MRI parallel imaging techniques
Parallel imaging is a technique used in MRI (Magnetic Resonance Imaging) to accelerate data acquisition and reduce scan times. It takes advantage of specialized hardware called multi-channel phased array coils, which are commonly found in modern clinical MRI scanners. These coils consist of multiple independent receiver channels, each with its own sensitivity profile.
The basic principle of parallel imaging involves exploiting the spatial sensitivity variations of the phased array coils. Each coil element is most sensitive to the magnetization closest to it and less sensitive to magnetization further away. By simultaneously acquiring data from multiple coils, parallel imaging techniques can reconstruct an image using less k-space data, which leads to faster scan times. By undersampling the data, the acquisition time is significantly reduced. However, this undersampling leads to aliasing artifacts in the resulting images.
The undersampled k-space data obtained through parallel imaging, however, leads to a phenomenon called aliasing, where the acquired data cannot be accurately assigned to their true spatial locations. To overcome this, specialized algorithms are employed to reconstruct artifact-free images from the aliased data or the undersampled data.
There are two commonly used parallel imaging algorithms: SENSE (Sensitivity Encoding) and GRAPPA (Generalized Autocalibrating Partially Parallel Acquisitions). In SENSE-type reconstruction, the aliased images are used to reconstruct artifact-free images. The receiver coil sensitivity information is utilized to separate the contributions from different coils and reconstruct the original image. This method relies on the fact that different receiver coils have different sensitivities to the underlying anatomy.
In GRAPPA-type reconstruction, the undersampled data itself is used to reconstruct the artifact-free images. The missing k-space data points are estimated by exploiting the correlations between the acquired and missing data points. The reconstruction process involves using a calibration region in the acquired data to estimate the reconstruction weights for the missing data points.
The advantages of parallel imaging in a clinical setting are significant. It allows for faster image acquisition, which can be utilized to reduce breath-hold times and minimize motion artifacts. Additionally, parallel imaging enables imaging of structures that are in motion or have rapidly changing contrast, such as the heart or flowing blood.
parallel imaging and image quality
Parallel imaging techniques in magnetic resonance imaging (MRI) offer faster scans by collecting data from multiple receiver coils simultaneously. However, they come with drawbacks. One significant disadvantage is the emergence of parallel imaging artifacts, compromising image quality.
Noise amplification artifact arises from the reconstruction of high-frequency information from aliased signals, magnifying noise levels and reducing image clarity. Geometric distortion artifact stems from coil array sensitivity and image reconstruction errors, distorting the image’s spatial accuracy.
Ghosting artifact emerges when incomplete phase correction leads to residual ghost-like images, marring diagnostic reliability. Additionally, signal loss artifact becomes an issue due to inconsistent coil sensitivity, resulting in incomplete signal capture in specific areas.
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