WO2013022333A2

WO2013022333A2 - Methodology and apparatus for early in vivo detection and grading of knee osteoarthritis

Info

Publication number: WO2013022333A2
Application number: PCT/MY2012/000195
Authority: WO
Inventors: Dileep Kumar; Raja Mohd Kamil RAJA AHMAD; Ahmad Fadzil Mohamad Hani
Original assignee: Institute Of Technology Petronas Sdn Bhd
Priority date: 2011-08-08
Filing date: 2012-06-29
Publication date: 2013-02-14
Also published as: MY155595A; WO2013022333A3

Abstract

The present invention relates generally to a methodology and apparatus for early in vivo detection and grading of knee osteoarthritis (OA) whereby combined assessment of cartilage thickness, water and proteoglycan (PG) contents is carried out by incorporating dual tuned knee coil (113) (²³Na/¹H) for data acquisition.

Description

METHODOLOGY AND APPARATUS FOR EARLY IN VIVO DETECTION AND GRADING OF KNEE OSTEOARTHRITIS

1. TECHNICAL FIELD OF INVENTION

The present invention relates generally to a methodology and apparatus for early in vivo detection and grading of knee osteoarthritis (OA) whereby combined assessment of cartilage thickness, water and proteoglycan (PG) content is carried out by incorporating dual tuned knee coil (²³Na/¹H) for data acquisition.

2. BACKGROUND OF THE INVENTION

Osteoarthritis (OA), or degenerative joint disease is one of the common type of arthritis. It is characterized by the breakdown of the joint's cartilage, causing bone to rub against bone and thus causing pain and loss of movement. It is most serious, painful and life altering joint disease that will lead to failure of synovial joint organ. Osteoarthritis can range from very mild to very severe and most commonly affects middle-aged and older people. The worldwide prevalence estimate for symptomatic OA is 9.6 % among men and 18 % among women. In 2008, the prevalence of OA in United States alone was estimated at 8.9% (27million). In addition, progression of early OA can take place within 10 years of a major injury. In case of injury at age 15, young people may have OA as early as age 25.

OA affect the joint of spine, fingers, hips, knees and toe but it is most common in the knee than any other synovial joint organs. According to the studies by World Health Organisation for the year 2000, knee OA is most prevalent in developing countries in Europe (EU BC) (13.3%) and South-East Asia Region (SEA) (7.9%) for the age group between 30 - 80 years. Disability due to OA has a huge negative impact to the economy due to the loss of productivity and health costs of any country. More importantly, there is an increasing trend of younger people diagnose having OA due to active life style. Early detection of knee osteoarthritis is thus important as it allows physicians to dispense advice such as weight control, avoidance of activities involving excessive use of weight bearing joints etc. and to start treatment to delay the further OA progression.

The main characteristic^' of knee OA is the gradual loss of articular cartilage (AC), causing bone to rub against bone and thus causing pain and loss of movement. AC is flexible yet soft tissue that exists on the end bones. At the microscopic scale between 100 micron (0.1mm) and 1 cm, AC can be viewed as a solid homogeneous material. At the same microscopic level, cartilage is organised into four different zones called: the superficial zone, middle zone, deep zone, and calcified cartilage zone. Each zone plays an important role to maintain the integrity of the cartilage and having different amounts of water, collagen and ground substance. At the same level, biochemical contents of articular cartilage can be observed. Articular cartilage tissue contains water in the range of 60% to 80%, and chondrocytes that are surrounded by extracellular matrix (ECM). The primary components of the extracellular matrix are type II collagen (5-10% of total cartilage composition) and large proteoglycan (PG) molecule (10-20% of total cartilage composition). Excessive use of joint cartilage and heavy load on knee joint would results in the unbalanced amount of biochemical compositions of articular cartilage. An unbalanced amount of biochemical compositions will lead to abnormalities in articular cartilage and other components of knee joint that can be categorised into three classes of features: (a) morphology, (b) biomechanical/bioelectrical properties and (c) molecular composition. Changes in selected features from the above three classes are most desirable for early OA detection. Research has so far shown that early detection of osteoarthritis is possible by assessing changes in the features associated with articular cartilage degeneration. However, the ability to detect feature changes sufficiently is somewhat limited by the resolution, sensitivity and invasiveness of the modalities. For a non-invasive, non-ionizing and in vivo early OA detection, selection of suitable features which are detectable at high sensitivity is required. Literature studies have shown that the earliest changes related to onset of OA that can be accurately and consistently measured using a non-invasive, non-ionizing and in-vivo modality for three categories of features are: - (1) cartilage thickness, (2) cartilage water content and (3) proteoglycan (PG) content. However, the absolute change of these features from normal to early OA has not been successfully measured with certainty in the past. In addition, measurements of a single feature and its changes vary from one study to another and result in inconclusive early detection of OA. This warrants the use of more than one unique feature to develop a diagnostic tool for early OA.

