ctrecon 0.1.8

an interface and some tools for computed tomography reconstruction

CTRECON: Some basic simulation tools for CT scanning

Development note:

I'm recording this here so I can make sure to undo at a future timepoint.

As of 0.1.7, with the addition of the ResultStore a few dependencies have "polluted" the tree:

• pandas
• smcore

smcore is included for serialization/deserialization (convenient for blob storage) pandas is is a nice tabular, familiar python interface to the SQLite backend.

Pandas may translate to a permanent dependency (about as close to a "fundamental" python package as numpy or matplotlib). smcore is not a dependency and must be removed at some point in the future.

The ResultStore is likely only a temporary resident in this package and is temporarily here for quick deployment and testing.

Motivation and Intent

More than "fast" or "efficient", I'm trying to bolt an interface on that feels intuitive and reusable. Something I want to reach for when developing a reserach question. The implemented math that backs this stuff should be swappable over time as better implementations are developed.

The idea is to apply some sane logical structure the many components required to go from simulated phantom, to CT sinogram, and back to image. My hope is that using the library feels like describing the experiment you're doing with normal language, and your code makes it feel obvious what experiment is being done.

• phantoms contains prebaked phantoms, as well as the tools to build your own
• scanners contains prebaked scan geometries, and the interface classes to build your own (and preserve compatibility with the rest of the toolchain).
• points contains different image types (collections of points for sampling) for reconstruction (such as pixel grids)
• recon contains code to perform reconstruction onto points objects

Environment setup

The package is available exclusively via pip. We strongly recommend using an environment manager (virtualenv, conda, micromamba, uv, etc.) to track and manage your dependencies.

pip install ctrecon

If you get a warning that matplotlib cannot show the window, you may also need to install a different backend for matplotlib (e.g., pip install PtQt5). Refer to the matplotlib documentation if you encounter any issues.

Hello world example

This example comes from the package's "unit tests". They're not actually unit tests because they're interactive, but they're great sanity checks to see if and how the package runs on your computer:

TODO

BYO Phantom

This is the full definition of our DebugPhantom:

class DebugPhantom(Phantom):
    def __init__(self):

        # tuneable elements, units are cm
        bg_attenuation = 0.0192
        ph_radius = 10.0

        elements = []

        bg = Circle(0, 0, ph_radius, bg_attenuation)

        findable_insert_1 = Circle(0, 5, 3.0, 0.04)
        findable_insert_2 = Circle(5, 0, 1.5, 0.04)

        # Intuitively, we draw from "bg" -> "fg"
        elements.append(bg)
        elements.append(findable_insert_1)
        elements.append(findable_insert_2)

        # When raycasting, its most efficient to intersect the fg first
        elements.reverse()

        # Save the elements of the phantom
        self.circles = elements

The concept is that we basically draw objects into the world space. We calculate sinograms via raycasting and each step iterates through the list of objects to check if we're inside. For that increment, we utilize the attenuation of the first object in the list that we encounter and move on.

The same phantom can be completely described via CSV as well. Note that the row order sets the object "precedence" in the sinogram generation process.

debug_phantom.csv:

5,0,1.5,0.04
0,5,3.0,0.04
0,0,10.0,0.0192

You can save a phantom a CSV file:

from ctrecon import phantoms

ph = phantoms.DebugPhantom()
ph.to_csv('debug_phantom.csv')

A phantom can be instantiate from such a CSV:

debug_ph = Phantom.from_csv("debug_phantom.csv")

This at least describes phantoms in a mostly portable manner. Free to use in other places and between research groups.

Scanning a phantom

scanners provides basic geometries and specific implementations of some geometries.

In keeping with our goal of language-like interaction to perform a scan:

ph = phantoms.Debug()
scanner = scanners.PencilBeam(...)
sinogram, metadata = scanner.scan(ph)

If a scanner is implemented correctly within our library, these three lines (using any phantom we provide and any scanner) must produce a valid sinogram. Moreover, the defaults should hopefully suffice for many applications.

The reason this can work is that we follow the philosophy of "pick good defaults" and require the scanner implementor to define a default_protocol.

class Scanner:
    def default_protocol(self) -> Protocol:
        raise NotYetImplemented

However specific scan protocols (including truncated scanning, sparse view, etc.) are among some of the most important ongoing research questions in the field.

Protocols

While scanners record physical details about the relationships between x-ray source and detector, as well as detector shape, etc., it doesn't desribe how that hardware should be used to scan an object.

How may projections we should acquire, at which angles, even "unconstrained" acquisitions coming from random locations, etc. is all modeled via an intermediate Trajectory data structure that represents a scan's geometric intent in an abstract sense so that it can be applied to multiple specific geometries.

Fundamentally, the Scanner object represents the physical constraints between the source and the detector.

Telemetry

If the protocol is the intent, telemetry represents what was actually executed by the scanner. In a simulation context, Telemetry will generally match the protocol exactly, however this abstraction layer could sit atop real hardware. Telemetry just allows us a dedicated path to report back information about the scan that was conducted.