Medical scanner teaching itself to optimize clinical protocols and image acquisition

A technology of medical images and scanners, applied in the field of medical scanners

Active Publication Date: 2018-02-09
SIEMENS HEALTHCARE GMBH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

As a result, even when an optimal set of parameters is learned for one study, those parameters cannot be easily reused for other dissimilar studies

Method used

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  • Medical scanner teaching itself to optimize clinical protocols and image acquisition
  • Medical scanner teaching itself to optimize clinical protocols and image acquisition
  • Medical scanner teaching itself to optimize clinical protocols and image acquisition

Examples

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Embodiment Construction

[0016] Systems, methods, and apparatus are described herein that relate generally to various techniques related to self-learning systems for medical scanners that automatically learn to optimize image acquisition. More specifically, the techniques described herein optimize image acquisition at a medical scanner using a self-learning model of operator parameter selection utilizing parameters available, for example, locally at the scanner or in a connected database. Collected parameters may include, for example, image data, patient data, attributes from clinical protocols and scanner parameters, and configurations. The examples provided in this paper describe techniques in the context of deep reinforcement learning frameworks, where an intelligent agent identifies relevant features from a pool of attributes, and then derives actions to automatically aggregate into target parameter settings. However, the general approach described in this paper can be extended to other forms of m...

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Abstract

The invention relates to a medical scanner teaching itself to optimize clinical protocols and image acquisition. A computer-implemented method for identifying an optimal set of parameters for medicalimage acquisition includes receiving a set of input parameters corresponding to a medical imaging scan of a patient and using a model of operator parameter selection to determine a set of optimal target parameter values for a medical image scanner based on the set of input parameters. The medical imaging scan of the patient is performed using the set of optimal target parameter values to acquire one or more images and feedback is collected from one or more users in response to acquisition of the one or more images. This feedback is used to update the model of operator parameter selection, thereby yielding an updated model of operator parameter selection.

Description

technical field [0001] The present invention generally relates to methods, systems and apparatus associated with medical scanners that use machine learning frameworks to self-teach to optimize image acquisition. The disclosed methods, systems and apparatus are applicable to scanners used in any imaging modality. Background technique [0002] Ensuring highly optimized image acquisition is one of the key factors for accurate clinical diagnosis in healthcare. However, medical scans depend on many input parameters, such as image information (eg, quality requirements), patient information (eg, target organs), clinical protocols (eg, scan time), utilized contrast agents, and various scan parameters. These parameters collectively represent a complex parameter space, which is often difficult to navigate in order to determine the optimal set of parameters for input. As a result, parameter selection can be a time-intensive process, as the operator must explore different parameter co...

Claims

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Application Information

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IPC IPC(8): G16H30/40G16H40/63G06N3/04G06N3/08
CPCG06N3/084G06N3/045G16H40/63G06N3/006G06N20/00G16H30/20G16H40/67G06F18/2178G06F18/2193
Inventor S.克卢克纳D.科马尼丘
Owner SIEMENS HEALTHCARE GMBH
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