Research has shown that the measurement of changes in selected early OA features is possible using several modalities. However, most of the studies reported involve in-vitro methods and results obtained are not consistent from one study to another. It is found that MRI is a non-invasive, nonionizing and in-vivo modality that has the capability to detect changes in morphology and physiology of articular cartilage. In addition, MRI is also the most suitable candidate to achieve single modality approach to detect early osteoarthritis at grade 1 or earlier. Therefore, it is expected that combining the above early OA features' and measuring the change in selected features using MRI as modality could improve the overall sensitivity and specificity of an early OA diagnostic tool. Hydrogen (Ή) based MRI is found suitable in measuring changes in water content and thickness of articular cartilage which are associated with early stage of OA progression. PG content of cartilage can be measured from TlGd relaxation time from of hydrogen based MR data which requires gadolinium- diethylenetriamine pentaacetic acid (Gd-DTPA) contrast enhancement agent to be injected in the body prior to scanning. However, this technique is invasive, time intensive and complicated because TlGd maps should be obtained as soon as possible before the concentration of injected contrast agent changes. To overcome the use of contrast agent, sodium (²³Na) based MRI is proposed due to the high sodium content in the articular cartilage. As the sodium concentration decreases due to the loss of PG, measurements of local sodium concentration can be used as marker to measure the PG loss directly. This warrants the use of specialised coil (Dual tune knee coil- ¹H/²³Na) that can resonate Ή to detect water content and ²³Na to detect PG content one at a time at early stage.

At present accurate diagnosis of osteoarthritis is in general possible only when the disease has progressed significantly. Physicians can do little more than make a diagnosis of osteoarthritis based on a physical examination and history of symptoms. At the very beginning, disruption of the collagen fibril and network releases proteoglycan (PG) that result in lesion on the superficial zone, tissue softening, fissures, fibrillation and increase in water content of the articular cartilage. These changes at early stage are detectable under microscope however, it requires an invasive procedure. Therefore detection of these changes at early stage using non-invasive imaging modalities is challenging.

Early OA detection is of great interest to orthopaedists, rheumatologists and radiologists where changes to the affected knee components are still reversible. Early detection allow physicians to begin hyaluronic acid treatment that would help to slow down the onset of OA, as well as dispensing weight control and lifestyle changing advice. Physicians can also advise patients with predisposition to OA from performing activities involving excessive movement of the knee or from taking up certain sports.

A number of grading systems that have been introduced based on radiographic, radiology, arthroscopy, histological and histochemical grading systems (HHGS) are suitable in monitoring the progression of OA. However, early detection of OA should focus only on the ability to detect grade 1 OA or possibly lower sub grades between normal and grade 1 OA. It has been found that the radiographic based grading systems are not sensitive in monitoring early OA. This is due to the use of Joint width space and osteophytes as key features in the formation of radiographic based grading, both are indicative of late stages. Grading systems based on radiology (MRI) has shown potential in measuring changes in signal intensities of T2 weighted images of the cartilage but further validation is required before they can be used in clinical settings. Whilst grade 1 of arthroscopy and HHGS based grading systems describe the minimal changes in the cartilage degeneration process that occur due to the changes in molecular components of AC. On the other hand, the Cartilage Histopathology Assessment System was introduced by Osteoarthritis Research Society International (OARSI) (OOCHAS) to devise a method to assess OA histopathology that would have wide application for OA assessment in clinical and experimental in vivo studies. In this system, increasing grade (OA depth progression into cartilage) indicates a more biologically aggressive disease; increasing stage (the horizontal extent of cartilage involvement within one side of a joint compartment irrespective of the underlying grade) indicates greater disease extent. As depth and horizontal extent are simpler features to assess than differences amongst particular OA features, it is likely that the OOCHAS can be applied more consistently by less experienced observers than the HHGS.

