Method for diagnosing proteoglycan deficiency in cartilage based on magnetic resonance image (MRI)

Proteoglycan deficiency in articular cartilage is diagnosed based on quantified signal intensities of pixels of a magnetic resonance image (MRI) extending across a depth of the articular cartilage. A pattern of the thus quantified signal intensities is indicative of proteoglycan distribution across the cartilage depth.

BACKGROUND OF THE INVENTION 
1. Field of the Invention 
This invention relates to the diagnosis of proteoglycan deficiency in 
articular cartilage based on magnetic resonance images (MRI) of the 
articular cartilage, and more particularly, based on a quantified signal 
intensity of each pixel of the magnetic resonance image extending across a 
depth of the articular cartilage. 
2. Description of the Related Art 
In magnetic resonance imaging (MRI), which is widely used for diagnostic 
purposes in medicine, a large magnet supported by complicated electronics 
and computers is employed for image acquisition. When a patient is made to 
lie in the magnet's magnetic field, individual atoms within the patient's 
tissues and organs become aligned with the magnetic field. Radiofrequency 
pulses are then transmitted at a defined rate to the magnetized atoms in 
the area of interest to thereby elevate the energy levels in the atoms. 
During a pause period between pulses, the atoms relax and part of the 
energy gained becomes released. This process represents the unique 
phenomenon of magnetic resonance. A receiving antenna collects the 
released energy signals which are then subjected to image processing to 
convert the energy signals into light dots (pixels) having an illumination 
level (e.g. a gray-scale) corresponding to an intensity of the energy 
signals. Thousands and thousands of such light dots form an image. The 
thus obtained MR image is displayed on a television monitor, printed as a 
hard copy image and/or stored on a magnetic tape or other recording 
medium. 
An MR image provides structural information, such as size and shape, 
regarding most tissues and organs in the body, and permits the visual 
detection of changes in such structural characteristics. For example, if a 
person develops a meniscal or anterior cruciate ligament tear as a result 
of a sports related injury, an MRI of the joint will reveal the presence 
of a discontinuity, swelling and/or shortening in the image of the torn 
tissue. The structural image information provided by the MRI helps to 
decide whether a patient requires surgical repair or other remedial 
action. 
FIG. 1 is a parasagittal section view (lateral to midline) of the human 
knee joint. Reference numeral 101 denotes the femur; 102 denotes the 
articularis genus muscle; 103 denotes the quadriceps femoris tendon; 104 
denotes 104 denotes the suprapatellar fat body; 105 denotes the 
suprapatellar synovial bursa; 106 denotes the patella; 107 denotes the 
subcutaneous prepatellar bursa; 108 denotes the articular cavity; 109 
denotes the infrapatellar fat body; 110 denotes the patellar ligament; 111 
denotes the synovial membrane; 112 denotes the subcutaneous infrapatellar 
bursa; 113 denotes the deep (subtendinous) infrapatellar bursa; 114 
denotes the lateral meniscus; 115 denotes the tuberosity of tibia; 116 
denotes the bursa under lateral head of gastrocnemius muscle; 117 denotes 
the synovial membrane; 118 denotes the articular cartilages; and 119 
denotes the tibia. Articular cartilage covers the opposing femur and tibia 
bone ends in the human knee joint. The articular cartilage, which is rich 
in extracellular matrix and poor in cellularity, has shock absorption and 
lubrication functions based on its visco-elasticity which depends on the 
high water content of its extracellular matrix. Normal human articular 
cartilage has a water content of 73%-81% (w/w) on a weight basis. 
Proteoglycans (PG) are the vital organic component required for the 
functions of articular cartilage. PG contains numerous sugar chains, 
namely glycosaminoglycans, which contain negatively charged groups such as 
carboxylates and sulfate groups. These water absorbing, i.e. hydrophilic, 
groups attract an excess of water hydrogen atoms and water carrying 
cations. The wide spread network of PG retains this water in the cartilage 
matrix. A decrease in PG causes changes in the amount and state of water 
contained therein, resulting eventually in cartilage dysfunction. Thus a 
PG depletion is indicative of cartilage degeneration and precedes such 
problems as osteoarthritis. 
