IP Library › Granted Patent US 12,491,373
Granted Patent B2
US 12,491,373 · App. 17/793,495 · Granted Dec 9, 2025

Planning and delivery of dynamically oriented electric field for biomedical applications

Inventors: Matthew Hebb (London, CA); Susanne Schmid (London, CA); Eugene Wong (London, CA); Terry Peters (London, CA); Erin Iredale (St. Marys, CA)
Assignee: London Health Sciences Centre Research Inc.
A61N1/40A61B5/055
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,491,373
App. No.
17/793,495
Granted
Dec 9, 2025
Kind
B2
Abstract

Methods, systems and non-transitory computer readable storage media of electric field treatment planning of a target tissue. A method of electric field treatment planning of a target tissue site includes: (a) obtaining an image of the target tissue site, (b) determining volume and one or more electric properties of the target tissue site, (c) using the volume and the one or more electric properties to: (i) determine a number of electrodes to treat the target tissue site with electric fields, each electrode having one or more contacts, (ii) determine a placement of the number of electrodes within the target tissue site, and (iii) relative to one of the contacts in one of the number of electrodes at the placement determined in (ii), determine an electric field that results in a prescribed electric field coverage of the target tissue site.

Claims (25)

1 . A computer optimized method of electric field treatment planning of a target tissue site, the method comprising:

(a) obtaining an image of the target tissue site and storing the image in a computer unit,

(b) determining a volume of the target tissue site and inputting into the computer unit the volume of the target tissue site and one or more electric properties of the target tissue site,

(c) the computer unit generating simulated electric field maps of the target tissue site using configurations of multiple electrodes implanted within the target tissue site, the volume and the electric properties of the target tissue site,

(d) the computer unit using an optimization algorithm and the simulated electric field maps to: (i) determine an optimal number of implantable electrodes to treat the target tissue site with electric fields, the optimal number of implantable electrodes being one or more than one implantable electrodes, each implantable electrode having one or more contacts, (ii) determine an optimal placement of the optimal number of implantable electrodes determined in (i) within the target tissue site, and (iii) determine optimal voltage and optimal phase shift of each of the contacts of the optimal number of implantable electrodes determined in (i) at the optimal placement determined in (ii) that results in a prescribed time average electric field coverage of the entire target tissue site, wherein the electric field is a dynamically oriented electric field,

wherein the optimal placement of the optimal number of implantable electrodes within the target tissue site includes an insertion location and a trajectory angle of the optimal number of implantable electrodes, and

(e) treating the target tissue with electrotherapy by implanting into the target tissue the optimal number of implantable electrodes determined in step (d) (i) at the optimal placement determined in step (d) (ii) and using the optimal voltages and phase shifts of each of the contacts of the optimal number of implantable electrodes determined in step (d) (iii).

2 . The method of claim 1 , wherein step (b) further comprises segmenting the image to delineate tissue surrounding the target tissue site (“surrounding tissue”) and determining volume of the surrounding tissue and inputting into the computer unit one or more electric properties of the surrounding tissue, and wherein step (d) further comprises using the volume and the one or more electric properties of the surrounding tissue to determine (i), (ii) and (iii) that results in the prescribed time average electric field coverage of the entire target tissue site while minimizes coverage of the volume of the surrounding tissue.

3 . The method of claim 1 , wherein the method further comprises (e) obtaining a post-electrode implant image of the target tissue to visualize an actual position of the implantable electrodes relative to the target tissue site, (f) using the image of (e) to assess the prescribed electric field coverage of the target tissue site, and (g) repeat steps (b) to (f) to optimize the prescribed electric field coverage based on the assessment in (f).

4 . The method of claim 1 , wherein the target tissue site is non-neoplastic tissue.

5 . The method of claim 1 , wherein the target tissue site is a tumor or neoplasm.

6 . The method of claim 1 , wherein the minimum prescribed time-average electric field is 1 V/cm.

7 . The method of claim 1 , wherein the optimal placement of the implantable electrodes within the target tissue site further includes an insertion depth of the implantable electrodes.