HHGS grading systems, in particular grade 1 of OOCHAS provide the most suitable description of the earliest changes in cartilage degeneration process at different grades and stages. However, the use of HHGS in the assessment of cartilage involves invasive procedures. The changes in molecular components of AC though minimal, can be detected using non-invasive qMRI technique. Therefore, future developments in grading system should be focused on quantitative values of MR signals to measure the onset and progression of OA.

It would hence be extremely advantageous to develop a non-invasive diagnostic tool for early detection of osteoarthritis and classify the cases into severity of OA at early stages. The present invention involves using of an apparatus which comprises of data acquisition unit, data processing and analysis unit as well as classification and grading unit for early in vivo detection and grading of knee osteoarthritis. Said early detection of OA includes the ability to detect grade 1 OA or lower sub grades between normal and grade 1.

3. SUMMARY OF THE INVENTION

Accordingly, it is the primary aim of the present invention to provide a methodology and apparatus for in vivo detection and grading of knee osteoarthritis which is a non invasive tool for early detection of OA.

It is yet another object of the present invention to provide a methodology and apparatus for early in vivo detection and grading of knee osteoarthritis to enable physicians to begin treatment as well as dispensing weight control and lifestyle changing advice that would help to slow down the onset of OA. It is yet another objective of the present invention to provide a methodology and apparatus for early in vivo detection and grading of knee osteoarthritis wherein magnetic resonance imaging (MRI) system is used by incorporating combined assessment of morphology and physiology of changes in articular cartilage.

It is yet another objective of the present invention to provide a methodology and apparatus for early in vivo detection and grading of knee osteoarthritis wherein dual tuned knee coil (Ή /²³Na) is used for the assessment without the need for changing the coil setup for multiple data acquisition. It is yet another objective of the present invention to provide a methodology and apparatus for early in vivo detection and grading of knee osteoarthritis which is capable of providing greater accuracy, sensitivity and specificity.

Other and further objects of the invention will become apparent with an understanding of the following detailed description of the invention or upon employment of the invention in practice.

According to a preferred embodiment of the present invention there is provided, An apparatus for early in vivo detection and grading of knee osteoarthritis (OA) comprising: at least one data acquisition unit; at least one data processing and analysis unit; at least one classification and grading unit; characterized in that said data acquisition unit comprises of a magnetic resonance imaging (MRI) device and a dual tuned knee coil (¹H/²³Na) to obtain the MRI data of a subject area, a MRI workstation for controlling operation of said MRI device as well as transforming of the received signal into images that can be made visible for sequence selection; wherein combined assessment of morphology and physiology of changes in articular cartilage (AC) is used in said apparatus to detect the early knee OA and classify the cases into sub grades.

In a second embodiment of the present invention there is provided, A methodology for early in vivo detection and grading of knee osteoarthritis (OA) comprising: i. data acquisition; ii. data processing and analysis; iii. OA classification; characterized in that said step of data acquisition is carried out by data acquisition unit which comprises of a magnetic resonance imaging (MRI) device and a dual tuned knee coil ^H/^Na) to acquire the MRI data of a subject area; said step of data processing and analysis is carried out by data processing means to process and to analyse said acquired MRI data of the subject area which includes measurement of AC thickness, water content and PG content by using single modality approach; said OA classification is carried out by classification and grading unit by using the measured value from the combined assessment of morphology and physiology of changes in articular cartilage (AC) to detect and classify the OA into sub grades. 4. BRIEF DESCRIPTION OF THE DRAWINGS

Other aspects of the present invention and their advantages will be discerned after studying the Detailed Description in conjunction with the accompanying drawings in which:

FIG. 1 shows a schematic diagram of apparatus for early in vivo detection and grading of and non-invasive knee osteoarthritis.

