Ultrasound Image Computation

After the beam formed RF data passes through all its processing stages, it becomes ready as an input to ultrasound image computation processes. Different types of ultrasound images are used in clinical practice, each conveying a particular type of information to the clinician.

A-Mode Data
The A-Mode data is the most basic type of ultrasound image derived from RF data, and forms the basis for other types of ultrasound images. With the analogy to telecommunications in mind, the A-Mode data is the result of amplitude demodulation of the RF data. In simpler terms, it is a signal that captures the variation in the amplitude of the RF data. The A-Mode data is therefore a 1D array of data, and strictly speaking not an image.

The RF data is an alternating sinusoid whose amplitude and phase are changing. The alternations by themselves carry little information about the structure of tissue being image. On the other hand, the amplitude of the sinusoid shows how much reflection and backscattering has happened at a particular depth in tissue. Therefore the amplitude demodulation is carried out to take out the alternations, and leave the amplitude signal in tact.

Techniques for amplitude demodulation in ultrasound imaging are adapted from telecommunications. The two most popular methods are through the Hilbert Transform, and IQ demodulation. In version 6.0.x of the Exam software, the demodulation technique is IQ Demodulation. It also includes filtering of the RF data in the base band. The cutoff frequency and the order of the filter can be adjusted flexibly through B-IQFILT.

B-Mode Images
A cross sectional image of the tissue can be formed by placing the A-Mode data for successive scan-lines side by side to form a 2D array of data. However, a number of processing steps are carried out on this array, before it becomes a presentable B-Mode image. These steps are described here.

Logarithmic Compression and Adjustment in the Dynamic Range
The variation in the amplitude of the RF data is relatively high. If the same image is mapped linearly to a 0-255 gray scale image, many important tissue structures will only have image values of 0-9. In other words, the presence of a few very high amplitude points in the image overshadows everything else. In order to achieve a balance (or a more even histogram), the amplitude values are mapped nonlinearly by a logarithmic-looking function which adjusts the dynamic range.

version 6.0.x of the Exam software, the dynamic range adjustment is performed by dividing the range of values into two portions at a pivot point. Below the pivot point a logarithmic compression is used, and above the pivot point a linear mapping is used. The parameters for both parts can be adjusted flexibly through B-DYNRNG