Projects:FieldmapFreeDistortionCorrection - Revision history

Melonakos: /* Publications */

2010-05-11T20:04:55Z

Publications

Melonakos at 20:06, 20 October 2009

2009-10-20T20:06:03Z

Polina: /* Publications */

2009-09-29T18:18:31Z

Publications

Cpoynton: /* Atlas Construction */

2009-09-29T15:14:11Z

Atlas Construction

Cpoynton: /* Fieldmap Estimation */

2009-09-29T15:13:36Z

Fieldmap Estimation

Cpoynton: /* Fieldmap Estimation */

2009-09-29T15:09:41Z

Fieldmap Estimation

Cpoynton: /* Fieldmap Estimation */

2009-09-29T15:09:19Z

Fieldmap Estimation

Cpoynton: /* Atlas-based Segmentation */

2009-09-29T15:07:35Z

Atlas-based Segmentation

Cpoynton: /* Atlas-based Segmentation */

2009-09-29T15:06:17Z

Atlas-based Segmentation

Cpoynton: /* Atlas-based Segmentation */

2009-09-29T15:03:48Z

Atlas-based Segmentation

← Older revision		Revision as of 20:04, 11 May 2010
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	= Publications =		= Publications =

−	[http://www.na-mic.org/publications/pages/display?search=Projects%3AFieldmapFreeDistortionCorrection&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked\| NA-MIC Publications Database]	+	[http://www.na-mic.org/publications/pages/display?search=Projects%3AFieldmapFreeDistortionCorrection&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked\| NA-MIC Publications Database on Fieldmap-Free EPI Distortion Correction]

@@ Line 1: / Line 1: @@
   Back to [[NA-MIC_Internal_Collaborations:fMRIAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]
+__NOTOC__
 = Fieldmap-Free EPI Distortion Correction =
@@ Line 50: / Line 50: @@
-= Atlas-Based Improved Prediction of Magnetic Field Inhomogeneity for Distortion Correction of EPI Data =
+== Atlas-Based Improved Prediction of Magnetic Field Inhomogeneity for Distortion Correction of EPI Data  Motivation ==
 In the Fieldmap-Free method described above, it was shown that tissue/air susceptibility models could be derived from structural MRI by using
@@ Line 63: / Line 61: @@
 fieldmaps, we hypothesized that improved segmentation methods would result in even greater accuracy.
-== Description ==
+= Description =
 In this work, anatomical information from a set of 22 whole-head CT data sets is used to achieve improved,

← Older revision		Revision as of 18:18, 29 September 2009
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	= Publications =		= Publications =

−	~~''In Print''~~	+	[http://www.na-mic.org/publications/pages/display?search=Projects%3AFieldmapFreeDistortionCorrection&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked\| NA-MIC Publications Database]
−
−	*[http://www.na-mic.org/publications/pages/display?search=Projects%3AFieldmapFreeDistortionCorrection&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked\| NA-MIC Publications Database]

@@ Line 83: / Line 83: @@
 {|
-|[[Image:Tissue air atlas.jpg|600px|thumb|Fig. 4. Results of Atlas Construction. Tissue / Air and Bone atlases constructed from 22 CT data sets.]]
+|[[Image:Tissue air atlas.jpg|600px|thumb|Fig. 4. Results of Atlas Construction. Tissue / Air (left and center) and Bone atlases (right) constructed from 22 CT data sets.]]
 |}

@@ Line 175: / Line 175: @@
 [4] Poynton, et al. MICCAI. 271-279, 2008.
-[5] Zubal IG, et al. Med. Phys. 21,
+[5] Zubal IG, et al. Med. Phys. 21, 299--302. 1994.
 [6] Poynton, et al. MICCAI. (in press). 2009.

