TrajectoryLogReader 2.0.0-beta.1

Trajectory Log Reader

Reads Varian TrueBeam trajectory log files (*.bin). This has only been tested with log-files of version 5.0.

Usage

Install from NuGet

dotnet add package TrajectoryLogReader

Load the log file:

TrajectoryLog log = LogReader.ReadBinary(filePath);

Data access

Log-file data can be accessed through either a "column-based" (axes) or "row-based" (snapshots) approach.

Axes data

log.Axes provides a wrapper around the raw-data for each axis.

var expected = log.Axes.Gantry.ExpectedValues;
var actual = log.Axes.Gantry.ActualValues;
var errors = log.Axes.Gantry.ErrorValues;
var expectedSubBeam1 = log.SubBeams.First().Axes.Gantry.ExpectedValues;

Snapshots

Reading all snapshots can be done via:

foreach (var snapshot in log.Snapshots)
{
    Console.WriteLine(snapshot.X1.Actual);
    Console.WriteLine(snapshot.X1.Expected);
}

The same can be performed for sub-beams:

foreach (var sub in log.SubBeams)
{
    foreach (var snapshot in sub.Snapshots)
    {
        Console.WriteLine(snapshot.X1.Actual);
        Console.WriteLine(snapshot.X1.Expected);
    }
}

Interpolation

There are some useful functions for interpolating data at various times. For example:

int timeInMs = 20;
var gantryExpected = log.InterpolateAxisData(Axis.Gantry, timeInMs, RecordType.ExpectedPosition);
var gantryActual = log.InterpolateAxisData(Axis.Gantry, timeInMs, RecordType.ActualPosition);

Data is linearly interpolated between sampling intervals.

MLC leaf positions can be interpolated at time t:

int bankA = 0;
int bankB = 1;
int leafIndex = 0;
int timeInMs = 25;
double mlc = log.InterpolateMLCPosition(timeInMs, bankA, leafIndex, RecordType.Actual);

Or, a 2D array of MLC positions interpolated at time t can be extracted:

int t = 25; // time in ms
float[,] mlc = log.InterpolateMLCPositions(t, RecordType.Expected);

Fluence Reconstruction

The library reconstructs the delivered 2D fluence by temporally integrating the beam intensity over the course of the delivery. This provides a high-fidelity representation of the actual modulation delivered by the linac.

var fluence = log.CreateFluence(options, RecordType.Actual); // for the entire log file
var fluenceSubBeam = log.SubBeams.First().CreateFluence(options, RecordType.Actual); // just for this beam

Dosimetric Principles:

• Aperture Integration: The total fluence is the summation of instantaneous apertures defined by the MLC and Jaws at each control point, weighted by the incremental MU delivered.
• Geometric Accuracy: The algorithm accounts for dynamic collimator rotation and asymmetric jaw tracking.
• Sub-pixel Resolution: To accurately model high-modulation VMAT arcs and small stereotactic fields, the system uses an exact area-intersection method (Sutherland-Hodgman) rather than simple center-point sampling. This eliminates aliasing artifacts and partial-volume errors at the leaf edges.

Gamma Analysis

The verification module implements the standard 2D Gamma Index analysis (Low et al., 1998) to quantify the agreement between the Reference (Plan) and Evaluated (Log) distributions.

Algorithm Specifics:

• Grid Resampling: To minimize discretization error in high-gradient regions (penumbra), the Reference distribution is automatically upsampled. The resolution is set to ensure it is significantly smaller than the Distance-to-Agreement (DTA) tolerance (default $\le \frac{1}{5} \text{DTA}$).
• Composite Metric: The algorithm evaluates the generalized $\gamma$ function, combining dose difference ($\Delta D$) and spatial distance ($\Delta d$) criteria. It supports both Global Normalization ( relative to $D_{max}$) and Local Normalization.
• Efficient Search: A localized search window (default radius $2 \times \text{DTA}$) is used for each evaluated point to find the minimum $\gamma$ value.