comparitive study of different mri quantification techniquesigmpublication.org/ijmeit issue/v2-i5/1...

Shivani Garg, Navdeep Singh IJMEIT Volume 2 Issue 5 May 2014 92

IJMEIT// Vol. 2 Issue 5 //May 2014 //Page No: 192-204//e-ISSN: 2348-196x 2014

Comparitive Study of Different MRI Quantification Techniques

Authors

Shivani Garg1, Er. Navdeep Singh2

1Research scholar, Master of Technology, Department Of Computer Engineering, Punjabi University, Patiala 2Assistant Professor, Department Of Computer Engineering, Punjabi University, Patiala

Email: [email protected] , [email protected]

Abstract:

With the advancement in techniques, the latest digital cameras are being used in clinics, the demand

for better image quality and image quantification has become high. In this paper, we have discussed

the various techniques for quantification of an MRI image. They not only will help in defect detection

inside the body but also helps in noise removal due to distortions. This paper also discusses about

various tools developed for the accurate and fast characterization and quantification of the defects.

Nowadays various MRI scanners are available in the market. MR techniques are most powerful in

providing indications of tissue diffusion. Thus, most of the studies are part of medical field and are

related to pathology. In order to analyze different qualitative results, various quantification methods

are required.

Keywords: Quantification, MRI, defects, pathology

INTRODUCTION

Imaging has transformed the practice of medicine

since the first X-rays were produced more than a 100

years ago. Since then medical imaging techniques

have continued to evolve in their capabilities and

have grown their importance to medical practice. X-

ray, X-ray CT, MRI, ultrasound, nuclear imaging

and optical imaging techniques have all been

adapted for specific applications in medicine. New

imaging techniques seek to improve one or more of

these imaging features. The future of medical

imaging lies in the continued improvement of

imaging devices, developing multimodality imaging

techniques and quantitative imaging techniques [3].

One of the main principles in maintenance

engineering is that the time to detect fault and

restore the system back to good working condition

should be as small as possible[1].Several processing

steps are required for the accurate characterization

mailto:[email protected]

mailto:[email protected]

Shivani Garg, Navdeep Singh IJMEIT Volume 2 Issue 5 May 2014 Page 193


and analysis of medical image data. The proper

combination and parameterization of the data

enables the development of such tools that can help

in the early diagnosis .Especially the evaluation of

cancer related pathology images is considered quite

important, since it requires years of theoretical and

practical training for a pathologist to be able to

recognize and diagnose rapidly and accurately the

status and the future evolution of tumor cells[4]. The

major principle of this type of study is to be able to

recognize an infected part of the body in an MRI

scanned image using the image processing

techniques and an effort to quantify the same.MRI

holds a very specific place among imaging

modalities as it is highly flexible from various points

of view; it provides a wide variety of contrast types

with the same equipment, accessible through a

unique imaging session, and it offers flexibility of

slice positioning at any angle in the three

dimensional space.

Magnetic Resonance Imaging (MRI) is an advanced

medical imaging technique used to produce high

resolution images of Image segmentation help to

automatically diagnose diseases for both qualitative

and quantitative analysis of image such as area of

detected portion.MRI can detect a variety of

conditions of the organ such as cysts, tumors,

bleeding, swelling, developmental and structural

abnormalities, infections, problems with the blood

vessels.MRI plays an important role in stem cell

therapies also. MRI does not depend on ionizing.

Pre-processing of MRI images is the primary step in

image analysis which perform image enhancement

and noise reduction techniques which are used to

enhance the image quality and then image

segmentation is done. The segmentation process is

usually based on gray level intensity, color, shape or

texture.

Advantages of MRI

1. It provides not only the phase image but also the

magnitude image.

2. It has high special resolution

3. Non-invasive in nature

4. It provides more perfect information for medical

explanation than any other medical images such as

CT scan, X-ray, etc [8]

Quantification is basically a technique used to

quantify the defects. The quantitative analysis of

MRI image allows obtaining useful key indicators of

disease progression. Segmentation becomes more

and more significant while normally dealing with

medical images; Segmentation and area calculation

from MRI data is an essential but fatigue and time

unbearable task when it completed manually by

medical professional when evaluate with present

day’s high speed computing machines which

facilitate us to visual study the area and position of

unnecessary tissues. Various types of quantification

is done and all their have own advantages and

disadvantages, most common advantage being that it

helps in accurate detection but at the same time

disadvantage is that it is time consuming.

