single-shell data. We refer to this method as "single-shell 3-tissue CSD" (SS3T-CSD). Both MSMT-CSD and SS3T-CSD require WM, GM and CSF response functions. These can be obtained from manually selected exemplary voxels of the tissue classes, or via the procedure described initially in the MSMT-CSD paper, which relies on a highly accurately co-registered T1 image. We propose an unsupervised procedure that does not depend on a T1 image, nor registration, and works for both single-shell and multi-shell data.},
	author = {Dhollander, Thijs and Raffelt, David and Connelly, Alan},
	month = sep,
	year = {2016},
	file = {Full Text PDF:/home/galkepler/Zotero/storage/JQQXDMM5/Dhollander et al. - 2016 - Unsupervised 3-tissue response function estimation.pdf:application/pdf},
}

@inproceedings{dhollander_2019,
	title = {Improved white matter response function estimation for 3-tissue constrained spherical deconvolution},
	abstract = {Multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD) and single-shell 3-tissue CSD (SS3T-CSD) decompose the diffusion MRI signal in a white matter (WM) fibre orientation distribution (FOD) and grey matter (GM) and cerebrospinal fluid (CSF) compartments. An unsupervised method was recently proposed to estimate the required WM/GM/CSF response functions. In this work, we improved WM response function estimation by leveraging WM properties across b-values, resulting in better 3-tissue CSD fit to most data. Slightly beyond the scope of this work, we also make an interesting observation in developing Human Connectome Project data.},
	author = {Dhollander, Thijs and Mito, Remika and Raffelt, David and Connelly, Alan},
	month = may,
	year = {2019},
	file = {Full Text PDF:/home/galkepler/Zotero/storage/PFCZ9XZB/Dhollander et al. - 2019 - Improved white matter response function estimation.pdf:application/pdf},
}
@article{tournier_SD_2012,
	title = {{MRtrix}: {Diffusion} tractography in crossing fiber regions},
	volume = {22},
	copyright = {Copyright © 2011 Wiley Periodicals, Inc.},
	issn = {1098-1098},
	shorttitle = {{MRtrix}},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/ima.22005},
	doi = {10.1002/ima.22005},
	abstract = {In recent years, diffusion-weighted magnetic resonance imaging has attracted considerable attention due to its unique potential to delineate the white matter pathways of the brain. However, methodologies currently available and in common use among neuroscientists and clinicians are typically based on the diffusion tensor model, which has comprehensively been shown to be inadequate to characterize diffusion in brain white matter. This is due to the fact that it is only capable of resolving a single fiber orientation per voxel, causing incorrect fiber orientations, and hence pathways, to be estimated through these voxels. Given that the proportion of affected voxels has been recently estimated at 90\%, this is a serious limitation. Furthermore, most implementations use simple “deterministic” streamlines tracking algorithms, which have now been superseded by “probabilistic” approaches. In this study, we present a robust set of tools to perform tractography, using fiber orientations estimated using the validated constrained spherical deconvolution method, coupled with a probabilistic streamlines tracking algorithm. This methodology is shown to provide superior delineations of a number of known white matter tracts, in a manner robust to crossing fiber effects. These tools have been compiled into a software package, called MRtrix, which has been made freely available for use by the scientific community. © 2012 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 22, 53–66, 2012},
	language = {en},
	number = {1},
	urldate = {2024-08-14},
	journal = {International Journal of Imaging Systems and Technology},
	author = {Tournier, J-Donald and Calamante, Fernando and Connelly, Alan},
	year = {2012},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/ima.22005},
	keywords = {crossing fibers, diffusion MRI, fiber tracking, tractography},
	pages = {53--66},
	file = {Full Text PDF:/home/galkepler/Zotero/storage/VAKS38NU/Tournier et al. - 2012 - MRtrix Diffusion tractography in crossing fiber r.pdf:application/pdf;Snapshot:/home/galkepler/Zotero/storage/DCH4H5S6/ima.html:text/html},
