CN-122023142-A - Methods, systems, and apparatus for medical image enhancement to optimize transducer array placement

Abstract

A method, system and apparatus for medical image enhancement to optimize transducer array placement. A computer-implemented method to generate a three-dimensional model, wherein the computer includes one or more processors and memory accessible by the one or more processors, and the memory stores instructions that, when executed by the one or more processors, cause the computer to perform the computer-implemented method including receiving first image data (1110) of a first portion of a patient's body in a first image modality, receiving second image data (1120) of a second portion of the patient's body in a second image modality, modifying the second image data from the second image modality to the first image modality (1160), and generating a three-dimensional model (1170) of the first and second portions of the patient's body based on the first image data in the first image modality and the modified second image data in the second image modality.

Inventors

• R. R. Shamir
• N. Ullman
• Y. Glozman

Assignees

• 诺沃库勒有限责任公司

Dates

Publication Date

20260512

Application Date

20220119

Priority Date

20220118

Claims (15)

1. 1. A computer-implemented method to generate a three-dimensional model, the computer comprising one or more processors and memory accessible by the one or more processors, the memory storing instructions that, when executed by the one or more processors, cause the computer to perform the method, the method comprising: receiving first image data of a first portion of a patient's body in a first image modality; Receiving second image data of a second portion of the patient's body in a second image modality; modifying the second image data from the second image modality to the first image modality, and A three-dimensional model of the first and second portions of the patient's body is generated based on the first image data in the first image modality and the modified second image data in the second image modality.
2. 2. The method of claim 1, further comprising generating an image modality conversion model, wherein modifying the second image data from the second image modality to the first image modality includes applying the image modality conversion model to the second image data in the second image modality.
3. 3. The method of claim 2, wherein generating an image modality conversion model includes: Receiving a plurality of image data of a first portion of a patient's body in a first image modality of a plurality of subjects, and A second plurality of image data of a second portion of the patient's body in a second image modality of the plurality of subjects is received, Wherein the image modality conversion model is generated based on an analysis of the first plurality of image data and the second plurality of image data.
4. 4. The method of claim 3, wherein the analysis comprises at least one of generating a challenge network (GAN) analysis, medGAN analysis, a super-resolution GAN analysis, a pix2pix GAN analysis, cycleGAN analysis, discoGAN analysis, a fila-sGAN analysis, a projected challenge network (PAN) analysis, a variational self-encoder (VAE) analysis, or a regression analysis.
5. 5. The method of claim 1, further comprising determining a transducer array map along at least one of the first and second portions of the patient's body based on the three-dimensional model of the first and second portions of the patient's body.
6. 6. A computer-implemented method to generate a three-dimensional model, the computer comprising one or more processors and memory accessible by the one or more processors, the memory storing instructions that, when executed by the one or more processors, cause the computer to perform the method, the method comprising: Receiving first image data of a first portion of a body part of a patient, wherein the first portion of the body part is less than a complete body part; Receiving a plurality of second image data of a body part of a plurality of subjects; determining a body part complete model based on the plurality of second image data; generating third image data of the second portion of the body part based on the body part complete model and the first image data, and Based on the first image data and the third image data, a three-dimensional model of the body part of the patient is generated.
7. 7. The method of claim 6, further comprising dividing each of the plurality of second image data into first partial image data and second partial image data, wherein the first partial image data comprises a first portion of a body part of the corresponding subject and the second partial image data comprises another portion of the body part of the corresponding subject.
8. 8. The method of claim 7, further comprising performing an analysis of the first portion of image data and the second portion of image data for each of the plurality of subjects.
9. 9. The method of claim 8, wherein the analysis comprises at least one of statistical shape analysis, active appearance analysis, or global image statistical analysis.
10. 10. The method of claim 6, further comprising determining image data of a second portion of the body part of the patient's body is required to generate a three-dimensional model of the body part of the patient based on the first image data.
11. 11. The method of claim 6, further comprising determining a transducer array map along the body part of the patient based on the three-dimensional model of the body part of the patient.
12. 12. The method of claim 6, wherein the body part is a head, and wherein the first image data of the first portion of the body part does not include a top portion of the patient's head.
13. 13. A computer-implemented method to generate a three-dimensional model, the computer comprising one or more processors and memory accessible by the one or more processors, the memory storing instructions that, when executed by the one or more processors, cause the computer to perform the method, the method comprising: Receiving first image data of a portion of a patient's body at a first image resolution; receiving a plurality of second image data of a plurality of subjects; determining a super-resolution model for increasing the resolution of the first image data based on the plurality of second image data, and Third image data of the portion of the patient's body at a second image resolution is generated based on the super-resolution model and the first image data, wherein the second image resolution is greater than the first image resolution.
14. 14. The method of claim 13, wherein receiving a plurality of second image data of the plurality of subjects comprises: receiving a first plurality of second image data of the same body part of the plurality of subjects as the portion of the patient's body at a first image resolution, and Receiving a second plurality of second image data of the same portion of the body of the plurality of subjects at a second image resolution, Wherein determining the super-resolution model includes performing an analysis of the first plurality of second image data and the second plurality of second image data.
15. 15. The method of claim 14, wherein the analysis comprises at least one of a regression analysis, a convolutional network analysis, a generated countermeasure network (GAN) analysis, medGAN analysis, a super-resolution GAN analysis, a pix2pix GAN analysis, a cycleGAN analysis, a discoGAN analysis, or a fila-sGAN analysis.

