2012 Winter Project Week: DTI Change Modeling - Revision history

Anuja: /* White matter analysis for PREDICT-HD DBP */

2012-08-19T11:30:48Z

White matter analysis for PREDICT-HD DBP

Anuja: /* References */

2012-08-19T11:10:01Z

References

Anuja: /* White matter analysis for PREDICT-HD DBP */

2012-01-13T15:58:11Z

White matter analysis for PREDICT-HD DBP

Anuja: /* White matter analysis for PREDICT-HD DBP */

2012-01-13T15:54:42Z

White matter analysis for PREDICT-HD DBP

Anuja: /* White matter analysis for PREDICT-HD DBP */

2012-01-13T15:49:45Z

White matter analysis for PREDICT-HD DBP

Anuja: /* White matter analysis for PREDICT-HD DBP */

2012-01-13T15:41:02Z

White matter analysis for PREDICT-HD DBP

Anuja: /* Key Partners */

2012-01-13T11:17:15Z

Key Partners

Anuja: /* Key Partners */

2012-01-13T11:15:43Z

Key Partners

Anuja: /* White matter analysis for PREDICT-HD DBP */

2012-01-13T11:11:58Z

White matter analysis for PREDICT-HD DBP

Anuja: /* White matter analysis for PREDICT-HD DBP */

2012-01-13T11:07:59Z

White matter analysis for PREDICT-HD DBP

@@ Line 82: / Line 82: @@
 |[[Image:ORIG_hd_cn.png|thumb|300px|center|FA curves (middle clipped portions) for one HD case (10027) and one control case (10004). Visual inspection clearly shows the huge decrease relative to the Control in the HD subject. On the other hand, the control shows normal expected decrease due to aging. Red-timepoint 1, Green-timepoint 2, Blue-timepoint 3. The left is HD case and the right plot is Control.]]
 |[[Image:Energy.png|thumb|300px|center|The energy plot for the gradient descent optimization procedure used in the framework.]]
-|[[Image:Params.png|thumb|300px|center|The estimated model parameters (Red-HD case, Green-Control case) for the two subjects. The top plot is the rate of change per unit time, per unit FA value. The middle plot is the function asymptote and the last plot is the common baseline curve estimation for providing a common reference frame for model estimation and comparison. The asymptote clearly shows a much lower FA range for HD when compared to the Control which already shows promising early prediction capabilities for HD cases where white matter decay is much faster than healthy patients.]]
+|[[Image:HD_vs_CN_params.png|thumb|300px|center|The estimated model parameters (Red-HD case, Blue-Control case) for the two subjects. The top plot is the rate of change per unit time, per unit FA value. The middle plot is the function asymptote and the last plot is the common baseline curve estimation for providing a common reference frame for model estimation and comparison. The asymptote clearly shows a much lower FA range for HD when compared to the Control which already shows promising early prediction capabilities for HD cases where white matter decay is much faster than healthy patients.]]
 |-
 |}
 {| border="0" style="background:transparent;"
-|[[Image:Final_3d.png|thumb|300px|center| 3D view of the estimated spatiotemporal change trajectories for each of the subject. Note that event though the estimation is joint and simultaneous for both subjects, the trajectories are patient specific. The left is HD case and the right plot is Control. We can see here that the FA for HD case decreases much more than the Control over a similar span of years and almost similar age range.]]
+|[[Image:HD_vs_CN_3d.png|thumb|300px|center| 3D view of the estimated spatiotemporal change trajectories for each of the subject. Note that event though the estimation is joint and simultaneous for both subjects, the trajectories are patient specific. The red is HD case and the blue is Control. We can see here that the FA for HD case decreases much more than the Control over a similar span of years.]]
-|[[Image:Final_3da.png|thumb|300px|center|Another view of the growth trajectories.]]
+|[[Image:Final_3da.png|thumb|300px|center|Another view of the growth trajectories (Left: HD, Right: CN).]]
 |[[Image:Final_3db.png|thumb|300px|center|Another view of the growth trajectories.]]
 |-

