Medical imaging system for accurate measurement evaluation of changes in a target lesion

Claim 2

An automated method for determining a bound on the error of a size change measurement, the method comprising the steps of: scanning a body part with an imaging system to produce a first set of imaging data; identifying at least one target lesion in the imaging data; rescanning the body part so as to produce a second set of imaging data; identifying the at least one target lesion in the second set of imaging data; measuring the at least one target lesion as imaged in both the first set of imaging data and the second set of imaging data to determine a first apparent target lesion size corresponding to the first set of imaging data and a second apparent target lesion size corresponding to the second set of imaging data; estimating a change in size by comparing the first and second apparent lesion sizes; and estimating a variance where the variance includes a set of variance measurements from a plurality of factors selected from the group consisting of nodule form, scanner parameters, patient factors, algorithm factors, operator factors, nodule tissue, scanner point spread function, patient motion, features of the at least one target lesion, features of adjacent structures, features of the imaging system including its inherent resolution, and noise present in the image so as to determine a bound on the change in size measurement.

Claim 3

The method of claim 1 wherein the size measurements comprise at least one size measurement selected from the group consisting of length, area and three-dimensional volume of the lesion.

Claim 4

The method of claim 1 further comprising the step of defining the edge of the at least one target lesion by adjusting the apparent image volume for the at least one target lesion according to the estimated variance to produce at least two adjusted image volumes.

Claim 5

The method of claim 3 wherein the step of defining the edge of the at least one target lesion further comprises the step of applying a threshold and/or a gradient function to the at least one target lesion to determine the boundary for the edge.

Claim 6

The method of claim 1 wherein each step is carried out by software that allows for interaction of a medical professional.

Claim 7

The method of claim 1 further including the step of automatically estimating a degree of motion for a particular structure.

Claim 8

The method of claim 6 wherein the step of automatically estimating a degree of motion for a particular structure includes measuring a degree of variation of surface structures and structures outside of the target lesion.

Claim 9

The method of claim 1 further comprising the step of automatically matching corresponding images of the at least one target lesion acquired at differing times.

Claim 10

The method of claim 8 further comprising the step of selecting the at least one target lesion as an object having a maximal area, maximal length or maximal volume and finding a comparable object obtained at a subsequent time.

Claim 11

The method of claim 1 further including the step of spatially calibrating an imaging system using at least one phantom and measuring quantities selected from the group consisting of bias, reproducibility, noise, scanner artifacts and image distortion.

Claim 12

The method of claim 10 wherein the step of spatially calibrating further includes the step of conducting phantom studies to establish maps that characterize the degree of noise, scanner artifacts and image distortion for all pertinent regions of the human body for a given scanner; and using the maps to determine a bound for the measurement error of the measurement of the target lesion.

Claim 13

The method of claim 1 wherein the body part is a lung further comprising the step of automatically segmenting other lung features from the at least one target lesion.

Claim 14

The method of claim 1 wherein the imaging system is selected from the group consisting of a computerized tomography scanner, magnetic resonance imager, positron emission imaging system, X-ray imaging system, vascular interventional and angiogram/angiography procedures, and an ultrasound imaging system.

Claim 15

The method of claim 1 further including the step of using a calibration device during scanning.

Claim 16

The method of claim 14 wherein the calibration device comprises a synthetic phantom.

Claim 17

The method of claim 14 wherein the calibration device comprises a set of synthetic phantoms of varying size.

Claim 18

The method of claim 14 wherein the calibration device is selected from the group consisting of wires, beads, rods and geometrical shapes.

Claim 19

The method of claim 14 wherein the calibration device is an in vivo device.

Claim 20

The method of claim 18 wherein the in vivo device is selected from the group consisting of wires, beads, catheters, implantable devices and items of known dimensions in the patient's body.

Claim 21

An automated method for determining a bound on the error of a size change measurement, the method comprising the steps of: scanning a body part with an imaging system to produce a first set of imaging data; identifying a plurality of target lesions in the imaging data; rescanning the body part so as to produce a second set of imaging data; identifying the plurality of target lesions in the second set of imaging data; measuring the plurality of target lesions as imaged in both the first set of imaging data and the second set of imaging data to determine a first apparent target lesion value corresponding to the first set of imaging data and a second apparent target lesion value corresponding to the second set of imaging data; estimating a change in target lesion value by comparing the first and second apparent lesion values; and estimating a variance so as to determine a bound on the change in value measurement where the variance includes a set of variance measurements from a plurality of factors selected from the group consisting of nodule form, scanner parameters, patient factors, algorithm factors, operator factors, nodule tissue, scanner point spread function, patient motion, features of the at least one target lesion, features of adjacent structures, features of the imaging system including its inherent resolution, and noise present in the image.

Claim 22

An automated method for determining a bound on the error of a size change measurement, the method comprising the steps of: scanning a body part with an imaging system to produce a first set of imaging data; identifying a plurality of target lesions in the imaging data; rescanning the body part so as to produce a second set of imaging data; identifying the plurality of target lesions in the second set of imaging data; measuring the plurality of target lesions as imaged in both the first set of imaging data and the second set of imaging data to determine a first apparent target lesion value corresponding to the first set of imaging data and a second apparent target lesion value corresponding to the second set of imaging data; estimating a change in target lesion value by comparing the first and second apparent lesion values; and estimating a variance so as to determine a bound on the change in value measurement where the variance includes a set of variance measurements from a plurality of factors selected from the group consisting of nodule form, scanner parameters, patient factors, algorithm factors, operator factors, nodule tissue, scanner point spread function, patient motion, features of the at least one target lesion, features of adjacent structures, features of the imaging system including its inherent resolution, and noise present in the image.

Claim 23

The method of claim 20 wherein at least one target lesion value is smaller than 1 cm.