Surgery – Diagnostic testing – Detecting nuclear – electromagnetic – or ultrasonic radiation
Reexamination Certificate
2000-07-03
2003-10-28
Leung, Philip H. (Department: 3742)
Surgery
Diagnostic testing
Detecting nuclear, electromagnetic, or ultrasonic radiation
C600S412000, C382S128000
Reexamination Certificate
active
06640130
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The invention is directed to an imaging apparatus and methods for performing assessment and monitoring with interpreted imaging. Embodiments of the invention are particularly useful in surgery, clinical procedures, tissue assessment, diagnostic procedures, health monitoring, and medical evaluations.
2. Description of the Background
Spectroscopy, whether it is visible, near infrared, infrared or Raman, is an enormously powerful tool for the analysis of biomedical samples. The medical community, however, has a definite preference for imaging methods, as exemplified by methods such as MRI and CT scanning as well as standard X-ray photography and ultrasound imaging. This is entirely understandable as many factors need to be taken into account for a physician to make a clinical diagnosis. Imaging methods potentially can provide far more information to a physician than their non-imaging counterparts. With this medical reality in mind, there has been considerable effort put into combining the power and versatility of imaging method with the specificity of spectroscopic methods.
Near-infrared (near-IR) spectroscopy and spectroscopic imaging can measure the balance between oxygen delivery and tissue oxygen utilization by monitoring the hemoglobin oxygen saturation in tissues (Sowa, M. G. et al., 1998
, Proc. SPIE
3252, pp. 199-207; Sowa, G. W. et al., 1999
, Journal of Surgical Research
, 86:62-29; Sow, G. W. et al., 1999
, Journal of Biomedical Optics
, 4:474-481; Mansfield, J. R., et al., 2000
, International Society of Optical Engineers
, 3920:99-197). For in-vivo human studies, the forearm or leg has been the investigational site for many of the noninvasive near-IR studies. Non-imaging near-IR applications have examined the local response of tissue to manipulations of blood flow (De-Blasi, R. A. et al., 1992
, Adv. Exp. Med. Biol
, 317:771-777). Clinically, there are situations where the regional variations in oxygenation saturation are of interest (Stranc, M. F. et al, 1998
, British Journal of Plastic Surgery
, 51:210-218). Near-IR imaging offers a means of accessing the spatial heterogeneity of the hemoglobin oxygenation saturation response to tissue perfusion. (Mansfield, J. R. et al., 1997
, Analytical Chemistry
, 69:3370-3374; Mansfield, J. R., et al., 1997
, Computerized Medical Imaging and Graphics
, 21:299-308; Salzer, R., et al., 2000
, Fresenius Journal of Analytical Chemistry
, 366:712-726; Shaw, R. A., et al., 2000
, Journal of Molecular Structure
(
Theochem
), 500:129-138; Shaw, R. A., et al., 2000
, Journal of Inorganic Biochemistry
, 79:285-293; Mansfield, J. R., et al., 1999
, Proc. SPIE Int. Soc. Opt. Eng
., 3597:222-233; Mansfield, J. R., et al., 1999
, Applied Spectroscopy
, 53:1323-1330; McIntosh, L. M., et al., 1999
, Biospectroscopy
, 5:265-275; Mansfield, R., et al.,
Vibrational Spectroscopy
, 19:33-45; Payette, J. R., et al., 1999
, American Clinical Laboratory
, 18:4-6; Mansfield, J. R., et al., 1998
, IEEE Transactions on Medical Imaging
, 6:1011-1018
Non-invasive monitoring of hemoglobin oxygenation exploits the differential absorption of HbO
2
and Hb, along with the fact that near-IR radiation can penetrate relatively deeply into tissues. Pulse oximetry routinely supplies a noninvasive measure of arterial hemoglobin oxygenation based on the differential red-visible and near infrared absorption of Hb and HbO
2
. Visible
ear-IR multispectral imaging permits the regional variations in tissue perfusion to be mapped on macro and micro scale. Unlike infrared thermography, hyperspectral imaging alone does not map the thermal emission of the tissues. Instead, this imaging method relies on the differential absorption of light by a chromophore, such as, Hb and HbO
2
, resulting in differences in the wavelength dependence of the tissue reflectance depending on the hemoglobin oxygen saturation of the tissue. (Sowa, M. G., et al., 1997
, Applied Spectroscopy
, 51:143-152; Leventon, M., 2000, MIT Ph.D. Thesis)
