# US 2026/0199705 A1 · Systems and methods for accelerated online adaptive radiation therapy > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0199705 A1 - Document ID: US20260199705A1 - Status: Published application - Kind: A1 - Application number: 19/440,254 - Filed: 2026-01-05 - Published: 2026-07-16 - Earliest claimed: 2018-06-26 - Assignees: The Medical College of Wisconsin, Inc. - Applicants: The Medical College of Wisconsin, Inc. - Inventors: X. Allen Li; Ying Zhang; Sara Lim; Jingqiao Zhang; Ergun Ahunbay; Ranjeeta Thapa; Haidy Nasief - Web page: https://patentgrep.com/patent/US20260199705A1 ## CPC - A61N 5/1031: {using a specific method of dose optimization} - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - A61N 5/1038: {taking into account previously administered plans applied to the same patient, i.e. adaptive radiotherapy} - G06N 20/00: Machine learning - G06T 5/40: using histogram techniques - G06T 7/11: Region-based segmentation - G06T 7/12: Edge-based segmentation - G06T 7/149: involving deformable models, e.g. active contour models - G06T 7/174: involving the use of two or more images - G06T 7/33: using feature-based methods - G16H 10/60: for patient-specific data, e.g. for electronic patient records - G16H 20/40: relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture - G16H 30/40: for processing medical images, e.g. editing - A61B 6/032: {Transmission computed tomography [CT]} - A61B 6/5217: {extracting a diagnostic or physiological parameter from medical diagnostic data} - G06T 2207/10081: Computed x-ray tomography [CT] - G06T 2207/10088: Magnetic resonance imaging [MRI] - G06T 2207/20081: Training; Learning - G06T 2207/30008: Bone - G06T 2207/30096: Tumor; Lesion - A61N: ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY (measurement of bioelectric currents A61B; surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body A61B18/00; anaesthetic apparatus in general A61M; incandescent lamps H01K; infrared radiators for heating H05B) - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) ## Abstract Systems and methods for accelerated online adaptive radiation therapy (“ART”) are described. The improvements to online ART are generally provided based on the use of textural analysis and machine learning algorithms implemented with a hardware processor and a memory. The described systems and methods enable more efficient and accurate online adaptive replanning (“OLAR”), which can also be implemented in clinically acceptable timeframes. For example, OLAR can be reduced from taking 10-30 minutes down to 5-10 minutes. ## Claims ### Claim 1 A method for assessing a quality of a radiation treatment plan, the method comprising: (a) accessing with a computer system, a first radiation treatment plan for a subject, wherein the first radiation treatment plan includes a first three-dimensional dose map; (b) accessing with the computer system, a second radiation treatment plan for the subject, wherein the second radiation treatment plan includes a second three-dimensional dose map; (c) generating at least one spatial dose feature based on the first three-dimensional dose map and the second three-dimensional dose map; and (d) selecting as a higher quality radiation treatment plan one of the first radiation treatment plan or the second radiation treatment plan based on an analysis of the at least one spatial dose feature. ### Claim 2 (depends on 1) The method as recited in claim 1, wherein the at least one spatial dose feature comprises a dose level size zone matrix (DLSZM). ### Claim 3 (depends on 2) The method as recited in claim 2, wherein the at least one spatial dose feature comprises a large cold spot feature computed from the DLSZM. ### Claim 4 (depends on 1) The method as recited in claim 1, wherein the at least one spatial dose feature comprises a dose level co-occurrence matrix (DLCM). ### Claim 5 (depends on 4) The method as recited in claim 4, wherein the at least one spatial dose feature comprises a hot contrast feature computed from the DLCM. ### Claim 6 (depends on 4) The method as recited in claim 4, wherein the at least one spatial dose feature comprises an inverse covariance feature computed from the DLCM. ### Claim 7 (depends on 1) The method as recited in claim 1, wherein the at least one spatial dose feature comprises a mutual objective function distance. ### Claim 8 (depends on 1) The method as recited in claim 1, wherein the at least one spatial dose feature comprises a neighbor level-dose difference matrix. ### Claim 9 (depends on 1) The method as recited in claim 1, wherein the at least one spatial dose feature comprises a coarseness feature computed from at least one of the first three-dimensional dose map and the second three-dimensional dose map. ### Claim 10 (depends on 1) The method as recited in claim 1, wherein the at least one spatial dose feature comprises a busyness feature computed from at least one of the first three-dimensional dose map and the second three-dimensional dose map. ### Claim 11 (depends on 1) The method as recited in claim 1, wherein the at least one spatial dose feature comprises a plurality of spatial dose features, and the higher quality radiation treatment plan is selected from the first radiation treatment plan and the second radiation treatment plan based on a general quality index value computed from plurality of spatial dose features. ### Claim 12 (depends on 11) The method as recited in claim 11, wherein the general quality index (GQI) is computed as: GQI = α 1 ⁢ H ⁢ C 1 → 2 + α 2 ⁢ I ⁢ C 1 → 2 + α 3 ⁢ L ⁢ C ⁢ S + α 4 ⁢ C ⁢ o + α 5 ⁢ B ⁢ u + α 6 ⁢ M ⁢ O ⁢ F ⁢ D 1 → 2 ; wherein HC1→2 is a hot contrast between the first radiation treatment plan and the second radiation treatment plan; IC1→2 is an inverse covariance between the first radiation treatment plan and the second radiation treatment plan; LCS is a large cold spot feature; Co is a coarseness feature; Bu is a busyness feature; MOFD1→2 is a mutual objective function distance between the first radiation treatment plan and the second radiation treatment plan; and αi for i=1, . . . , 6 are coefficients. ### Claim 13 (depends on 12) The method as recited in claim 12, wherein the coefficients are determined from ground truth data. ### Claim 14 (depends on 11) The method as recited in claim 11, wherein the higher quality radiation treatment plan is selected from the first radiation treatment plan and the second radiation treatment plan having a lower general quality index value. ### Claim 15 (depends on 1) The method as recited in claim 1, wherein the higher quality radiation treatment plan is selected based also on an analysis of a dose volume parameter computed from at least one of the first radiation treatment plan and the second radiation treatment plan. ### Claim 16 (depends on 15) The method as recited in claim 15, wherein the dose volume parameter is a dose volume histogram.