In the following sections we have discussed about

the significance of the quantification and MRI in

section II. In section III all the related work and

methods presented by scientists have been discussed.

In section IV we have concluded our study.



SIGNIFICANCE OF QUANTIFICATION AND

MRI

The improvements in medical imaging have been

beneficial to the practice of medicine. In recent

years, the high quality of imaging has resulted in

high sensitivity to suspicious tissue features.

Imaging technologies are very good at detecting

small abnormalities. Unfortunately, these

improvements in sensitivity have not always been

paralleled by improvements in specificity. Currently,

we are facing many health issues and medical

problems. People are affected with various kinds of

diseases. In order to study these diseases in internal

organs we need MRI, which is the latest technique.

MRI provides valuable information on glands and

organs within the abdomen, and accurate

information about the structure of the joints, soft

tissues, and bones of the body. Often, surgery can be

deferred or more accurately directed after knowing

the results of an MRI scan.MRI basically takes the

images of the infected area. But the images produced

by MRIs are not of very high quality. So detection of

problems like tumor defects are problematic;

because the MRI images are affected by various

factors like noise, shadowing by other organs etc. So

identification of the exact region of

infection/problem is very cumbersome. A large

amount of information is generated by several image

modalities and computational techniques are

required to support decisions in tasks related to

diagnosis, surgical planning and evaluation of

treatments. Our work is to study the different

methods to quantify the identified defect. Once we

are able to accurately detect and quantify the region

of infection, we can provide more effective

treatment to the patient. So our work will provide a

big help to the researchers in the in this field. On the

other hand, the validation of medical image

segmentation methods has been an important

problem from the beginning. Evidently, there are

very time consuming approaches, and subject to high

variability.

RELATED WORK

A number of previous works have been done

addressing different methods for quantification of

different images.

1)In 2001, L Shao et al [6] discussed various latest

techniques like singles transmission attenuation

correction, the Fourier rebinning algorithm, etc

plus scatter and random correction to image from a

dual head coincidence camera towards the goal of

quantification.

Method

The performance of the camera is examined through

the resolution, the target-to-background ratio and the

image distortion using several phantoms. The results

indicate that attenuation correction improves the

quality of image. To avoid the emission image

quality compromise, transmission study is performed

after each emission study. The quality of

reconstructed images is improved with combinations

of Fourier rebinning (FORE) and OSEM

reconstruction with attenuation correction, scatter

correction built in wiener filter while keeping the

noise in the background area low. During



transmission acquisition, three types of corrections

are applied.

1. Emission contamination

2. Self contamination

3. Dead time correction

Advantages

1. Attenuation correction improves the image

quality.

2. It can be widely used for lung and cardiac studies.

3. Distortions due to attenuations are removed.

Disadvantages

1. For attenuation correction, the registration

between emission and transmission scams is more

critical.

2) In 2002, F.Calamante et al[5] presented a bolus

tracking MRI technique for the evaluation of

perfusion in cerebral ischemia.This study aimed at

improving the characterization of tissue which is at

risk of stroke.Perfusion is the term used for the

proper blood supply to all body parts.Recent studies

are concerned with predicting the eventual infarct

volume suffering ischemia.Many studies have shown

a relation between perfusion MRI parameters and

lesion outcome measures but could not specify the

reasons.Many attempts have been made to find

absolute measurement of perfusion parameters and

to find a threshold values that will help to predict

tissue viability.