Description

Methods, systems, and apparatus for medical image enhancement to optimize transducer array placement The scheme is a divisional application. The parent application entitled "method, system and apparatus for medical image enhancement to optimize transducer array placement" filed on day 2022, 1-month 19, and 202280023809.9. Cross Reference to Related Applications The present application claims priority from U.S. provisional application No. 63/140,635, filed on 22 months 1, 2021, and U.S. non-provisional application No. 17/578,241, filed on 18 months 1, 2022, which are incorporated herein by reference in their entirety for all purposes. Background The tumor treatment field (TTField) is a low-intensity alternating electric field in the mid-frequency range that can be used to treat tumors as described in U.S. patent 7,565,205. Ttfields are induced non-invasively in a region of interest by transducers placed on the patient's body and applying an AC voltage between the transducers. To determine the effective positioning of the transducer on the patient's body, a three-dimensional model of a portion of the patient's body may be evaluated. However, sufficient image data of the patient may not be available to generate the three-dimensional model because the available image data of the patient may lack a portion of the body, because the resolution of the image data may be insufficient to generate the three-dimensional model, or because the image data of a first portion of the body has a different image modality than the image data of a second portion of the body. As such, any of these problems may hinder the generation of a three-dimensional model of a portion of the patient's body and thereby hinder the efficient positioning of the transducer on the patient's body to sense TTField. Disclosure of Invention One aspect of the invention is directed to a computer-implemented method to generate a three-dimensional model, the computer including one or more processors and memory accessible by the one or more processors, the memory storing instructions that, when executed by the one or more processors, cause the computer to perform the method including receiving first image data of a first portion of a patient's body in a first image modality, receiving second image data of a second portion of the patient's body in a second image modality, modifying the second image data from the second image modality to the first image modality, and generating a three-dimensional model of the first and second portions of the patient's body based on the first image data in the first image modality and the modified second image data in the second image modality. The above aspects of the invention are exemplary and other aspects and variations of the invention will become apparent from the detailed description of the embodiments below. Drawings FIG. 1 is a flow chart of an example method for generating a three-dimensional image of a body part of a patient based on two image scans of the patient. Fig. 2 is a flow chart of an example method for generating a three-dimensional image of a body part of a patient based on a single image scan of the patient. FIG. 3 is a flow chart of an example method for generating a high resolution three-dimensional image of a patient body part based on a low resolution image of the patient body part. FIG. 4 is a flow chart of an example method for determining a transducer array layout for TTField delivery to a portion of a patient's body. FIG. 5 is a block diagram depicting an example operating environment. Fig. 6 illustrates an example apparatus for electrotherapy treatment. Detailed Description As found by the inventors, the disclosed subject matter provides methods and systems for generating a three-dimensional model of a portion of a patient's body given an incomplete or inconsistent image set. The three-dimensional model may then be used to determine the location at which to place the transducer on the patient's body to generate TTField. The incomplete or inconsistent image set of the patient's body may be, for example, an image set of a portion of the patient's body missing, an image set having insufficient resolution to generate a three-dimensional model, or an image set of a first portion of the patient's body having a different image modality than image data of a second portion of the patient's body. Using one or more of the techniques of the present invention, a three-dimensional model of a portion of a patient's body may then be generated given such incomplete or inconsistent image sets. FIG. 1 is a flow chart of an example method 1100 for generating a three-dimensional image of a patient body part based on two image scans of a patient, wherein at least a portion of the two image scans comprise different portions of the patient body part, and wherein the two image scans have different image modalities. The two images of the patient in different image modalities may each be images of the same patient body