Projects:DTI DWI QualityControl - Revision history

MahshidLightMoon at 18:53, 2 January 2013

2013-01-02T18:53:03Z

MahshidLightMoon at 18:20, 2 January 2013

2013-01-02T18:20:25Z

MahshidLightMoon at 18:19, 2 January 2013

2013-01-02T18:19:55Z

MahshidLightMoon at 18:18, 2 January 2013

2013-01-02T18:18:50Z

MahshidLightMoon at 18:07, 2 January 2013

2013-01-02T18:07:43Z

MahshidLightMoon at 17:42, 2 January 2013

2013-01-02T17:42:16Z

MahshidLightMoon at 17:41, 2 January 2013

2013-01-02T17:41:06Z

MahshidLightMoon at 17:40, 2 January 2013

2013-01-02T17:40:44Z

MahshidLightMoon at 17:32, 2 January 2013

2013-01-02T17:32:31Z

MahshidLightMoon at 17:26, 2 January 2013

2013-01-02T17:26:26Z

← Older revision		Revision as of 18:53, 2 January 2013
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	== Entropy Based Diffusion Imaging Quality Control ==		== Entropy Based Diffusion Imaging Quality Control ==
−	We have proposed new QC step for detecting drop-out signal intensities which can be caused by mechanical vibration artifacts. This step detects and potentially removes these residual artifacts that are not commonly detected in the individual DWIs. The artifacts appear in color-FA images in either widespread or local dominant direction ~~( see Fig?? )~~. In order to detect such artifacts, we proposed a new approach via the entropy of the Principal Direction (PD) histogram computed over the major region of the image (e.g. the full brain). Given a prior knowledge of expected entropy values for acceptable scans, the quality of the DTI image is categorized into acceptable, suspicious and highly suspicious/rejection categories using calculated the standard scores.	+	We have proposed new QC step for detecting drop-out signal intensities which can be caused by mechanical vibration artifacts. This step detects and potentially removes these residual artifacts that are not commonly detected in the individual DWIs. The artifacts appear in color-FA images in either widespread or local dominant direction. In order to detect such artifacts, we proposed a new approach via the entropy of the Principal Direction (PD) histogram computed over the major region of the image (e.g. the full brain). Given a prior knowledge of expected entropy values for acceptable scans, the quality of the DTI image is categorized into acceptable, suspicious and highly suspicious/rejection categories using calculated the standard scores.

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 In experimental results, we applied our method on acquisition schemes and also individual scans post-QC. These results show that the proposed rejection metrics provide an effective assessment of post-QC individual scan and also acquisition protocols. Furthermore, our results confirm that higher degrees of uniformity in the sampling gradients results in lower overall bias. Thus, determination of diffusion properties with minimal error requires an evenly distributed gradient directions before and after QC. This method will be incorporated in DTIPrep, used for QC of DWI/DTI data.
-[[Image:Mc schemes.png|700px|thumb|center| Bias analysis of different DWI schemes via MC simulation. (a-c) Gradient direction distribution for 3 acquisition schemes: 42-direction quasi-uniform, Phillips 32-direction non-uniform, and 6-direction uniform. (d- f) Estimated error distribution in PD computation. (g-i) Estimated error distribution in FA computation as a percentage of true FA. Number of iteration 200,000, true FA value of 0.4 and SNR = 10. ]]
+[[Image:Mc schemes.png|500px|thumb|center| Bias analysis of different DWI schemes via MC simulation. (a-c) Gradient direction distribution for 3 acquisition schemes: 42-direction quasi-uniform, Phillips 32-direction non-uniform, and 6-direction uniform. (d- f) Estimated error distribution in PD computation. (g-i) Estimated error distribution in FA computation as a percentage of true FA. Number of iteration 200,000, true FA value of 0.4 and SNR = 10. ]]
-[[Image:Mc scans.png|700px|thumb|center| Scan-specific bias analysis via MC simulation. (a-b) The gradient direction distribution after excluding 20% of the total number of gradients due to artifacts, in two configurations: non-clustered and clustered exclusion (blue: excluded gradients, green: included gradients). (c) Estimated error distribution of PD computation given the gradient sampling schemes in (a) and (b). (d) Estimated error distribution in FA computation, shown as percentage of true FA. MC simulation was performed for 200,000 iterations for this experiment, with a true FA value of 0.4 and SNR = 10.]]
+[[Image:Mc scans.png|600px|thumb|center| Scan-specific bias analysis via MC simulation. (a-b) The gradient direction distribution after excluding 20% of the total number of gradients due to artifacts, in two configurations: non-clustered and clustered exclusion (blue: excluded gradients, green: included gradients). (c) Estimated error distribution of PD computation given the gradient sampling schemes in (a) and (b). (d) Estimated error distribution in FA computation, shown as percentage of true FA. MC simulation was performed for 200,000 iterations for this experiment, with a true FA value of 0.4 and SNR = 10.]]
 = Publications =