Although the MRI is used to visually detect structural changes in the 
articular cartilage of post-trauma joints, currently no non-invasive 
diagnostic tool is available to detect a biochemical change such as PG 
depletion in the articular cartilage at very early stages of cartilage 
degradation. Detection of PG depletion in cartilage prior to a structural 
change taking place could be extremely beneficial because steps could be 
initiated to preserve the cartilage by therapeutic intervention. Once PG 
depletion has started, the surface of the cartilage begins to break after 
1-2 years (fibrillation). As a result of fibrillation, usually in about 5 
years the cartilage becomes thinned resulting in a narrowing of the joint 
space. Therefore any attempt to protect or preserve the cartilage must be 
made before initiation of surface breaking. 
Present techniques employing MRI are not capable of detecting biochemical 
changes, particularly PG depletion, that develop in articular cartilage 
following joint trauma several years in advance of structural 
disorganization referred to as osteoarthritis (OA). 
SUMMARY OF THE INVENTION 
An object of the present invention is to provide a non-invasive method 
employing quantitative techniques for diagnosing a proteoglycan deficiency 
in articular cartilage, preferably prior to the onset of fibrillation and 
structural disorganization. 
Another object of the invention is to provide a non-invasive method for 
tracking the progression or remission of proteoglycan depletion in 
articular cartilage which would be useful, for example, in a cartilage 
preserving drug development/screening program. 
Another object of the invention is to provide a non-invasive method for 
diagnosing an arthritic joint. 
In achieving the above and other objects, the method of this invention 
includes quantifying a signal intensity of a magnetic resonance image of 
the cartilage and correlating the thus quantified signal intensity with at 
least one predetermined reference signal intensity indicative of cartilage 
proteoglycan content, e.g. with an expected or normal peak signal 
intensity indicative of an expected or normal proteoglycan content, or 
with an expected or normal signal intensity pattern indicative of an 
expected or normal proteoglycan concentration across cartilage depth. 
Generally, the MRI signal intensity is quantified across the depth of the 
cartilage as a gray-scale illumination of pixels of the image. The signal 
intensity variation across the depth of the cartilage is correlated with 
an expected or normal bell-shaped variation. Also, a peak signal intensity 
in a middle portion of the cartilage is compared with predetermined 
reference signal intensities indicative of expected or normal PG content. 
Further, the signal intensity within a same pixel layer of the cartilage 
may be analyzed, and a comparison of signal intensity variation in the 
medial vs. lateral condyles may be carried out.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
Previous research of the present inventors has shown that the cartilage 
thickness measurable on magnetic resonance images of rabbit knee 
cartilage, obtained using spin echo pulse sequences with TR=1000 msec and 
TE=30 msec, correlated well with changes in the proteoglycan content 
brought about by intraarticular injection of the plant-derived proteolytic 
enzyme papain. (See Paul et al., "Magnetic Resonance Imaging Reflects 
Cartilage Proteoglycan Degradation in the Rabbit Knee", Skeletal Radiol, 
1991.) 
Such injection of papain is known to profoundly deplete the cartilage 
proteoglycan content, but the process following a single injection is 
known to be reversed through increased synthesis by the chondrocytes which 
returns the cartilage proteoglycan content toward normality. In the 
inventors' experiments in connection with the above-mentioned previous 
research, the depletion of proteoglycan content appeared to correlate with 
a decrease, and the repletion with an increase, in the cartilage thickness 
which was measured using the MR image. 
