User:Jadrian Miles/Paper list - Revision history

Jadrian Miles: Added a couple more ideas inspired by the toy problem initialization and goodness-of-fit stuff

2013-03-15T23:46:50Z

Added a couple more ideas inspired by the toy problem initialization and goodness-of-fit stuff

← Older revision		Revision as of 23:46, 15 March 2013
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	==== Diffusion model parsimony ====		==== Diffusion model parsimony ====
	Compare multiple diffusion models (single-tensor, multi-tensor, higher-order tensor, higher-order ODF [e.g. QBI], multi-compartment biological [e.g. CHARMED]) in terms of their "parsimony" on real data. Quantify parsimony with chi-squared goodness-of-fit on top of the probability integral transform for Rician noise. This will hopefully demonstrate that most models under- or over-fit, and that the multi-compartment models are juuuuuust right. Come to think of it, a Goldilocks reference would make for a snappy title. See dhl/cad/jadrian email from 2011-12-15 "a quickie Dagstuhl thought experiment".		Compare multiple diffusion models (single-tensor, multi-tensor, higher-order tensor, higher-order ODF [e.g. QBI], multi-compartment biological [e.g. CHARMED]) in terms of their "parsimony" on real data. Quantify parsimony with chi-squared goodness-of-fit on top of the probability integral transform for Rician noise. This will hopefully demonstrate that most models under- or over-fit, and that the multi-compartment models are juuuuuust right. Come to think of it, a Goldilocks reference would make for a snappy title. See dhl/cad/jadrian email from 2011-12-15 "a quickie Dagstuhl thought experiment".

			Could potentially extend this to a "constructive" algorithm: something that picks the degrees of freedom for an RBF or other higher-order diffusion model.


			==== Improved Hough-Peak--Finding by Topological Persistence ====
			Put the toy-problem initialization technique on a solid theoretical basis. Rather than blurring the histogram, use topological persistence to disregard "sub-peaks".


			==== Feature Identification in Multispectral Images Using the Hough Transform ====
			Think about a "support" function for prospective line segments, with Gaussian falloff on the image plane. Finding an application area for this one might be hard.

Jadrian Miles: New paper: diffusion model parsimony

2012-06-12T17:10:01Z

New paper: diffusion model parsimony

← Older revision		Revision as of 17:10, 12 June 2012
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	* Prescribe a simple pipeline for deriving the noise equation for any combination of algorithms		* Prescribe a simple pipeline for deriving the noise equation for any combination of algorithms
	* Maybe also consider writing up the application to PICo: probability of connection to a cortical ROI is the integral of the uncertainty PDF over that subcortical surface. But the benefit from this technique (gain in precision) may be minimal, as symmetric PDFs on either side of an ROI boundary would tend to contribute the same on either side; results might be about the same without the PDF spreading, just because you've got a lot of curves.		* Maybe also consider writing up the application to PICo: probability of connection to a cortical ROI is the integral of the uncertainty PDF over that subcortical surface. But the benefit from this technique (gain in precision) may be minimal, as symmetric PDFs on either side of an ROI boundary would tend to contribute the same on either side; results might be about the same without the PDF spreading, just because you've got a lot of curves.


			==== Diffusion model parsimony ====
			Compare multiple diffusion models (single-tensor, multi-tensor, higher-order tensor, higher-order ODF [e.g. QBI], multi-compartment biological [e.g. CHARMED]) in terms of their "parsimony" on real data. Quantify parsimony with chi-squared goodness-of-fit on top of the probability integral transform for Rician noise. This will hopefully demonstrate that most models under- or over-fit, and that the multi-compartment models are juuuuuust right. Come to think of it, a Goldilocks reference would make for a snappy title. See dhl/cad/jadrian email from 2011-12-15 "a quickie Dagstuhl thought experiment".

Jadrian Miles at 15:38, 22 May 2012

2012-05-22T15:38:35Z

← Older revision		Revision as of 15:38, 22 May 2012
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	** Generate synthetic HARDI scans from 2-tensor fit of averaged LARDI scans for tons of healthy normals		** Generate synthetic HARDI scans from 2-tensor fit of averaged LARDI scans for tons of healthy normals
	* Prescribe a simple pipeline for deriving the noise equation for any combination of algorithms		* Prescribe a simple pipeline for deriving the noise equation for any combination of algorithms
	* Maybe also consider writing up the application to PICo: probability of connection to a cortical ROI is the integral of the uncertainty PDF over that subcortical surface.		* Maybe also consider writing up the application to PICo: probability of connection to a cortical ROI is the integral of the uncertainty PDF over that subcortical surface. But the benefit from this technique (gain in precision) may be minimal, as symmetric PDFs on either side of an ROI boundary would tend to contribute the same on either side; results might be about the same without the PDF spreading, just because you've got a lot of curves.


