Organ model production system
| Title: | Organ model production system |
|---|---|
| Patent Number: | 12105,501 |
| Publication Date: | October 01, 2024 |
| Appl. No: | 18/471285 |
| Application Filed: | September 20, 2023 |
| Abstract: | The methods, process, and apparatus of the present invention produces a structurally representative organ model using magnetic resonance imaging scan information of a organ and a patient's medical history and physiology. This is accomplished by mathematically convolving the scan information with a second order ranked tensor matrix encoded with the patient's physiological information as it relates to the scanned organ and their medical profile. The convolved scan information and encoded matrix are computer processed to produce a 3D printer driver file which is used to print a structurally representative organ model conforming to the patient's physiology. |
| Inventors: | Eul, Alexander Paul (Camarillo, CA, US) |
| Claim: | 1. A method of producing a three dimensional model of a biological organ the method comprising: accepting a set of independent variables; accepting a set of dependent variables; accepting a set of scaling values; vector embedding a second order ranked tensor with information contained in the set of independent variables, contained in the set of dependent variables and contained in the set of scaling values resulting in an encoded second order ranked tensor; storing the encoded second ranked tensor on a client computer system; reading and unpacking a K-Space dataset content transferred from a host computer console computer system; superimposing the encoded second order ranked tensor onto the K-Space dataset content resulting in a superimposed dataset where the superimposed dataset processing is executed on the client computer system; time acquisition optimizing the superimposed dataset using a tensor decomposition software operation resulting in a tensor decomposition result where the tensor decomposition result processing is executed on the client computer system; simultaneously applying an image technique and a surface registration technique to the tensor decomposition result producing a volumetric dataset where the volumetric dataset processing is performed on the client computer system; performing a data type file conversion on the volumetric dataset producing a driver sequence file for controlling an additive layering printer where the driver sequence file processing is performed on the client computer system; and transferring the driver sequence file to a 3D printer system where the 3D printer system translates the driver sequence file into a set of instructions for controlling an additive layering printer filament type and an additive layering printer extruder motion to produce the three dimensional model where the three dimensional model represents the biological organ. |
| Claim: | 2. The method of claim 1 , wherein the vector embedding further includes: establishing in first array the set of independent variables containing encoding information for cellular composition; establishing in a second array the set of dependent variables containing encoding information for elasticity, plasticity, and fracture; establishing in a third array the set of scalar values containing encoding information for a patient's age, race, and state of health; and combining the first array, the second array, the third array into an input array to encode the second order ranked tensor where the encoded second ranked tensor defines the deformation of continuous mediums as a plurality of voxel value parameterization for the three dimensional model of the biological organ when 3D printed. |
| Claim: | 3. The method of claim 1 , wherein the time acquisition optimizing further comprises the steps of: evaluating the convolution dataset to determine a dimensionality reduction parameter where the dimensionality reduction parameter linearly corresponds to the convolution dataset size; and transforming an initial dimensional space of the convolution dataset to a lower dimensional space of a size reduced convolution dataset where the size reduced convolution dataset is reduced proportionally according to the dimensionality reduction parameter. |
| Claim: | 4. The method of claim 1 where the image technique and the surface registering technique further comprises the steps of: merging multiple data resources where the multiple data resources include image features for edges, corners, and blobs which conform structurally to the biological organ; supervising learning gradients where the learning gradients include information derived from the biological organ; and representing surfaces with transformation manifolds where the transformation manifolds eliminate sharp surface discontinuities in the three dimensional model of the biological organ. |
| Claim: | 5. The method of claim 1 where the volumetric dataset processing further comprises the steps of: projecting embedded data onto a feature space embodying anatomic components of the biological organ in the three dimensional model of the biological organ; and interpolating the embedded data to a function space embodying physiological components of the biological organ in the three dimensional model of the biological organ. |