Since changes in cartilage thickness coincided with comparable changes in 
the measurable signal intensity, a quantitative MRI technique was 
developed and applied to healthy human knees to investigate whether the 
MRI signal intensity is truly related to the proteoglycan content. The 
goal was to quantify the signal intensity of all pixels in a particular 
region of the knee cartilage using computer-based image analysis. This was 
done in order to assess whether the signal intensity varied across the 
cartilage depth, and whether such variations correspond with the 
distribution of any of the three major biochemical constituents of 
cartilage, i.e. water, collagen and proteoglycans, known to be 
differentially distributed across the cartilage depth. 
Six healthy volunteers ranging in age from 20-40 years old were studied. 
The right knee joint of each individual was scanned using a 1.5T (Signa, 
Ge, Milwaukee, Wis.) magnet and a dedicated transmit/receive extremity 
coil with the subject laying in the supine position. Three series of 
images were obtained in 5 of the 6 subjects, two using spin echo (SE) 
pulse sequences with TR msec/TE msec=700/20 and 1000/20. The third series 
was obtained using gradient refocused echo (GRE) 3D volume acquisition 
(60/15) and a flip angle of 15.degree.. This pulse sequence was chosen 
since it reduces the scanning time while improving cartilage image 
contrast. The sixth subject was studied using only the GRE pulse sequence 
as explained below but using three different flip angles to further 
evaluate the signal intensity variation in cartilage images. 
For the spin echo, a coronal localizer (500/20) was followed by two 
sagittal acquisitions using first 700/20 and then 1000/20 pulse sequences. 
Either an 8 or 12 cm FOV was used. Other parameters were as follows: slice 
thickness 4 mm and interslice gap 1.5 mm, matrix 256.times.256 and two 
averages (in plane resolution 300 and 450 microns). For the GRE sequence, 
the same FOV and plane were used. Twenty-eight to sixty contiguous slice 
locations were obtained using either 1.5 m or 3 mm slice thicknesses (in 
plane resolution 300 and 450 microns). Other parameters were kept similar 
for all the 5 subjects studied. Finally, to further determine the degree 
of T1 weighted dependence of the signal intensity, in the sixth subject 
the knee was imaged using only the GRE sequence, but at 15.degree., 
30.degree., and 50.degree. flip angles. 
For each study, femoral and tibial cartilage images obtained in the 
sagittal plane were analyzed on a Sun Sparc 1+workstation (Sun 
Microsystems, Saddle Brook, N.J.) using modified image display computer 
software that permitted x16 magnification of the MR cartilage image, and 
allowed measurement of the signal intensity on a pixel-by-pixel basis both 
across and along the full depth of the cartilage image. 
More particularly, reference is made to the imaging process flow chart of 
FIG. 2. As noted previously, the conventional MRI apparatus converts 
received signal intensities into image data denoting an illumination level 
of each pixel of the image corresponding to a location within the MRI 
slice or plane. This image data is received at the workstation (step 1) 
and displayed (step 2) in a conventional manner. Then, the operator 
chooses a window or area of interest (e.g. the femoral cartilage) within 
the display image. The chosen area is then magnified (step 3) for display. 
Responsive to user inputs individual pixels are selected (e.g by defining 
a region of interest within the overall magnified display image by tracing 
a boundary with the assistance of a computer mouse device), and the 
gray-scale illumination representative of the MR signal intensity of 
selected individual pixels is recorded (step 4). Then, as will be 
discussed in more detail below, the recorded signal intensities of the 
selected pixels are subjected to appropriate averaging and interpolation 
(step 5), for the purpose of plotting the signal intensity (y-axis) for 
each pixel along the depth (x-axis) of the cartilage (step 6). 
In this manner, signal intensity was measured from the individual pixels of 
the femoral and tibial cartilage images. For example, FIG. 3 is a femoral 
cartilage image with x16 magnification showing individual pixels in the 
cartilage depth. The white arrow in FIG. 3 indicates a direction across 
the cartilage (i.e. a "depth direction"), and the black arrow indicates a 
direction along, i.e. within, each cartilage pixel layer. (The pulse 
sequence for the image obtained in FIG. 3 was GRE with TR=60 msec and 
TE=15 msec.) 
