JP-2026514416-A - Methods for real-time targeting and monitoring of organs at risk for radiotherapy

Abstract

This specification describes variations of methods for monitoring and/or tracking a region of interest within a patient during a radiotherapy session. For example, a method for monitoring a region of interest may include comparing an image of the target region acquired during real-time radiation delivery with a reference image of the same target region acquired before treatment delivery and determining the difference between the two images. If it is determined that the treatment image differs from the reference image, radiation delivery may be stopped to prevent radiation from being delivered to healthy tissue as a result of an anatomical shift in the target region. This specification also describes systems that may be used to implement such methods. [Selection Diagram] Figure 9A

Inventors

• シー， リンシー
• バル， ハルシャリ
• ラザラキス， テオドルス
• シュー， シーユー
• シャオ， リンション

Assignees

• リフレクション メディカル， インコーポレイテッド

Dates

Publication Date

20260511

Application Date

20240327

Priority Date

20230331

Claims (20)

1. A method for real-time radiotherapy treatment, To generate a set of pre-scan images that include a region of interest (ROI) within the patient, To acquire a set of real-time images including the aforementioned ROI, The set of real-time images is compared with the set of pre-scan images, If the set of real-time images deviates from the set of pre-scan images by a predetermined threshold, the radiotherapy treatment session will be terminated. The method, including the method described above.
2. Calculating pre-scan image metrics, The method according to claim 1, further comprising calculating a real-time image metric, wherein comparing the set of real-time images of the ROI with the set of pre-scan images of the ROI includes comparing the pre-scan image metric with the real-time image metric.
3. The method according to claim 1, further comprising generating a graphical notification or other notification of the deviation status of the set of real-time images from the set of pre-scan images.
4. The generation of the aforementioned pre-scan image set is Acquiring a subset of pre-scan images at each of multiple control points, The method according to claim 1, comprising combining a subset of the pre-scan images.
5. The method according to claim 4, further comprising combining a plurality of subsets of pre-scan images corresponding to the plurality of control points in order to generate a pre-scan image with the sum of the ROIs.
6. The method according to claim 4, wherein each of the plurality of control points includes a beam station in a radiotherapy treatment system configured to deliver radiotherapy to a portion of the ROI within the patient.
7. The method according to claim 1, wherein acquiring the set of real-time images includes acquiring one or more subsets of real-time images at control points.
8. The method according to claim 1, further comprising delivering radiotherapy treatment to the patient from a control point if the set of real-time images does not deviate from the set of pre-scan images by a predetermined threshold amount.
9. The method according to claim 1, wherein the ROI includes an inner ROI within an outer ROI.
10. Identifying the medial ROI before the real-time radiotherapy treatment, The method according to claim 9, further comprising localizing the lateral ROI before the real-time radiotherapy treatment.
11. The method according to claim 2, wherein the set of prescan images includes a sum of multiple subsets of prescan images, the calculation of the prescan image metric includes calculating the auto-cross-correlation between each of the sum of the multiple subsets of prescan images, and the calculation of the real-time image metric includes calculating the local cross-correlation between the set of real-time images and the sum of the prescan images.
12. The method according to claim 11, wherein each of the pre-scan image metric and the real-time image metric includes one or more of the maximum cross-correlation signal intensity for the control point and the location of the maximum cross-correlation signal intensity for the control point.
13. The method according to claim 2, wherein each of the real-time image metric and the pre-scan image metric includes the rate of change of the maximum signal intensity.
14. The set of pre-scan images includes a combined pre-scan image comprising multiple subsets of pre-scan images, and the method is To determine the pre-scan image metric, the rate of change of the maximum signal intensity is calculated for a plurality of subsets of the pre-scan images, A hybrid image including the ROI is generated by replacing the corresponding subset of pre-scan images in the aggregated pre-scan image with the aforementioned set of real-time images. The method according to claim 13, further comprising calculating the rate of change of the maximum signal intensity for the hybrid image in order to determine the real-time image metric.
15. The method according to claim 13, wherein the ROI includes an inner ROI within an outer ROI, and the maximum signal intensity includes the maximum signal intensity within the inner ROI.
16. The method according to claim 15, wherein the rate of change of the maximum signal intensity indicates the movement of the inner ROI relative to the outer ROI.
17. The method according to claim 2, wherein each of the real-time image metric and the pre-scan image metric includes the rate of change of the standard deviation of the signal.
18. The set of pre-scan images includes a combined pre-scan image comprising multiple subsets of pre-scan images, and the method is To determine the pre-scan image metric, the rate of change of the standard deviation of the signal is calculated for each of a plurality of subsets of the pre-scan images, A hybrid image including the ROI is generated by replacing the corresponding subset of pre-scan images in the aggregated pre-scan image with the aforementioned set of real-time images. The method according to claim 17, further comprising calculating the standard deviation of the signal for the hybrid image in order to determine the real-time image metric.
19. The method according to claim 18, wherein the pre-scan image metric includes the maximum rate of change of the standard deviation of the signal intensity in a plurality of subsets of the pre-scan images.
20. The method according to claim 17, wherein the ROI includes an inner ROI within an outer ROI, and calculating the standard deviation of the signal includes excluding the signal in the inner ROI and calculating the standard deviation of the signal in the outer ROI.