@@ Line 33: / Line 33: @@
 In experimental results, we applied our method on acquisition schemes and also individual scans post-QC. These results show that the proposed rejection metrics provide an effective assessment of post-QC individual scan and also acquisition protocols. Furthermore, our results confirm that higher degrees of uniformity in the sampling gradients results in lower overall bias. Thus, determination of diffusion properties with minimal error requires an evenly distributed gradient directions before and after QC. This method will be incorporated in DTIPrep, used for QC of DWI/DTI data.
-[[Image:Mc schemes.png|700px|thumb|center| ]]
+[[Image:Mc schemes.png|700px|thumb|center| Bias analysis of different DWI schemes via MC simulation. (a-c) Gradient direction distribution for 3 acquisition schemes: 42-direction quasi-uniform, Phillips 32-direction non-uniform, and 6-direction uniform. (d- f) Estimated error distribution in PD computation. (g-i) Estimated error distribution in FA computation as a percentage of true FA. Number of iteration 200,000, true FA value of 0.4 and SNR = 10. ]]
-[[Image:Mc scans.png|700px|thumb|center| ]]
+[[Image:Mc scans.png|700px|thumb|center| Scan-specific bias analysis via MC simulation. (a-b) The gradient direction distribution after excluding 20% of the total number of gradients due to artifacts, in two configurations: non-clustered and clustered exclusion (blue: excluded gradients, green: included gradients). (c) Estimated error distribution of PD computation given the gradient sampling schemes in (a) and (b). (d) Estimated error distribution in FA computation, shown as percentage of true FA. MC simulation was performed for 200,000 iterations for this experiment, with a true FA value of 0.4 and SNR = 10.]]
 = Publications =

@@ Line 29: / Line 29: @@
 == Ongoing Research about Simulation-based Bias Analysis==
-It is important to note that there are some pitfalls associated with these QC approaches in the result of QC after correcting these artifacts. The correction processes modify a configuration of gradient sampling from a scan either by changing the gradients directions or excluding individual DWI’s along a subset of the gradients due to artifacts. Conventionally, QC procedures exclude artifact-affected DWIs from subsequent computations leading to a cleaned, reduced set of DWIs, called DWI-QC. Often, a rejection threshold is heuristically/empirically chosen above which the entire DWI-QC data is rendered unacceptable and thus no DTI is computed. In this work, we propose a simulation-based automatic DTI QC to assess the resulting tensor properties from DWI-QC techniques. We define two error metrics based on the directional distribution of bias for FA and PD. These metrics can provide a promising benchmark for post-QC assessment of the remaining DWIs.
+It is important to note that there are some pitfalls associated with these QC approaches in the result of QC after correcting these artifacts. The correction processes modify a configuration of gradient sampling from a scan either by changing the gradients directions or excluding individual DWI’s along a subset of the gradients due to artifacts. Conventionally, QC procedures exclude artifact-affected DWIs from subsequent computations leading to a cleaned, reduced set of DWIs, called DWI-QC. Often, a rejection threshold is heuristically/empirically chosen above which the entire DWI-QC data is rendered unacceptable and thus no DTI is computed. In this work, we propose a simulation-based automatic DTI QC to assess the resulting tensor properties from DWI-QC techniques. We define two error metrics based on the directional distribution of bias for FA and PD. These metrics can provide a promising benchmark for post-QC assessment of the remaining DWIs. Based on our simulation results, we introduce rejection metrics (automatic thresholds) with respect to magnitude and directional distribution of bias for FA and PD.
 = Publications =