Method

Dynamic susceptibility contrast (DSC) MRI,

referred to as bolus tracking, is the MRI technique

most commonly used for the clinical evaluation of

perfusion in cerebral ischemia. It involves the rapid

intravenous injection of an MR contrast agent and

the serial measurement of the signal loss during the

passage of the bolus through the tissue[5].New

generation of MR scanner provide the necessary

software and hardware to calculate perfusion maps

by using deconvolution.This has increased the use of

deconvolution maps at the clinical level evaluation

of stroke. There are 3 main assumptions in the

quantification of DSC MRI data and the potential

implications for the quantitative measurement of

CBF in stroke namely, arterial input function (AIF),

tissue characteristics, and cross calibration. By

estimating AIF in major artery, the delay and

dispersion of the bolus are accounted from the

injection site. The model used interprets this delay

and dispersion occurring within the tissue. With the

increasing availability of DSC MRI as a “black-box”

technique on modern scanners, caution in

interpretation of perfusion maps should be taken.

This is necessary when absolute quantification is

attempted, but in some circumstances for relative

measurements as well. Work is currently under way

in a number of centers to address the problems

underlying the quantification of CBF.

Advantages

1. It is a powerful technique for perfusion evaluation

in cerebral ischemia.

2. It provides indicators of tissue perfusion.

3. It helps in the assessment and management of

patients with cerebrovascular disease



Disadvantages

1. Some assumptions can behave invalid in cerebral

ischemia

2. There can be significant errors in quantification of

perfusion due to some assumptions in kinetic model

used

3. Interpretation of perfusion maps should be done

with caution.

3) In 2003, studying [11-13], resulted in the

development of a protocol named”Quantitative PCR

for Determination of Angiogenic Factors”. In this

protocol two main quantification methods were

discussed which are important for calculating

concentration of DNA.PCR(Polymerase Chain

Reaction) is an in vitro techniques for rapid

synthesizing of large quantities of DNA.

Quantitative PCR can be used to quantify target

DNA using absolute or relative quantification.

Method

1. Absolute quantification uses external standards to

determine the absolute amount of target nucleic acid.

Equivalent amplification efficiencies between the

target and external standard are necessary for

absolute quantification.

2. Relative quantification calculates the ratio

between the amount of target template and a

reference template in a sample. The accuracy of

relative quantification depends on the appropriate

choice of a reference template for standards. The

difference in amplification efficiencies of target and

reference is negligible. Finally, the amplification

efficiencies of both the reference and target are

measured, and a correction factor is determined. This

process called normalization requires a sample

containing known concentrations of both target and

reference and the generation of two standard curves.

The PCR efficiency between a reference sample and

a target sample is determined by preparing a dilution

series for each target. The difference in CT values

calculated by subtracting either target or reference

from the other is then plotted against the log of the

template amount. If the resulting slope of the straight

line is less than ±0.1, the amplification efficiencies

are similar.

Advantage

1. Using an internal standard can minimize

variations in sample preparation and handling.

Disadvantage

1. Variability of the standard will influence the

results.

4) In 2004, Christophe et al [10] developed a method

in which muscle is studied directly by segmenting

3D MRI. Various studies have been done by the

research teams in order to quantify the importance of

infarcts. They dealt with myocardium, a middle

muscular layer in the heart, which has become one

of the main reasons of mortality in industrial

countries.

Method

In this method, MRI of the left ventricle is analyzed

after infarcts. The volume of infracted and sane

muscles are measured .It evaluates the percentage of

infracted muscles. In this method segmentation



techniques are necessary so that a semi-automatic

tool could be easily used. In semi-automated tool,

first of all watershed algorithm is computed on the

whole image, then recursively on the region

delimited by the previous segmentation. The method

is divided into three parts:-

1. Region of Interest (ROI) selection

2. Slice-by-Slice interactive segmentation

3.3D reconstruction, smoothing and volume

estimation.

Advantages

1. Tool developed is fast during segmentation.

2. Results can be modified easily by medical staff.

3. Volume computation time is less with 5%

precision.

4. More precise than manual segmentation.

5. Precision degree can be chosen by user.

6 .For contrasted images, automatic selection and

watershed algorithms give good results.

Disadvantages

1. Precision degree can be chosen manually so

chances are there for incorrect results.

2. Manual segmentation is faster than this method

3. The structure presented is applicable to grey scale

images.