	==== Toy Problem / Probability Integral Transform ====		==== Toy Problem / Probability Integral Transform ====
	Write up the toy problem as an example use of the PIT in model testing for DWIs. Or maybe piggyback on Leemans/Sijbers MRM'05 synthetic DWIs from tractography, using PIT to get p-values for synthetic images. This may not be worth its own paper; maybe better to hold off on PIT stuff until it can be put in context of my own "real-problem" work.		Write up the toy problem as an example use of the PIT in model testing for DWIs. Or maybe piggyback on Leemans/Sijbers MRM'05 synthetic DWIs from tractography, using PIT to get p-values for synthetic images. This may not be worth its own paper; maybe better to hold off on PIT stuff until it can be put in context of my own "real-problem" work.


			==== Curve clustering based on chi-squared reconstruction error from a boundary model ====
			I've already written up an outline of this paper. Clustering proceeds by building a boundary model of clusters/bundles, and the objective function is chi-squared goodness-of-fit derived from the tractography PDFs.

Jadrian Miles at 15:30, 22 May 2012

2012-05-22T15:30:03Z

Jadrian Miles at 15:29, 22 May 2012

2012-05-22T15:29:13Z

← Older revision		Revision as of 15:29, 22 May 2012
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			==== Curve noise statistics ====
			An upgraded version of my ISMRM abstract.
			* Present original findings (expanded to range over FA + MD)
			** Potentially also investigate multiple algorithms: nonlinear DT fitting, multi-DT, Q-ball, other tractography algorithms, etc.
			* Explain results with first-order model
			* Validate the model (repeat experiment 2) on more "realistic" data with ground truth. Some options:
			** Brain-based computational phantom (constructed like so: T1+HARDI scan, 2-tensor fit, tractography, filter curves for T1 termination criteria, lowpass curves, synthesize DWIs from curves)
			** Popular phantom (tractography cup?)
			** HARDI atlas
			** Generate synthetic HARDI scans from 2-tensor fit of averaged LARDI scans for tons of healthy normals
			* Prescribe a simple pipeline for deriving the noise equation for any combination of algorithms
			* Maybe also consider writing up the application to PICo: probability of connection to a cortical ROI is the integral of the uncertainty PDF over that subcortical surface.


			==== Toy Problem / Probability Integral Transform ====
			Write up the toy problem as an example use of the PIT in model testing for DWIs. Or maybe piggyback on Leemans/Sijbers MRM'05 synthetic DWIs from tractography, using PIT to get p-values for synthetic images.


	==== Automatic Shape-Sensitive Curve Clustering (§4.1.2) ====		==== Automatic Shape-Sensitive Curve Clustering (§4.1.2) ====
	Distance measure definition, distributed clustering algorithm, spectral clustering refinement, comparison to other techniques. Base on q-ball and DTI tractography from DTK; use Mori's atlas for ground truth? Moberts et al.'s ground truth clusterings? [moberts/van_wijk/vilanova might have a ground truth. Song Zhang had a clustering ground truth paper. Cagatay played with this at some point. bang not huge, how big is the buck? but I could be convinced -- not sure this is a vis paper? but it could be. Application of curve similarity to other areas (bat flight trajectories) would be convincing at Vis too.]		Distance measure definition, distributed clustering algorithm, spectral clustering refinement, comparison to other techniques. Base on q-ball and DTI tractography from DTK; use Mori's atlas for ground truth? Moberts et al.'s ground truth clusterings? [moberts/van_wijk/vilanova might have a ground truth. Song Zhang had a clustering ground truth paper. Cagatay played with this at some point. bang not huge, how big is the buck? but I could be convinced -- not sure this is a vis paper? but it could be. Application of curve similarity to other areas (bat flight trajectories) would be convincing at Vis too.]

