Method for Determining a Type of Reconstruction of Image Data from a Magnetic Resonance Measurement of an Examination Object Using a Magnetic Resonance Device

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This invention describes a method for selecting the optimal type of image reconstruction for magnetic resonance imaging (MRI) data, based on a patient's breathing patterns. The system uses parameters that represent the patient's respiratory process, compares them with a database (which can learn and improve over time), and then chooses the appropriate reconstruction method for the MRI images.

Use CasesContent extracted from patent full text and abstract with AI.

• Improving MRI image quality for patients who may have irregular breathing patterns
• Enabling more accurate diagnostic MRI imaging under challenging clinical conditions
• Automating the selection of reconstruction techniques to reduce operator workload
• Enhancing image consistency in research studies using MRI
• Supporting advanced MRI protocols in hospitals and diagnostic centers

BenefitsContent extracted from patent full text and abstract with AI.

• Provides more accurate and reliable MRI images by considering patient-specific respiration data
• Reduces the need for manual adjustments by technicians, saving time and minimizing errors
• Adapts and improves over time through its self-learning database system
• Makes MRI diagnostics more robust in cases with patient movement or irregular breathing
• Enables better clinical decision-making due to improved image quality

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Patent Abstract

A method is disclosed for determining a type of reconstruction of image data from a magnetic resonance measurement of an examination object by way of a magnetic resonance device. In an embodiment, the method includes determining at least one first parameter which includes information about a breathing process of the examination object; comparing the at least one first parameter with at least one second parameter from a maintained database; and determining a type of reconstruction of image data of the magnetic resonance measurement according to the comparison that has been made. In an example embodiment, at least one of the at least one first parameter and the at least one second parameter is derived from a histogram of respiratory phases and the maintained database includes a self-learning system.