Image analysis – Applications – Biomedical applications
Reexamination Certificate
2002-10-02
2004-12-28
Couso, Jose L. (Department: 2621)
Image analysis
Applications
Biomedical applications
Reexamination Certificate
active
06836557
ABSTRACT:
FIELD OF THE INVENTION
The present invention is directed to the assessment of certain biologically or medically significant characteristics of bodily structures, known as biomarkers, and more particularly to the assessment of biomarkers by quantitative measurement of their response to a stimulus.
DESCRIPTION OF RELATED ART
The measurement of internal organs and structures from computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET), and other imaging data sets is an important objective in many fields of medicine. These imaging modalities are quantitative assessments that, when used, are typically based on manual intervention by a trained technician or radiologist. Examples illustrating current applications of medical imaging include the measurement of the hippocampus in patients with epilepsy (Ashton E. A., Parker K. J., Berg M. J., and Chen C. W. “A Novel Volumetric Feature Extraction Technique with Applications to MR Images,”
IEEE Transactions on Medical Imaging
16:4, 1997), the measurement of the biparietal diameter of the fetal head, and the measurement of cartilage thickness in bone (Stammberger, T., Eckstein, F., Englmeier, K-H., Reiser, M. “Determination of 3D Cartilage Thickness Data from MR Imaging: Computational Method and Reproducibility in the Living,”
Magnetic Resonance in Medicine
41, 1999; and Stammberger, T., Hohe, J., Englmeier, K-H., Reiser, M., Eckstein, F. “Elastic Registration of 3D Cartilage Surfaces from MR Image Data for Detecting Local Changes in Cartilage Thickness”,
Magnetic Resonance in Medicine
44, 2000).
The need for accurate and precise measurements of organs, tissues, structures, and sub-structures continues to increase. For example, in following the response of a disease to a new therapy, the accurate representation of three-dimensional (3D) structures is vital in broad areas such as neurology, oncology, orthopedics, and urology. In human and animal anatomy texts, there are a great number of named organs, structures, and sub-structures. Furthermore, in disease states modifications to normal structures are possible and additional pathological structures or lesions can be present. Despite the imposing number of defined sub-structures and pathologies, within the major disease categories there are specific objects that serve as indicators of disease. For example, liver metastases, brain lesions, atherosclerotic plaques, and meniscal tears are some examples of specific indicators of different conditions. The topological, morphological, radiological, and pharmacokinetic characteristics of biological structures and sub-structures are called biomarkers, and specific measurements of the biomarkers can provide a quantitative assessment of disease progress. The ability to clearly and precisely quantify, distinguish and identify these biomarkers represents a needed and important step for an accurate, image-based assessment of both normal and disease states. Currently, medical imaging techniques such as MRI, CT, and ultrasound are used to assess biological structures and substructures and offer a limited degree of resolution.
In following the response of a person or animal to therapy, or to monitor the progression of disease, it is desirable to accurately and precisely monitor the trends in biomarkers over time. It is also very useful to obtain accurate measurements of these biomarkers over time, particularly to judge the degree of response to a new therapy, or to assess the trends often associated with increasing age. The prior art is capable of assessing gross changes over time. However, the conventional measurements are not well suited to assessing and quantifying subtle changes in lesion size, and are incapable of describing complex topology or shape in an accurate manner or of addressing finer details of biological structure(s).
In consideration of current medical imaging and tracking techniques, it becomes apparent that there are many disadvantages in using such technologies. As was noted earlier, many current imaging modalities require manual or semi-manual intervention by trained personnel. Interventions often include the usage of calipers (to derive measurement from radiographic films) and/or the use of a trackball or mouse. Additionally, user assisted interfaces are also employed to initiate some semi-automated algorithms (Ashton et al). The need for intensive and expert manual intervention is a disadvantage, since the demarcations can be tedious and prone to a high inter- and intra-observer variability. Furthermore, the typical application of manual measurements within two-dimensional (2D) slices, or even sequential 2D slices within a 3D data set, is not optimal since tortuous structures, curved structures, and thin structures are not well characterized within a single 2D slice, leading again to operator confusion and high variability in results. If these measurements are repeated over time on successive scans, then inaccurate trend information can unfortunately be obtained.
Yet another problem with conventional methods is that they lack sophistication and are based on “first order” measurements of diameter, length, or thickness. These traditional measurements can be insensitive to small but important changes. As previously mentioned, the manual and semi-manual tracings of images lead to high intra- and inter-observer variability, and also lead to uneven or “ragged” 3D structures. The accuracy of these measurements and their sensitivity to subtle changes in small sub-structures are highly dependent upon the resolution of the imaging system. Unfortunately, most CT, MRI, and ultrasound systems have poor resolution in the out-of-plane, or “z” axis. While the in-plane resolution of these systems can commonly resolve objects that are just less than one millimeter in separation, the out-of-plane (slice thickness) is commonly set at 1.5 mm or even greater. For assessing subtle changes and small defects using “higher order” structural measurements, it is desirable to have better than one millimeter resolution in all three orthogonal axes. Manual and semi-manual assessments of conventional biomarkers (such as major axis length or cross-sectional area) have a high inherent variability, so as successive scans are traced the variability can hide subtle trends. This means that only gross changes, sometimes over very long time periods, can be verified using conventional methods.
Some references for the prior work include:
Eckenstein F., Gavazzeni H. S., Sittek H., Haubner, M., Losch, A., Milz, S., Englmeier, K-H., Schulte, E., Putz, R, Reiser, M. “Determination of Knee Joint Cartilage Thickness using Three-Dimensional Magnetic Resonance Chondro-Crassometry (3D MR-CCM),”
Magnetic Resonance in Medicine
36: 256-265, 1996.
Solloway, S., Hutchinson, C. E., Waterton, J. C., Taylor, C. “The Use of Active Shape Models for Making Thickness Measurements of Articular Cartilage from MR Images,”
Magnetic Resonance in Medicine
37: 943-952, 1997.
Stammberger, T., Eckstein, F., Englmeier, K-H., Reiser, M. “Determination of 3D Cartilage Thickness Data from MR Imaging: Computational Method and Reproducibility in the Living,”
Magnetic Resonance in Medicine
41: 529-536, 1999.
Ghosh, S., Ries, M., Lane, N., Majundar, S. “Segmentation of High Resolution Articular Cartilage MR Images,” 46th Annual Meeting, Orthopaedic Research Society, Mar. 12-15, 2000, Orlando Fla.
Dardzinski, B. J., Mosher, T. J., Li, S., Van Slyke, M. A., Smith, M. B. “Spatial Variation of T2 in Human Articular Cartilage.
Radiology
205: 546-550, 1997.
Therasse, P., et al. “New Guidelines to Evaluate the Response to Treatment in Solid Tumors,”
Journal of National Cancer Institute
, February 2000(92) 3: 205-216. This paper describes the standard (RECIST) for unidimensional tumor measurement.
Barseghian, T. “Uterine Fibroid Embolization Offers Alternative to Surgery,”
Diagnostic Imaging
, September 1997, 11-12. This paper illustrates the awkwardness of the conventional mouse-driven manual outlining of lesions.
Yang, W., et al., “Comparison of Dynami
Ashton Edward
Tamez-Peña José
Totterman Saara Marjatta Sofia
Blank Rome LLP
Couso Jose L.
VirtualS{tilde over (c)}opics, LLC
LandOfFree
Method and system for assessment of biomarkers by... does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Method and system for assessment of biomarkers by..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Method and system for assessment of biomarkers by... will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-3317426