Patient monitor

• US 9,028,422 B2
• Filed: 02/12/2009
• Issued: 05/12/2015
• Est. Priority Date: 09/05/2006
• Status: Active Grant

• Current Assignee: Vision RT Limited
• Original Assignee: Vision RT Limited
• Inventors: Meir, Ivan Daniel, Smith, Norman Ronald, Ruto, Anthony Christopher
• Primary Examiner(s): D'ANGELO, MICHAEL J
• Application Number:
  US12/379,108
• Publication Number:
  US 20090187112A1
• Time in Patent Office: 2,280 Days
• Field of Search: 600/416, 600/534, 600/595, 382/128, 382/132, 378/64, 378/65
• US Class Current: 600/534
• CPC Class Codes: A61B 2576/02 specially adapted for a particular organ or body part
• CPC Class Codes: A61B 5/0077 Devices for viewing the surface of the body, e.g. camera, magnifying lens
• CPC Class Codes: A61B 5/113 occurring during breathing
• CPC Class Codes: A61B 5/7285 for synchronising or triggering a physiological measurement or image acquisition with a physiological event or waveform, e.g. an ECG signal A61B6/5205, A61B6/541, A61B8/5207, A61B8/543 take precedence
• CPC Class Codes: A61B 6/541 involving acquisition triggered by a physiological signal
• CPC Class Codes: A61N 2005/1059 using cameras imaging the patient
• CPC Class Codes: A61N 5/1049 for verifying the position of the patient with respect to the radiation beam
• CPC Class Codes: A61N 5/1064 for adjusting radiation treatment in response to monitoring
• CPC Class Codes: G06T 2207/10021 Stereoscopic video; Stereoscopic image sequence
• CPC Class Codes: G06T 2207/10076 4D tomography; Time-sequential 3D tomography
• CPC Class Codes: G06T 2207/10081 Computed x-ray tomography [CT]
• CPC Class Codes: G06T 2207/30096 Tumor; Lesion
• CPC Class Codes: G06T 7/593 from stereo images

Abstract

A patient monitor is disclosed for detecting patient movement or abnormal breathing. Images of a patient are obtained by a stereoscopic camera. These images are then processed by a 3D position determination module which determines measurements indicative of positions of at least part of a patient. The obtained measurements are then passed to a model generation module which generates a breathing model of the variation in position of the at least part of a patient during a breathing cycle. Subsequently abnormal breathing or patient movement can be detected by processing further images obtained by the stereoscopic camera to determine more measurements indicative of positions of at least part of a patient. These measurements are then compared with a stored breathing model by a comparison module. If abnormal breathing or patient movement is detected the comparison module sends a signal to a treatment apparatus to interrupt treatment until normal breathing resumes or alternatively to a mechanical couch to reposition the patient to account for the detected movement.

Claims

1. A patient monitor comprising:

a 3D position determination module operable to determine measurements indicative of shape and location of at least part of a patient;

a clock module operable to associate measurements indicative of the shape and location of at least part of a patient determined by the 3D position determination module with timing data, a model generation module operable to partition the shape and location and timing data into a plurality of groups of data each associated with different portions of the breathing cycle and utilize the portioned data to generate a plurality of component analysis models of the variation in shape and location of the at least part of a patient during the respective different portions of the breathing cycle; and

a comparison module operable to utilize shape and location and timing data and a plurality of component analysis models generated by the model generation module to identify breathing which differs from that occurring in the images used to generate the component analysis models.

2. The patient monitor in accordance with claim 1 wherein the model generation module is operable to partition shape and location and timing data into groups of data each associated with a specific amplitude range.

3. The patient monitor in accordance with claim 1 wherein the model generation module is operable to partition shape and location and timing data into groups of data each associated with a specific period within the breathing cycle.

4. The patient monitor in accordance with claim 1 wherein the comparison module is operable to:

select from the plurality of component analysis models generated by the model generation module a model associated with the portion of the breathing cycle shape, location and timing data is determined to relate to;

model a representation of the position of the at least part of a patient corresponding to obtained shape and location and timing data using the selected model; and

compare obtained position and timing data with the modelled representation.

5. The patient monitor in accordance with claim 1 wherein comparison module is operable to:

select from the plurality of component analysis models generated by the model generation module a model associated with the portion of the breathing cycle timing and position data is determined to relate to;

determine a set of weights for modeling a representation of the shape and location and timing data using the selected model;

calculate a plausibility measure indicative of the probability of the representation of the shape and location and timing data arising based on the set of weights determined for modeling the representation; and

compare the plausibility measure of with a threshold value.

6. The patient monitor in accordance with claim 5 wherein the model generation module is operable to associate each of the plurality of component analysis models with a mean model value, wherein the comparison module is operable to determine weighting factors for weighting models based upon a comparison of obtained shape and location and timing data and the mean model values associated with the models.

7. The patient monitor in accordance with claim 5 wherein the model generation module is operable to associate each of the plurality of component analysis models with a mean model value, wherein the comparison module is operable to set weighting factors for weighting models based on the identification of the component analysis model associated with a mean model value which most closely matches obtained shape and location and timing data.

8. The patient monitor in accordance with claim 5 wherein the model generation module is operable to associate each of the plurality of component analysis models with a mean model value, wherein the comparison module is operable to set weighting factors for weighting models based on the differences between mean model values associated with each of the plurality of models and obtained shape and location and timing data.

9. The patient monitor in accordance with claim 1 wherein the comparison module is operable to:

determine a weighting factor for each of the plurality of component analysis models generated by the model generation module; and

determine the extent obtained shape and location and timing data correspond to a modeled representation of the shape and location and timing data utilizing the plurality of models weighted in accordance with the weighting factors.

10. The patient monitor in accordance with claim 1 wherein the comparison module is operable to:

determine a set of weights for modelling a representation of obtained shape and location and timing data using the plurality of component analysis models weighted in accordance with the weighting factors;

calculate a plausibility measure indicative of the probability of a modelled representation of the obtained shape and location and timing data arising based on the set of weights determined for modelling the representation; and

compare the plausibility measure of with a threshold value.

11. The patient monitor in accordance with claim 1 wherein the comparison module is operable to:

determine a weighting factor for each of the plurality of component analysis models;

determine a set of weights for modeling a representation of the obtained shape and location and timing data using the plurality of models weighted in accordance with the weighting factors; and

compare obtained shape and location and timing data with the modeled representation.