8 . A system for electric fields treatment of a target tissue site comprising: a generator for generating electrical field parameters, and at least one data processor, wherein the data processor, wherein the data processor includes instructions that when executed perform the following operations: (a) receiving, by the at least one data processor, data relating to a volume and one or more electric properties of the target tissue site, (b) using configurations of multiple electrodes implanted within the target tissue site, the volume and the one or more electric properties of the target tissue site, (c) using an optimization algorithm and the simulated electric field maps to: (i) determine an optimal number of implantable electrodes to treat the target tissue site with electrical field, the optimal number of implantable electrodes being one or more than one implantable electrodes, each electrode having one or more contacts, (ii) determine an optimal placement of the optimal number of implantable electrodes determined in (i) within the target tissue site, and (iii) determine optimal voltages and phase sifts of each of the one or more contacts of the optimal number of implantable electrodes at the optimal placement determined in (ii) that results in a prescribed time average electric field coverage of the entire target tissue site, wherein the electric field is a dynamically oriented electric filed,

wherein the optimal placement of the optimal number of implantable electrodes within the target tissue site includes an insertion location and a trajectory angle of the optimal number of implantable electrodes, and

(d) providing an output to treat the target tissue with electrotherapy by implanting into the target tissue the optimal number of implantable electrodes determined in step (c)(i) at the optimal placement determined in step (c)(ii) and using the optimal voltages and phase shifts of each of the contacts of the optimal number of implantable electrodes determined in step (c)(iii).

9 . The system of claim 8 , wherein operation (a) further comprises receiving data relating to volume and electric properties of tissue surrounding the target tissue site (“surrounding tissue”), operation (c) further comprises using the volume and the one or more electric properties of the surrounding tissue to determine (c)(i), (c)(ii) and (c)(iii) that results in a prescribed time average electric field coverage of the target tissue site while minimizes the volume of the surrounding tissue receiving the prescribed time average electric field.

10 . The system of claim 8 , wherein the operations further include (d) receiving data relating to one or more electric properties of the target tissue having information of an actual position of the implantable electrodes relative to the target tissue site, (e) using the data of (d) to assess the prescribed time average electric field coverage of the target tissue site, and (f) repeat steps (b) to (e) to optimize the prescribed time average electric field coverage based on the assessment in (e).

11 . The system of claim 8 , wherein the optimal placement of the optimal number of implantable electrodes within the target tissue further includes an insertion depth of the optimal number of implantable electrodes determined in step (c)(i).

12 . A non-transitory computer readable storage medium, wherein code embodied in the computer readable storage medium executed by at least one processor performs operations, the operations including: (a) receiving, by at least one data processor, data relating to a volume and one or more electric properties of a target tissue site, (b) generating simulated electric field maps of the target tissue site using configurations of multiple electrodes implanted within the target tissue site, the volume and the one or more electric properties of the target tissue site, (c) using an optimization algorithm and the simulated electric field maps to: (i) determine an optimal number of implantable electrodes to treat the target tissue site with electric fields, the optimal number of implantable electrodes being one or more than one implantable electrodes, each implantable electrode having one or more contacts, (ii) determine an optimal placement of the optimal number of implantable electrodes determined in (i) within the target tissue site, and (iii) determine optimal voltages and phase sifts of each of the one or more contacts of the optimal number of implantable electrodes at the optimal placement determined in (ii) that results in a prescribed time average electric field coverage of the entire target tissue site, wherein the electric field is a dynamically oriented electric filed,

wherein the optimal placement of the optimal number of implantable electrodes within the target tissue site includes an insertion location and a trajectory angle of the optimal number of implantable electrodes, and

(d) providing an output to treat the target tissue with electrotherapy by implanting into the target tissue the optimal number of implantable electrodes determined in step (c)(i) at the optimal placement determined in step (c)(ii) and using the optimal voltages and phase shifts of each of the contacts of the optimal number of implantable electrodes determined in step (c)(iii).