Jadrian Miles: moved User:Jadrian Miles/paper list to User:Jadrian Miles/Paper list: Want to match capitalization of other subpages

2012-05-22T14:45:53Z

moved User:Jadrian Miles/paper list to User:Jadrian Miles/Paper list: Want to match capitalization of other subpages

← Older revision	Revision as of 14:45, 22 May 2012
(No difference)

Jadrian Miles: /* Automatic Tractography Repair / "Healing a Broken Tractogram" */

2010-04-29T15:39:38Z

Automatic Tractography Repair / "Healing a Broken Tractogram"

← Older revision		Revision as of 15:39, 29 April 2010
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	* '''Contributions'''		* '''Contributions'''
	*# A slicing-based (or alpha-shape contraction--based) algorithm for generating a cluster boundary surface with orientation, spreading, and medial axis metadata.		*# A slicing-based (or alpha-shape contraction--based) algorithm for generating a cluster boundary surface with orientation, spreading, and medial axis metadata.
	*# An algorithm for "sampling" novel un-broken curves from the cluster boundary surface model.		*# An algorithm for "sampling" novel un-broken curves from the cluster boundary surface model ("sparsifying")
	*# An algorithm for extrapolating the curve cluster along its axis.		*# An algorithm for extrapolating the curve cluster along its axis ("lengthening")
	*# An algorithm for joining axially-aligned curve clusters based on extrapolation and refinement against underlying DWIs.		*# An algorithm for joining axially-aligned curve clusters based on extrapolation and refinement against underlying DWIs ("bridging")
			*# Maybe others: "fattening", "smoothing"
	* '''Proofs of Contributions'''		* '''Proofs of Contributions'''
	*# Demonstration (with figures) of the boundary surface algorithm on synthetic and real-world tractography data.		*# Demonstration (with figures) of the boundary surface algorithm on synthetic and real-world tractography data.

Jadrian Miles: /* Automatic Tractography Repair / "Healing a Broken Tractogram */

2010-04-29T14:00:42Z

Automatic Tractography Repair / "Healing a Broken Tractogram

@@ Line 13: / Line 13: @@
 The benefit of the initial form of the macrostructure model is its ability to reconstruct its input curves.  The evaluation on this should be relatively easy, as there is no comparison to other techniques.  A manual clustering is acceptable but automatic clustering that implies some bound on reconstruction error would probably be better.  Good for Vis, SIGGRAPH, EG, EV, I3D, ISMRM.  [but why does anyone care?  This could be a way of establishing the minimal information required to represent brain datasets... but again, does anyone care about that?  Must find related work.  dhl suggests that this may be "importance filtering" for curves, but what's the benefit?]
-==== Automatic Tractography Repair / "Healing a Broken Tractogram ====
+==== Automatic Tractography Repair / "Healing a Broken Tractogram" ====
-ISMRM poster: demonstrate repairing broken curves by clustering, macrostructure generation, and then bridging gaps (&sect;4.1.5).
+ISMRM poster / Neuroimage paper / TVCG paper?  Demonstrate the utility of the cluster boundary surface algorithm for repairing broken curves (&sect;4.1.5).
 ==== Automatic Tractography-Based DW-MRI Segmentation ====

David Laidlaw: /* An Orientation-Aware Boundary Surface Representation of Space Curve Clusters */

2010-03-18T00:49:35Z

An Orientation-Aware Boundary Surface Representation of Space Curve Clusters

← Older revision		Revision as of 00:49, 18 March 2010
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	*# Superior results versus naive algorithms (and previously published work to solve the same problem? "Competitors" to consider include Gordon's crease surfaces, flow critical surfaces, etc. [dhl mentions Ken Joy's work...] [define "superior". Faster? more accurate? If you mean more "natural" then this may be the same thing as above]).		*# Superior results versus naive algorithms (and previously published work to solve the same problem? "Competitors" to consider include Gordon's crease surfaces, flow critical surfaces, etc. [dhl mentions Ken Joy's work...] [define "superior". Faster? more accurate? If you mean more "natural" then this may be the same thing as above]).
	* '''Proofs:'''		* '''Proofs:'''
	*# Prose argument that contrasts cross-section-based boundary surfaces to other representations of curve clusters. A bunch of curves is a mess and does not lend itself to operations on the cluster volume (smoothing, joining, etc.). Median curves have no width. Rasterization creates surface artifacts and loses orientation information. Alpha shapes lose orientation information.		*# Prose argument that contrasts cross-section-based boundary surfaces to other representations of curve clusters. A bunch of curves is a mess and does not lend itself to operations on the cluster volume (smoothing, joining, etc.). Median curves have no width. Rasterization creates surface artifacts and loses orientation information. Alpha shapes lose orientation information. [nice!]
	*# Alpha shapes is the main "competitor". Expected results show topological defects resulting from a global choice of alpha. Run both algorithms on phantom and real data and discuss features. [I suspect that there are some other shrink-wrap approaches that might be competitors, unless alpha-shapes are always superior]		*# Alpha shapes is the main "competitor". Expected results show topological defects resulting from a global choice of alpha. Run both algorithms on phantom and real data and discuss features. [I suspect that there are some other shrink-wrap approaches that might be competitors, unless alpha-shapes are always superior]

