Accuracy evaluation of automatic quantification of the articular cartilage surface curvature from MRI
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Accuracy evaluation of automatic quantification of the articular cartilage surface curvature from MRI. / Folkesson, Jenny; Dam, Erik B; Olsen, Ole F; Christiansen, Claus.
In: Academic Radiology, Vol. 14, No. 10, 10.2007, p. 1221-8.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Accuracy evaluation of automatic quantification of the articular cartilage surface curvature from MRI
AU - Folkesson, Jenny
AU - Dam, Erik B
AU - Olsen, Ole F
AU - Christiansen, Claus
PY - 2007/10
Y1 - 2007/10
N2 - RATIONALE AND OBJECTIVES: To study the articular cartilage surface curvature determined automatically from magnetic resonance (MR) knee scans, evaluate accuracy of the curvature estimates on digital phantoms, and an evaluation of their potential as disease markers for different stages of osteoarthritis (OA).MATERIALS AND METHODS: Knee MR data were acquired using a low-field 0.18T scanner, along with posteroanterior x-rays for evaluation of radiographic signs of OA according to the Kellgren-Lawrence index (KL). Scans from a total of 114 knees from test subjects with KL 0-3, 59% females, ages 21-79 years were evaluated. The surface curvature for the medial tibial compartment was estimated automatically on a range of scales by two different methods: Euclidean shortening flow and boundary normal comparison on a cartilage shape model. The curvature estimates were normalized for joint size for intersubject comparisons. Digital phantoms were created to establish the accuracy of the curvature estimation methods.RESULTS: A comparison of the two curvature estimation methods to ground truth yielded absolute pairwise differences of 1.1%, and 4.8%, respectively. The interscan reproducibility for the two methods were 2.3% and 6.4% (mean coefficient of variation), respectively. The surface curvature was significantly higher in the OA population (KL > 0) compared with the healthy population (KLi = 0) for both curvature estimates, with P values of .000004 and .000006, respectively. The shape model based curvature estimate could also separate healthy from borderline OA (KL = 1) populations (P = .005).CONCLUSION: The phantom study showed that the shape model method was more accurate for a coarse-scale analysis, whereas the shortening flow estimated fine scales better. Both the fine- and the coarse-scale curvature estimates distinguished between healthy and OA populations, and the coarse-scale curvature could even distinguish between healthy and borderline OA populations. The highly significant differences between populations demonstrate the potential of cartilage curvature as a disease marker for OA.
AB - RATIONALE AND OBJECTIVES: To study the articular cartilage surface curvature determined automatically from magnetic resonance (MR) knee scans, evaluate accuracy of the curvature estimates on digital phantoms, and an evaluation of their potential as disease markers for different stages of osteoarthritis (OA).MATERIALS AND METHODS: Knee MR data were acquired using a low-field 0.18T scanner, along with posteroanterior x-rays for evaluation of radiographic signs of OA according to the Kellgren-Lawrence index (KL). Scans from a total of 114 knees from test subjects with KL 0-3, 59% females, ages 21-79 years were evaluated. The surface curvature for the medial tibial compartment was estimated automatically on a range of scales by two different methods: Euclidean shortening flow and boundary normal comparison on a cartilage shape model. The curvature estimates were normalized for joint size for intersubject comparisons. Digital phantoms were created to establish the accuracy of the curvature estimation methods.RESULTS: A comparison of the two curvature estimation methods to ground truth yielded absolute pairwise differences of 1.1%, and 4.8%, respectively. The interscan reproducibility for the two methods were 2.3% and 6.4% (mean coefficient of variation), respectively. The surface curvature was significantly higher in the OA population (KL > 0) compared with the healthy population (KLi = 0) for both curvature estimates, with P values of .000004 and .000006, respectively. The shape model based curvature estimate could also separate healthy from borderline OA (KL = 1) populations (P = .005).CONCLUSION: The phantom study showed that the shape model method was more accurate for a coarse-scale analysis, whereas the shortening flow estimated fine scales better. Both the fine- and the coarse-scale curvature estimates distinguished between healthy and OA populations, and the coarse-scale curvature could even distinguish between healthy and borderline OA populations. The highly significant differences between populations demonstrate the potential of cartilage curvature as a disease marker for OA.
KW - Adult
KW - Aged
KW - Female
KW - Humans
KW - Magnetic Resonance Imaging
KW - Male
KW - Middle Aged
KW - Osteoarthritis, Knee
KW - Reproducibility of Results
KW - Journal Article
U2 - 10.1016/j.acra.2007.07.001
DO - 10.1016/j.acra.2007.07.001
M3 - Journal article
C2 - 17889339
VL - 14
SP - 1221
EP - 1228
JO - Academic Radiology
JF - Academic Radiology
SN - 1076-6332
IS - 10
ER -
ID: 187555073