13 . The non-transitory computer readable storage medium of claim 12 , wherein operation (a) further comprises receiving data relating to volume and one or more electric properties of tissue surrounding the target tissue site (“surrounding tissue”), operation (c) further comprises using the volume and the one or more electric properties of the surrounding tissue to determine (i), (ii) and (iii) that results in the prescribed time average electric field coverage of the target tissue site while minimizes the volume of the surrounding tissue receiving the prescribed time average electric field.

14 . The non-transitory computer readable storage medium of claim 12 , wherein the operations further include (d) receiving data relating to one or more electric properties of the target tissue having information of an actual position of the implantable electrodes relative to the target tissue site, (e) using the data of (d) to assess the prescribed time average electric field coverage of the target tissue site, and (f) repeat steps (b) to (e) to optimize the prescribed time average electric field coverage based on the assessment in (e).

15 . The non-transitory computer readable storage medium of claim 12 , wherein the optimal placement of the optimal number of implantable electrodes within the target tissue further includes an insertion depth of the optimal number of implantable electrodes determined in step (c)(i).

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 5, 2025
From: IREDALE, ERIN
To: LONDON HEALTH SCIENCES CENTRE RESEARCH INC.
Reel/Frame 072784/0539 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 11, 2022
From: HEBB, MATTHEW; SCHMID, SUSANNE; WONG, EUGENE; PETERS, TERRY
To: LONDON HEALTH SCIENCES CENTRE RESEARCH INC.
Reel/Frame 061383/0721 →
Continuity (2)
Provisional Application 62962553 · Jan 17, 2020
Related Publication 20230045652A1 · Feb 9, 2023
References Cited (40)
US 4612934A · Borkan · 1986 [cited by examiner]
US 7616998B2 · Nuttin · 2009 [cited by examiner]
US 8027738B2 · Palti · 2011 [cited by examiner]
US 8914117B2 · Valente · 2014 [cited by examiner]
US 10537741B2 · Bradley · 2020 [cited by examiner]
US 20050209642A1 · Palti · 2005 [cited by examiner]
US 20110196455A1 · Sieracki · 2011 [cited by examiner]
US 20170072198A1 · Makous · 2017 [cited by examiner]
US 20180001078A1 · Kirson · 2018 [cited by examiner]
US 20180160933A1 · Urman · 2018 [cited by examiner]
US 20190133683A1 · Matloubian · 2019 [cited by examiner]
US 20190308016A1 · Wenger · 2019 [cited by examiner]
WO 2016090239 · 2016 [cited by applicant]
Dmochowski, J.P., et al., Optimized multi-electrode stimulation increases focality and intensity at target, Journal of Neural Engineering, Jun. 2011, V. 8, 1-16. [cited by applicant]
Valle, G. and Micera, S., Modeling to Guide Implantable Electrode Design, River Publishers Series in Biomedical Engineering, Chapter 4, Dec. 2019. [cited by applicant]
International Search Report and Written Opinion of the International Searching Authority, PCT/CA2021/050044, Mar. 31, 2021. [cited by applicant]
Xu, H., Bihari F, Whitehead S, et al. In vitro validation of intratumoral modulation therapy for glioblastoma. Anticancer Res. 2016; 36: 71-80. [cited by applicant]
Di Sebastiano AR, Deweyert A, Benoit S, et al. Preclinical outcomes of intratumoral modulation therapy for glioblastoma. Sci. Rep. 2018; 8(1):7301. [cited by applicant]
Deweyert A, Iredale E, Xu Hu, et al. Diffuse intrinsic pontine glioma cells are vulnerable to low intensity electric fields delivered by intratumoral modulation therapy. J Neurooncol. 2019; 143: 49-56. [cited by applicant]
Iredale E, Deweyert A, Hoover DA, et al. Optimization of multi-electrode implant configurations and programming for the delivery of non-ablative electric fields in intratumoral modulation therapy. Med Phys. 2020; 47(11)… [cited by applicant]
