MR Workflow Analysis with Timestamped Video Event Annotations

The patent introduces video cameras as intermediary devices that capture human activities during imaging examinations. These cameras serve as mediators between the imaging device and the workflow analysis system, providing visual evidence of operator actions, patient movements, and procedural steps that were previously unrecorded. The video data is then processed through annotation algorithms that extract meaningful events and link them to machine log entries, thereby completing the workflow information without requiring direct integration of complex monitoring systems into the imaging device itself.

The patent segments the workflow monitoring system into independent components: video capture modules, annotation processing algorithms, timestamp synchronization mechanisms, and log integration interfaces. This segmentation allows each component to be developed, optimized, and maintained separately. The video data is segmented into discrete events through automated annotation, and the workflow information is segmented into distinct phases that can be analyzed independently, reducing the overall system complexity while maintaining information completeness.

The patent extracts only the essential information from video data through automated annotation algorithms. Instead of storing and processing entire video files, the system identifies and extracts key events such as patient positioning, contrast injection timing, scan sequence execution, and operator interventions. These extracted events are represented as concise text annotations with timestamps, which are then linked to corresponding machine log entries. This extraction process retains all necessary human activity information while reducing data storage requirements from gigabytes of video to kilobytes of structured annotations.

The patent applies partial action by selectively processing only the portions of video data that contain meaningful workflow information. The annotation algorithms scan video streams and process only segments where human activities occur, ignoring static periods or redundant footage. This partial processing approach maintains complete human activity information while significantly reducing the computational burden and storage requirements compared to processing entire video datasets.

The patent applies preliminary action by pre-processing video data during or immediately after the imaging examination. Annotation algorithms continuously analyze video streams in real-time or near-real-time, extracting events and generating timestamps before the workflow analysis is formally initiated. Machine log entries are also pre-synchronized with standardized timestamp formats. This preliminary preparation ensures that when workflow analysis is performed, the data is already structured and ready for rapid matching, thereby improving accuracy without significantly increasing the actual analysis time.

The patent replaces manual workflow analysis with automated computational systems. Instead of radiologists or technicians manually reviewing video footage and correlating it with machine logs, the system uses automated annotation algorithms and timestamp-matching mechanisms to perform the correlation. This substitution of mechanical human analysis with computational automation dramatically improves measurement precision in identifying workflow events while reducing the time required from minutes or hours of manual review to seconds of automated processing.