}
@article{tournier_iFOD2_2010,
	title = {Improved probabilistic streamlines tractography by 2nd order integration over fibre orientation distributions},
	volume = {18},
	journal = {Proc. Intl. Soc. Mag. Reson. Med. (ISMRM)},
	author = {Tournier, Jacques-Donald and Calamante, F. and Connelly, Alan},
	month = jan,
	year = {2010},
}
@article{sift_2013,
	title = {{SIFT}: {Spherical}-deconvolution informed filtering of tractograms},
	volume = {67},
	issn = {1095-9572},
	shorttitle = {{SIFT}},
	doi = {10.1016/j.neuroimage.2012.11.049},
	abstract = {Diffusion MRI allows the structural connectivity of the whole brain (the 'tractogram') to be estimated in vivo non-invasively using streamline tractography. The biological accuracy of these data sets is however limited by the inherent biases associated with the reconstruction method. Here we propose a method to retrospectively improve the accuracy of these reconstructions, by selectively filtering out streamlines from the tractogram in a manner that improves the fit between the streamline reconstruction and the underlying diffusion images. This filtering is guided by the results of spherical deconvolution of the diffusion signal, hence the acronym SIFT: spherical-deconvolution informed filtering of tractograms. Data sets processed by this algorithm show a marked reduction in known reconstruction biases, and improved biological plausibility. Emerging methods in diffusion MRI, particularly those that aim to characterise and compare the structural connectivity of the brain, should benefit from the improved accuracy of the reconstruction.},
	language = {eng},
	journal = {Neuroimage},
	author = {Smith, Robert E. and Tournier, Jacques-Donald and Calamante, Fernando and Connelly, Alan},
	month = feb,
	year = {2013},
	pmid = {23238430},
	keywords = {Algorithms, Brain, Connectome, Diffusion Tensor Imaging, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Nerve Fibers, Myelinated, Reproducibility of Results, Sensitivity and Specificity, Software},
	pages = {298--312},
}

@article{sift2_2015,
	title = {{SIFT2}: {Enabling} dense quantitative assessment of brain white matter connectivity using streamlines tractography},
	volume = {119},
	issn = {1095-9572},
	shorttitle = {{SIFT2}},
	doi = {10.1016/j.neuroimage.2015.06.092},
	abstract = {Diffusion MRI streamlines tractography allows for the investigation of the brain white matter pathways non-invasively. However a fundamental limitation of this technology is its non-quantitative nature, i.e. the density of reconstructed connections is not reflective of the density of underlying white matter fibres. As a solution to this problem, we have previously published the "spherical-deconvolution informed filtering of tractograms (SIFT)" method, which determines a subset of the streamlines reconstruction such that the streamlines densities throughout the white matter are as close as possible to fibre densities estimated using the spherical deconvolution diffusion model; this permits the use of streamline count as a valid biological marker of connection density. Particular aspects of its performance may have however limited its uptake in the diffusion MRI research community. Here we present an alternative to this method, entitled SIFT2, which provides a more logically direct and computationally efficient solution to the streamlines connectivity quantification problem: by determining an appropriate cross-sectional area multiplier for each streamline rather than removing streamlines altogether, biologically accurate measures of fibre connectivity are obtained whilst making use of the complete streamlines reconstruction.},
	language = {eng},
	journal = {Neuroimage},
	author = {Smith, Robert E. and Tournier, Jacques-Donald and Calamante, Fernando and Connelly, Alan},
	month = oct,
	year = {2015},
	pmid = {26163802},
	keywords = {Algorithms, Brain, Diffusion, Diffusion Magnetic Resonance Imaging, Diffusion Tensor Imaging, Humans, Image Processing, Computer-Assisted, Magnetic resonance imaging, SIFT, Streamlines, Structural connectivity, Tractography, White Matter},
	pages = {338--351},
}