@@ Line 95: / Line 95: @@
 ==References==
 * Casey B. Goodlett, P. Thomas Fletcher, John H. Gilmore, Guido Gerig. Group Analysis of DTI Fiber Tract Statistics with Application to Neurodevelopment. NeuroImage 45 (1) Supp. 1, 2009. p. S133-S142
 * [http://www.na-mic.org/Wiki/index.php/Projects:TractLongitudinalDTI Details of the framework]
 </div>

@@ Line 91: / Line 91: @@
 |-
 |}
 ==References==

@@ Line 80: / Line 80: @@
 {| border="0" style="background:transparent;"
-|[[Image:ORIG_hd_cn.png|thumb|300px|center|FA curves (middle clipped portions) for one HD case (10027) and one control case (10004). Visual inspection clearly shows the huge decrease relative to the Control in the HD subject. On the other hand, the control shows normal expected decrease due to aging. Red-timepoint 1, Green-timepoint 2, Blue-timepoint 3. ]]
+|[[Image:ORIG_hd_cn.png|thumb|300px|center|FA curves (middle clipped portions) for one HD case (10027) and one control case (10004). Visual inspection clearly shows the huge decrease relative to the Control in the HD subject. On the other hand, the control shows normal expected decrease due to aging. Red-timepoint 1, Green-timepoint 2, Blue-timepoint 3. The left is HD case and the right plot is Control.]]
 |[[Image:Energy.png|thumb|300px|center|The energy plot for the gradient descent optimization procedure used in the framework.]]
-|[[Image:Params.png|thumb|300px|center|The estimated model parameters (Red-HD case, Green-Control case) for the two subjects. The top plot is the rate of change per unit time, per unit FA value. The middle plot is the function asymptote and the last plot is the common baseline curve estimation for providing a common reference frame for model estimation and comparison.]]
+|[[Image:Params.png|thumb|300px|center|The estimated model parameters (Red-HD case, Green-Control case) for the two subjects. The top plot is the rate of change per unit time, per unit FA value. The middle plot is the function asymptote and the last plot is the common baseline curve estimation for providing a common reference frame for model estimation and comparison. The asymptote clearly shows a much lower FA range for HD when compared to the Control which already shows promising early prediction capabilities for HD cases where white matter decay is much faster than healthy patients.]]
 |-
 |}
 {| border="0" style="background:transparent;"
-|[[Image:Final_3d.png|thumb|300px|center| 3D view of the estimated spatiotemporal change trajectories for each of the subject. Note that event though the estimation is joint and simultaneous for both subjects, the trajectories are patient specific.]]
+|[[Image:Final_3d.png|thumb|300px|center| 3D view of the estimated spatiotemporal change trajectories for each of the subject. Note that event though the estimation is joint and simultaneous for both subjects, the trajectories are patient specific. The left is HD case and the right plot is Control. We can see here that the FA for HD case decreases much more than the Control over a similar span of years and almost similar age range.]]
 |[[Image:Final_3da.png|thumb|300px|center|Another view of the growth trajectories.]]
 |[[Image:Final_3db.png|thumb|300px|center|Another view of the growth trajectories.]]