5) In 2006,charalampos et al[9] developed and

proposed an automated image analysis method that

would give an un biased quantification of the micro-

vessel density and growth in angiogenic CAM

images. Angiogenesis is the physiological process

through which new blood vessels form from pre-

existing vessels. Stimulation of new blood vessel

growth has been proposed as a therapeutic approach

to treat conditions that

include ischemic heart disease, neurodegenerative

disorders and hair loss. On the other hand, an

excessive and deregulated angiogenic response can

contribute to cancer. It is an interactive process

between tumor, endothelial and stromal cells in

order to create a network for oxygen and nutrients

supply, necessary for tumor growth. In this method,

focus was put on to test and develop an automated

image analysis method that would give an unbiased

quantification of the micro-vessel density.

Method

This angiogenic potential is calculated by counting

number of blood vessels in particular section.

Quantification of the angiogenic response in the

Chorio Allantoic membrane (CAM) is done by

manual scoring or by morphometric analysis of

CAM pictures using appropriate software, such as

Scion Image or Image-Pro plus. The presented

method provided an automated angiogenetic

assessment based on vessel length, branching points

and texture quantification. First of all stereoscopic

images of the tissues developed are taken. Then, the

automated processing and angiogenesis

quantification is done in the following three phases:-

1. Image is converted into grey scale.

2. Adaptive thresholding and noise removal

3. Image is skeletonized and it produces final output

that contains vessels thickness.



Fig1: various steps involving angiogenesisquantification

Finally the developed tool calculates automatically

the total vessel length, vessel branching points,

vessel density over whole image area and vessel

texture.

Advantages

1. Suitable method for tumor growth detection.

2. Extensively used tool by biologists.

3. Highly accurate and reproducible tool.

Disadvantage

1. Quiet a long process, thus time consuming

6) In 2006, Jerry et al [7] presented a MRI

acquisition and analysis protocol to quantify the cell

number and cell volume and was validated in future

in vivo studies. In vivo stands for something that is

present inside the living body of a plant or animal.

This study presented an MR technique for

quantifying the number, density and distribution of

labeled stem cells after injection or

transportation.MRI is a powerful tool to investigate

biological processes at molecular and cellular level

in vivo lengthwise.

Method

Various steps involved in this method are:-

1. Cell labeling

2. Labeled cells suspended uniformly in Agarose Gel

3. Cell number quantification

Stem cells which are labeled magnetically using MR

contrast agents can be used to check/monitor the

repair or progression of different ischemic (deficient

supply of blood to body part) diseases.

Advantages

1. This technique can be applied and used for

monitoring and optimizing stem cell therapy.

2. It gives assessment of stem cell localization and

migration.

3. Good agreement between actual cell number and

estimated cell numbers is found.

Disadvantages

1. Computational error can take place

2. Quality of raw weighted images can affect

quantification.

3. Error may arise from quantifying high number of

cells in small region.

4. Relationship between apparent transverse

relaxation rate (AR)

And cell concentration may not be linear.

7) In 2010, Logeshwari et al [2] described a

segmentation method consisting of two phases. In

the first phase, the MRI brain image is taken from

patient’s database and artifact and noise are removed

from the image. After that Hierarchical Self

Organizing Map (HSOM) is applied for image

segmentation. The HSOM is the extension of the



conventional self organizing map (SOM) used for

row by row classification of the image.

Method

New unsupervised learning Optimization algorithm

SOM is implemented to extract the suspicious region

in the Segmentation of MRI tumor. The textural

features can be used to classify them. In this method

starting from the first row and the first column, the

intensity value greater than that of the threshold

value is removed from MRI. During removal of film

artifacts, the image consists of salt and pepper noise.

To remove this, image is enhanced. A self-

organizing map (SOM) or self-organizing feature

map (SOFM) is a type of artificial neural network

for unsupervised learning. It describes a mapping

from a higher dimensional input space to a lower

dimensional map space. SOMs operate in two

modes: training and mapping, Training is a

competitive process or we call it vector quantization,

whereas Mapping automatically classifies a new

input vector. Image segmentation techniques can be

classified as

based on edge detection, region or surface growing,

threshold level, classifier such as Hierarchical Self

Organizing Map (HSOM), and feature vector

clustering or vector quantization [2].There are two

processes involved in vector quantization:

1. The training process which determines the set of

codebook vector according to the probability of the

input data.

2. The encoding process which assigns input vectors

to the code book vectors. Thus number of tumor

cells of different pixel windows is calculated.

