What happens to SNR as the Field of View (FOV) increases?

What happens to SNR as the Field of View (FOV) increases?

• A. SNR increases
• B. SNR decreases
• C. SNR remains unchanged
• D. SNR fluctuates

Answer: (B)

As the Field of View (FOV) increases, the Signal-to-Noise Ratio (SNR) typically decreases. This phenomenon can be understood by considering how SNR is influenced by image acquisition parameters. When the FOV is expanded, the same amount of signal is dispersed over a larger area. This dispersion results in a dilution of the signal relative to the noise, which is not dependent on the FOV size. Essentially, while the signal remains constant, the background noise remains relatively uniform in distribution. Therefore, as you increase the FOV, you are essentially spreading the same signal across a larger area while the noise level stays the same, leading to a lower SNR. This relationship highlights the trade-offs involved in imaging techniques: larger fields of view can provide more anatomical information but may compromise the clarity of the signal captured, resulting in decreased SNR. Understanding this principle is crucial for optimizing imaging parameters in practice.

As the Field of View (FOV) increases, the Signal-to-Noise Ratio (SNR) typically decreases. This phenomenon can be understood by considering how SNR is influenced by image acquisition parameters.

When the FOV is expanded, the same amount of signal is dispersed over a larger area. This dispersion results in a dilution of the signal relative to the noise, which is not dependent on the FOV size. Essentially, while the signal remains constant, the background noise remains relatively uniform in distribution. Therefore, as you increase the FOV, you are essentially spreading the same signal across a larger area while the noise level stays the same, leading to a lower SNR.

This relationship highlights the trade-offs involved in imaging techniques: larger fields of view can provide more anatomical information but may compromise the clarity of the signal captured, resulting in decreased SNR. Understanding this principle is crucial for optimizing imaging parameters in practice.