FIG. 2 shows a flow chart of methodology for early in vivo detection and grading of knee osteoarthritis.

FIG. 3 shows a detailed flow chart of data acquisition stage for the methodology of the present invention.

FIG. 4 shows an exemplary of slice selection module.

FIG. 5-A shows the different zones of articular cartilage at microscopic level.

FIG. 5-B shows the two difference zones of articular cartilage, i.e. deep zone and superficial zone. FIG. 5-C shows the full femoral cartilage which is divided into 6 different compartments. FIG. 6 shows a process flow of thickness measurement for the methodology of the present invention.

FIG. 7 shows a process flow of water content measurement for the methodology of the present invention. FIG. 8 shows a process flow chart of PG content measurement for the methodology of the present invention.

5. DETAILED DESCRIPTION OF THE DRAWINGS

In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be understood by those or ordinary skill in the art that the invention may be practised without these specific details. In other instances, well known methods, procedures and/ or components have not been described in detail so as not to obscure the invention.

The invention will be more clearly understood from the following description of the methods thereof, given by way of example only with reference to the accompanying drawings. In the descriptions that follow, like numerals represent like elements in all figures. For example, where the numeral (2) is used to refer to a particular element in one figure, the numeral (2) appearing in any other figure refers to the same element. Referring now to FIG. 1, there is shown a schematic diagram of apparatus for early in vivo and non-invasive detection and grading of knee osteoarthritis (OA). Said apparatus is used as a combined assessment of morphology and physiology of changes in articular cartilage (AC) to detect the OA and thereafter to classify the cases into sub grades. Said assessment of morphology includes the assessment of cartilage thickness and said assessment of physiology includes the assessment of changes on the water and proteoglycan (PG) content of AC. Said apparatus comprises of at least one data acquisition unit (110), at least one data processing and analysis unit (130) and at least one classification and grading unit (150). Said data acquisition unit (110) comprises of an imaging device for obtaining the image and quantitative data of a subject area which is connected to a control unit through connecting means. Said imaging device is a magnetic resonance imaging (MRI) device (111) whereas said control unit is a MRI workstation (115) for controlling operation of said MRI device(lll) as well as transforming of the received signal into images that can be made visible. Said control unit is any of a number of workstations known to those skilled in the arts where sequence selection and data acquisition is carried out. The obtained data from said acquisition unit (110) is then being transferred to said data processing and analysis unit (130) through another connecting means. Said data processing and analysis unit (130) comprises of at least a data processing means (131) which may be a software program for processing and analysing said captured MRI data of the subject area. Said processing and analysing of said acquired MRI data includes measurement of AC thickness, water content and PG content. The measured values obtained are then transferred to classification and grading unit (150) for early OA grading. A multivariate classifier (151) can be used to classify the severity of OA based on the measured value of cartilage thickness, water content and PG content at early stages.

In clinical practices, 3.0T field strength is commonly available and used. The trend in research using MRI is moving towards the application of high and ultra-high magnetic field strengths which range between 3T to 9.4T.

However, research has not demonstrated any significant advantage in using high magnetic field strength (>1.5Tesla) to detect OA at an early stage. A study by Kornaat et al. has reported that there is no significant difference in average cartilage thickness measured on MR images acquired at 1.5T and 3.0T. This is because the segmentation algorithm used in the process of cartilage thickness determination performs adequately on the MRI data acquired at 1.5T, despite the 3.0T MRI data having approximately twice the signal-to-noise ratio (SNR) and contrast to noise ratio (CNR) efficiencies. This suggests that at least 1.5T MR scanner could provide sufficient spatial resolution and CNR in measuring thickness of AC. This can be achieved by selecting a suitable imaging technique such as 3D Spoiled-Gradient Echo (SPGR) and Double-echo steady-state sequences (DESS). In addition, it is suggested that using a lower field such as 1.5T will result in similar T2 variation as at higher magnetic field strength.

