RESUMO
We have developed 3-dimensional (3D) magnetic resonance imaging (MRI) analysis software that allows measurement of the projected cartilage area ratio with a particular thickness intended to allow quantitation of the cartilage in the knee. Our aims in this study were to validate the projected cartilage area ratio in both pig and human knees and to examine the ratio in patients reporting knee pain. METHODS: After 3D MRI reconstruction, the femoral cartilage was projected onto a flat surface. The projected cartilage area was determined in pig knees using our 3D MRI analysis software, and was compared with the area obtained with other software. The projected cartilage area ratio (for cartilage thickness ≥1.5 mm) at 4 segments was also validated in human knees. Finally, changes in the projected cartilage area ratio were examined in 8 patients with knee pain who had undergone 2 MR images at 3 to 21-month intervals. RESULTS: The projected cartilage areas determined with our 3D MRI analysis software were validated in pig knees. The projected cartilage area ratio at each segment in human knees had an intraclass correlation coefficient (ICC) of 0.87 to 0.99 (n = 16) between readers and 0.76 to 0.99 (n = 20) between measurements on repeat MR images. The projected cartilage area ratio (for cartilage thickness ≥1.5 mm) at the most affected segment in 8 human patients significantly decreased between the pairs of MR images obtained at intervals of 3 to 21 months. CONCLUSIONS: We proposed a novel evaluation method using 3D MRI to quantify the amount of cartilage in the knee. This method had a low measurement error in both pig and human knees. CLINICAL RELEVANCE: The projected cartilage area ratio based on a particular thickness may serve as a sensitive method for assessing changes in cartilage over time.
RESUMO
A temporal subtraction image, which is obtained by subtraction of a previous image from a current one, can be used for enhancing interval changes (such as formation of new lesions and changes in existing abnormalities) on medical images by removing most of the normal structures. However, subtraction artifacts are commonly included in temporal subtraction images obtained from thoracic computed tomography and thus tend to reduce its effectiveness in the detection of pulmonary nodules. In this study, we developed a new method for substantially removing the artifacts on temporal subtraction images of lungs obtained from multiple-detector computed tomography (MDCT) by using a voxel-matching technique. Our new method was examined on 20 clinical cases with MDCT images. With this technique, the voxel value in a warped (or nonwarped) previous image is replaced by a voxel value within a kernel, such as a small cube centered at a given location, which would be closest (identical or nearly equal) to the voxel value in the corresponding location in the current image. With the voxel-matching technique, the correspondence not only between the structures but also between the voxel values in the current and the previous images is determined. To evaluate the usefulness of the voxel-matching technique for removal of subtraction artifacts, the magnitude of artifacts remaining in the temporal subtraction images was examined by use of the full width at half maximum and the sum of a histogram of voxel values, which may indicate the average contrast and the total amount, respectively, of subtraction artifacts. With our new method, subtraction artifacts due to normal structures such as blood vessels were substantially removed on temporal subtraction images. This computerized method can enhance lung nodules on chest MDCT images without disturbing misregistration artifacts.