David Laidlaw: /* An Orientation-Aware Boundary Surface Representation of Space Curve Clusters */

2010-03-18T00:49:00Z

An Orientation-Aware Boundary Surface Representation of Space Curve Clusters

← Older revision		Revision as of 00:49, 18 March 2010
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	==== An Orientation-Aware Boundary Surface Representation of Space Curve Clusters ====		==== An Orientation-Aware Boundary Surface Representation of Space Curve Clusters ====
	* '''Contributions:'''		* '''Contributions:'''
	*# "Natural" representation of clusters of curves, useful for higher-level operations.		*# "Natural" representation of clusters of curves, useful for higher-level operations. [I think that "natural" is not descriptive enough. I would try to refine that. Is the orientation a tangency constraint? There's something about it that seems important, but "orientation aware" doesn't quite seem like what it is... . I would give an example or two of the kind of higher-level operations you mean.
	*# Superior results versus naive algorithms (and previously published work to solve the same problem? "Competitors" to consider include Gordon's crease surfaces, flow critical surfaces, etc. [dhl mentions Ken Joy's work...]).		*# Superior results versus naive algorithms (and previously published work to solve the same problem? "Competitors" to consider include Gordon's crease surfaces, flow critical surfaces, etc. [dhl mentions Ken Joy's work...] [define "superior". Faster? more accurate? If you mean more "natural" then this may be the same thing as above]).
	* '''Proofs:'''		* '''Proofs:'''
	*# Prose argument that contrasts cross-section-based boundary surfaces to other representations of curve clusters. A bunch of curves is a mess and does not lend itself to operations on the cluster volume (smoothing, joining, etc.). Median curves have no width. Rasterization creates surface artifacts and loses orientation information. Alpha shapes lose orientation information.		*# Prose argument that contrasts cross-section-based boundary surfaces to other representations of curve clusters. A bunch of curves is a mess and does not lend itself to operations on the cluster volume (smoothing, joining, etc.). Median curves have no width. Rasterization creates surface artifacts and loses orientation information. Alpha shapes lose orientation information.
	*# Alpha shapes is the main "competitor". Expected results show topological defects resulting from a global choice of alpha. Run both algorithms on phantom and real data and discuss features.		*# Alpha shapes is the main "competitor". Expected results show topological defects resulting from a global choice of alpha. Run both algorithms on phantom and real data and discuss features. [I suspect that there are some other shrink-wrap approaches that might be competitors, unless alpha-shapes are always superior]

	==== A Sparse, Volumetric Representation of Space Curve Clusters (§4.1.4) ====		==== A Sparse, Volumetric Representation of Space Curve Clusters (§4.1.4) ====

← Older revision		Revision as of 15:30, 22 May 2012
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	==== Toy Problem / Probability Integral Transform ====		==== Toy Problem / Probability Integral Transform ====
	Write up the toy problem as an example use of the PIT in model testing for DWIs. Or maybe piggyback on Leemans/Sijbers MRM'05 synthetic DWIs from tractography, using PIT to get p-values for synthetic images.		Write up the toy problem as an example use of the PIT in model testing for DWIs. Or maybe piggyback on Leemans/Sijbers MRM'05 synthetic DWIs from tractography, using PIT to get p-values for synthetic images. This may not be worth its own paper; maybe better to hold off on PIT stuff until it can be put in context of my own "real-problem" work.


