Development of a new Python-based cardiac phantom for myocardial SPECT imaging

Abstract

Purpose

The aim of this work was to develop a digital dynamic cardiac phantom able to mimic gated myocardial perfusion single photon emission computed tomography (SPECT) images.

Methods

A software code package was written to construct a cardiac digital phantom based on mathematical ellipsoidal model utilizing powerful numerical and mathematic libraries of python programing language. An ellipsoidal mathematical model was adopted to create the left ventricle geometrical volume including myocardial boundaries, left ventricular cavity, with incorporation of myocardial wall thickening and motion. Realistic myocardial count density from true patient studies was used to simulate statistical intensity variation during myocardial contraction. A combination of different levels of defect extent and severity were precisely modeled taking into consideration defect size variation during cardiac contraction. Wall thickening was also modeled taking into account the effect of partial volume.

Results

It has been successful to build a python-based software code that is able to model gated myocardial perfusion SPECT images with variable left ventricular volumes and ejection fraction. The recent flexibility of python programming enabled us to manipulate the shape and control the functional parameters in addition to creating variable sized-defects, extents and severities in different locations. Furthermore, the phantom code also provides different levels of image filtration mimicking those filters used in image reconstruction and their influence on image quality. Defect extent and severity were found to impact functional parameter estimation in consistence to clinical examinations.

Conclusion

A python-based gated myocardial perfusion SPECT phantom has been successfully developed. The phantom proved to be reliable to assess cardiac software analysis tools in terms of perfusion and functional parameters. The software code is under further development and refinement so that more functionalities and features can be added.

Appendix

Code structure, function and parameters

class SPECT.oop_model.Gate (index, maxthick, maxcount)

The class Gate creates a gate of the heart cycle with specific parameters that are specific for each gate

defect_maker (gate, start, slices_to_be_modified, extent, severity)

Making defects in each volume of each gate with specific size (extent) and degree (severity)

Parameters

Gate – a list of images of specific gate Start – the index of first slice that will be modified Slices_to_be_modified – no. of slices that will be modified Extent – extent Severity – severity

Dicom_implementation (slices, gateslices, base_repeation, path, name, start, slices_to_be_modified, extent, severity)

Very important function for implementation of generated images in the software with dicom format with specific dicom headers

Parameters

Slices – number of all generated slices that need to be implemented Gateslices – number of slices in each gate Base_repeation – number of repeated myocardial base images Path – path of slices that need to be implemented Name – patient name _as you want to name it

The rest of the arguments are used by the previous functions and defined in the docstrings of these functions

elipse(y_point, endo_long_axis, base_radius)

the function used to produce shape

Parameters:	y_point – y_point Endo_long_axis – max long axis value (distance from base to apex) Base_radius – max short axis value (diameter of base)
Return output:	The x_point related to the inserted

Gray_scale_converter (imgno)

Modification of slices that are grey scale_converted by reverse the grey scale of them ‘before dicom implementation

Parameters:	Imgno – image _slice name
Return output:	The same image with grey scale reversed

noise_maker (gate)

Generating poisson noise in the empty pixels of each image

Parameters:	Gate – a list of images of specific gate
Return output:	A list of images of specific gate with noised background

Poisson (lampda)

generating randomly distributed Poisson series used for noise.

Parameters:

lampda – the standard deviation of the series

Slicemaker (r1, r2, x_var, k, SD, points, num, midcount)

used to make each slice

Parameters:

r1 – outer circle diameter. r2 – inner circle diameter. x_var – increment between mini_circles that produce each circle variation in r1 and r2. k – initial count of mini circles. SD – standard deviation of the iscount function that used to make counts in each slice iscount distributed. Points – related to number of mini_circles used to produce each slice. Num – numerator to name images when saving them. Midcount – mid_myocardial count of a volume

Return output:

Of this function is one gate with specific parameters

Smoothing_maker(gate, sigma = 1, order = 0, mode = 'constant', cval = 0.0)

to make smoothing _but before adding noise with sigma = 1, order = 0,mode = constant.

Parameters:

Gate – a list of images of specific gate in shape of 2d matrix. Sigma – standard deviation for Gaussian kernel. The standard deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes. Order – {0, 1, 2, 3} or sequence from same set. The order of the filter along each axis is given as a sequence of integers, or as a single number. An order of 0 corresponds to convolution with a Gaussian kernel. An order of 1, 2, or 3 corresponds to convolution with the first, second or third derivatives of a Gaussian. Higher order derivatives are not implemented. Mode – {‘reflect’, ‘constant’, ‘nearest’, ‘mirror’, ‘wrap’}, optional. The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to ‘constant’. Default is ‘reflect’. cval – scalar, optional Value to fill past edges of input if mode is ‘constant’. Default is 0.0

Return output:

List of images of specific gate _after smoothing in shape of 2d matrices

Starter()

used to generate some empty images required for implementation in the software

used_pixels_counter(gate)

counter of the used pixels of images _non zero _pixels in images useful for accurate volume calculation used before adding noise

Parameters:

Gate – a list of images of specific gate