Arterial Spin Labeling MR Imaging Planning via Blood Flow Optimization
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Solution Overview
Problem
Current arterial spin labeling (ASL) MR imaging techniques in perfusion-weighted imaging face challenges in optimizing selective labeling due to insufficient visualization of blood flow parameters, making the planning process time-consuming and affecting image quality, as it relies heavily on operator experience and lacks real-time feedback on blood flow dynamics.
Innovation Solution
Acquiring angiographic MR signal data to derive quantitative blood flow parameters, which are then used to compute and optimize the labeling efficiency of the ASL sequence, allowing for interactive or automated adjustment of sequence parameters to maximize labeling efficiency and improve image quality by adapting to specific vascular conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If selective ASL labeling is performed without quantitative blood flow parameter optimization, then the planning process is simpler, but the image quality and labeling efficiency are reduced
Solution Approach 1:
The system performs preliminary acquisition of angiographic MR signal data and derivation of quantitative blood flow parameters before the actual ASL imaging. This preliminary action allows the planning process to be based on actual measured blood flow characteristics rather than operator estimation, improving labeling efficiency while the automated computation keeps the additional complexity manageable.
Solution Approach 2:
The system computes labeling efficiency based on derived blood flow parameters and provides feedback to optimize sequence parameters. This feedback loop allows the planning process to adapt to actual patient-specific blood flow conditions, improving labeling efficiency while the automation of the feedback computation prevents excessive complexity increase.
2Manufacturing precision
If quantitative blood flow parameters are derived and used to optimize ASL sequence, then image quality improves, but the planning process becomes more time-consuming
Solution Approach 1:
The system automatically derives quantitative blood flow parameters from angiographic MR signal data and computes optimal ASL sequence parameters without requiring manual measurement or estimation by the operator. This self-service approach improves image quality through precise parameter optimization while reducing planning time by eliminating manual processes.
Solution Approach 2:
The system changes the parameters of the ASL sequence based on derived blood flow parameters to optimize labeling efficiency. By automatically adjusting sequence parameters such as labeling duration, gradient strength, and timing based on measured blood flow characteristics, the system improves image quality while the automated parameter computation prevents excessive time consumption.
3Ease of operation
If automated computation of labeling efficiency is implemented, then operator experience dependency is reduced, but computational requirements increase
Solution Approach 1:
The system automatically computes labeling efficiency and optimizes sequence parameters without requiring operator expertise in blood flow dynamics or ASL sequence design. This self-service capability reduces operator experience dependency while the automated computation, performed by the MR device's existing processing resources, keeps the computational burden manageable.
Solution Approach 2:
The system replaces the manual, experience-based planning process with an automated computational approach. Instead of relying on operator knowledge and manual adjustment, the system uses algorithmic computation to derive blood flow parameters and optimize sequence settings, reducing operator dependency while utilizing the MR device's existing computational capabilities.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Facilitates easier and more intuitive planning of ASL MR imaging sessions, leading to enhanced image quality by providing real-time feedback on blood flow parameters and allowing for optimized selective labeling, thus improving perfusion-weighted imaging across various patient groups.
Implementation Method 1
The magnetic field B0 produces different energy levels for the individual nuclear spins in dependence on the magnetic field strength which can be excited (spin resonance) by application of an electromagnetic alternating field (RF field) of defined frequency (so-called Larmor frequency, or MR frequency).
Implementation Method 2
the overall magnetization which can be deflected out of the state of equilibrium by application of an electromagnetic pulse of appropriate frequency (RF pulse) while the corresponding magnetic field B1 of this RF pulse extends perpendicular to the z-axis, so that the magnetization performs a precessional motion about the z-axis.
Implementation Method 3
After termination of the RF pulse, the magnetization relaxes back to the original state of equilibrium, in which the magnetization in the z direction is built up again with a first time constant T1 (spin lattice or longitudinal relaxation time)
Implementation Method 4
the magnetization in the direction perpendicular to the z direction relaxes with a second and shorter time constant T2 (spin-spin or transverse relaxation time).
Implementation Method 5
In order to realize spatial resolution in the body, time-varying magnetic field gradients extending along the three main axes are superposed on the uniform magnetic field B0, leading to a linear spatial dependency of the spin resonance frequency.
Data Source
AI summary
The invention relates to a method of MR imaging of at least a portion of a body (10) placed in a main magnetic field within the examination volume of a MR device (1). It is an object of the invention to facilitate the planning of an arterial spin labeling (ASL) MR imaging session and to improve the image quality in perfusion weighted MR imaging. The method of the invention comprises the following steps: acquiring angiographic MR signal data by subjecting the portion of the examined body (10) to one or more MR angiography scans; deriving quantitative blood flow parameters from the angiographic MR signal data; —computing a labeling efficiency of an ASL sequence from the sequence parameters of the ASL sequence and from the quantitative blood flow parameters; optimizing the sequence parameters by maximizing the labeling efficiency; acquiring perfusion weighted MR signal data by subjecting the portion of the body to the ASL sequence; and—reconstructing a MR image from the perfusion weighted MR signal data. Moreover, the invention relates to a MR device (1) and to a computer program for a MR device (1).