Typically, measurement of early OA features using MRI is influenced by the selection of nuclei and imaging pulse sequence. Selection of MR pulse sequences used in cartilage imaging influences the visibility, contrast between cartilage and surrounding tissues, as well as SNR and scan time. In said MRI system, single modality approach is used to measure morphology and quantitative data for physiology of changes in AC features. Dual tuned knee coil (113) which can excite both hydrogen and sodium nuclei using single coil is provided for the assessment of changes in articular cartilage (AC) features where two channels, i.e. Ή and ²³Na are used without changing the coil setup for multiple data acquisition. The first channel is used to acquire data for cartilage thickness and water content while the second channel is used to acquire data for proteoglycan (PG) content measurement by taking an advantage of sodium content of AC. As sodium concentration decreases due to the loss of PG, accurate measurement of local sodium concentration loss can be used as marker to measure the PG loss directly. Said dual tuned knee coil (113) ^H/^Na) is applied in resonating both Ή and ²³Na without changing the coil setup for multiple data acquisition during MRI scan. In addition, a single dual tuned knee coil can be used to detect water and proteoglycan content in articular cartilage.

In order to design an ideal pulse sequence, several factors such as image spatial resolution, scanning time, image signal, and multi-nuclei imaging have been taken into consideration. A number of parameters that affects the final MRI data are repetition time (TR), echo time (TE), flip angle (FA), number of acquisitions, matrix size, field of view (FOV), slice thickness (ST), slice location/ selection (SS), slice gap (SG), phase encoding and bandwidth etc. In the methodology of the present invention, single MRI pulse sequence is used to acquire data for water and thickness measurement in said data acquisition unit (110) by using hydrogen based MRI with RF of 63.6 Mhz. Whilst, another pulse sequence can be used for sodium imaging without the need for changing the coil setup by using sodium based MRI with RF 16.8 Mhz. Thus by having such an arrangement, dual tuned knee coil (113) (^Na/¹!!) which is operated at least at 1.5T is able to produce hydrogen based high resolution images and multi echo data that is suitable for T2 relaxation whilst sodium excitation will produce information on PG content.

Referring now to FIG. 2, there is shown a flow chart of methodology for early in vivo detection and grading of knee osteoarthritis. Typically said methodology comprises of three stages, which are data acquisition, data processing and analysis as well as early OA classification. Referring now to FIG. 3, there is shown a detailed flow chart of data acquisition stage for the methodology of the present invention. During the stage of data acquisition, a scanning protocol is provided for each of said AC thickness, water content and PG content. This begins with the image type selection for each feature. For example, T2-weighted in sagittal plane is used for thickness, T2 in sagittal plane is used for water content and sodium density in sagittal plane is used for PG content. This is followed by MRI pulse sequence selection as well as optimization of the selected sequence. The set parameters comprises of slice thickness (TR), repetition time (TR), echo time (TE), flip angle (FA), field of view (FOV) resolution, slice thickness (ST) etc. After that, dual tuned knee coil (113) (²³Na/^l ) is used for scanning of the subject area. Ή is used to acquire data for cartilage thickness and water content while ²³Na is used to acquire data for proteoglycan (PG) content. The acquired MRI data is then transferred for data processing and analysis.

After the acquired MRI data is being transferred to data processing and analysis stage, slice selection step will enable selection of same slice from each data set and exclusion of slice with artefacts, which may due to patient movement during scan, blurring in image and other noise. FIG. 4 shows the exemplary of slice selection module. Clear separation between femur and patellar cartilage, and fixed landmark or reference feature such as muscles and fluid can be used for suitable slice selection and exclusion criteria.