Medial and lateral compartments were identified from anatomical and MR 
landmarks. Two mid-medial and two midlateral slices in the SE sequence, 
and five mid-medial and five mid-lateral slices in 3D volume GRE acquired 
cartilage images were analyzed. The region of the posterior femoral and 
tibial articular cartilages located above and below the middle third of a 
line connecting the base and the apex of the hypointense triangular shaped 
posterior horn of the medial and lateral meniscus was always analyzed. In 
each sagittal slice, signal intensity was measured from contiguous pixels 
along both the antero-posterior and supero-inferior planes (X and Y axes, 
respectively). 
The data were processed to obtain the following: 
1. Within pixel layer signal intensity variation (i.e. in the plane of the 
black arrow of FIG. 3): The differences between the signal intensity of 
411 adjacent pairs of pixels were measured and statistically evaluated. 
2. Across pixel layer signal intensity variation (i.e. in the plane of the 
white arrow of FIG. 3): To minimize errors from susceptibility artifacts, 
pixels at the edges near the bone and surface of the cartilage regions 
examined were excluded from the assessment. Secondly, since both the 
femoral and tibial cartilages have a curved geometry, the pixels are not 
oriented in a linear plane. Therefore, to facilitate the averaging process 
it was necessary to choose analysis of pixel columns containing either 
even or odd numbers of units. Odd numbers were selected, and columns 
containing even number of pixels were interpolated across the entire depth 
to an odd number. Thirdly, the signal intensity of 10 pixels present in 
the same row antero-posteriorly (black arrow, FIG. 3) was averaged to 
obtain the mean signal intensity value for each zone or pixel layer. Such 
measurements provided the inter-zonal or across the pixel layer signal 
variation curve in the cartilage region examined. This procedure was 
repeated in all of the medial and lateral slices studied. 
3. Pixel layers and cartilage thickness. The number of pixel layers present 
across the depth of the cartilage was counted at each of the 10 sites. The 
average multiplied by the resolution of each pixel (e.g. 300 microns) 
provided a measure of cartilage thickness. 
The measured signal intensities from images of medial and lateral 
compartments of femoral and tibial cartilages obtained in the subjects 
with the 3 pulse sequences noted above were compared. 
Typical mid-lateral sagittal images of the same region of the right knee 
obtained using the three pulse sequences are shown in FIGS. 4(a) through 
4(c). More particularly, FIG. 4(a) is an MR image of a healthy knee 
obtained using a spin echo sequence TR=1000 msec and TE=20 msec, showing 
the meniscus, femoral and tibial cartilage in the sagittal plane. FIG. 
4(b) is a similar view obtained using a spin echo sequence TR=700 msec and 
TE=20 msec. FIG. 4(c) is a similar view obtained using a GRE pulse 
sequence TR=60 msec and TE=15 msec. Compared with spin echo images, 
cartilage contrast was greater in the GRE images. Also, as can be seen 
from FIG. 3, a x16 magnified image showed differential contrast across but 
not within the pixel layers. Across the pixel layers the observed contrast 
was maximal in the middle and minimal at the surface and deep edges of the 
cartilage. 
As noted previously, the signal intensity of each pixel in the region was 
measured to obtain the average pixel-by-pixel variation both across the 
pixel layers, i.e. along the cartilage depth, and within each pixel layer 
from a 10 pixel wide area measuring between 3 and 4.5 mm.sup.2, depending 
upon the FOV used. Signal intensity within a pixel did not vary with 
magnification. 
The results for images obtained using the spin echo 1000/20 sequence are 
described first in detail. Those for the other two sequences are then 
compared. 
SPIN ECHO 1000/20 
1. Pattern of Signal Intensity Variation 
Within pixel layers. Signal intensity within each pixel layer was found to 
vary randomly. The median value for the differences between adjacent pairs 
of pixels equalled 7.0 (mean of 9.1 and mode of 5.0). 
