Professor: F.Yaghmayi

Guided by:Dr. F.YaghmayiTumor growth parameters estimation Application to LG GliomasPresented By: Behrooz HosseiniSemnan University

Spring 1392Image Processing ContentPrimary definitions and IntroductionMotivations & Literature ReviewMaterials and MethodSynthetic ResultsClinical ResultsDiscussion And Conclusion

IntroductionMulti discipline

Physiology

The Human body Cell ConstructionBenign and malignant Cancers.

IntroductionGliomas are classified by cell type, by grade, and by location.

Gliomas are further categorized according to theirgrade, which is determined bypathologicevaluation of the tumor.

Introduction

A glioma is a type of tumor that starts in the brain or spine. It is called a glioma because it arises from glial cells. The most common site of gliomas is the brain. Gliomas make up ~30% of all brain and central nervous system tumors and 80% of all malignant brain tumors.Introduction

IntroductionLow-gradegliomas [WHO grade II] are well-differentiated (notanaplastic); these arebenignand portend a better prognosis for the patient. Low grade gliomas (LGG) are slow invaders of brain tissue as they keep growing for many years, presenting one of the most contro-versial decision treatment areas.

High-grade[WHO grade IIIIV] gliomas are undifferentiated oranaplastic; these aremalignantand carry a worse prognosis. High grade gliomas (HGG) remain unfortunately incurable with an average life expectancy of maximum one year after its discovery,

IntroductionMedical ImagingCT SCAN: A computed tomography (CT-SCAN) uses X-rays, a type of ionizing radiation, to acquire its images, making it a good tool for examining tissue composed of elements of a relatively higher atomic number than the tissue surrounding them, such as bone and calcifications (calcium based) within the body (carbon based flesh), or of structures (vessels, bowel) is better suited for bone injuries, Lung and Chest imaging, and detecting cancers.MRI: Magnetic Resonance Imaging uses non-ionizing radio frequency (RF) signals to acquire its images and is best suited for non-calcified tissue and soft tissue, (e.g. ligament and tendon injury, spinal cord injury, brain tumors etc.)IntroductionMedical Imaging

Sample : Para-sagittal MRI of the headMRI immerses you in a massive magnetic field, and then flood you with radio waves, and try and work out what strength of magnetic field will cause the nuclei to resonate at which radio frequencies, and so judge which chemicals exist within a locality of the body.

IntroductionMedical ImagingMRI is more expensive than CT Scan.MRI Takes about 30 ~45 minute to take the image but CT takes 40 seconds.CT has high radiation exposer risk but MRI completely controls the radiation.MRI is more flexible in angle and plane changes.Patient must not move during the MRI ScanningPatients with metal inside thaeir body or tatto might expose problems in MRIMRI can give a well plenty of images with detailed constructions.CT can only give on image but with a reasonable resolution

Pros and ConsGlioma DiagnosisLiterature & Motivation1-initial time (T0): the analysis of various time series of MRI sequences of the brain (Reaction Diffusion Model )

2- tumor diffusion tensors :determine the grade of the gliomas (Progress Step) _ tumor cells migrate faster on white matter ber tracts myelin sheaths (Anisotropic Diffusion)

3- proliferation rate : Estimate their current and further spatial extent

4- initial point :if possible the source main location (center of tumor)

5- mathematical models of Brain tumors Growth DynamicsSimilar WorksLiterature & MotivationAEE (Anisotropic Eikonal Equation): Models the time at which a tumor front reaches a given point. By minimizing the distance between the segmented tumor and the simulated one, estimate and test the prediction of future tumor evolution from at least a pair of images.

POD (Proper Orthogonal Decomposition) uses a reduced model based on Proper Orthogonal Decomposition in order to identify growth parameters of pulmonary nodules in CT images.

Some previous works also just proposed an approach to automatically and accurately personalize brain tumor models for just a single personA New MethodologyNew Method

A New MethodologyNew Methodaim at characterizing the nature of the Glioma , more precisely LGG, from a single MR image

Given a segmented brain Glioma from an MR image, it solves an inverse problem in order to estimate the diffusivity ratio d w , dg and the tumor source position