This is followed by the segmentation of AC to select cartilage region of interest. At the same microscopic level, cartilage is organised into four different zones, i.e. the superficial zone, middle zone, deep zone and calcified cartilage zone, as illustrated in FIG. 5-A. Each zone plays an important role to maintain the integrity of the cartilage and having different amounts of water, collagen and ground substance. In the methodology of the present invention, full cartilage is first segmented, followed by selecting at least one zone, for examples the deep zone and the superficial zone for further segmentation to at least one compartment at each zone. FIG. 5-B shows the two difference zones of articular cartilage, i.e deep zone and superficial zone. While the full femoral cartilage is divided into 6 different compartments namely MA (medial condyle, anterior portion), MM (medial condyle, middle portion), MP (medial condyle, posterior portion), LA (lateral condyle, anterior portion), LM (lateral condyle, middle portion) and LP (lateral condyle, posterior portion) as depicted in FIG. 5-C. Therefore, measurement on the average thickness of cartilage is carried out in at least one zone (full cartilage) and at least one compartment at each zone.

Referring now to FIG. 6, there is shown a process flow of thickness measurement for the methodology of the present invention. From the segmented MR image, thickness can be measured by using for example distance transform (proximity method- shows the closest neighbours on the opposite surface) function that computes the distance between the inner and outer boundary of cartilage. Before measurement is being carried out, calibration shall be conducted on said segmented MR images whereby image resolution and FOV are used for calibration. Measurement is then carried out for each pair of selection. After that, the data obtained from the measurement is stored for thickness averaging.

Referring now to FIG. 7, there is shown the process flow of water content measurement for the methodology of the present invention. After the process of slice selection and cartilage site segmentation, quantitative measurement is carried out from the segmented MR image to calculate T2 value of the cartilage region. Quantitative measurement of MR parameters allows differentiating between tissues that can be measured by fitting signal intensities pixel by pixel to a signal intensity equation or model. Software program which is able to generate T2 colour coded parametric maps and quantitative values will be used to establish a baseline values. The data obtained from the measurements are then stored for T2 relaxation time averaging. Sodium calculation is different process than T2 relaxation time calculation. For sodium, a calibration curve which correlates pixel intensity with sodium parameter has to be estimated. Then, the pixel values will be measured.

Referring now to FIG. 8, there is shown the process flow of PG content measurement respectively for the methodology of the present invention. After the process of slice selection and cartilage site segmentation, a calibration curve which correlates pixel intensity with sodium parameter is estimated. Using sodium concentration map, the pixel values are then measured. The data obtained from the measurements are then stored for sodium parameter averaging.

Upon completion of quantitative MR parameter measurement for thickness measurement, water content and PG content measurement, the severity of OA can be classified at early stage based on the measured value obtained from the combined assessment. Multivariate classifier (151) can be used for the early OA grading. Said multivariate classifier (151) will grade the cases into sub-grades between 0 (Normal) and 1 (Early) based on the variation in values of thickness, water and PG. Said sub-grades between 0 (Normal) and 1 (Early) is referring to standards such as Osteoarthritis Research Society International (OARSI) Cartilage Histopathology Assessment System (OOCHAS) standard or any other standards which is able to generate said sub-grades. While the preferred embodiment of the present invention and its advantages has been disclosed in the above Detailed Description, the invention is not limited there to but only by the scope of the appended claim.

Claims

WHAT IS CLAIMED IS:

1. An apparatus for early in vivo detection and grading of knee osteoarthritis (OA) comprising: at least one data acquisition unit (110); at least one data processing and analysis unit (130); at least one classification and grading unit (150); characterized in that said data acquisition unit (110) comprises of a magnetic resonance imaging (MRI) device (111) and a dual tuned knee coil (113) to obtain the MRI data of a subject area, a MRI workstation (115) for controlling operation of said MRI device (111) as well as transforming of the received signal into images that can be made visible for sequence selection; wherein combined assessment of morphology and physiology of changes in articular cartilage (AC) is used in said apparatus to detect the early knee OA and classify the cases into sub grades.

2. An apparatus for early in vivo detection and grading of knee osteoarthritis (OA) as claimed in Claim 1 wherein said assessment of morphology includes the assessment of cartilage thickness and said assessment of physiology includes the assessment of changes on the water and proteoglycan (PG) content of AC.

3. An apparatus for early in vivo detection and grading of knee osteoarthritis (OA) as claimed in Claim 1 wherein two channels, i.e. ¹H and ²³Na are used in said dual tuned knee coil (113) without the need of changing the coil setup for multiple data acquisition; wherein first channel is resonating Ή to acquire data for cartilage thickness and water content; wherein second channel is resonating ²³Na to acquire data for proteoglycan (PG) content.