Across pixel layers. Across the pixel layers, the signal intensity varied 
by a significantly greater margin, by as much as 200.0. FIGS. 5(a) through 
5(f) respectively illustrate the signal intensities across cartilage depth 
seen in images of the medial and lateral tibial plateau obtained using a 
spin echo 1000/20 pulse sequence for the five subjects and the mean for 
all subjects (mean differences between the signal intensities in cartilage 
zones 2 and 6 failed to be statistically significant). FIGS. 6(a) through 
6(f) illustrate the same in images of the medial and lateral femoral 
condyles obtained using the spin echo 1000/20 pulse sequence. The signal 
intensity was maximal in pixel layers of the middle zone and minimal at 
both the superficial and deep edges. This resulted in the presence across 
the cartilage depth of a bell-shaped signal variation curve, which was 
present in all tibial and all except one out of ten femoral cartilages 
examined. 
2. Peak Signal Intensity 
The highest signal intensity was invariably present in pixel layers of the 
middle zone of the cartilage. In the medial compartment, the mean peak 
signal intensity was 250.5.+-.44.2 (SD) and 272.4.+-.39.7 for the tibial 
and femoral cartilages, respectively. The differences were not 
statistically significant. In the lateral compartment the mean peak signal 
intensity values were 264.8.+-.39.1 and 275.3.+-.38.6 for the tibial and 
femoral cartilages, respectively. 
3. Number of pixel layers 
The number of pixel layers varied as shown in FIGS. 5 and 6 between 
individuals and depending on both the FOV and the anatomic site. For 
example, in the tibial cartilage the number varied between 3 and 5 and in 
the femur between 3 and 9, irrespective of the compartment. 
SPIN ECHO 700/20 
The bell-shaped signal variation curve remained unchanged in all the 
subjects and in all the regions examined. The mean peak signal values for 
the tibial cartilages Were: 173.2.+-.35.9 on the medial and 192.0.+-.29.6 
on the lateral side. Those for the femoral cartilages were: 219.8.+-.24.8 
on the medial and 207.8.+-.29.0 on the lateral side. Without exception 
these values were significantly lower than the already stated 
corresponding values observed using the 1000/20 sequence (p&lt;0.01 to 
0.0001). 
GRE 60/15 
The signal variation curve was bell-shaped in all, including subject number 
3 in whose lateral femoral cartilage the corresponding curves observed 
using the two spin echo sequences were flat. 
The mean peak signal intensity values were the lowest. For example, in the 
tibial cartilages they were: 90.7.+-.17.1 on the medial, and 86.2.+-.9.7 
on the lateral side. For the femoral cartilages they were: 98.8.+-.11.8 on 
the medial, and 99.3.+-.7.3 on the lateral side. Again without exception 
the differences between these and the corresponding results for the 
1000/20 SE images were statistically significant (p&lt;0.0002 to 0.0003). 
FIG. 7 shows the effect of changing TR/TE from SE 1000/20 to either 700/20 
or GRE 60/15 on signal intensities across cartilage depth seen on the 
images of the lateral femoral condyle of the right knee in subject 4. 
Results shown are the mean for all the slices obtained. Compared with 
1000/20, the bell-shaped variation curve and the number of pixel layers 
seen on images obtained using the 700/20 pulse sequence remain unchanged, 
but there was a definite decrease in the signal intensity. The decrease 
occurred nearly uniformly across the cartilage depth. GRE resulted in an 
even greater decrease in signal intensity but, once again, the shape of 
its variation across the pixel layers remained virtually unchanged. 
The results for the group of 5 subjects as a whole summarized below show 
that as compared with the 1000/20 findings, the peak cartilage signal 
intensity values observed using the 700/20 pulse sequence were 
intermediate and those using the GRE pulse sequences were the lowest. 