By having tissue characteristic of patient (dg & dw) and origination Locale , a given index provided by current method will help the physician to diagnosis from the rst acquired MR image and save the valuable time for surgical and/or radio-therapeutic treatment.A New MethodologyNew MethodTake one MRI FLAIR & one Diffusion Tensor (DT-MRI) MRIThe extent of the tumor has been manually segmented in FLAIR images. The Tumor Shape is not usually GeometricTwo main Questions? 1-the tumor source position2-the diffusivity ratio between white matter and gray matterAcquired DT-MRI may have various anomalies like blackholes, low resolution and signal distortionDT-MRINew MethodThe DT MRI is asymmetric especially in white matter where about 50% of the contralateral tumor volume has zero Fractional Anisotropy (FA) values, where the tumor grew, the diffusion signal exists with a remarkable distortion

signal-missNew Methodthe presence of large black hole a barrier to diffusion tensor-guidedglioma evolution simulation. Comparison is so vague.

algorithm based on isotropic diffusion (solving the heat equation) was applied to estimate the missing tensors from neighboring regions. This is done by applying a Gaussian convolution separately on the six components of the diffusion tensorsSolutionTumor growth modelingNew MethodData Collection is not so easy from clinics.Glial cells dynamics are essentially governed by two biological phenomena: proliferation and invasion (reactiondiffusion)u: Change of Tumor density over time:the proliferation rate (Logistic Growth)D: the local diffusion tensor (invade into neighboring neural bers)n: the normal vector at the domain boundary surface

The second equation indicates that there is no ow of tumor cells outside the domain

White Tumor EstimateNew Method

The diffusion tensor(D) is a denite positive and symmetric 3 *3matrix whose value may be linked to Diffusion Tensor MRI (DT-MRI) Indeed, it characterizes the movement of tumor cells thatis considered to be isotropic in gray matter but anisotropic in whitematter. More precisely, the tumor diffusion tensor (TDT) may bewritten as D(x)=dgI3 in gray matter, where dg is the diffusivitycoefcient.But for inhomogeneous anisotropy ratio of White matter:where dw is the white matter diffusivity coefcient, V (x) prepresentsthe matrix of sorted eigen vectors of Dwhite (x) and e1(x), is the normalized largest eigen value (between 0 and 1) of Dw(x).Anisotropic Eikonal Equation (AEE)New MethodAnisotropic Eikonal Equation (AEE) describes the time T(x) at which the evolving tumor front passes through the location x. In its simplest form, the AEE writes as:

assuming that the visible con-tour is associated with isodensity contour u =0.4asymptotic behavior implies that we look at tumors way of growing, regardless of their size and position , at larger times

Parameter estimation from unique MR-imageNew Methodwe can simulate the growth of a glioma given its initial source

Reverse ProblemNew Methodgiven a visible tumor boundary like SSeg in an MR -image, canwe extract the growth parameters S(x); dw ; dg ; ; T(0) that best explain the observed tumor boundary.it is sufcient in this inverse problem to consider a xed value of the proliferation rate corresponding to the tumor grade and to estimate the remaining parameters. the simulated tumor isocontours do not depend on absolute value of dw ; dg but on the diffusivity ratio

inverse problem parameters: 1-the source location 2- spikiness index (r) represents a biology-driven estimated measure which quanties the indirect boundary of the tumor

Reverse ProblemNew Methodestimate the patient specic parameters by minimizing the followingCriterion:

N is number of points in tumor boundary T is time and C gives the standard deviationmotivation to use this criterion is to get good estimates of our unknown initial pointand r rate with fixed proliferation rateSynthetic and Clinical ResultsResults

ResultsRobustness of Estimates Syntheticto validate parameter estimation Synthetic tumor MR images are produced where the initial tumor location and diffusivity ratio are known with fixed proliferation rate

The fact that the minimization criterion appears to be convex at the vicinity of the ground truth parameters is reassuring about the observability of the four parameters

For 100 Test on different synthetic tumorsPoint mean error = 0.42 mmPoint standard deviation =0.36 mmdiffusivity ratio(r) mean error = 0.18diffusivity ratio (r) standard deviation=0.06

Synthetic Simulation

Different (r) value or Start pointClinical ResultThe estimated contour for one real patient Red: Real Blue: Estimate Glioma Grows buttom up during 900 days

Clinical ResultFour different axial slices illustrating the prediction of the tumor evolution Red:Predict Blue: Real

Future Possible MethodImage Registration Application:

Estimation of tumor evolution parameters combined with a multimodal deformable registration framework.

This approach focuses on the MR image registration with an Atlas (Reference) providing the estimation of the initial seed and the current patient status.Discussion, ConclusionThanks for your AttentionAny Questions?Discussion, Conclusion

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