@@ Line 152: / Line 152: @@
 subject in column 3. These are especially noticable in areas that have lower signal in MR, such as in the ventricles and major sulci. Fieldmap results from the
 atlas-based segmentations are shown in the second column of Fig. 7 and show
-improved agreement with acquired fieldmaps. Quantitative analysis of the abso-
+improved agreement with acquired fieldmaps. Quantitative analysis of the absolute error in the B0 field between these images is given in the table in Fig. 7.
 Since the bandwidth/pixel for the EPI data acquired in this study is 22.3 Hz,
 % of the voxels in the atlas-based fieldmaps show subvoxel error. The mean
@@ Line 159: / Line 158: @@
 and atlas-based classifiers. The intensity classifier shows a slight improvement
 over the results reported by Koch et al [3] for a single subject. The atlas-based
-classifier out performs both the intensity and Koch methods. Paired t-tests com-
+classifier out performs both the intensity and Koch methods. Paired t-tests comparing the means of the intensity and atlas-based results shows this improvement
 is statistically significant (all p-values < 0.05).
 {|

@@ Line 150: / Line 150: @@
 The first column of Fig. 7 shows fieldmaps computed from the intensity-based segmentations, which
 show significant differences relative to the acquired fieldmaps shown for each
-subject in column 3. These are especially noticable in areas that have lower sig-
+subject in column 3. These are especially noticable in areas that have lower signal in MR, such as in the ventricles and major sulci. Fieldmap results from the
 atlas-based segmentations are shown in the second column of Fig. 7 and show
 improved agreement with acquired fieldmaps. Quantitative analysis of the abso-

@@ Line 121: / Line 121: @@
 {|
-|[[Image:Bone air seg.jpg|600px|thumb|Fig. 6. Bone Segmentation. Segmentation of bone from MR using intensity information alone is ineffective, while atlas-based segmentation shows good overall agreement with CT.]]
+|[[Image:Bone air seg.jpg|600px|thumb|Fig. 6. Bone Segmentation. Segmentation of bone from MR using intensity information alone is ineffective (middle), while atlas-based segmentation shows good overall agreement with CT (right).]]
 |}

@@ Line 108: / Line 108: @@
 Results of the segmentation of bone from structural MR for a representative subject is shown in Fig. 6. The
 CT shown on the left can be easily thresholded to segment bone from air and
-soft tissue. The the results of using the intensity and atlas-
+soft tissue. The results of using the intensity and atlas-
 based classifiers are shown in the middle and right images, respectively. While the intensity classifier has some success in
 segmenting MR into tissue/air classes, it is much less effective in segmenting

@@ Line 96: / Line 96: @@
-Results of atlas-based segmentation of structural MR is shown in Fig. 5 below. Fig. 5a
+Results of atlas-based segmentation of structural MR is shown in Fig. 5 below.
-shows T1 of the sinus region. Fig. 5b shows the limitations of using the intensity
+T1 of the sinus region is shown on the left. The limitations of using the intensity
-classifier to segment the MR. While it produces reasonable results for many of the voxels in the sinuses,
+classifier to segment the MR is shown in the middle image. While the classifier produces reasonable results for many of the voxels in the sinuses,
 voxels outside this region which are clearly soft tissue or
 bone are mislabeled with values close to zero. In contrast, using the atlas-based
-segmenter (as shown in Fig. 5c) achieves similar results for the highly variable
+segmenter (right image) achieves similar results for the highly variable
 subject-specific anatomy within the sinus region, while producing fewer errors
 in the surrounding area.
@@ Line 107: / Line 107: @@
 Results of the segmentation of bone from structural MR for a representative subject is shown in Fig. 6. The
-CT shown in Fig. 6a can be easily thresholded to segment bone from air and
+CT shown on the left can be easily thresholded to segment bone from air and
-soft tissue. Fig. 6b and Fig. 6c show the results of using the intensity and atlas-
+soft tissue. The the results of using the intensity and atlas-
-based classifiers, respectively. While the intensity classifier has some success in
+based classifiers are shown in the middle and right images, respectively. While the intensity classifier has some success in
 segmenting MR into tissue/air classes, it is much less effective in segmenting
-bone (Fig. 6b). Inspection of the the atlas-based segmentation (Fig. 6), however, shows good general agreement with the CT, with a dice score of 0.780 for
+bone. Inspection of the the atlas-based segmentation, however, shows good general agreement with the CT, with a dice score of 0.780 for
 this subject.