Beamform Parameters API V2: A Comprehensive Guide

This document provides a detailed overview of the Beamform Parameters API V2, outlining its structure, key components, and functionalities. This API is designed to facilitate the exchange of critical parameters related to beamforming, ensuring consistency and interoperability across different systems and applications. It is crucial for researchers, developers, and engineers working with ultrasound and similar technologies to understand these parameters for accurate data processing and analysis. This guide will help you navigate the API, understand its elements, and use it effectively in your projects.

Core Structure and Standards

The Beamform Parameters API V2 follows a structured approach to organize and represent beamforming parameters. Here are the foundational elements:

• Magic Number: Every data structure begins with a magic number, 0x5042504D455AFECA, represented as a uint64. This magic number serves as a quick validity check to ensure that the data being read is indeed a valid Beamform Parameters data structure. It's like a secret handshake for data files!
• Version Number: Following the magic number is the version number, represented as a uint32. Currently, the version is set to 2. This version number allows for future compatibility and evolution of the API. By checking the version, applications can determine how to interpret the subsequent data fields, ensuring backward and forward compatibility. Think of it as knowing which edition of a textbook you're reading – each edition might have slightly different content or organization.

These two fields provide a crucial foundation for any system reading beamform parameter files, ensuring data integrity and proper interpretation.

Key Enumerations

Enumerations (enums) play a vital role in defining specific modes and types within the Beamform Parameters API V2. Here's a breakdown of some key enums:

• Acquisition Mode: The acquisition_mode enum specifies the method used to acquire data (FORCES, HERCULES, etc.). The standard is that 0 should always be a reasonable default, and -1 is an optional invalid value. This setting determines how the data was captured, which affects subsequent processing steps. For example, FORCES mode might involve specific triggering and data collection sequences different from HERCULES mode.
• Decode Mode: The decode_mode enum details how the acquired data should be decoded or interpreted. As with other enums, 0 is the default, and -1 represents an invalid value. The decode mode could specify different algorithms for converting raw data into meaningful information, such as different envelope detection or demodulation techniques.
• Sampling Mode: The sampling_mode enum defines the method used for sampling the incoming signal, whether it's standard sampling or bandpass sampling. The default value of 0 indicates a standard sampling approach. Different sampling modes have implications for the reconstruction and analysis of the signal. Bandpass sampling, for instance, is useful when the signal of interest lies within a specific frequency band.
• Data Type: The data_type enum specifies the format of the raw data, such as complex numbers, floating-point values, or integers. Understanding the data type is crucial for proper interpretation and processing. Different data types require different scaling and conversion methods. This parameter is critical for ensuring that the software correctly interprets the binary data.

The convention across all enums is that 0 represents a reasonable default value, and -1 signifies an invalid value. This standardization promotes consistency and simplifies error handling.

Essential Parameters

Several key parameters define the data acquisition and processing pipeline within the Beamform Parameters API V2. Let's explore these:

