Surgery – Diagnostic testing – Detecting nuclear – electromagnetic – or ultrasonic radiation
Reexamination Certificate
2001-01-19
2003-07-01
Lateef, Marvin M. (Department: 3737)
Surgery
Diagnostic testing
Detecting nuclear, electromagnetic, or ultrasonic radiation
C600S443000, C128S916000
Reexamination Certificate
active
06585647
ABSTRACT:
BACKGROUND
1. Technical Field
This disclosure relates to medical imaging, and in particular to a system and method for imaging and determining body organs.
2. Description of the Related Art
The noninvasive visualization of the internal anatomy of organ systems, and the supporting vascular network, provide invaluable medical diagnostic information of the patient. There have been considerable studies over the past ten years investigating volume visualization techniques for representing anatomical structures, using direct volume rendering and surface-fitting algorithms. These volume visualization techniques have been applied to various imaging modalities, such as ultrasound (US), magnetic resonance imaging (MRI), and computer tomography (CT).
In the field of diagnostic ultrasound, the use of conventional 2-D images requires the operator to try to mentally reconstruct and visualize the 3-D properties of the anatomy and related pathology. However, the ability to “think 3-D” varies considerably among clinicians and depends on their experience and innate ability in spatial perception. It is sometimes very difficult for the radiologist to develop a 3-D “picture” from the 2-D slices, to detect some lesions even with multiple views, and to visualize the supporting vascular system.
Three-dimensional ultrasound (3-D US) images are derived from two-dimensional contigous slices from conventional ultrasound scans. The tissue volume is spatially sampled, digitally stored and simultaneously displayed in a multiplanar array format to provide any three perpendicular anatomic planes desired, with rotation, thresholding and dissection (electronic scalpel), as needed, in order to optimally view the structures of interest. By maintaining the entire volume of data, analysis can be performed off-line, after the patient has left the clinic. This allows the multiplanar images to be reviewed in many arbitrary planes and with various processing options. For example, analysis can obtain specific region-of-interest statistics and their variation with time, merge information from multiple modalities, and allow for motion description and compensation.
3-D US imaging has been very effective in Ob/Gyn studies. It has been successfully used for detecting congenital abnormalities in fetal surface features (gestational ages 10 to 39 weeks), such as cleft lip and cleft palate, and for the early detection of chromosomal anomalies in the first trimester, distinguishing between cystic hygroma colli and physiologic nuchal translucency. 3D US has also permitted measurement of fetal organ volume for assessment of fetal growth and fetal abnormalities. For the first time, 3-D US permits the possibility of measuring the fetal lung volume and relating it to gestational age and fetal weight. 3-D multiplanar US can also be effective in identifying and assessing the standard cardiac planes from 14 weeks to term; clinical tests have shown that a 3-D measure of cardiac volume can be used to improve screening for fetal cardiac anomalies, with best results between 22 and 27 weeks gestation.
3-D US has also been effective in improving visualization of vessels and tumors in the prostate gland and breast, for visualizing the cardiac chambers, uterine anatomy, carotid artery and endoluminal structures. In recent clinical prostate studies, 3-D US gave a greater confidence level in identifying permanent transperineal radioactive seed implants, for the goal of real-time optimization of prostate brachytherapy. In breast studies, intraoperative and 3-D US are very effective in detecting and localizing areas of free silicone from ruptured breast implants when the ruptured implants are surgically removed. 3-D US gives a more accurate spatial localization of lesions for biopsy, compared to conventional 2-D US, and provides an accurate assessment of vascular structures and their related pathologies. In a broad range of clinical studies, 3-D endoluminal US provided unique information about spatial relationships of anatomic structures, such as the size and shape of the vascular lumen and the distribution, location and type of plaque, that could not be obtained with conventional 2-D imaging. 3-D US can present a more accurate distribution of tumor along the ureter, and its relationship adjacent structures, and provide a measure of the tumor volume. It can also greatly facilitate the visualization and staging of colorectal masses.
Some major limitations to 3-D ultrasound imaging are (1) the considerable number of “looks” or “slices” that are required for image reconstruction (typically several hundred slices in magnetic resonance imaging (MRI) and computed tomography (CT) and about 64 slices in ultrasound), (2) the long data acquisition time required for imaging, (3) the accuracy required for multi-plane anatomical registration (for example, resolutions less than about 0.5 mm), and (4) the requirements for large memory storage and rapid computation. There is a need for improved imaging technology to address such limitations; for example, a shortened acquisition time greatly minimizes the effects of target movement, such as fetal motion, and results in less exposure time with the accompanying less risk of bioeffects from normal biological activity or from sudden movements.
Other imaging techniques have been used for improved detection and classification of objects. For example, Synthetic Structural Imaging (SSI) techniques use low frequency transmissions for the successful detection and classification in both radar and sonar, of aircraft and of acoustic mines and submarines, respectively.
The SSI concept has been demonstrated to provide acoustical target identification and structural feature estimation in sonar applications. Test results have indicated that the acoustic transient response is uniquely characteristic of target identity, with features strongly related to simple geometrical shape features. It has been shown that such signatures may be used for a narrow bandwidth to provide pictorial information of sufficient quality as well as volume estimates of sufficient accuracy for substantially accurate target identification.
SSI employs ramp response analysis, which was developed in radar studies of airwing identification, and has also been successfully applied to imaging underwater (scaled) targets. Similar to conventional high frequency imaging, the SSI method is direction-dependent, but is considerably more robust. It has less resolution than conventional techniques but its correlation to shape is much stronger. SSI technology has been shown in previous experimental studies to be very promising for specific radar and sonar applications.
The application of SSI techniques to biological mediums may provide an estimate of the volume of biological organs, tumors and other structures. To date, there has been little progress in estimating the primary tissue classifiers of volume, size and shape in a clinical environment and relating them to tissue pathology. In addition, the discrimination of normal tissue from abnormal tissue has not been successfully accomplished using SSI.
SUMMARY
A novel, non-invasive, acoustic measurement and imaging system and method is disclosed which uses SSI techniques to provide unique information concerning the size and shape of biological tissue structures for classification and visualization of normal and abnormal tissues, organs, tumors, etc.
The SSI system includes a processor and memory for generating low frequency ultrasound signals to be applied to a biological structure to generate a synthetic structural image of the structure. The SSI system analyzes the low frequency ramp response of the tissue structure which is used to generate a graphic representation of the tissue structure as well as to estimate the volume of the tissue structure and to classify the tissue structure as to type and condition of the tissue using a set of stored tissue data. The classifier may include a neural network and/or a nearest neighbor rule processor.
The disclosed system and method utilize low frequency ultrasound transmissions for de
Carter DeLuca Farrell & Schmidt LLP
Imam Ali M.
Lateef Marvin M.
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