Advantages

1. Execution time is the main parameter and it varies

window to window e.g. 3*3, 5*5, etc

2. Directly used in clinics.

3. Lowest level of weight vector, a higher value of

tumor pixels, computation speed is achieved by the

HSOM with vector quantization.

Disadvantages

1. The number of neural units in the competitive

layer needs to be approximately equal to the number

of regions desired in the Segmented image.

2Performance varies on the quality of enhanced

image.

8) In 2012, Theodosios et al [4] presented an

advanced image analysis tool quantification of

cancer and apoptotic cells in microscopy images

utilizing adaptive thresholding. In this, a

combination vector machine and majority voting and

watershed algorithm was proposed to characterize

and quantize different types of cells.

Method

First of all, tumor images training set is taken. The

image imported is edited by the classification model.

The image is enhanced using adaptive thresholding

segmentation and noise generated is removed by

adopting morphological operations e.g. majority

voting and watershed filtering. Finally size

correction procedure was developed for the accurate

quantification of each type of cells.

Advantages



1. Accurate and fast characterization of cancer.

2. Accurate and reproducible results of cancer

images

Disadvantages

1. Some miscounts occur due to existence of

extremely merged cells.

2. Merged cells cannot be separated using watershed

algorithm.

9) In2012, S.Roy et al[8] proposed a fully automatic

algorithm to detect defects preferably tumors by

using symmetry analysis. In this firstly the defect is

detected, segmented and then area is calculated.

Method

A basic concept to detect tumor is used. Basically,

the component of image holding the tumor has extra

concentration the other segments and from that area,

shape and radius of the tumor can be estimated,

which is calculated in pixels. In preprocessing steps,

image is enhanced and noise is removed. Then

filtering and segmentation is done with a purpose to

show only infected portion which has more intensity

and area as compared to other portions.

Advantages

1. It produces good results in complex situations

also.

2. As it’s an automatic segmentation, it frees

physicians from manual labeling thus saving time.

3. A well organized technique which combines

region and edge information.

4. Valid for dissimilar types of tumors with MRI

images.

Disadvantages

1. Tumors of different color, shape, texture and

position often deformed by anatomical structures.

2. Fails when pathological tissues cannot be captured

by discriminative model.

10) In 2012, Abdulfattah et al [1] presented a novel

brain tumor quantification method based on step

response algorithm utilizing a model which itself

was based on step response model resulting in smart

and rapid quantification of tumor volume. The

method developed aims at determining the volume

of tumor for both intra and inter slice by involving

its integral form.

Method

The general pattern of growth of brain tumor on

slice by slice basis is governed by:

Where Tu(0)-Tu(∞) and Tu(h)-Tu(∞) are α and β

respectively, h is slice thickness, and ψ is the tumor

growth and decay model called behavioral function.

It is expanded as:

∞

∞



Where Tu(h),Tu(0) and Tu(∞) stand for tumor area at

any point within slice thickness (h), tumor area at the

lower surface of slice, and tumor area at the upper

surface of slice assuming that the tumor area at the

lower surface is less than that of the upper surface.

The brain tumor model equation is

Tum(h)=Tu(∞) +βe(-h/

)………..(1)

Where

∞ ∞

for determining the instantaneous tumor area at any

given point within the slice thickness (h). .The

integral of the equation (1)gives the volume of tumor

i.e. quantizes.

∞

Tu∞]e−0.91Tu∞−Tu0hdh

Advantages

1. Smart and rapid quantification of tumor volume.

2. It can be used by new or inexperienced surgeons

easily.

3. No time is spent at planning stage, so it saves our

time.

Disadvantage

1. Radiological images cannot be directly taken for

use.

11) In 2012,Michael et al [3] presented two methods,

spectral-based techniques and envelope statistics at

clinical frequencies and at high ultrasonic

frequencies (> 15 MHz) that will examine their

abilities to improve diagnostic ultrasound.