	==== Automatic Shape-Sensitive Curve Clustering (§4.1.2) ====		==== Automatic Shape-Sensitive Curve Clustering (§4.1.2) ====
	Distance measure definition, distributed clustering algorithm, spectral clustering refinement, comparison to other techniques. Base on q-ball and DTI tractography from DTK; use Mori's atlas for ground truth? Moberts et al.'s ground truth clusterings? [moberts/van_wijk/vilanova might have a ground truth. Song Zhang had a clustering ground truth paper. Cagatay played with this at some point. bang not huge, how big is the buck? but I could be convinced -- not sure this is a vis paper? but it could be. Application of curve similarity to other areas (bat flight trajectories) would be convincing at Vis too.]		Distance measure definition, distributed clustering algorithm, spectral clustering refinement, comparison to other techniques. Base on q-ball and DTI tractography from DTK; use Mori's atlas for ground truth? Moberts et al.'s ground truth clusterings? [moberts/van_wijk/vilanova might have a ground truth. Song Zhang had a clustering ground truth paper. Cagatay played with this at some point. bang not huge, how big is the buck? but I could be convinced -- not sure this is a vis paper? but it could be. Application of curve similarity to other areas (bat flight trajectories) would be convincing at Vis too.]


	==== An Orientation-Aware Boundary Surface Representation of Space Curve Clusters ====		==== An Orientation-Aware Boundary Surface Representation of Space Curve Clusters ====
Line 27:		Line 28:
	*# Prose argument that contrasts cross-section-based boundary surfaces to other representations of curve clusters. A bunch of curves is a mess and does not lend itself to operations on the cluster volume (smoothing, joining, etc.). Median curves have no width. Rasterization creates surface artifacts and loses orientation information. Alpha shapes lose orientation information. [nice!]		*# Prose argument that contrasts cross-section-based boundary surfaces to other representations of curve clusters. A bunch of curves is a mess and does not lend itself to operations on the cluster volume (smoothing, joining, etc.). Median curves have no width. Rasterization creates surface artifacts and loses orientation information. Alpha shapes lose orientation information. [nice!]
	*# Alpha shapes is the main "competitor". Expected results show topological defects resulting from a global choice of alpha. Run both algorithms on phantom and real data and discuss features. [I suspect that there are some other shrink-wrap approaches that might be competitors, unless alpha-shapes are always superior]		*# Alpha shapes is the main "competitor". Expected results show topological defects resulting from a global choice of alpha. Run both algorithms on phantom and real data and discuss features. [I suspect that there are some other shrink-wrap approaches that might be competitors, unless alpha-shapes are always superior]


	==== A Sparse, Volumetric Representation of Space Curve Clusters (§4.1.4) ====		==== A Sparse, Volumetric Representation of Space Curve Clusters (§4.1.4) ====
	The benefit of the initial form of the macrostructure model is its ability to reconstruct its input curves. The evaluation on this should be relatively easy, as there is no comparison to other techniques. A manual clustering is acceptable but automatic clustering that implies some bound on reconstruction error would probably be better. Good for Vis, SIGGRAPH, EG, EV, I3D, ISMRM. [but why does anyone care? This could be a way of establishing the minimal information required to represent brain datasets... but again, does anyone care about that? Must find related work. dhl suggests that this may be "importance filtering" for curves, but what's the benefit?]		The benefit of the initial form of the macrostructure model is its ability to reconstruct its input curves. The evaluation on this should be relatively easy, as there is no comparison to other techniques. A manual clustering is acceptable but automatic clustering that implies some bound on reconstruction error would probably be better. Good for Vis, SIGGRAPH, EG, EV, I3D, ISMRM. [but why does anyone care? This could be a way of establishing the minimal information required to represent brain datasets... but again, does anyone care about that? Must find related work. dhl suggests that this may be "importance filtering" for curves, but what's the benefit?]


	==== Automatic Tractography Repair / "Healing a Broken Tractogram" ====		==== Automatic Tractography Repair / "Healing a Broken Tractogram" ====
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	* '''Conclusions'''		* '''Conclusions'''
	* '''Methods'''		* '''Methods'''


	==== Automatic Tractography-Based DW-MRI Segmentation ====		==== Automatic Tractography-Based DW-MRI Segmentation ====
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	[The above two could each be ISMRM posters or talks, and should be quickly followed up by an MRM paper.]		[The above two could each be ISMRM posters or talks, and should be quickly followed up by an MRM paper.]


	==== A ??? (§4.2.1) ====		==== A ??? (§4.2.1) ====