Parkins KM, Dubois VP, Kelly JJ, et al. Engineering circulating tumor cells as novel cancer theranostics. Theranostics. 2020; 10(17): 7925-7937. [cited by applicant]
Hamilton A, Foster PJ, Ronald JA. Evaluating non-integrating lentiviruses as safe vectors for noninvasive reporter-based molecular imaging of multipotent mesenchymal stem cells. Hum Gene Ther. 2018; 9(10): 1213-1225. Ab… [cited by applicant]
Ghiaseddin AP, Shin D, Melnick K, et al. Tumor treating fields in the management of patients with high grade gliomas. Curr. Treat. Options in Oncol. 2020; 21: 76. [cited by applicant]
Arnold WM, Fuhr G. Increasing the permittivity and conductivity of cellular electromanipulation media [abstract]. Proceedings of 1994 IEEE Industry Applications Society Annual Meeting. 1994; 2:1470-1476. [cited by applicant]
Chen MT, Jiang C, Vernier PT, et al. Two-dimensional nanosecond electric field mapping based on cell electropermeabilization. PMC Biophys. 2009; 2(1): 9. [cited by applicant]
Latikka J, Eskola H. The resistivity of human brain tumours in vivo. Ann Biomed Eng. 2019; 47(3): 706-713. [cited by applicant]
Palti Y. Stimulation of internal organs by means of external applied electrodes. J. Appl. Physiol. 1966; 21: 1619-1623. [cited by applicant]
Taghian T. 2015. Thesis. Interaction of an electric field with vascular cells. Avaliable at https://etd.ohiolink.edu/. [cited by applicant]
Gabriel, C., et al. “Electrical conductivity of tissue at frequencies below 1 MHz,” Phys. Med. Biol., vol. 54, No. 16, pp. 4863-4878, Aug. 2009. [cited by applicant]
Latikka, J., et al. “Conductivity of living intracranial tissues.,” Phys. Med. Biol., vol. 46, No. 6, pp. 1611-1616, Jun. 2001. [cited by applicant]
Stoy, R.D., et al. “Dielectric properties of mammalian tissues from 0.1 to 100 MHz: a summary of recent data,” 1982 Phys. Med. Biol. vol. 27, p. 501. [cited by applicant]
De Giorgi, U., et al., “Circulating Tumor Cells and [ 18 F]Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography for Outcome Prediction in Metastatic Breast Cancer,” J. Clin. Oncol., vol. 27, No. 20, pp. 3… [cited by applicant]
Wenger, C., et al. “The electric field distribution in the brain during TTFields therapy and its dependence on tissue dielectric properties and anatomy: A computational study,” Phys. Med. Biol., vol. 60, No. 18, pp. 733… [cited by applicant]
Wenger, C., et al. “Improving Tumor Treating Fields Treatment Efficacy in Patients With Glioblastoma Using Personalized Array Layouts,” Int. J. Radiat. Oncol., vol. 94, No. 5, pp. 1137-1143, Apr. 2016. [cited by applicant]
Amon, A. and Alesch, F. “Systems for deep brain stimulation: review of technical features,” J. Neural Transm., vol. 124, No. 9, pp. 1083-1091, Sep. 2017. [cited by applicant]
Anderson, D.N., et al. “Optimized programming algorithm for cylindrical and directional deep brain stimulation electrodes,” J. Neural Eng., vol. 15, No. 2, p. 026005, Apr. 2018. [cited by applicant]
Butson, C.R. and McIntyre, C.C. “Role of electrode design on the volume of tissue activated during deep brain stimulation.,” J. Neural Eng., vol. 3, No. 1, pp. 1-8, Mar. 2006. [cited by applicant]
Alonso, F., et al. “Investigation into Deep Brain Stimulation Lead Designs: A Patient-Specific Simulation Study.,” Brain Sci., vol. 6, No. 3, p. 39, Sep. 2016. [cited by applicant]
Amaran, S., et al. “Simulation optimization: a review of algorithms and applications,” Ann. Oper. Res., vol. 240, No. 1, pp. 351-380, May 2016. [cited by applicant]
Morgan-Fletcher, S.L. “Prescribing, Recording and Reporting Photon Beam Therapy (Supplement to ICRU Report 50), CRU Report 62 . ICRU, pp. ix+52, 1999 (ICRU Bethesda, MD) $65.00 ISBN 0-913394-61-0,” Br. J. Radiol., vol. … [cited by applicant]