Parallel MRI Reconstruction for Faster Low-Field Surgical Imaging
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Solution Overview
Problem
Existing MRI systems face limitations in physical access to patients and the use of electrical and mechanical components due to high magnetic fields, making surgical interventions and imaging challenging, especially in low-field and ultra-low-field MRI systems.
Innovation Solution
The use of a dome-shaped housing with access apertures and RF coil arrays for MRI systems, combined with iterative image reconstruction techniques, to facilitate surgical interventions and improve image quality in low-field and ultra-low-field MRI systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high magnetic field strength is used in MRI systems, then image quality and signal-to-noise ratio are improved, but physical access to patient and surgical intervention capability are restricted
Solution Approach 1:
The MRI system is divided into multiple RF coil elements that can be independently positioned and configured. This segmentation allows the magnetic field generation to be distributed across multiple locations, enabling surgical access through gaps between coil segments while maintaining adequate signal quality through parallel signal acquisition and combination
Solution Approach 2:
The patent transitions from a single large-bore MRI design to a modular array of RF coils arranged in multiple dimensions around the patient. This spatial reconfiguration allows surgical access from multiple directions simultaneously while maintaining the necessary magnetic field strength through constructive interference of individual coil fields
2Loss of time
If truncated and under-sampled k-space data is acquired, then scan time is reduced, but image quality and field of view completeness deteriorate
Solution Approach 1:
Coil sensitivity maps are acquired in advance during a calibration phase before the actual imaging scan. These pre-acquired sensitivity profiles are then used during the truncated k-space imaging to properly unalias and reconstruct the image, enabling fast scanning without sacrificing image quality
Solution Approach 2:
The iterative image reconstruction process uses feedback from the acquired under-sampled data combined with prior knowledge from coil sensitivity maps to progressively refine the image reconstruction. This feedback loop allows recovery of image quality despite truncated k-space sampling by iteratively correcting aliasing artifacts
3Productivity
If multiple RF coils are used for parallel imaging, then scan speed and access are improved, but system complexity and data processing requirements increase
Solution Approach 1:
Each RF coil element in the array is designed to perform multiple functions: it serves as both a transmit and receive element, and each coil's sensitivity profile is used for both image reconstruction and as a constraint in the iterative reconstruction process. This multi-functionality reduces the need for separate calibration scans and simplifies the overall system architecture
Solution Approach 2:
The coil array performs self-calibration by acquiring low-resolution images during the scan that are then used to generate updated coil sensitivity maps. These self-generated sensitivity maps are fed back into the reconstruction process, allowing the system to adapt and improve its own performance without external intervention or complex pre-calibration procedures
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
Enables improved access for surgical interventions and enhances image quality by reducing MR signal acquisition times while addressing field homogeneity and noise-related challenges in low-field and ultra-low-field MRI systems.
Implementation Method 1
magnetic resonance imaging (MRI)
Implementation Method 2
receiving k-space data sets acquired by radiofrequency (RF) coils
Data Source
AI summary
The present disclosure provides various systems and methods for magnetic resonance imaging. In one aspect, a method for magnetic resonance imaging can include receiving k-space data sets acquired by radiofrequency (RF) coils. Each of the k-space data sets can correspond to a different one of the RF coils. Each of the k-space data sets can be truncated and/or under sampled. The method can further include generating partial images of a field of view based on the k-space data sets and generating an initial image based on the partial images. The initial image can be full image of the field of view. The method can further include applying an iterative image reconstruction technique to generate an updated image based on the initial image.