RAW file load defect (initial slice\Film view projection): fixed.
This was due to recently introduced feature of remembering the initial View in the FVX file format, an unintended consequence.
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Friday, August 8, 2014
Thursday, August 7, 2014
Tuesday, August 5, 2014
Monday, August 4, 2014
Blood Vessel map segmentation: Implemented additional controls to adjust the how aggressive the segmentation is:
a) Introduced "vessel time delay" parameter due to dispersion
b) L2-Normalization of TAC comparison, since the numerical values of TACs and IFs are quite different due to dispersion and better shape comparison is expected that way.
Parallelized segmentation algorithm.
a) Introduced "vessel time delay" parameter due to dispersion
b) L2-Normalization of TAC comparison, since the numerical values of TACs and IFs are quite different due to dispersion and better shape comparison is expected that way.
Parallelized segmentation algorithm.
Sunday, August 3, 2014
Saturday, August 2, 2014
Thursday, July 31, 2014
FireVoxel Build 146A is released.
1. FVX file format, version4: implemented format extension to be able to save current dynamic model and the Input functions. This would be a great productivity multiplier both for end-User and developer.
2. General 4D "Blood vessel segmentation" algorithm and UI with User-provided input function(s).
3. Implemented and added an UI item "MainMenu>Segment>Segment dark ridges" which complements the existing "Segment bright ridges" functionality.
4. Implemented Liver DCDI Blood Vessel Segmentation 3D: this is an alternative algorithm that uses a single pre-contrast timepoint assuming the vessels are darker than the liver tissue. Existing "Dark Ridge detection" algorithm is used.
5. Fixed a defect in the IDIF function.
6. Added several "Keep Highest voxels for each individual timepoint" options as specified by Jean Logan.
1. FVX file format, version4: implemented format extension to be able to save current dynamic model and the Input functions. This would be a great productivity multiplier both for end-User and developer.
2. General 4D "Blood vessel segmentation" algorithm and UI with User-provided input function(s).
3. Implemented and added an UI item "MainMenu>Segment>Segment dark ridges" which complements the existing "Segment bright ridges" functionality.
4. Implemented Liver DCDI Blood Vessel Segmentation 3D: this is an alternative algorithm that uses a single pre-contrast timepoint assuming the vessels are darker than the liver tissue. Existing "Dark Ridge detection" algorithm is used.
5. Fixed a defect in the IDIF function.
6. Added several "Keep Highest voxels for each individual timepoint" options as specified by Jean Logan.
Wednesday, July 30, 2014
Monday, July 28, 2014
Implemented Liver DCDI Blood Vessel Segmentation 3D: this is an alternative algorithm that uses a single pre-contrast timepoint assuming the vessels are darker than the liver tissue. Existing "Dark Ridge detection" algorithm is used. Existing Ridge dialog box is called.
Two ROIs are returned one for organ and the other for blood vessel. There is no differentiation between Arterial and Venous vessels as in Segment4D variant.
Two ROIs are returned one for organ and the other for blood vessel. There is no differentiation between Arterial and Venous vessels as in Segment4D variant.
Sunday, July 27, 2014
Initial result for Liver Blood vessel map segmentation. Procedure is general and is not Liver-specific. User provides 1 or more Input Function by standard loading into the Dynamic Module. Whole organ ROI (liver in this case) is provided. Result is the set of new ROIs for (NumVessels+1). In this test Arterial ROI was added for correctness verification.
Tuesday, July 15, 2014
Monday, July 14, 2014
Saturday, July 12, 2014
Friday, July 11, 2014
Thursday, July 10, 2014
Saturday, July 5, 2014
LIVER DCDI case {LI}: comparison of parametric maps {Total Flow,fa,EMTT,Ki} REGISTERED vs. UNREGISTERED
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| Total flow, UNREG (68.4 +/-41.4) - REG (133 +/- 55) |
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| fa-Arterial fraction, UNREG (0.64 +/- 0.35) - REG (0.37 +/- 0.34) |
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| EMTT, UNREG (26.1 +/- 12.1) - REG (17.7 +/-10) |
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| Ki, UNREG (4.67 +/- 1.37) - UNREG (5.28 |
Friday, July 4, 2014
Monday, June 30, 2014
Friday, June 27, 2014
Thursday, June 26, 2014
Wednesday, June 25, 2014
Liver DCDI modelling, case SK: processed the whole 71-timepoint dataset (100 min). Vertical motion in the coronal plane is almost entirely eliminated.
