Surgery – Diagnostic testing – Detecting nuclear – electromagnetic – or ultrasonic radiation
Reexamination Certificate
2000-05-08
2003-04-15
Lateef, Marvin M. (Department: 3737)
Surgery
Diagnostic testing
Detecting nuclear, electromagnetic, or ultrasonic radiation
C600S419000, C600S407000, C382S128000, C382S131000
Reexamination Certificate
active
06549803
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to the delivery of material to tissue within a subject or patient. In particular, delivery of materials such as therapeutic, image enhancing, bio-active, pharmacological, nanotechnical or otherwise active materials is enhanced, particularly under real-time observable imaging systems (such as MRI, sonograms, and X-ray fluoroscopy). The invention further relates to the field of predictive mass transport or diffusion analysis for enhancing material delivery within a subject or patient.
2. Background of the Art
It is increasingly common to administer a drug or other material to a carefully targeted part of the body, rather than to insert it into the bloodstream and rely on some of it finding the target by carriage through the circulatory system. This targeted delivery has multiple advantages, when it can be performed effectively. Among the advantages of targeted delivery are that much less of the drug is needed, which itself represents a double gain. The drug itself is often costly, so reducing the volume of drug used in a treatment can represent a significant cost savings. It is also rare that any drug is wholly without negative effects (‘side effects’ with reference to the desired result). Where these adverse effects arise only where the drug reaches a specific non-targeted tissue, restricting the drug to a target that does not include that non-targeted tissue avoids the side effects completely. Even where this complete exclusion is impossible, the side effects may be far more acceptable if limited to a small region around the target, with reduced impact on the body at large, while delivering the desired result on the target tissue at full strength. Even where the side effects are not directly life-threatening, it can be important to avoid them: for example, to keep cancer chemotherapy drugs from the sites where they cause nausea and hair loss is good both for patient morale and for patient persistence in taking the drug. The importance of targeted delivery will increase for nanodevices (that is, devices with dimensions that are measured with one or two orders of magnitude of nanometers), which will often be designed for highly specific activity in a particular environment. Apart from the waste of resources in failure to reach the target site, their action in unintended (and less studied) sites may be hard to predict.
However, this format of targeted material delivery adds to the traditional complexity of computing dosage and delivery rates. Instead of a single figure of blood concentration, controlled by the rates at which the drug enters the circulatory system and at which it diffuses, flows and is absorbed, metabolized or excreted by various tissues, concentration becomes a distinct time-varying value (number) at each part of the body. Since the effects of a drug vary in complex ways depending upon local levels of concentration (scopolamine, for example, is an anti-nausea drug in a narrow range of levels), its administration must ensure that at active points in the target tissue the concentration is correct across the entire region of targeted tissue to obtain the desired effect, while at other non-active treatment points minimizing undesired effects (often, but not always, by minimizing concentration at such points). Planning delivery commensurate with desired treatment effects thus requires a difficult prediction task.
The need for such prediction of delivery and diffusion profiles has increased, particularly with the recent development of methods of tracking a diffusing or flowing material in real time in the patient (see U.S. Pat. No. 6,026,316) and with the advent of direct drug infusion techniques (see U.S. Pat. No. 5,720,720, which describes a catheter-based technique for high-flow microinfusion, and U.S. Pat. No. 5,735,814, which discusses drug infusion into brain tissue by means of an implantable pump and catheter). The physician can thus observe the changes in concentration in the various tissues around the entry point and delivery region, and modify plans according to observed events. It is important to note that the ability to modify or alter the significant results are not effectable instantaneously. Unlike an artist applying paint to a canvas, where the result is immediate, the physician must control the administration process according to events that will subsequently provide observable or therapeutic effects that result over a period of seconds or minutes. However, the decision itself must be immediate, so the prediction of consequences must be available immediately in advance of the alteration of procedures.
The physician's brain, or a computer in assistance, must model the concentration dynamics prospectively much faster than they occur in real time, to be useful in real-time decision making. However, current computational methods of predicting concentration dynamics in tissue take longer than the actual events in the process of delivery. These current methods rely on two steps, both of which are slow.
The first step, once a scan of the target region is available (in either preoperative or real time during initial steps of the medical procedure), is to build a structural model of the tissue structures present. This requires first the labeling of points according to the type of tissue present (this process is often called ‘segmentation’, since it categorizes the points into three-dimensional ‘segments’).
FIG. 1
illustrates this with a 2D slice of a brain scan, with the Globus Pallidus Medialis on each side extracted and marked visually (
101
) by a uniform gray shade.
FIG. 2
shows the layered 3D region constructed from such slices. The planner then creates a best-fit geometric model of each structure present, such as bone, hippocampus, cortex, etc.
FIG. 3
shows such a 3D model, for one of the segments identified in FIG.
1
. Note that ‘model’ in this sense is a description of a geometrical shape, by (for instance) specifying vertices and faces, rather than a statistical model of a relationship, found by such methods as least squares. The decision as to what model fits the data best is somewhat heuristic: usually the objectives in fitting a geometric surface model to a bone are that points inside it should mostly be ‘probably bone’ on the evidence of local scan values, that they should form a connected region, that points immediately outside should be ‘probably not bone’, and that the surface should be reasonably smooth. (This last criterion tends both to reduce the impact of noisy data, and to allow a model that uses fewer vertices faces.) Methods for constructing such a surface model range from local definition of a surface that separates points according to whether they are above or below a threshold value, such as the Marching Cubes technique [W E Lorensen and H E Cline, System and method for the display of surface structures contained within the interior region of a solid body, U.S. Pat. No. 4,710,876] to active ‘balloons’ that move over the 3D image and attach themselves to boundary-like points, while resisting extremes of bending. (See for example L Cohen, L D Cohen, and N Ayache, “Using deformable surfaces to segment 3-D images and infer differential structures,” CVGIP:19, Image Understanding 56(2):242-263, September 1992)
These models are divided into finite elements with simple geometric forms, such as tetrahedra (as in
FIG. 3
) or skewed cuboids, spheres or other geometric or mathematical shapes. On each such element, a partial differential equation governing concentration dynamics, which by the definition of ‘differential’ involves values at an infinitude of points, is approximated. This approximation is by a system of equations with a small or at least controlled number of variables. Typically each variable multiplies a fixed function of position before it is added to an approximation of the concentration function. In the simplest cases of current art, these functions may be constants and linear functions, such as functions proportional to x,
Poston Timothy
Raghavan Raghu
Viswanathan Raju R.
Image-Guided Neurologics Inc.
Lateef Marvin M.
Mark A. Litman & Assoc. P.A.
Qaderi Runa Shah
LandOfFree
Method and apparatus for targeting material delivery to tissue 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 apparatus for targeting material delivery to tissue, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Method and apparatus for targeting material delivery to tissue will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-3019736