4. An apparatus for early in vivo detection and grading of knee osteoarthritis (OA) as claimed in Claim 1 wherein said dual tuned knee coil (113) is operated at least at 1.5 Tesla.

5. An apparatus for early in vivo detection and grading of knee osteoarthritis (OA) as claimed in Claim 1 wherein single pulse sequence is used to acquire data for water and thickness measurement in said data acquisition unit (110).

6. An apparatus for early in vivo detection and grading of knee osteoarthritis (OA) as claimed in Claim 1 wherein said data processing and analysis unit (130) comprises of at least a data processing means (131) which may be a software program for processing and analysing said captured MRI data of the subject area which includes measurement of AC thickness, water content and PG content.

7. An apparatus for early in vivo detection and grading of knee osteoarthritis (OA) as claimed in Claim 1 wherein a multivariate classifier (151) can be used in said classification and grading unit (150) to classify the severity of OA into sub-grades between 0 (Normal) and 1 (Early) of the OOCHAS standard based on the measured value from said combined assessment.

8. A methodology for early in vivo detection and grading of knee osteoarthritis (OA) comprising: i. data acquisition; ii. data processing and analysis; iii. OA classification; characterized in that said step of data acquisition is carried out by data acquisition unit (110) which comprises of a magnetic resonance imaging (MRI) device and a dual tuned knee coil (113) to capture the MRI data of a subject area; said step of data processing and analysis is carried out by data processing means (131) to process and to analyse said acquired MRI data of the subject area which includes measurement of AC thickness, water content and PG content by using single modality approach; said OA classification is carried out by classification and grading unit (150) by using the measured value from the combined assessment of morphology and physiology of changes in articular cartilage (AC) to detect and classify the OA into sub grades.

9. A methodology for early in vivo detection and grading of knee osteoarthritis (OA) as claimed in Claim 8 wherein said steps of data acquisition for each assessment is done by the sub-steps of: i. selection of image type; ii. selection of pulse sequence; iii. optimization of said selected pulse sequence based on the set parameters; iv. using dual tuned knee coil (113) for scanning of the subject area to acquire MRI data.

10. A methodology for early in vivo detection and grading of knee osteoarthritis (OA) as claimed in Claim 9 wherein said set parameters comprises of slice thickness (ST), repetition time (TR), echo time (TE), flip angle (FA), field of view (FOV), resolution, and slice thickness (ST) etc.

11. A methodology for early in vivo detection and grading of knee osteoarthritis (OA) as claimed in Claim 8 wherein said step of data processing and analysis comprising the following sub-steps: i. selection of slice from each data set and exclusion of slice with artefacts; ii. segmentation of AC based on at least one zone (full cartilage) and at least one compartment at each zone; thickness measurement for assessment of morphology; iv. quantitative measurement on water content and proteoglycan (PG) content for assessment of physiology.

12. A methodology for early in vivo detection and grading of knee osteoarthritis (OA) as claimed in Claim 11 wherein examples of said zone in segmentation of AC comprises of deep zone and superficial zone; wherein examples of said compartment at each zone comprises of MA (medial condyle, anterior portion), MM (medial condyle, middle portion), MP (medial condyle, posterior portion), LA (lateral condyle, anterior portion), LM (lateral condyle, middle portion) and LP (lateral condyle, posterior portion).

13. A methodology for early in vivo detection and grading of knee osteoarthritis (OA) as claimed in Claim 11 wherein said quantitative measurement on water content and proteoglycan (PG) content for assessment of physiology is carried out from the segmented image to calculate T2 values for water content and sodium parameters for PG content of the cartilage region.

14. A methodology for early in vivo detection and grading of knee osteoarthritis (OA) as claimed in Claim 8 wherein said steps of OA classification can be carried out using multivariate classifier (151) to grade the cases into sub-grades between 0 (Normal) and 1 (Early) of the OOCHAS standard based on the values obtained from the combined assessment.