Neuroanatomy-based search to optimize trajectory selection during DBS targeting

Patent metadata

Field	Value
Publication number	US-12567495-B2
Application number	US-202318488684-A
Country	US
Kind code	B2
Filing date	Oct 17, 2023
Priority date	Oct 19, 2022
Publication date	Mar 3, 2026
Grant date	Mar 3, 2026

Abstract

Methods and systems for planning a trajectory for implanting electrical stimulation leads in a patient's brain are described. The methods and systems rank candidate trajectories based on their expected therapeutic efficacies, as well as other criteria. Optimized stimulation parameters are determined for each of the candidate trajectories and therapeutic efficacies using the optimized parameters are predicted.

First claim

What is claimed is:

1 . A method for planning a position for a stimulation lead for neurostimulation of one or more target structures of a patient's brain, wherein the stimulation lead comprises a tip, a longitudinal axis, and a plurality of electrode contacts, wherein the method is executable using a machine comprising a user interface (UI) and a processor, the method comprising:
receiving, via the UI, an indication of the one or more target structures, receiving, via the UI, indications of a plurality of candidate positions for the stimulation lead; using the processor to:
execute a reverse programming algorithm to determine a set of optimized stimulation parameters for each of the candidate positions based on the target structures;
predict a volume of tissue activated (VTA) for each of the candidate positions' set of optimized stimulation parameters;
determine an overlap of each of the predicted VTA with the target structure, and
rank the plurality of candidate positions based at least partially on the overlaps, and
present an indication of the ranking of each of the plurality of candidate positions on the UI.

2 . The method of claim 1 , wherein each candidate position is defined by a tip location, a rotation angle, and a longitudinal axis angle.

3 . The method of claim 2 , wherein the indication of a plurality of candidate positions comprises an indication of a basis position and of values for one or more of the tip location, rotation angle, and/or longitudinal axis angle.

4 . The method of claim 1 , wherein the reverse programming algorithm comprises optimizing current fractionalization among the electrode contacts based on stimulation field models (SFMs) modeled for each current fractionalization.

5 . The method of claim 4 , wherein the reverse programming algorithm comprises a cost function that includes (i) overlap of the SFMs with the target structure for each current fractionalization, and (ii) a cost associated with increasing a size of the SFM.

6 . The method of claim 5 , wherein the cost function is further a function of (iii) overlap of the SFMs with an avoidance structure for each current fractionalization.

7 . The method of claim 1 , wherein ranking the plurality of candidate positions is further based on one or more bounding parameters or additional scoring functions.

8 . The method of claim 7 , wherein the bounding parameters comprise maximum power usage.

9 . The method of claim 7 , wherein the bounding parameters specify one or more of stimulation amplitude values, total charge values, pulse width, or frequency.

10 . An apparatus for planning a position for a stimulation lead for neurostimulation of one or more target structures of a patient's brain, wherein the stimulation lead comprises a tip, a longitudinal axis, and a plurality of electrode contacts, the apparatus comprising:
a user interface (UI), and a processor configured to:
receive, via the UI, an indication of the one or more target structures,
receive, via the UI, indications of a plurality of candidate positions for the stimulation lead;
execute a reverse programming algorithm to determine a set of optimized stimulation parameters for each of the candidate positions based on the target structures;
predict a volume of tissue activated (VTA) for each of the candidate positions' set of optimized stimulation parameters;
determine an overlap of each of the predicted VTA with the target structure,
rank the plurality of candidate positions based at least partially on the overlaps, and
present an indication of the ranking of each of the plurality of candidate positions on the UI.

11 . The apparatus of claim 10 , wherein each candidate position is defined by a tip location, a rotation angle, and a longitudinal axis angle.

12 . The apparatus of claim 11 , wherein the indication of a plurality of candidate positions comprises an indication of a basis position and of values for one or more of the tip location, rotation angle, and/or longitudinal axis angle.

13 . The apparatus of claim 10 , wherein the reverse programming algorithm comprises optimizing current fractionalization among the electrode contacts based on stimulation field models (SFMs) modeled for each current fractionalization.

14 . The apparatus of claim 13 , wherein the reverse programming algorithm comprises a cost function that includes (i) overlap of the SFMs with the target structure for each current fractionalization, and (ii) a cost associated with increasing a size of the SFM.

15 . The apparatus of claim 14 , wherein the cost function is further a function of (iii) overlap of the SFMs with an avoidance structure for each current fractionalization.

16 . The apparatus of claim 10 , wherein ranking the plurality of candidate positions is further based on one or more bounding parameters.

17 . The apparatus of claim 16 , wherein the bounding parameters comprise maximum power usage.

18 . The apparatus of claim 16 , wherein the bounding parameters specify one or more of stimulation amplitude values, total charge values, pulse width, or frequency.

Assignees

• Boston Scient Neuromodulation Corp

Inventors

• Malekmohammadi Mahsa
• Moore Lisa
• Steinke G Karl
• Serrano Carmona Raul

Classifications

• A61N1/36146

  specified by the stimulation parameters · CPC title

• A61N1/36125

  Details of circuitry or electric components · CPC title

• G16H50/70

  for mining of medical data, e.g. analysing previous cases of other patients · CPC title

• G16H50/20

  for computer-aided diagnosis, e.g. based on medical expert systems · CPC title

• G16H20/40 Primary

  relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture · CPC title