Conventional imaging techniques often lack the

ability to detect anomalous tissue features. So results

can be negative. QUS(quantitative ultrasound) helps

in improving ultrasound diagnosis. There are many

techniques that can be included in this e.g. spectral

based parameterization of ultrasound signals, flow

estimation through Doppler, tissue elastrography,

shear wave imaging and envelope statistics. In this,

spectral based parameterization and envelope

statistics are discussed. Both ability to produce new

sources of contrast and provide improved diagnostic

potential for several disease states.

Method

1. Spectral based parameterization

These are based on obtaining a good representation

of the power spectrum of the backscattered signal

through the periodogram. The estimate of the power

spectrum is normalized by observing

Scattered volume and the characteristics of the

transducer and

Excitation pulse. The backscatter coefficient (BSC)

is related to the normalized power spectrum .BSC is

a fundamental property of the tissue. The BSC is

both system independent and operator. Estimates of

the scatterer properties (effective scatterer diameter

(ESD) and effective acoustic concentration (EAC))

can be obtained using an estimator that compares the

BSC calculated from measurements to a theoretical



BSC and minimizes some cost function versus trial

values of ESD and EAC. Different estimators are

used to reduce the scattered size distribution on the

final ESD estimate effects.

2. Envelope Statistics

Envelope statistics include the estimation of the

number density of scatterers and quantification of

coherent to incoherent signals produced from the

tissue. Envelope statistics were estimated from the

homodyned K distribution, an algorithm was

developed to estimate the parameters of the

homodyned K distribution. The algorithm made use

of fractional order moments of the envelope SNR

(R), skewness (S) and kurtosis (K) to provide

estimates of the k (ratio of coherent to incoherent

signal due to scattering) and p (number of scatterers

per unit resolution cell parameters). During

estimation, the R, S, and K are estimated for a data

block and are used to generate level curves for two

sets of fractional order moments for different values

of k and p. These curves are generated through

comparison with a theoretical evaluation of R, S, and

K.

Advantages

1. It provides specific features related to tissues that

help in improving ultrasound diagnosis.

2. Detection and classification of solid tumors and

lymph nodes.

3. Detection and quantification of fatty liver disease.

4. Monitoring and assessment of thermal therapy on

solid tumors.

5. It is good at identification of lesions.

6. Reduction in number of biopsies results in cost

reduction.

7. Anxiety reduction and risks reduction.

8. Reduced time burden.

Disadvantages

1. Techniques frequency dependent

2. Incorrect attenuation estimates results in incorrect

results

3. New attenuation estimation algorithms are

required.

12) In 2014, Paredes et al [14] conducted an

experiment on whole fixed mice for developing a

method to calculate the pancreatic volume.

Generally, patients having pancreatic disorders

undergo cross sectional imaging of pancreas on

routine basis. This diagnosis is done using MRI.

Method

In this a thin sliced, optimized sequence protocol is

developed using a high field MRI so that pancreatic

volume could be calculated correctly. They used

pancreas size as a key element to assess pancreatic

growth, development and recovery from injuries .A

7 Telsa Bruker micro-MRI system is used and

images of the object under observation are taken in

all the three standard sets of orthogonal planes:

axial, sagittal and coronal. A vitrea software is used

to trace the contour of the pancreas an then it

transforms it into a 3D reconstruction, which helps

in calculating volumetric measurements. For image

optimization, heart perfusion fixation, T1 sequence

analysis and 0.2 to 0.4mm thick slices are used. As a



result, we found that the volume of pancreas

depends directly on the body weight which is further

dependant on the age.

Advantages

1. This method helps in improving the non-

invasively quantify changes in pancreatic size.

2. It can be used on both animals and humans for

determining their pancreas volume.

3. It is operator independent method.

4. Patient usage time is reduced due its non- invasive

nature.

Disadvantages

1. Technically a difficult method.

2. MRIs with higher resolution are required.

3. Time consuming method as per calculation view

IV. CONCLUSION

Comprehensively, we have discussed various

quantification techniques along with their

advantages and disadvantages. The paper also

discusses the applications where these techniques are

applicable. The paper can be used to give more

clarity regarding various existing techniques so that

a researcher can select the method wisely. In future

,the developed methods should be improved by

adapting more methods to suit the different medical

image segmentation and quantification. The current

methodologies are being used by several medical

labs which are entirely based on either on the visual

count or on the commercially available image

analysis.

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