Changing the flip angle influenced the magnitude of signal intensity but 
not the shape of its variation curve. FIG. 8 shows the effect of changing 
the flip angle from 30.degree. to either 15.degree. or 50.degree. on 
signal intensities across cartilage depth seen on the image of the medial 
femoral condyle of the right knee obtained using the GRE pulse sequence in 
subject 6. Results shown are the mean of all slices obtained. Changing the 
flip angle from the generally used 15.degree. to 30.degree. increased the 
peak signal intensity value by almost 30%, shifted the curve to the left, 
reduced the number of pixel layers by 2, but importantly left its 
bell-shaped form unchanged. 
Further, an increase in the flip angle to 50.degree. had the opposite 
effect. As can be seen from FIG. 8, the peak signal intensity value was 
decreased by nearly 15% compared to that observed with the 15.degree. 
angle. The decrease was uneven across the cartilage depth. It was greatest 
in the pixels present in the middle layers and virtually nil in those at 
the cartilage periphery. The signal intensity curve was shifted to the 
right and the number of pixels were reduced by 2, although, once again, 
its outline remained essentially bell-shaped. 
The data shows that the signal intensity seen on MR images of healthy human 
cartilage varied in a bell-shaped manner across, but not within, the pixel 
layers. The variation was found to be independent of magnification used, 
section location, anatomic site in the knee and even in the healthy 
individual examined. 
Adjacent pixels within the same layer experience the same set of 
acquisition parameters including temperature and their biochemical 
composition is also likely to be comparable. This could explain why signal 
intensity variation within the pixel layers were found to vary only 
randomly and within a narrow range. 
Findings for the medial and lateral compartments revealed that across the 
pixel layers the signal intensity varied by a significantly greater 
margin, by as much as 200.0. This resulted in the presence of a 
bell-shaped signal variation curve across both the femoral and tibial 
cartilages that was either identical or comparable in all except one 
subject. 
The biochemical composition of cartilage varies across its depth. 
Therefore, MR signal intensity of pixels across the cartilage can be 
expected to vary too. However, this expectation has to be balanced against 
the question: to what extent is the bell-shaped signal variation due to 
technical factors associated with MR imaging? For reasons discussed below, 
the signal variations across pixel layers are judged to be intimately 
related to the biochemical factors, particularly PG content, rather than 
the possible influence of technical factors. 
Biochemical data shows variations in the distribution of major matrix 
constituents across cartilage zone, but not within them, not at least in 
areas as small as examined in the present invention. Moreover, as shown in 
FIG. 9, the reported distribution of collagen, water, and proteoglycans 
shows the pattern of variation for each of them to clearly differ. 
Significantly, only the curve for the variation in the proteoglycan 
content is clearly bell-shaped and clearly similar to the curve for signal 
variation observed across the cartilage in the present study. 
It is proposed that the signal variation curve seen across the MR images of 
healthy human knee cartilage obtained with the three pulse sequences used 
in the inventors, study arises from the water protons associated with the 
negatively charged anions of proteoglycans and not from those of free 
water nor from those associated with collagen. Therefore, the signal 
variation curve resembles the curve for variation in cartilage 
proteoglycan content (FIG. 9) because o this association. 
That is, the signal variation curve appears to be related to the state, 
rather than the amount of water in the cartilage. Were it to be solely 
related to the water amount, its shape would be expected to resemble that 
of the water content shown in FIG. 9. Collagen tends to be more abundant 
at the surface than the middle of the cartilage. Thus, were the signal 
variation curve to be due to water associated with collagen, it would also 
vary in a downward sloping manner as does the collagen content shown in 
FIG. 9. 
Water interacts with proteoglycans electrostatically, and in this respect 
its protons (H.sup.+) resemble cations such as Na.sup.+, Ca.sup.2+, and 
Mn.sup.2+, all of which interact with the abundant anionic charges 
(sulfates and carboxylates) available on the sugar side chains, i.e. the 
acidic glycosaminoglycans (GAGs), present on the core protein chain on the 
proteoglycans. The cationic dyes, such as for example, toludine blue used 
to stain cartilage for the presence of proteoglycans also interact on the 
same physiochemical basis. This may explain why in the inventors' earlier 
experiments in the rabbit knee the decrease followed by the increase in 
the signal intensity was found to mirror changes in not only the 
biochemically measurable GAG content, but also the intensity of the 
toludine stain. 