← Older revision		Revision as of 18:07, 2 January 2013
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	[[Image:Correction.png\|700px\|thumb\|center\| The correction results show visible improvement in contrast within the cingulum and fornix tracts (left) and fiber tractography of splenium (right). ]]		[[Image:Correction.png\|700px\|thumb\|center\| The correction results show visible improvement in contrast within the cingulum and fornix tracts (left) and fiber tractography of splenium (right). ]]
		+
		+
		+	== Ongoing Research about Simulation-based Bias Analysis==
		+
		+	It is important to note that there are some pitfalls associated with these QC approaches in the result of QC after correcting these artifacts. The correction processes modify a configuration of gradient sampling from a scan either by changing the gradients directions or excluding individual DWI’s along a subset of the gradients due to artifacts. Conventionally, QC procedures exclude artifact-affected DWIs from subsequent computations leading to a cleaned, reduced set of DWIs, called DWI-QC. Often, a rejection threshold is heuristically/empirically chosen above which the entire DWI-QC data is rendered unacceptable and thus no DTI is computed. In this work, we propose a simulation-based automatic DTI QC to assess the resulting tensor properties from DWI-QC techniques. We define two error metrics based on the directional distribution of bias for FA and PD. These metrics can provide a promising benchmark for post-QC assessment of the remaining DWIs.

	= Publications =		= Publications =

← Older revision		Revision as of 17:42, 2 January 2013
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−	[[Image:Vibration artifact.png\|800px\|thumb\|center\| ]]	+	[[Image:Vibration artifact.png\|800px\|thumb\|center\| Vibration artifacts. a) Artifact free scan. Top-left, a representative axial slice of the color-FA map. Top-right, spherical histogram of the PD distribution within the entire brain. Bottom-right, tractography of genu and splenium of the corpus callosum. Bottom-left, genu tract in more detail. (b) Vibration artifacts may manifest as localized (prefrontal region for this example) signal-loss in the DWI image or as dominant L-R (red) direction. (c) Vibration artifact in the absence of localize DWI signal loss. Spherical viewpoints chosen to show locations of highest histogram frequency.]]

	For our correction step, we employed a simple, iterative leave-one-out-strategy over all individual DWI images by recomputing DTI images and correspond- ing entropies. At each iteration, the DWI with maximal improvement is removed and all leave-one-out entropies are recomputed. This process is continued until either the z-score is in acceptable range or a maximum threshold for exclusion is reached.		For our correction step, we employed a simple, iterative leave-one-out-strategy over all individual DWI images by recomputing DTI images and correspond- ing entropies. At each iteration, the DWI with maximal improvement is removed and all leave-one-out entropies are recomputed. This process is continued until either the z-score is in acceptable range or a maximum threshold for exclusion is reached.