| Claim: | 6. The method of claim 1 where the driver sequence file processing further comprises the steps of: accepting binary data conforming to anatomic and physiological structure of the biological organ; reading a set of binary instructions from a memory of the first client computer system; decoding the set of binary instructions read from the memory of the first client computer system; and executing the decoded set of binary instructions read from the memory of the client computer system to transform the binary data conforming to the anatomic and physiological structure of the biological organ to a plurality of voxel structures; and outputting a result of the executed set of decoded binary instructions to a client processor interface in communication with a 3D printer interface. |
| Claim: | 7. A non-transitory machine-readable storage device comprising instructions stored thereon, the instructions when executed by a computing processor causing: loading a computer port scanning set up file into a first computer memory of a client computer; execution of the port scanning set up file to initialize a plurality of communication ports connected to a network architecture; execution of a dynamic memory management controller of a client computer mountable file system to operatively configure the mountable file system where the dynamic memory management controller resides in a second computer memory of the client computer; collection of a K-space dataset produced by a magnetic resonance imaging device where the K-space dataset is stored in a first computer memory of a host console memory and the K-space dataset represents a scanned organ's physical characteristics; transferring the K-space dataset stored in the first computer memory of the host console memory to the client computer mountable file system; determination of a first set of values representing the scanned organ's structural properties; determination of a second set of values representing the scanned organ's cellular composition; determination of a third set of values representing a set of scaling factors related to a set of patient health quantifiable factors; execution of vector database instructions where the vector database instructions generates a plurality of second order ranked tensors then storing the plurality of second order ranked tensors into a second plurality of arrays written to the mountable file system; merging the first set of values, the second set of values, the third set of values with the K-space dataset into a first plurality of arrays where each array represents the scanned organ's physical characteristics contained within the K-space dataset; storing the first plurality of arrays in the mountable file system; execution of a time acquisition optimization process using the second plurality of arrays stored in the mountable file system and storing the result of the time optimization process in a third plurality of arrays stored in the mountable file system; loading an image and surface registration instruction set into a third computer memory of a client computer; executing the image and surface registration instruction set where the surface and registration instruction set processes the contents of the third plurality of arrays where the processes result is stored in a fourth plurality of arrays representing the scanned organ's structure where the fourth plurality of arrays is stored in the mountable file system; generation of a time optimized volumetric dataset representing the organ model's structural properties using the content of the fourth plurality of arrays as input; conversion of the time optimized volumetric dataset into a datatype file used to generate an additive printer driver file; and loading the additive printer driver file into an internal memory of an additive 3D printing device to construct a 3D organ model product containing the scanned organ's physical characteristics which includes a tissue variance, a cellular structure, and a physical structure. |
| Claim: | 8. The non-transitory machine-readable storage device of claim 7 further comprises instructions causing: buffering a first portion of the K-Space dataset in a first memory location in the mountable file system; storing a second portion of the K-Space dataset in a second memory location in the mountable file system; monitoring the available memory in the first memory location; monitoring the available memory in the second memory location; moving the first portion and the second portion of the K-space data out of the first memory location in the mountable file system to the second memory location when the first memory of the mountable file system is full; and storing the extracted K-space dataset into a first internal memory. |
| Claim: | 9. The non-transitory machine-readable storage device of claim 7 further includes instructions for: configuring a scalar portion of a vector database with the contents of a patient health history form; configuring a dependent variable portion of the vector database with mechanical information where the mechanical information is elasticity, plasticity, and fracture; configuring an independent variable portion of the vector database with cellular composition information; and combination of the scalar portion of a vector database, the dependent variable portion of the vector database, the independent portion of the vector database into an encoding array which defines the deformation of continuous mediums as a plurality of voxel value parameterization for the three dimensional model of the biological organ when 3D printed. |
| Patent References Cited: | 9968257 May 2018 Burt 20180144219 May 2018 Kalisman 20230225698 July 2023 Pernot 113920213 January 2022 |
| Assistant Examiner: | Tang, Michael |
| Primary Examiner: | Cao, Chun |
| Attorney, Agent or Firm: | Lerma, Robert R. |
| Accession Number: | edspgr.12105501 |
| Database: | USPTO Patent Grants |
| Language: | English |
|---|