This explanation is consistent with the principles governing magnetic 
contrast relation in biological tissues. Most, if not all, the NMR signal 
in MR imaging comes from the protons of water rather than other organic 
molecules. 
It is noted that load, i.e. weight-bearing, largely determines the 
cartilage proteoglycan content. Therefore, it is possible that deviations 
may be observed in the shape of signal variation curve in association with 
changes in the distribution of proteoglycans in situations where load is 
redistributed e.g., owing to immobilization, trauma, congenital 
dislocation of the hip, laxity of ligaments and recurrent dislocation of 
the patella. 
Also, pixel values in MRI inherently include a certain amount of random 
noise. In the present study this was minimized, while preserving tissue 
signal contrast, by using both temporal and spatial averaging. Temporal 
averaging involved combining data from multiple acquisitions; spatial 
averaging involved combining signal intensity data from ten adjacent 
pixels. The operation was judged to be valid because the signal intensity 
profile of the ten pixels was similar. At worse, the operation could 
flatten the resultant signal variation curve but would not create a 
bell-shaped curve where one did not exist. 
The magnitude of the signal intensity is known to vary according to the 
type of pulse sequence used, and this proved to be the case in the present 
study too. For example, the peak signal intensity value was found to be 
the highest using SE 1000/20, the lowest using the GRE, and intermediate 
using the SE 700/20 sequence. More importantly though, the data reveal 
that the shape of the signal variation curve was independent of the pulse 
sequence used. It was found to be bell-shaped not only using the two spin 
echo sequences, but also using the GRE sequence. This may be because at 
flip angles larger than 10.degree., but smaller than 90.degree., GRE 
detects a T1 weighted vector. The observed differences between the 
magnitude of signal intensity at the three flip angles that were used in 
the present study further support this explanation, since the degree of T1 
weighing varies with the angle. 
Cartilage comprises a mixture of collagen and proteoglycans unevenly 
distributed, and is organized in varying directions across the cartilage 
depth, which features could be responsible not only for the presence of 
signal intensity variations at each flip angle but also for the 
qualitative differences that were observed in its pattern on changing the 
flip angle. 
Additional acquisition associated factors that might affect signal 
intensity include the sequence gradient magnitudes, magnet field strength, 
the RF pulse shape, and excitation frequency. These factors only influence 
signal intensity through their effects on image T1, T2, or proton density 
weighting and thus produce global as opposed to zonal effects within an 
organ. 
Other technical factors potentially include, e.g., magnet and gradient 
inhomogeneities, partial volume, chemical shift, temperature variations, 
and bulk susceptibility (leading to localized magnetic field 
inhomogeneities) effects, all of which may lead to localized signal 
intensity variations. 
The observed pattern o signal intensity variations within the cartilage did 
not vary from slice to slice making partial volume effects unlikely. 
Chemical shift effects occur when the structure of interest contains 
components of different resonant frequencies (such as lipid and water) 
leading to signal cancellation and misregistration. The symmetrical 
appearance of the signal variation curve observed in the cartilage in this 
study makes this unlikely as the cause of the observed bell-shaped signal 
intensity variations. Temperature is not a relevant factor in this 
isothermal environment. Finally, the physical environment of the cartilage 
is quite different at its periphery. For example, at its upper surface it 
is in contact with the joint fluid, whereas at its deeper surface it is in 
contact with the bone. If bulk susceptibility were a relevant factor, for 
example, it would be expected to produce signal intensity variations that 
differ between the two sides giving rise to asymmetry. The independence of 
signal intensity variation symmetry to pulse parameter changes further 
supports the conclusion that technical factors were not the cause of the 
signal intensity variation pattern. 