Description

Cross-reference of related applications: This application claims priority to U.S. Provisional Patent Application No. 63/493,488, filed on 31 March 2023, the disclosure of which is incorporated herein by reference in its entirety. This disclosure generally relates to systems and methods for monitoring a target region of interest and/or organs at risk in real time and controlling therapeutic delivery during a radiotherapy session. These methods may be used for radiation-induced high-energy photon delivery, such as in biologically induced radiotherapy. Radiotherapy, or radiation therapy, uses high-energy photons or other particles to treat a wide variety of diseases. For example, radiotherapy is commonly used to treat cancerous tumors. During treatment, a gantry equipped with a radiation source moves around the patient, emitting radiation beams from several locations to a region of interest within the patient. Precisely tracking the region of interest (e.g., tumor location) is a crucial factor in maximizing the radiation dose to the target area and limiting radiation exposure to the patient's healthy tissues. Furthermore, precisely tracking organs near the target area (i.e., organs at risk) that are particularly vulnerable to radiotherapy-related toxicity can be used to help provide information on how to limit radiation exposure to the patient's healthy tissues. Some radiotherapy systems use image guidance to track the region of interest and facilitate radiation delivery to that area. However, current image-guided radiotherapy systems and methods require high-quality and/or complete images of the region of interest to guide therapeutic delivery. Such images are time-consuming to acquire and reconstruct, and typically do not characterize the region of interest in real time. Furthermore, current radiotherapy systems do not currently offer a way to track the region of interest in real time within the treatment zone (e.g., due to anatomical shifts) and to stop treatment when the region of interest is no longer within the treatment zone. Therefore, a new and improved method of real-time monitoring of the region of interest in radiotherapy is needed to reduce the risk of radiotoxicity to patients by monitoring and responding to real-time changes in the region of interest. An exemplary schematic diagram of a radiotherapy patient with three regions of interest monitored during a radiotherapy session is shown. This diagram shows an exemplary schematic representation of deformation patterns in the region of interest for monitoring during radiotherapy sessions. This diagram illustrates a conceptual variation of a method for acquiring images of a region of interest during a radiotherapy session. Figures A and B show conceptual diagrams of modified forms of methods for generating images of a region of interest during a radiotherapy session. A schematic diagram of an exemplary radiotherapy treatment system for use in the method described herein is shown. This diagram shows an exemplary flowchart representation of a modified form of a method for monitoring a region of interest during a radiation delivery session. An exemplary flowchart is shown illustrating an example of a method for monitoring a region of interest during a radiation delivery session. The following shows simulation results of processing image data to determine imaging monitoring metrics using the exemplary methods described herein. An exemplary flowchart is shown illustrating an example of a method for monitoring a region of interest during a radiation delivery session. Three images of a region of interest monitored during a radiation delivery session are shown using the exemplary method described herein. The graphical representation of image monitoring metrics calculated during a radiation delivery session is shown using the exemplary methods described herein. An exemplary flowchart is shown illustrating an example of a method for monitoring a region of interest during a radiation delivery session. Two graphical representations of the distribution plots used to determine the image monitoring metrics as described herein are shown. Various embodiments and non-limiting examples of the present invention are described herein and shown in the accompanying drawings. This specification describes methods for real-time monitoring and/or tracking of a region of interest within a treatment zone during a radiation delivery session, where the radiation delivery session may, in some variations, be a radiotherapy treatment session. Generally, the methods provide various ways of determining whether the region of interest remains within the treatment zone during a treatment session and stopping treatment if the region of interest moves outside the treatment zone, for example, due to anatomical movement, patient movement, etc. The methods described herein are also useful for real-time monitoring and/or tracking of organs at risk in relation to the treatment zone. Generally, t