@@ Line 24: / Line 24: @@
 For our correction step, we employed a simple, iterative leave-one-out-strategy over all individual DWI images by recomputing DTI images and correspond- ing entropies. At each iteration, the DWI with maximal improvement is removed and all leave-one-out entropies are recomputed. This process is continued until either the z-score is in acceptable range or a maximum threshold for exclusion is reached.
-[[Image:Correction.png|600px|thumb|right| The correction results show visible improvement in contrast within the cingulum and fornix tracts (left) and fiber tractography of splenium (right). ]]
+[[Image:Correction.png|700px|thumb|center| The correction results show visible improvement in contrast within the cingulum and fornix tracts (left) and fiber tractography of splenium (right). ]]
 = Publications =

@@ Line 20: / Line 20: @@
-[[Image:Vibration artifact.png|700px|thumb|center| ]]
+[[Image:Vibration artifact.png|800px|thumb|center| ]]
 For our correction step, we employed a simple, iterative leave-one-out-strategy over all individual DWI images by recomputing DTI images and correspond- ing entropies. At each iteration, the DWI with maximal improvement is removed and all leave-one-out entropies are recomputed. This process is continued until either the z-score is in acceptable range or a maximum threshold for exclusion is reached.
-[[Image:Correction_Figure_copy.png|400px|thumb|right| The correction result (top right image) shows visible improvement in contrast within the cingulum and fornix tracts. The FA profiles of the genu and splenium tracts (bottom, red: uncorrected, blue: corrected image) show higher FA profiles for the corrected image.]]
+[[Image:Correction.png|600px|thumb|right| The correction results show visible improvement in contrast within the cingulum and fornix tracts (left) and fiber tractography of splenium (right). ]]
 = Publications =

← Older revision		Revision as of 17:32, 2 January 2013
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−	[[Image:~~Diagram2_copy~~.png\|~~400px~~\|thumb\|~~right~~\| Visualization of dominating direction artifact using the color FA DTI image (left column) and the corresponding spherical histogram of the principal directions within the brain. Top: Example of an acceptable DTI dataset. Middle: Example of a "green" direction (anterior-posterior) dominating artifact. Bottom: "Red" direction (left-right) artifact.]]	+	[[Image:Vibration artifact.png\|700px\|thumb\|center\| ]]

	For our correction step, we employed a simple, iterative leave-one-out-strategy over all individual DWI images by recomputing DTI images and correspond- ing entropies. At each iteration, the DWI with maximal improvement is removed and all leave-one-out entropies are recomputed. This process is continued until either the z-score is in acceptable range or a maximum threshold for exclusion is reached.		For our correction step, we employed a simple, iterative leave-one-out-strategy over all individual DWI images by recomputing DTI images and correspond- ing entropies. At each iteration, the DWI with maximal improvement is removed and all leave-one-out entropies are recomputed. This process is continued until either the z-score is in acceptable range or a maximum threshold for exclusion is reached.

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-== Ongoing extension using DTI QC ==
+== Entropy Based Diffusion Imaging Quality Control  ==
 [[Image:Diagram2_copy.png|400px|thumb|right| Visualization of dominating direction artifact using the color FA DTI image (left column) and the corresponding spherical histogram of the principal directions within the brain. Top: Example of an acceptable DTI dataset. Middle: Example of a "green" direction (anterior-posterior) dominating artifact. Bottom: "Red" direction (left-right) artifact.]]
-This approach can also be employed to correct data: Using a leave-one-out scheme, we iteratively determine the best candidate DWI image for exclusion as the one with optimal improvement in the corresponding DTI's entropy measure.  This removal of DWI's continues until either DTI image is not longer classified in the rejection class.
+For our correction step, we employed a simple, iterative leave-one-out-strategy over all individual DWI images by recomputing DTI images and correspond- ing entropies. At each iteration, the DWI with maximal improvement is removed and all leave-one-out entropies are recomputed. This process is continued until either the z-score is in acceptable range or a maximum threshold for exclusion is reached.
 [[Image:Correction_Figure_copy.png|400px|thumb|right| The correction result (top right image) shows visible improvement in contrast within the cingulum and fornix tracts. The FA profiles of the genu and splenium tracts (bottom, red: uncorrected, blue: corrected image) show higher FA profiles for the corrected image.]]