Finally magnet related inhomogeneities, while localized, cause signal 
intensity variations over a much larger area than that as small as those 
examined in the present study. 
These findings strongly suggest that the bell-shaped pattern of proton 
signal intensity across the cartilage depth is due to corresponding 
changes in proteoglycan concentration. To validate this hypothesis, two 
human cadaver patellae cartilages were imaged using a spin-echo sequence 
with TE/TR of 700/20 and sectioned (200 u) across their depth and 
proteoglycan content was measured from each slice. 
As shown in FIG. 10, the patterns of the MRI signal intensity and direct 
proteoglycan measurement were similar and bell-shaped, which confirms that 
the proton signal intensity across the depth of the cartilage reflects the 
proteoglycan content of the tissue matrix. 
This method was also tested in 7 rheumatoid arthritis patient. The mean 
peak proton signal intensity (SI) of medial and lateral femoral and tibial 
cartilages of these patients were compared with the corresponding 
measurement of mean peak signal intensity of 5 age and sex matched normal 
volunteers. These findings illustrated in FIG. 11 show a profound drop in 
the signal intensity measurement of both compartments, indicating that 
cartilage in rheumatoid arthritic knees can be differentiated from normal 
cartilage. A 70% (approx.) drop in RA mean peak intensity is different 
from the 30% (approx.) drop found in osteoarthritis (OA). 
FIG. 12(a) shows the signal intensity variation pattern for the femoral 
cartilage of the first tested RA patient and FIG. 12 (b) shows the same 
for the tibial cartilage of the same patient. FIGS. 13(a) through 18(b) 
are respectively for RA patients two through seven. (In FIGS. 12-18, the 
signal intensity numbers shown do not have a background illumination level 
removed, thus resulting in larger numbers than those shown in FIG. 11. The 
background illumination level varies according to the MRI magnet/software 
version employed.) 
Also, it is anticipated that PG depletion in RA patients will occur more 
rapidly than in OA patients, which could be confirmed by conducting 
measurements of various patients over time to track the PG depletion rate. 
Thus, it is contemplated that RA could be differentiated from OA at early 
PG depletion stages based on a measured PG depletion rate. For example, it 
may be possible to differentiate RA from OA based on the amount of time 
during which cartilage MRI signal intensity went from a 5% drop to a 15% 
drop. 
It is further anticipated the signal intensity patter and corresponding 
distribution of proteoglycan depletion will be disease specific. The 
techniques of the present invention can be applied to identifying such 
differences and so further help differentiate arthritis. For example, 
depending on the particular disease, proteoglycan depletion may occur 
diffusely across and along the cartilage, or in a spotty random-like 
manner, or in a localized manner. 
In summary, progression of arthritis is characterized by degradation of the 
articular cartilage. The cartilage loss is initiated by a loss of 
proteoglycan. The present inventor's have discovered that PG depletion may 
be detected by the magnetic resonance (MR) proton signal intensity 
measurements using the present method. Thus, a significant drop in signal 
intensity is indicative of the loss of proteoglycan. 
In conclusion, the present inventors have discovered a novel method of 
diagnosing PG depletion in articular cartilage, based upon a correlation 
between the signal intensity of NMR images of the cartilage and its PG 
content. Since PG depletion is a precursor to cartilage degeneration, this 
method provides a very useful means for diagnosing a potential cartilage 
degeneration in a patient so that preventive measures may be taken if 
necessary. 
Also, the techniques of the present invention can be applied to track the 
progression or remission of proteoglycan depletion in a patient. That is, 
by monitoring the MRI signal intensity of a patient's articular cartilage 
over time, an increase or decrease in proteoglycan of that cartilage can 
be diagnosed. Such would be particularly useful in testing the 
effectiveness of therapeutic drugs intended to halt proteoglycan depletion 
and/or replenish proteoglycan content. For example, by comparing MRI 
signal intensities of cartilage before and after drug administration, an 
indication of the drug's effectiveness can be obtained.