# US 11,432,737 B2 · Systems and methods for real-time motion prediction in dynamic imaging

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- Number: US 11,432,737 B2
- Document ID: US11432737B2
- Status: Granted
- Kind: B2
- Application number: 16/492,295
- Filed: 2018-03-17
- Published: 2020-01-09
- Granted: 2022-09-06
- Earliest claimed: 2017-03-17
- Assignees: The Regents of the University of California
- Applicants: The Regents of the University of California
- Inventors: Xinzhou Li; Holden H. Wu
- Web page: https://patentgrep.com/patent/US11432737B2

## CPC

- G06T 7/277: involving stochastic approaches, e.g. using Kalman filters
- A61B 5/11: Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb {(A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721)}
- A61B 5/725: {using specific filters therefor, e.g. Kalman or adaptive filters (specific diagnostics methods using using bioelectric or biomagnetic signals A61B5/316)}
- A61B 6/032: {Transmission computed tomography [CT]}
- A61B 6/5264: {due to motion}
- A61B 8/5276: {due to motion}
- G01R 33/56509: {due to motion, displacement or flow, e.g. gradient moment nulling (G01R33/567 takes precedence)}
- G06T 7/0016: {involving temporal comparison}
- G06T 7/11: Region-based segmentation
- A61B 5/7207: {of noise induced by motion artifacts}
- A61B 6/037: {Emission tomography}
- A61B 2576/00: Medical imaging apparatus involving image processing or analysis (A61B1/00009, A61B6/52 and A61B8/52 take precedence)
- G01R 33/5608: {Data processing and visualization specially adapted for MR, e.g. for feature analysis and pattern recognition on the basis of measured MR data, segmentation of measured MR data, edge contour detection on the basis of measured MR data, for enhancing measured MR data in terms of signal-to-noise ratio by means of noise filtering or apodization, for enhancing measured MR data in terms of resolution by means for deblurring, windowing, zero filling, or generation of gray-scaled images, colour-coded images or images displaying vectors instead of pixels (image data processing or generation, in general G06T)}
- G06T 2207/10088: Magnetic resonance imaging [MRI]
- G06T 2207/20104: Interactive definition of region of interest [ROI]
- G06T 2207/30004: Biomedical image processing
- G16H 30/40: for processing medical images, e.g. editing
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G01R: MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES (indicating correct tuning of resonant circuits H03J3/12)
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

Systems and methods for predicting motion of a target using imaging are provided. In one aspect, a method includes receiving image data, acquired using an imaging system, corresponding to a region of interest (“ROI”) in a subject, and generating a set of reconstructed images from the image data. The method also includes processing the set of reconstructed images to obtain motion information associated with a target in the ROI, and applying the motion information in a motion prediction framework to estimate a predicted motion of the target. The method further includes generating a report based on the predicted motion estimated.

## Claims

### Claim 1

A method for predicting motion of a target using imaging, the method comprising:
receiving image data, acquired using an imaging system, corresponding to a region of interest (“ROI”) in a subject;
generating a set of multi-rate images from the image data, wherein the set of multi-rate images comprises a first set of images and a second set images, wherein a temporal rate of the second set of images is higher than a temporal rate of the first set of images and a spatial resolution of the second set of images is lower than a spatial resolution of the first set of images;
processing the set of multi-rate images to obtain motion information associated with a target in the ROI, the processing of the set of multi-rate images comprising generating a first motion waveform using the first set of images and a second motion waveform using the second set of images, wherein the second motion waveform is shifted in time relative to the first motion waveform; and
utilizing the motion information in a motion prediction framework to estimate a predicted motion of the target; and
generating a report based on the predicted motion.

### Claim 2 (depends on 1)

The method of claim 1, wherein the image data comprises magnetic resonance imaging (“MRI”) data, X-ray data, computed tomography (“CT”) data, positron emission tomography (“PET”) data, ultrasound (“US”) data, or optical image data.

### Claim 3 (depends on 1)

The method of claim 1, further comprising using a least squares registration algorithm to generate at least one of the first motion waveform and the second motion waveform.

### Claim 4 (depends on 1)

The method of claim 1, further comprising performing a multi-rate adaptive filtering technique using the first motion waveform and the second motion waveform, in combination with supplementary measurements, to estimate a motion state variable.

### Claim 5 (depends on 1)

The method of claim 1, further comprising performing a multi-rate adaptive filtering technique, using the first motion waveform and the second motion waveform, to estimate a motion state variable.

### Claim 6 (depends on 5)

The method of claim 5, wherein the multi-rate adaptive filtering comprises applying a Kalman filter technique.

### Claim 7 (depends on 5)

The method of claim 5, further comprising estimating the predicted motion of the target using at least one of the multi-rate adaptive filtering technique or motion state variable.

### Claim 8 (depends on 5)

The method of claim 5, further comprising performing an adaptive calibration to at least one of the multi-rate adaptive filtering technique or motion state variable when a motion prediction error exceeds a threshold.

### Claim 9 (depends on 5)

The method of claim 5, wherein the motion state variable is estimated before or during an intervention.

### Claim 10 (depends on 1)

The method of claim 1, further comprising providing the report to an interventional system or a graphical user interface.

### Claim 11

A system for predicting motion of a target using imaging, the system comprising:
an input in communication with an imaging system configured to acquire image data from a subject correspond to a region of interest (“ROI”) in a subject;
at least one processor configured to:
receive the image data from the imaging system using the input;
reconstruct a set of images using the image data, wherein the set of images include multi-rate images, and the set of multi-rate images comprises a first set of images and a second set images, wherein a temporal rate of the second set of images is higher than a temporal rate of the first set of images and a spatial resolution of the second set of images is lower than a spatial resolution of the first set of images;
process the set of images to obtain motion information associated with a target selected in the ROI, the processing of the set of images comprising generating a first motion waveform using the first set of images and a second motion waveform using the second set of images, wherein the second motion waveform is shifted in time relative to the first motion waveform;
estimate, using a motion prediction framework, a predicted motion of the target using the motion information; and
generate a report based on the predicted motion; an output for providing the report.

### Claim 12 (depends on 11)

The system of claim 11, wherein the input is further configured to obtain supplementary measurements from the subject.

### Claim 13 (depends on 11)

The method system of claim 11, wherein the at least one processor is further configured to perform a multi-rate adaptive filtering technique, using the first motion waveform and the second motion waveform, to estimate a motion state variable.

### Claim 14 (depends on 13)

The system of claim 13, wherein the at least one processor is further configured to perform an adaptive calibration to at least one of the adaptive filter or motion state variable when a motion prediction error exceeds a threshold.

### Claim 15 (depends on 11)

The system of claim 11, wherein the at least one processor is further configured to perform an adaptive filtering technique, using the first motion waveform and the second motion waveform, in combination with supplementary measurements, to estimate a motion state variable.

### Claim 16 (depends on 11)

The system of claim 11, wherein the output is in communication with an intervention system, and the report generated is configured to control operation of the interventional system.