The science behind the products we build

Elements AI Tumor Segmentation is the first artificial intelligence AI-based segmentation solution developed by Brainlab. Learn how our new tumor segmentation tool uses artificial intelligence to segment one or multiple supported cranial tumors within a patient MRI in under a minute.

Explore how Elements Fibertracking addresses inherent limitations of Diffusion Tensor Imaging (DTI) and deterministic tractography to improve white matter tracking by embedding a cutting edge Constrained Spherical Deconvolution (CSD)-based probabilistic tracking algorithm.

Magnetic resonance imaging is prone to geometric image distortions caused by hardware- and patient-induced disturbances of magnetic field homogeneity. Whereas hardware-related non-linearities can be compensated by the scanner, patient-related distortions are not known a priori. Discover how Elements Distortion Correction Cranial enables the correction of cranial MRI data to support accurate image geometry and precise targeting.

Multi-modal image co-registration is important to clinical workflows. The goal is to find the spatial transformation that best aligns the position of features and properties of two three-dimensional image series. This article describes an automated and accurate image coregistration algorithm available with Elements Image Fusion, part of the modular Brainlab Elements software.

Multi-modal image coregistration is required in many clinical settings. In spine treatments, accounting for differences in patient positioning between scans, such as CT and MRI, is essential to ensure accurate image coregistration. Explore how Elements Curvature Correction Spine facilitates the coregistration of spinal imaging by enabling elastic fusion of MRI and CT scans.

Distinguishing tumor progression from pseudo-progression following radiation treatment of intracranial lesions can be challenging with standard follow-up MRI alone. Elements Contrast Clearance Analysis is an MRI-based methodology that helps clinicians differentiate regions of contrast clearance from contrast accumulation in brain tumor datasets, providing additional insights to support clinical decision making.

Elements Segmentation introduces a novel approach whereby a Synthetic Tissue Model is employed to simulate the patient's anatomy and to generate a patient-specific atlas, showing same imaging characteristics as the analyzed image set. The Synthetic Tissue Model is a core technology in a variety of Brainlab Elements applications and facilitates reliable segmentation.

Commissioning of a treatment planning system requires a high number of beam data measurements to be performed. Reference Beam Models by Brainlab were specifically developed to facilitate this commissioning process, reducing the measurement effort and allowing users to begin clinical use quickly. This white paper gives an overview of how the models were developed and validated.

Elements Image Fusion Angio enables fast, accurate fusion of 2D vessel imaging with 3D volumetric data , all without requiring an additional frame-based angiography procedure. Learn more in this white paper about the powerful algorithm behind the software and its clinical applications.

A growing body of literature suggests that total tumor burden, rather than the absolute number of metastases, may be a more relevant criterion for radiosurgery. This raises questions about the clinical value of treatment planning margins that increase the total target volume by including additional healthy tissue. In this white paper, we explore how advanced treatment planning, positioning and monitoring technologies can work together to give clinicians the confidence to reduce margins while maintaining precision.