MEDICAL IMAGING PROJECT By: Vanya Vabrina Valindria (V3) Vega Valentine (V2) VIBOT 2010 Management and Post- Processing of Prostate Perfusion MRI
Jun 03, 2015
MEDICAL IMAGING PROJECT
By:Vanya Vabrina Valindria (V3)Vega Valentine (V2)VIBOT
2010
Management and Post-Processing of Prostate Perfusion MRI
Introduction
The anatomy
Prostate cancer leading cause of cancer death in males
Diagnosis:Conventional MR DCE (Dynamic Contrast Enhance) – MRI : Perfusion imaging Link between contrast material up-take in tumors and micro-vascular observation from signal-intensity time curve
Aim
Generate Parametric Images on a pixel- by – pixel basis
Follow the kinetics of the contrast agent within the prostate to localize cancer/tumor area
Build user interface for post-processing of Perfusion MR Imaging
Med_Toolbox_V3V2 Aim to manage and post-process Prostate Perfusion MRI Available in MATLAB GUI
How do we come up with this Toolbox?
Toolbox ‘Browse patient’:
Select Patient Slice ROI of prostateROI
Normalization
Parametric calculation:
Rectangular ROI of the prostate – selected by the user
We use Standardized signal-intensity curves divide the signals by the muscle
Muscle: Same coordinates for each patient
Average of intensity inside ROI muscle before the contrast injection
(in time series ~ 1 -4)
Signal Intensity-Time Curve Comparison between tissues curves
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Normal PZ
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Parametric Calculation
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Wash out
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Max.Contrast Enhancement
Toolbox ‘Image Post-Processing’: Parametric Images Select Analyses Wash IN
Wash In
Wash-in : the mean rate of increase in intensity between the onset time and the maximum-signal time.
Estimated the degree of early strong enhancement of cancerous tissue.
Formula:
MATLAB command:
x_in = double(delta_t*t_onset: delta_t : delta_t*tmax);coeff_wi = polyfit(x_in, double(TIC(t_onset:tmax)),1);wash_in(i,j) = double(coeff_wi(1))*100;
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Wash-in Results
Patient 313; Slice 11
Patient 405, Slice 14
Patient 409, Slice 9
Toolbox ‘Image Post-Processing’: Parametric Images Select Analyses Wash OUT
Wash Out
Wash Out: the decreasing slope after the maximum intensity signal
Formula:
MATLAB code:
x_out = double(delta_t*tmax: delta_t : delta_t*series);coeff_wo = polyfit(x_out, double(TIC(tmax:series)),1);wash_out(i,j) = double(-1*coeff_wo(1))*100;
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Wash-Out Results
Patient 313, Slice 12 Patient 405, Slice 15
Patient 409, Slice 7
Toolbox ‘Image Post-Processing’: Parametric Images Select Analyses Maximum Contrast Enhancement
Maximal Contrast Enhancement (MCE)
MCE: Relative difference between the maximum signal and the baseline signal
Formula:
MATLAB Code:
max_enh(i,j) = double(S_max - S_base)/double(S_base)*100;
MCE Results
Patient 313, Slice 12 Patient 405, Slice 15
Patient 409, Slice 10
Toolbox ‘Image Post-Processing’: Select Analyses Combine Parametric Images
Combined Parametric Image
Combination of all normalized parametric images
MATLAB Code:
washin_norm = (wash_in - min(min(wash_in)))/(max(max(wash_in)) - min(min(wash_in)));washout_norm = (wash_out - min(min(wash_out)))/(max(max(wash_out)) - min(min(wash_out)));max_enh_norm = (max_enh - min(min(max_enh)))/(max(max(max_enh)) - min(min(max_enh)));params_norm = (washin_norm + washout_norm + max_enh_norm)./3;
Combined Image Results Patient 313, Slice 12 Patient 405, Slice
15
Patient 409, Slice 10
Toolbox ‘Extra Features’:
Check signal intensity-time curve for a selected point
Toolbox ‘Extra Features’:
Check signal intensity-time curve for a selected point
Toolbox ‘Extra Features’:
Display parametric images value in Zoom ROI
Toolbox ‘Extra Features’:
Display parametric images value in Zoom ROI
Toolbox ‘Extra Features’:
SAVE in DICOM format
Save all as DICOM
Save all images in DICOMSame DICOM information
MATLAB Code:metadata = dicominfo([direct '\Image00' id]);
dicomwrite(I_wi, ['D:\MEDICAL IMAGING\Project\Datasets\ParametricImg\' dir(37:47) '_Slice' num2str(slice) '_wash_in.dcm'], metadata);
Toolbox ‘Extra Features’:
SAVE all in JPEG format
Toolbox ‘Image Post-Processing’: See the Segmented Cancer Result
Cancer Segmentation
Method: Region Growing Manually selected seeds (for each patient)
Homogeneity criteria:
a,b : is the position of the evaluated pixel
f : the intensity value of the image in Wash In, Wash Out and MCE Images (3 features)
Mean :the value of n-region mean
Cancer Segmentation
Criteria for Cancer Region:
Wash in value > 4Wash out value > 0.2MCE value > 200Max.Area < 500 pixels
Global threshold for all of the images: T(cancer) = 50
The region is consider as cancer region and keep growing when:
Segmentation Results Patient 313
Segmentation results Patient 405
Patient 405
Segmentation Results
Patient 409
Segmentation Results
Segmentation Results Patient 409
Registration
Problem of Patient 407 in time series: 25 – 29
Deformable registration Method: Control point selection Dense SIFTWrapping image Thin Plate Spline
Registration Flow Chart
Reference Image
Unregistered Image
Extract ROI
Extract ROI
SIFT Dense
SIFT Dense
Find Matching
Points
TPS Warping
Registered Image
Registration Result For patient 407, Slice: 8
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Registered Images
Time series: 25 Time series: 26 Time series: 28
Time series: 25 Time series: 26 Time series: 28
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Conclusion
User interface for prostate cancer in MR-perfusion images had been done using MATLAB GUI
Parametric images obtained are useful to characterize the prostate tissues.