@@ Line 65: / Line 65: @@
 {| border="0" style="background:transparent;"
-|[[Image:HRsubj.png|thumb|500px|center|]]
+|[[Image:HRsubj.png|thumb|500px|center|HD cases, complete diffusion curves]]
-|[[Image:HRsubj-mid.png|thumb|500px|center|]]
+|[[Image:HRsubj-mid.png|thumb|500px|center|HD cases, middle portion of the clipped diffusion curves]]
 |-
 |}
 {| border="0" style="background:transparent;"
-|[[Image:CNsubj.png|thumb|500px|center|]]
+|[[Image:CNsubj.png|thumb|500px|center|Control cases, complete diffusion curves]]
-|[[Image:CNsubj-mid.png|thumb|500px|center|]]
+|[[Image:CNsubj-mid.png|thumb|500px|center|Control cases, middle portion of the clipped diffusion curves]]
 |-
 |}
@@ Line 80: / Line 80: @@
 {| border="0" style="background:transparent;"
-|[[Image:ORIG_hd_cn.png|thumb|300px|center|]]
+|[[Image:ORIG_hd_cn.png|thumb|300px|center|FA curves (middle clipped portions) for one HD case (10027) and one control case (10004). Visual inspection clearly shows the huge decrease relative to the Control in the HD subject. On the other hand, the control shows normal expected decrease due to aging. Red-timepoint 1, Green-timepoint 2, Blue-timepoint 3. ]]
-|[[Image:Energy.png|thumb|300px|center|]]
+|[[Image:Energy.png|thumb|300px|center|The energy plot for the gradient descent optimization procedure used in the framework.]]
-|[[Image:Params.png|thumb|300px|center|]]
+|[[Image:Params.png|thumb|300px|center|The estimated model parameters (Red-HD case, Green-Control case) for the two subjects. The top plot is the rate of change per unit time, per unit FA value. The middle plot is the function asymptote and the last plot is the common baseline curve estimation for providing a common reference frame for model estimation and comparison.]]
 |-
 |}
 {| border="0" style="background:transparent;"
-|[[Image:Final_3d.png|thumb|300px|center|]]
+|[[Image:Final_3d.png|thumb|300px|center| 3D view of the estimated spatiotemporal change trajectories for each of the subject. Note that event though the estimation is joint and simultaneous for both subjects, the trajectories are patient specific.]]
-|[[Image:Final_3da.png|thumb|300px|center|]]
+|[[Image:Final_3da.png|thumb|300px|center|Another view of the growth trajectories.]]
-|[[Image:Final_3db.png|thumb|300px|center|]]
+|[[Image:Final_3db.png|thumb|300px|center|Another view of the growth trajectories.]]
 |-
 |}

@@ Line 62: / Line 62: @@
 '''HD and Control subjects'''
-Shown below are the assembled along-tract diffusion profiles for a number of HD and Control subjects parametrized by arc length of the tract. The scalar diffusion measures (FA shown here) have been averaged across perpendicular cross sections along the tract's length.
+Shown below are the assembled along-tract diffusion profiles for a number of HD and Control subjects parametrized by arc length of the tract. The scalar diffusion measures (FA shown here) have been averaged across perpendicular cross sections along the tract's length. From what we see, the data is noisy as we do not see a consistent change pattern from timepoint 1 (TP1) to timepoint 3 (TP3) diffusion curves. At some places along the length, the FA values increase and then decrease while for some subjects, we see an overall decrease along length. Moreover, the ends of the tract are specially noisy. This could probably be improved by a more accurate registration and tractography procedure. However, since for this week our aim was a concept demonstration, we have worked on the middle of the diffusion curves (by clipping off the ends). Seen here are the plots for the complete tract as well as the middle portion for both HD and Control cases.
 {| border="0" style="background:transparent;"

@@ Line 31: / Line 31: @@
 <h3>Progress</h3>
-Co-registered a couple of HD and Control subjects. Performed tractography and back mapping to get discrete-time scalar diffusion curves for these subjects. Concept demonstration was done by quantifying the longitudinal change trajectory for two subjects (one HD and one Control) by estimating a continuous spatiotemporal model and capturing the changes through model parameters. Details of the processing and preliminary results are discussed below.
+Co-registered a couple of HD and Control subjects. Performed tractography and back mapping to get discrete-time scalar diffusion curves for these subjects. Concept demonstration was done by quantifying patient specific longitudinal change trajectory for two subjects (one HD and one Control) simultaneously by estimating their individualized continuous spatiotemporal model and capturing the changes through model parameters. Details of the processing and preliminary results are discussed below.
 </div>
 </div>

← Older revision		Revision as of 11:11, 13 January 2012
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−	We demonstrate the feasibility of the method by applying the framework to 2 subjects- one being a HD patient (10027) with a high burden factor (higher factor value (of factor 12) implying that the subjects is closer to the onset) and the other being a control subject (10004). Both have a similar baseline scan age (42 and 43 years respectively) as well as a similar time separation between follow up scans. This allows an intuitive comparison of the estimated growth trajectories.	+	We demonstrate the feasibility of the method by applying the framework ([http://www.na-mic.org/Wiki/index.php/Projects:TractLongitudinalDTI Check link for details of the framework]) to 2 subjects- one being a HD patient (10027) with a high burden factor (higher factor value (of factor 12) implying that the subjects is closer to the onset) and the other being a control subject (10004). Both have a similar baseline scan age (42 and 43 years respectively) as well as a similar time separation between follow up scans. This allows an intuitive comparison of the estimated growth trajectories.