OptimMRI

OptimMRI is a medical image processing software designed to aid neurosurgeons by processing pre-operative MR images to create 3D annotated models of the brain for functional neurosurgical planning. It employs semi-automatic segmentation and machine learning to localize key structures like the Subthalamic Nuclei (STN) and Ventral Intermediate Nucleus (VIM), demonstrating high accuracy in performance studies for these targets.

OptimMRI is a software application designed to assist qualified medical professionals in the processing, visualization, and interpretation of anatomical structures from medical images. Its primary function is to process pre-operative DICOM compatible MR images of the brain, generating 3D annotated models that aid users in neurosurgical functional planning by allowing them to display, review, analyze, annotate, interpret, and export data. Specifically, OptimMRI is indicated for use with other clinical methods to help localize critical brain regions such as the Subthalamic Nuclei (STN) and the Ventral Intermediate Nucleus (VIM). The software employs semi-automatic segmentation and machine learning models to create specialized annotations, marking regions of interest with 3D crosses. Performance studies, which involved 44 cerebral MRIs for STN analysis and 31 for VIM analysis, demonstrated high accuracy, with at least 90% of surface distances for STN or VIM being within 2.0mm when compared to reference devices. OptimMRI's STN localization performance is reported as identical to its predicate device, SIS Software, despite OptimMRI's limitation to pre-operative use, reliance on semi-automatic processes, and lack of fusion imaging. A distinguishing feature is OptimMRI's capability to localize the VIM, a function not available in its predicate device.

AI Regulatory Concerns

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• Q1. How does the performance of OptimMRI's semi-automatic segmentation for STN and VIM localization compare to fully manual segmentation by expert neuroradiologists in terms of accuracy and time efficiency?; What specific machine learning models are employed for the annotation of anatomic structures, and what was the training dataset size and diversity for these models?; Given that OptimMRI annotates regions of interest with 3D crosses rather than 3D segmented volumes, what is the clinical impact of this methodological difference on surgical planning precision for procedures targeting STN and VIM?; How does the lack of fusion imaging capability in OptimMRI affect its utility in complex neurosurgical cases where multi-modal image integration is typically beneficial?; What are the specific output formats beyond DICOM, and how seamlessly does OptimMRI integrate with various existing neurosurgical planning systems commonly used in clinical practice?