Spectroscopic imaging methodologies and data are becoming increasingly common in analytical laboratories, whether it be magnetic resonance (MRI), mid-IR, Raman, fluorescence and optical microscopy, or near-IR/visible-based imaging. However, the volume of information contained in spectroscopic images can make standard data processing techniques cumbersome. Furthermore, there are few techniques that can demarcate which regions of a spectroscopic image contain similar spectra without a priori knowledge of either the spectral data or the sample's composition. The objective of analyzing spectroscopic images is not only to determine what the spectrum is at any particular pixel in the sample, but also to determine which regions of the sample contain similar spectra; i.e., what regions of the sample contain chemically related compounds. Multivariate analysis methodologies can be used to determine both the spectral and spatial characteristics of a sample within a spectroscopic imaging data set. These techniques can also be used to analyze variations in the temporal shape of a time series of images either derived for extracted from a time series of spectroscopic images.
There are few techniques that can demarcate which regions of a sample contain similar substances without a priori knowledge of the sample's composition. Spectroscopic imaging provides the specificity of spectroscopy while at the same time relaying spatial information by providing images of the sample that convey some chemical meaning. Usually the objective in analyzing heterogeneous systems is to identify not only the components present in the system, but their spatial distribution. The true power of this technique relative to traditional imaging methods lies in its inherent multivariate nature. Spatial relationships among many parameters can be assessed simultaneously. Thus, the chemical heterogeneity or regional similarity within a sample is captured in a high dimensional representation which can be projected onto a number of meaningful low dimensional easily interpretable representations which typically comprise a set of composite images each having a specific meaning.
While it is now clear that both spectroscopy and spectroscopic imaging can play roles in providing medically relevant information, the raw spectral or imaging measurement seldom reveals directly the property of clinical interest. For example using spectroscopy, one cannot easily determine whether the tissue is cancerous, or determine blood glucose concentrations and the adequacy of tissue perfusion. Instead, pattern recognition algorithms, clustering methods, regression and other theoretical methods provide the means to distill diagnostic information from the original analytical measurements.
There are however various methods for the collection of spectroscopic images. In all such cases, the result of a spectroscopic imaging experiment is something termed a spectral image cube, spectroscopic imaging data cube or just hypercube. This is a three dimensional array of data, consisting of two spatial dimensions (the imaging component), and one spectral dimension. It can be thought of as an array of spatially resolved individual spectra, with every pixel in the first image consisting of an entire spectrum, or as a series of spectrally resolved images. In either representation, the 3D data cube can be treated as a single entity containing enormous amounts of spatial and spectral information about the sample from which it was acquired.
As an extension of the three dimensional array acquired in a spectroscopic imaging experiment, one can collect data cubes as a function of additional parameters such as time, temperature or pH. Numerous algorithms can be used to analyze these multi-dimensional data sets so that chemical and spectral variations can be studied as additional parameters. However, taken together, they can allow one to more fully understand the variations in the data. This can be done in a gated or sequential fashion.
Multi-modal image fusion, or image registration, i
Freeman Jenny E.
Hopmeier Michael J.
Leventen M.
Lewis Edgar Neil
Mansfield James
Hypermed, Inc.
Leung Philip H.
Morrison & Foerster / LLP
LandOfFree
Integrated imaging apparatus does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Integrated imaging apparatus, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Integrated imaging apparatus will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-3131404