# US 2018/0025131 A1 · System and methods for performing medical physics calculations > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2018/0025131 A1 - Document ID: US20180025131A1 - Status: Published application - Kind: A1 - Application number: 15/721,277 - Filed: 2017-09-29 - Published: 2018-01-25 - Earliest claimed: 2010-04-28 - Assignees: Stc.Unm - Applicants: Stc.Unm - Inventors: Roy William Keyes; Christian Romano; Shuang Luan; Dorian C. Arnold - Web page: https://patentgrep.com/patent/US20180025131A1 ## CPC - G16H 20/40: relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture - G06F 9/5066: {Algorithms for mapping a plurality of inter-dependent sub-tasks onto a plurality of physical CPUs (mappping at compile time, see G06F8/451)} - G16H 50/50: for simulation or modelling of medical disorders - G06F 9/5083: {Techniques for rebalancing the load in a distributed system} - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract A method of calculating radiation fluence and energy deposition distributions on a networked virtual computational cluster is presented. With this method, complex Monte Carlo simulations that require expansive equipment, personnel, and financial resources can be done efficiently and inexpensively by hospitals and clinics requiring radiation therapy dose calculations. ## Claims ### Claim 1 A method of determining distributions of energy deposition and fluence due to ionizing radiation comprising the steps of: accessing a distributed processing framework connecting a medical provider network; uploading from the medical provider network to the distributed processing framework a Monte Carlo task comprising calculation parameters including a patient anatomy and a beam configuration; determining by a master node of the distributed processing framework a number of virtual nodes required for the Monte Carlo task, wherein the number of virtual nodes includes one or more additional virtual nodes determined by a threshold amount; distributing by the master node to each virtual node the calculation parameters; performing by each virtual node a portion of a Monte Carlo calculation using the calculation parameters to compute a dose calculation result; meeting a threshold value by the number of virtual nodes that completes the Monte Carlo calculation; using a linear model to aggregate by the master node all dose calculation results from each virtual node to obtain a final dose calculation result comprising a distribution of energy deposition; storing the final dose calculation result in a data storage of the distributed processing framework; and communicating by the distributed processing framework to the medical provider network the final dose calculation result. ### Claim 2 (depends on 1) The method of claim 1, wherein the distributed processing framework is accessed by the medical provider network using a secure platform interface. ### Claim 3 (depends on 1) The method of claim 1, wherein the number of virtual nodes is dynamic. ### Claim 4 (depends on 1) The method of claim 1 further comprising a plurality of master nodes. ### Claim 5 (depends on 1) The method of claim 1, wherein the number of virtual nodes equals one. ### Claim 6 (depends on 1) The method of claim 1, wherein the distributed processing framework is a cloud computing network. ### Claim 7 (depends on 1) The method of claim 1, wherein the distribution of energy deposition includes fluence due to ionizing radiation. ### Claim 8 (depends on 1) The method of claim 1, wherein the distribution of energy deposition includes physical energy absorbed per unit mass. ### Claim 9 (depends on 1) The method of claim 1, wherein the distribution of energy deposition includes linear energy transfer (LET). ### Claim 10 (depends on 1) The method of claim 1, wherein the distribution of energy deposition includes a track of an individual particle including energy loss by the individual particle along the track. ### Claim 11 (depends on 1) The method of claim 1, wherein the storing step further comprises the step of recording in the data storage of the distributed processing framework a network addresses for each node. ### Claim 12 (depends on 1) The method of claim 1, wherein the threshold value is 95% or 5%. ### Claim 13 (depends on 1) The method of claim 1, wherein the using step further includes the step of using a tree-based distribution model to aggregate by the master node all dose calculations results. ### Claim 14 (depends on 13) The method of claim 13, wherein the tree-based distribution model reduces data aggregation to logd n, where d is the tree topology and n is the number of nodes. ### Claim 15 (depends on 13) The method of claim 13, wherein the tree topology is a binary tree topology (d=2). ### Claim 16 A method of determining distributions of energy deposition and fluence due to ionizing radiation comprising the steps of: accessing a cloud computing network by a medical provider network using a secure platform interface; uploading from the medical provider network to the cloud computing network a Monte Carlo task comprising calculation parameters including a patient anatomy and a beam configuration; determining by a master node of the cloud computing network a number of virtual nodes required for the Monte Carlo task, wherein the number of virtual nodes includes one or more additional virtual nodes determined by a threshold amount of 5%; distributing by the master node to each virtual node the calculation parameters; performing by each virtual node a portion of a Monte Carlo calculation using the calculation parameters to compute a dose calculation result; meeting a threshold value of 95% by the number of virtual nodes that completes the Monte Carlo calculation; aggregating by the master node all dose calculation results from each virtual node to obtain a final dose calculation result comprising a distribution of energy deposition, wherein the distribution of energy deposition includes one selected from the group comprising fluence due to ionizing radiation, physical energy absorbed per unit mass, and linear energy transfer (LET); storing the final dose calculation result in a data storage of the cloud computing network; and communicating by the cloud computing network to the medical provider network the final dose calculation result. ### Claim 17 (depends on 16) The method of claim 16, wherein the aggregating step uses a tree-based distribution model to aggregate by the master node all dose calculations results. ### Claim 18 (depends on 17) The method of claim 17, wherein the tree-based distribution model reduces data aggregation to logd n, where d is the tree topology and n is the number of nodes. ### Claim 19 (depends on 17) The method of claim 17, wherein the tree topology is a binary tree topology (d=2). ### Claim 20 (depends on 16) The method of claim 16, wherein the aggregating step uses a linear model to aggregate by the master node all dose calculations results.