Koninklijke Philips Corporation N.V.

Vijayananda Jagannatha

Umesh Suryanarayana Rudrapatna

Jaladhar Neelavalli

Rupesh Vakkachi Kandi

Vidya Ms

Suja Saraswathy

Sharun S Thazhackal

Srinivasa Rao Kundeti

Srinivas Prasad Madapusi Raghavan

Vinay Pandit

Ashvin Srinivasan

Vidya Madapusi Srinivas Prasad

Manikanda Krishnan V

Disclosed herein is a method and system for identifying abnormal images in a set of medical images for optimal assessment of the medical images. A plurality of global features from each medical image is extracted based on pretrained weights associated with each global feature. Similarly, plurality of local features from each medical image is extracted analyzing a predefined number of image patches generated from a higher resolution image corresponding to each medical image. Further, an abnormality score for each medical image is determined based on weights associated with a combined feature set obtained by concatenating the plurality of global features and the plurality of local features. Thereafter, the medical image is identified as an abnormal image when the abnormality score of the medical image is higher than a predefined first threshold score.

Identifying abnormal images in set of medical images based on combined feature set with global features extracted using first CNN and local features extracted using second CNN analyzing high resolution image patches

Proposed concepts thus aim to provide schemes, solutions, concept, designs, methods and systems pertaining to updating a magnetic field (B) map of a subject during a magnetic resonance imaging (MRI) examination or during image reconstruction. In particular, when a subject moves, the magnetic field inside the magnetic bore changes. As a result, any Bmap obtained prior to the movement of the subject may be inaccurate. Accordingly, an initial Bmap is updated to reflect changes in the Bmap caused by the movement of the subject. This can be achieved by determining a Bmap of the subject based on a Bprediction model, instead of spending additional scanning time to acquire another Bmap.

Method for updating a magnetic field map during a MRI examination

Systems and methods are disclosed for generating device test cases for medical imaging devices, which are not only reflective of current actual field usage of the device but also provide outlook on future usage. A probabilistic model of usage patterns is generated from historic data present in the device field logs by mining the current usage patterns. A Deep Long Short Term Memory Neural Network model of the usage patterns is constructed to predict the future usage patterns. Additionally, to capture the changing trends of device usage patterns in the field, predictive models are continuously updated in real time, and the test cases generated log files by the models are integrated into an automated testing system.

System and a method for improving reliability of medical imaging devices

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Bengaluru, India

Vijayananda Jagannatha