Robust for cancer segmentation and deformable registration for prostate region
Time for Toolbox DEMO!!
References
Shen, Kaikai. Parametric Image Formation of Human Prostate Cancer Vascularisation from MRI Perfusion Data. 2008. Laboratoire Electronique, Informatique et Image University of Burgundy.
Jeong Kon Kim, Seong Sook Hong. Rate on the Basis of Dynamic ContrastEnhanced MRI: Usefulness for Prostate Cancer Detection and Localization. Journal of magnetic resonance imaging 22:639–646 (2005)
Olivier Rouvière, et al. Characterization of time-enhancement curves of benign and malignant prostate tissue at dynamic MR imaging. Eur Radiol (2003) 13:931–942 DOI 10.1007/s00330-002-1617-6.
Deanna Lyn Langer . Multi-parametric Magnetic Resonance Imaging (MRI) in Prostate Cancer. 2010. University of Toronto.
Baowei Fei. Image registration for the prostate . 2009. Case Western Reserve University .
Satish Viswanath, et al. A Comprehensive Segmentation, Registration, and Cancer Detection Scheme on 3 Tesla In Vivo Prostate DCE-MRI. 2008. American Cancer Society
Thank You...
Signal-intensity Time CurveSignal enhancement seen on a DCE-MR images
can be assessed in two ways:
Analysis of signal intensity changes (semi quantitative)
Quantifying contrast agent concentration change (pharmacokinetics modeling)
Enhancement parameterized by examining changes in signal intensity over time
Advantages using Muscle Norm?? The enhancement is normalized (same range) for all
patients.
MR signal units are arbitrary and not reproducible from one patient to another
Ease to used for segmentation on post-processing because all patients have the same threshold values in signal-time intensity curve
Signal-intensity Time Curve Comparison between un-normalized and standardized curve
Interval time between series: delta_t = 6.828 s from str2num(info.AcquisitionTime)
There are 40 series in each slice Series time = 40*delta_t
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Un-normalized Curve Standardized Curve
Comparison between other Equation??
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Linear least square fitting of signal data from 10% to 90% maximum intensity enhancement
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Our calculation Our calculation
Use in Patient 313, Slice 11 and Patient 409, Slice 15
Save all as JPEG
Save all images in JPEG format
To visualize directly
MATLAB Code:imwrite(I_wi , ['D:\
MEDICAL IMAGING\Project\Datasets\ColorImg\' dir(37:47) '_Slice' num2str(slice) '_WashIn.jpg'], 'jpg');
Flowchart Segmentation by Region Growing
Label (u*v)
Visited (u*v)
Queue (1:uv)
Initialize the seed (in the top Queue)
Mark Visited and calculate homogeneity criteria
Check its neighbours/ adjacency pixels (1:n)
Have not been Visited?
Does it fit the criteria?
Add pixel to the region and mark Label
Add the pixel to the Queue list
Retrive the pixel from the Queue
Image (u*v)
Starts for each Regions (1:r)
Update region size and region’s statistics
All pixels in Queue are visited?
END
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REGISTRATION Problem Deformable image
registration,: particularly because of bladder and rectum filling
Hard to use only rigid-body registration
Hard to select control points and find the matching points
Register the prostate back inside the ROI the reference position
Control point selection SIFT
Dense Scale Invariant Feature Transform (vl_feat toolbox) to automatically select control points
Descriptors are obtained for densely sampled key points with identical size and orientation.
Optimal parameter
Extract a descriptor each STEP = 5 pixels
A spatial bin covers SIZE = 5 pixels
Matching points the minimum squared Euclidean distance between the matches.
Automatic Control Points Selection
Input Prostate Reference Prostate
Input Prostate Reference Prostate
Matching Control Points from Dense SIFT Descriptors. Some examples in Patient 407:
Input Prostate Reference Prostate
Input Prostate Reference Prostate
Wrapping: TPS (Thin Plate Shape)
The transformation is modeled using TPS Used as the non-rigid transformation model in image
alignment Fits a mapping function between corresponding control
points by minimizing the energy function
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Wrapped Image by TPS
Registration Result For Patient 407, Slice 11
Original Images
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Time series: 25 Time series: 27 Time series: 28
Time series: 27Time series: 25 Time series: 28
Registered Images
Reference Image
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Parametric Images after registration
Patient 407, Slice 8
Wash In Wash Out MCE
Combined Segmented
Non cancer detection…? Patient 313, Slice 3
Wash In Wash Out MCE
Combined Segmented