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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Parametric Calculation

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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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Control Points in Input Image

Control Points in Ref.Image

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