Begin by creating a control noise profile for quality control, or QC purposes. Use a specific customized 2D GRE sequence, including a high field of view, to capture the maximum signal from the area, a high bandwidth per pixel to identify nearby noise resonances and the lowest possible repetition time, or TR, and echo time, or TE.Acquire the QC for noise profile using a xenon vest or a loop coil. Obtain an image with no hyperpolarized xenon 129 sample in the coil.
This image will characterize the noise profile. Examine the acquired noise data, particularly the K-space for non-Gaussian elements, such as spikes, patterns, or discretized or binned values. Create a quantile-quantile, or QQ plot, by plotting the acquired real or imaginary data against a synthesized Gaussian dataset with identical mean, standard deviation, and vector length, both ordered from the smallest to largest.
Deviations from the line Y is equal to X in the QQ plot, indicate the presence of non-Gaussian components within the acquired data, requiring further investigation. Proceed to identify the noise distribution pattern and potential outliers with a suitable plot of choice. To rule out the scanner as a noise source, acquire images using the standard site protocol with various pulse sequence parameters disabled, and electronic components powered off.
Refer to the list on the screen for possible noise sources. To eliminate noise sources from the room, use a simple surface loop coil tuned to the xenon 129 frequency to sniff around the magnet room for noise sources. Physically place the xenon coil element near potential problematic devices and run a test sequence to detect amplified noise.
Examine K-space and image data to pinpoint the exact source of coherence noise. If a specified source is identified, attempt to disable it or cover it with aluminum foil, flashing or a copper mesh to reduce noise. Rerun the scan after disabling or covering noise sources to see if the noise resolves.
Continue this process until all noise sources are eliminated, leaving only low root mean square Gaussian noise. Identify irregular noise as high signal spikes in individual K-space pixels with abnormally high or low signals in the real or imaginary channels. Perform imaging in different phase encoding orientations, including anterior to posterior, head to foot, and left to right.
Eliminate potential issues with X-Y-or Z-gradients, by enabling or disabling individual gradients selectively. Systematically examine the resulting images to identify which specific gradient direction is contributing to the noise. The results of the noise characterization analysis performed on the noise scan demonstrated the impact of both regular and irregular noise on the K-space.
Regular noise led to a continuous pattern in the K-space, while irregular noise resulted in high value outliers in the QQ plot. A series of lung images acquired using HPG MRI showed that a distinct bright spot centered in the K-space indicated a clear lung signal with low noise. Conversely, the presence of regular noise was spread throughout the images.
Irregular noise evidently caused high value spikes in the K-space, and resulted in a striped pattern in image space. A scenario where both regular and irregular noises were present simultaneously also affected the lung image.