• Sampling Frequency: The sampling_frequency parameter, represented as a single-precision floating-point number, specifies the rate at which the signal is sampled. This is a fundamental parameter as it dictates the maximum frequency that can be accurately represented in the data (Nyquist frequency). A higher sampling frequency allows for the capture of higher-frequency signals, but it also results in larger data volumes. For instance, a sampling frequency of 40 MHz means that 40 million samples are acquired every second.
• Demodulation Frequency: The demodulation_frequency parameter, also a single-precision floating-point number, indicates the frequency used for demodulating the signal. Demodulation shifts the signal's frequency spectrum, often bringing the signal of interest closer to baseband for easier processing. This parameter is particularly relevant in systems employing heterodyne detection techniques. Correct demodulation is vital for extracting the relevant information from the carrier signal.
• Raw Data Dimensions: The raw_data_dimensions parameter is an array of four unsigned 32-bit integers (uint32[4]) that defines the dimensions of the raw data array. These dimensions correspond to: [Samples * Receive Events, Channels, Sections, Ensembles]. Understanding these dimensions is crucial for properly accessing and processing the raw data. Each dimension represents a different level of organization in the data. For example, 'Samples' refers to the number of data points collected per channel for each receive event, while 'Ensembles' represents a group of acquisitions.
• Sample Count: The sample_count parameter, a uint32, indicates the number of samples acquired for each channel in each receive event. This parameter directly influences the temporal resolution of the data. A higher sample count provides more detailed information about the signal over time.
• Channel Count: The channel_count parameter, also a uint32, specifies the number of channels in the data. This is relevant for multi-channel systems, such as phased arrays, where data is acquired simultaneously from multiple elements. Knowing the channel count is essential for beamforming and other multi-channel processing techniques. Understanding the arrangement and relationships between channels is a prerequisite for advanced processing methods.
• Receive Event Count: The receive_event_count parameter, a uint32, represents the number of receive events in the data. A receive event typically corresponds to a single transmit-receive cycle in an ultrasound system. Multiple receive events might be acquired to improve signal-to-noise ratio or to capture dynamic changes over time. The receive event count is critical for synchronization and averaging techniques.
• Transducer Element Pitch: The transducer_element_pitch parameter, represented as a single-precision floating-point number, defines the spacing between transducer elements. This parameter is crucial for beamforming calculations. The element pitch directly affects the spatial resolution and beam steering capabilities of the transducer. Accurate knowledge of the element pitch is essential for creating focused and steered beams.
• Transducer Transform Matrix: The transducer_transform_matrix parameter is a 4x4 matrix of single-precision floating-point numbers that describes the transformation from transducer coordinates to world coordinates. This matrix is vital for accurately positioning the acquired data in space. It accounts for the orientation and position of the transducer relative to the target being imaged. This parameter is indispensable for 3D imaging and accurate spatial representation of the data.
• Speed of Sound: The speed_of_sound parameter, a single-precision floating-point number, specifies the speed of sound in the medium being imaged. This parameter is critical for calculating distances and delays in beamforming. The accuracy of the speed of sound estimate directly affects the focusing and image quality. Variations in the speed of sound due to temperature or tissue properties can significantly impact image fidelity.
• Time Offset: The time_offset parameter, a single-precision floating-point number, includes time delays due to the starting depth, transmit delay profile, and the peak excitation time (typically half the length of the pulse). This parameter is vital for accurately aligning the received signals in time. It compensates for inherent delays in the system, ensuring that the data is properly synchronized.
• Group Acquisition Time: The group_acquisition_time parameter, represented as a single-precision floating-point number, specifies the time from the beginning of an acquisition mode to its end. It is typically equal to 1/prf * receive_event_count, where prf is the pulse repetition frequency. This parameter is essential for understanding the timing of the data acquisition process. It defines the duration of a single acquisition sequence.
• Ensemble Repetition Interval: The ensemble_repetition_interval parameter, a single-precision floating-point number, defines the time between the beginning of two acquisitions in an ensemble. It is restricted to be at least group_acquisition_time * section_count. This parameter is crucial for systems that acquire data in ensembles, such as those used for motion estimation or perfusion imaging. The repetition interval determines the temporal sampling rate of the ensemble data. This parameter ensures data integrity by imposing a minimum duration based on the acquisition time and section count.

Data Offsets

Offsets play a crucial role in locating additional data structures within the file. The API defines several offset parameters:

• Channel Mapping Offset: The channel_mapping_offset parameter points to the location of channel mapping information within the file. If the value is -1, it indicates that no channel mapping data is present. Otherwise, the offset points to a location relative to the base of the file where the channel mapping data is stored. Channel mapping is crucial for reordering or selecting specific channels for processing.
• Emission Parameters Offset: The emission_parameters_offset parameter points to the location of emission parameters within the file. If the value is -1, it indicates that no emission parameter data is present. Otherwise, the offset points to a location relative to the base of the file where the emission parameters are stored. Emission parameters typically include information about the transmit pulse, such as its frequency, amplitude, and duration.
• Acquisition Mode Parameters Offset: The acquisition_mode_parameters_offset parameter points to the location of acquisition mode-specific parameters within the file. If the value is -1, it indicates that no acquisition mode parameters are present. Otherwise, the offset points to a location relative to the base of the file where the acquisition mode parameters are stored. These parameters are specific to the chosen acquisition mode and might include settings related to triggering, data buffering, or synchronization.

All offsets are represented as signed 32-bit integers (int32). A value of -1 indicates that the corresponding data is not present in the file. Otherwise, the offset value provides the location of the data relative to the beginning of the file. Proper interpretation of these offsets is essential for accessing and utilizing the auxiliary data structures.

Acquisition Modes: A Note on OPTIMUS

It's important to note that while OPTIMUS has its own acquisition mode, its data is often stored as if it were HERCULES with multiple sections. This means that the data layout might resemble that of HERCULES, but with the sections dimension used to differentiate between different OPTIMUS acquisitions. This approach allows for a unified data format, simplifying data processing and analysis across different acquisition modes.

Conclusion

The Beamform Parameters API V2 provides a comprehensive and standardized way to represent beamforming parameters. By understanding the structure, enumerations, parameters, and offsets defined in this API, developers and researchers can effectively exchange and process beamforming data across different systems. This API promotes interoperability and facilitates the development of advanced beamforming applications. Leveraging the API correctly is essential for anyone working with beamforming-based technologies, enabling accurate and efficient data processing and analysis.

For more in-depth information and resources on beamforming, visit the IEEE website. This organization offers publications, conferences, and standards related to signal processing and beamforming techniques.