Preliminary evaluation of the PV-IF show a substantial sharpening of this function. Exactly the same manually constructed ROI is used in this diagram both for registered and unregistered case.
Preliminary evaluation of the PV-IF show a substantial sharpening of this function. Exactly the same manually constructed ROI is used in this diagram both for registered and unregistered case.
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| RED - IF obtained from unregistered data, BLUE- from registered. |
Tuesday, June 24, 2014
Thursday, June 19, 2014
Wednesday, June 18, 2014
Monday, June 16, 2014
Sunday, June 15, 2014
Employing Affine transform further improves the quality of registration.![]() |
| SK: t=19, before registration |
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| SK: t=19, after registration |
Saturday, June 14, 2014
Friday, June 13, 2014
Preliminary result: Substantial registration improvement is achieved on Liver-GRASP-DCDI exam
First, User contructs a fairly precise "Whole Liver ROI" on a single timepoint (SK,t=25).
This is done by outlining the liver on every 2nd slice and applying Fill&Morph operation (total time=5 min). This Whole Liver ROI will be used in registration and further liver function evaluation.
First, User contructs a fairly precise "Whole Liver ROI" on a single timepoint (SK,t=25).
This is done by outlining the liver on every 2nd slice and applying Fill&Morph operation (total time=5 min). This Whole Liver ROI will be used in registration and further liver function evaluation.
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| ROI contours are manually drawn on Axial projection and simultaneously displayed on Coronal. |
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| Before registration: SK-case, timepoint t=20, and the Target Liver ROI (t=25) |
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| After registration: Anatomy and the Target ROI are aligned. |
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| Parameter set that has been used. |
Thursday, June 12, 2014
Wednesday, June 11, 2014
Monday, June 9, 2014
Friday, June 6, 2014
Wednesday, June 4, 2014
Monday, June 2, 2014
Sunday, June 1, 2014
Saturday, May 31, 2014
Friday, May 30, 2014
Wednesday, May 28, 2014
Tuesday, May 27, 2014
Friday, May 23, 2014
Wednesday, May 21, 2014
PET brain "Ridge Segmentation" function: finalized interface and functionality for 2D and 3D cases. Simplistic skeletonization algorithm that works well in 2D, for some reason is not performing well in 3D and would require more time to integrate more precise 3D skeletonization.
Therefore, due to a limited time available, "Process slices individually" option is always on in 3D case for the time being.
Therefore, due to a limited time available, "Process slices individually" option is always on in 3D case for the time being.
Friday, May 16, 2014
Wednesday, May 14, 2014
Monday, May 12, 2014
Added the "Detect Bright Ridges" function to the User Interface.
Operating volume - user specified increase in the resolution of the resulting "Ridges". It is practical experience that skeletons might benefit from the increase in resolution since their width of 1 voxels is used to simulate the objects of 0-thickness. "Increase resolution coefficient" in practice range from 1 to 3.
Interpolation - specifies the algorithm used in image upscaling.
Background detection is a part of the algorithm. Background is detected by overall smoothing of the volume. User has to specify "Radius (vox)" and the type of the smoothing Kernel.
Output parameter - specifies the result of the processing.
Elevation Map - Signal intensity volume specifying the likelihood that corresponding voxel is a ridge.
Ridge Mask: Binary ROI representing wide ridges.
Skeleton Mask: Centerlines of the Ridges represented as binary volumes.
Operating volume - user specified increase in the resolution of the resulting "Ridges". It is practical experience that skeletons might benefit from the increase in resolution since their width of 1 voxels is used to simulate the objects of 0-thickness. "Increase resolution coefficient" in practice range from 1 to 3.
Interpolation - specifies the algorithm used in image upscaling.
Background detection is a part of the algorithm. Background is detected by overall smoothing of the volume. User has to specify "Radius (vox)" and the type of the smoothing Kernel.
Output parameter - specifies the result of the processing.
Elevation Map - Signal intensity volume specifying the likelihood that corresponding voxel is a ridge.
Ridge Mask: Binary ROI representing wide ridges.
Skeleton Mask: Centerlines of the Ridges represented as binary volumes.
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