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Bullsai Confirm provides functionality to assist medical professionals in planning the programming of stimulation for patients receiving approved Abbott deep brain stimulation (DBS) devices.

Bullsai Confirm is intended to assist medical professionals in planning the programming of deep brain stimulation (DBS) by visualizing the Volume of Tissue Activated (VTA) relative to patient anatomy. It is used to visualize patient-specific information within the patient's anatomy. Integrated magnetic resonance imaging (MRI) and computed tomography (CT) images are uploaded to Bullsai Confirm and can be navigated through in multiple 2D projections and 3D reconstructions. Abbott DBS lead models are positioned in the corresponding artifacts and potential stimulation settings and electrode configurations entered. Bullsai Confirm mathematically combines finite element (FE) based electric field model of the lead with an axon based neural activation model to translate potential stimulation settings and electrode configurations into a visualized VTA field to indicate the shape and the area or volume of anatomy that will be activated by the stimulation. Results, including input image quality assessments, are shared in an output PDF report and visualized in a web-based software interface. Bullsai Confirm is used to do the following: - Import DICOM images from a picture archiving and communication system (PACS), including . MRI and CT DICOM images. - . Import preoperative planning outputs (including tractography, structural ROIs, etc.) from AWS S3 Cloud Storage - . Combine MR images, CT images, and patient specific 3D structures for more detail - Localize graphical compatible DBS lead models (based on preoperative imaging) . - . Visualize VTA fields relative to structures of interest in the patient anatomy or lead position The software provides a workflow for clinicians to: - Create patient-specific stimulation plans for DBS programming . - . Export reports that summarize stimulation plans for patients (PNG screenshot)

Bullsai is composed of a set of modules intended for analysis and processing of medical images and other healthcare data. It includes functions for image manipulation, basic measurements, and planning. Bullsai is indicated for use in image processing, registration, atlas-assisted visualization and segmentation, and target export creation and selection of structural MRI images, where an output can be generated for use by a system capable of reading DICOM image sets. Bullsai is indicated for use in the processing of diffusion-weighted MRI sequences into 3D maps that represent white matter tracts based on diffusion reconstruction methods and for the use of said maps to select and create exports. Typical users of Bullsai are medical professionals, including but not limited to surgeons, clinicians. and radiologists.

Bullsai is a software-only, cloud-deployed, image processing package which can be used to perform DICOM image processing and analysis. Bullsai can receive ("import") DICOM images from picture archiving and communication systems (PACS), including Diffusion Weighted Imaging (DWI) and structural brain imaging. Bullsai removes protected health information (PHI) and links the dataset to an encryption key, which is then used to relink the data back to the patient when the data is exported to a medical imaging platform such as a hospital PACS or other DICOM device. The software provides a workflow for a clinician to: - Select an image for planning and visualization, - Validate image quality, - Export black and white and color DICOMs for use in systems that can view DICOM images. Bullsai uses advanced MRI processing to deliver patient-specific anatomy and tractography results to support physicians in neurosurgical planning. Bullasi preprocessing steps include denoising, debiasing, skull stripping, susceptibility distortion and head motion correction to ensure input data can support generation of tractography and segmentation results. Bullsai uses generalized q-sampling imaging (GQI) to estimate the voxel-level white matter microstructure: GQI is a model-free diffusion reconstruction method that uses the Fourier transform relationship between the diffuse MR signal and the potential diffusion displacement for resolving fiber orientations and the anisotropy of water. Patient-specific anatomical segmentation and GQI estimated fiber orientations are used in Bullsai as part of an iterative heuristic tractography algorithm that emulates the manual work of neuroanatomists who iteratively seed tracts and remove aberrant fibers. Results are shared as DICOM outputs which can readily be viewed and edited in standard neurosurgical planning software packages.