Annual Progress Seminar

Medical Signal & Image Processing

Presented By

PRASHANT BANSODQIP-RESEARCH SCHOLAR

SCHOOL OF BIOSCIENCES & BIOENGINEERINGROLL NO. 05430304

Guided by

PROF. U.B. DESAIELECTRICAL ENGG. DEPARTMENT

INDIAN INSTITUTE OF TECHNOLOGY, POWAI, MUMBAIAugust 2006

Imaging modalities for medical applications

X-ray imaging Nuclear Medicine imaging Computed tomography PET and SPECT MRI imaging Ultrasonography Thermal imaging

Comparison of various Imaging modalities

Characteristics Nuclear Medicine

Ultrasound CT MRI Computed Radiology

2D/3D 2D 2D/3D 3D 3D 3D

Spatial Resolution

Low Moderate Moderate Low High

Contrast Resolution

Low<15 mm

Low<1.5 mm

High<0.1 mm

High<0.1 mm

Low

CNR Low Low High High --

Temporal Resolution

High High Moderate Low Low

No. of Images per study

30 30 + time series

60 100 2

Real time Possible Possible - - -

Radiation Moderate None Moderate None Moderate

Cost Moderate Low High High Moderate

Portability Yes Yes No* No* Some

Distinct Features of Ultrasonography

Safe Non-invasive Easy to use Real time Low cost Comparatively Portable

Applications of Ultrasonography

Obstetric Gynecology Abdominal imaging Echocardiography Superficial structure scanning Vasculature & blood flow Interventional ultrasound Detection, diagnosis & staging of many

diseases

Valvular diseases and assessment of Stenosis Myocardial motion Congenital heart disease Ventricular function Wall thickness Coronary Artery Disease Cardio-myopathy Structural abnormalities Progression of a disease

Diagnostics Applications in Cardiology

Recent Trends in ultrasound imaging

Use of contrast agents High Frequency imaging Tissue motion Intravascular imaging Three & four Dimensional imaging Portable scanners

2D-Imaging: An example

3D-Imaging: An example

Limitations of 2D echo cardiology

Mentally transformation of multiple 2D images to form a 3D impression of the anatomy and pathology.

Such transformation is time-consuming and inefficient.

It is variable and subjective, leading to incorrect decisions in diagnosis.

Conventional 2D ultrasound techniques calculate volume from simple measurements of height, width and length in two orthogonal views, by assuming an idealized shape. This practice can potentially lead to low accuracy, high variability and operator dependency.

Limitations of 2D echo cardiology….

Conventional 2D ultrasound is suboptimal for monitoring therapeutic procedures, or for performing quantitative prospective or follow-up studies, due to the difficulty in adjusting the transducer position so that the 2D image plane is at the same anatomical site and in the same orientation as in a previous examination.

Location and orientation of conventional 2D ultrasound images are determined by the transducer.

Some views are impossible to achieve because of restrictions imposed by the patient’s anatomy or position.

Specific Advantages of 3D & 4D imagingin Cardiology Fast & efficient assessment thus reduction in

acquisition & study time

The C-plane obtained for anatomical views not possible with 2D

Easier for cardiologist to view spatial orientation of heart.

Complete examination through increased perspective from volume data

Better qualitative and quantitative information for diagnosis

Specific Advantages of 3D & 4D imagingin Cardiology

All planes of view reproducible: virtual patient

Simplifies orientation for the referring physician

Better correlation between valves, chambers and vessels.

Volume calculation of heart cavities

Complete analysis possible in one rotation.

Diagnose fetal heart defects in utero, Advance preparation of pathologic conditions for treatment at birth.

Global Cardiac parameters

S.No Parameter Expression01 Left Ventricular Volume

LVVVolume of single shape calculated by integration

02 Left Ventricular VolumeLVM

1.05 x Vm

Vm = Vt (epi) – Vc(endo)

03 Stroke Volume (SV) EDV - ESV04 Ejection Fraction (EF) SV/ EDV x 100 %05 Cardiac Output (CO) SV x HR

EDV= End diastolic volume, ESV=End systolic volumeHR=Heart rate, Vt(epi)= Volume contained in epicardial borders, Vc(endo)= Volume contained within chamber

Regional Cardiac parameters

Wall Thickness Strain Strain rate Shape index Shape spectrum Local stretching Deformation gradient Mean & Gaussian curvature Model specific parameters

Steps for determination of Cardiac parameters

Image Acquisition Preprocessing Registration Segmentation Post processing Visualization Analysis

Research issues

Need for efficient, accurate and robust segmentation of myocardial borders for 2D as well as 3D echo cardiology

Fully automated segmentation

Appropriate model for 3D/4D visualization of heart

3D model based segmentation

Accurate estimation of functional cardiac parameters to assist diagnosis and staging of cardiac disease

Segmentation Difficulties

Tracking of various myocardial boundaries in clinical images which are noisy & blurring.

Transducer motion artifacts modify the brightness

Shape variations are encountered in case of abnormalities

Presence of discontinuities in the acquired images

Inter-observer & intra-observer variability

Existing Methods

Model free techniques- Segment from image by low level processing- Considerable expert intervention- Difficult for real time applications- Suffer from sampling artifacts- Suffer from spatial aliasing and noise- Poor image quality degrades the situation- Difficult for 3D applications

Existing Methods……

Model based approaches- Are based on shape, appearance, surface,

volumetric, deformable model- Suitable for dynamic applications- Can be extended to 3D applications- Accuracy is better due to spatio- temporal

data- High level image processing algorithm

minimize the effect of noise, variability and transducer orientation

Active Appearance Model

This was originally proposed by Cootes. Two steps involved are: Model building and Model matchingModel building consists of generation of statistical models for shape and texture1. Modeling shapex x (1)

denotes mean shape, the shape eigenvector and are shape parameters

2. Modeling texture (2)

denotes mean intensity, the intensity eigenvector and the intensity para

s s

g g

bx s bs

g g b

g g bg

1,

,

metersNow the shape and texture are combined to express components of AAM as functions of model coefficients c:

(3)

(4)

where is a diagonal matrix relating to

s s c s

g c g

s

x x W c

g g c

W

different units of shape & intensity

Active Appearance Model

New shape is generated by eq. 3 by applying similarity transformation to x,y coordinate system of the image

New texture vector is generated by eq.4 and transform to image frame.

AAM is used for segmentation by minimizing the difference between model appearance and target image by gradient descent minimization

Limitations to Active Appearance Model

Disturbances in shape and appearance of objects in images

Large training database is required Training data should be free from

disturbances Computational complexity is more Attempt to enhance robustness further

increases computational complexity

Suggested Modifications

To incorporate a priori shape occurrences possible

To extend the problem for 3D environment for better estimation

To introduce adaptive methods specially suited for ultrasound images

Proposed Work

Algorithm for improvement in AAM & motion models

Possible extension of improved model to 3D data sets

Simulation of electromechanical model of heart

Techniques for mapping image data to electromechanical model for 3D visualization

Estimation of Cardiac functional parameters

Ultimate Aim: 3D visualization & quantification

Thanking you