MRF-SMVA Simultaneous Multivolume Acquisition Reduces MRI Scan Time
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Solution Overview
Problem
Conventional magnetic resonance imaging (MRI) techniques are constrained by long acquisition times due to the limited variation of acquisition parameters, which restricts the speed of data collection and image generation, especially in multi-slice imaging.
Innovation Solution
The implementation of magnetic resonance fingerprinting (MRF) with simultaneous multivolume acquisition (MRF-SMVA) allows for the simultaneous excitation and data acquisition from multiple slices using varied flip angle trains and slice-encoding gradients, producing pseudorandom signal evolutions that can be matched against a dictionary to estimate MR parameters like T1, T2, and proton density.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional MRF acquires data serially slice by slice with 1000-2000 time points per slice, then measurement precision of MR parameters is improved, but acquisition time increases to 20-40 minutes for volumetric imaging
Solution Approach 1:
The patent segments the acquisition space-time domain into multiple simultaneously acquired slices, each with its own pseudorandom sampling trajectory. By dividing the volumetric imaging task into parallel slice acquisitions rather than serial processing, the method reduces total acquisition time while maintaining parameter estimation precision through independent fingerprinting in each slice.
Solution Approach 2:
The patent introduces simultaneous multivolume acquisition by adding a slice dimension to the traditional single-volume MRF approach. Multiple slices are excited and acquired at the same time using slice-selective RF pulses and gradient encoding, transforming the acquisition from a single-time-series problem into a multi-dimensional parallel processing problem that reduces overall scan time.
2Productivity
If conventional MRI uses limited variation of acquisition parameters, then device complexity is reduced, but productivity decreases due to long acquisition times
Solution Approach 1:
The patent employs dynamic, pseudorandom variation of acquisition parameters including flip angle, echo time, and inversion time within each slice's time course. These parameters are modulated according to pseudorandom patterns that create unique signal evolutions for different tissue types, enabling precise parameter mapping while maintaining manageable system complexity through systematic control of the variations.
Solution Approach 2:
The patent systematically changes multiple acquisition parameters simultaneously including RF pulse flip angles, echo times, inversion times, and gradient amplitudes according to predetermined pseudorandom patterns. This multi-parameter modulation creates distinctive signal fingerprints for different tissues while allowing parallel acquisition across multiple slices, improving productivity without excessive complexity increase.
3Adaptability or versatility
If MRF uses pseudorandom signal evolutions with arbitrary phase relationships, then adaptability for characterizing different resonant species is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The patent uses dictionary matching as a feedback mechanism where measured signal evolutions from multiple slices are compared against pre-computed signal templates for various tissue types and parameter combinations. This feedback loop enables automatic identification of tissue characteristics and MR parameters by finding the best match between acquired data and reference dictionaries, simplifying the analysis of complex pseudorandom signals.
Solution Approach 2:
The patent creates copies of signal evolution patterns by pre-computing dictionaries of expected signal responses for different tissue types and parameter values. These copied reference patterns are then matched against actual acquired signals from multiple simultaneously acquired slices, enabling efficient parameter estimation without directly solving the complex inverse problem from scratch for each measurement.
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
This approach significantly reduces acquisition time by enabling the simultaneous estimation of MR parameters across multiple slices, facilitating faster and more efficient image production while maintaining accuracy and precision.
Implementation Method 1
MRF has more freedom based, at least in part, on how MRF produces nuclear magnetic resonance (NMR) that produces signal evolutions that include complex values with arbitrary phase relationships
Implementation Method 2
MRF employs a series of varied sequence blocks that simultaneously produce different signal evolutions in different resonant species (e.g., tissues) to which the RF is applied
Implementation Method 3
Conventional multi-slice MRI may also be constrained by the number of acquisition parameters or acquisition conditions that can be varied from slice to slice during a single image acquisition
Data Source
AI summary
Magnetic resonance fingerprinting (MRF) with simultaneous multivolume acquisition (SMVA) is described. One example nuclear magnetic resonance (NMR) apparatus includes an NMR logic that repetitively and variably samples (k, t, E) spaces associated with different volumes (e.g., slices) in an object to simultaneously acquire sets of NMR signals that are associated with different points in the (k, t, E) spaces. Sampling is performed with t and/or E varying in a non-constant way. The NMR apparatus may also include a signal logic that produces an NMR signal evolution from the NMR signals and compares the NMR signal evolution to reference signal evolutions. Since different volumes are excited differently, resulting signal evolutions can be acquired simultaneously from the different volumes and NMR parameters may be simultaneously determined for the multiple volumes, which reduces acquisition time and parameter map creation time.