Matrix Operations for Applications in Quantum Annealing

Quantum annealing is a promising technology for quantum
computing to solve quadratic problems. D Wave makes
quantum annealers and provides an open source Python
interface: Ocean [1]. Ocean’s hybrid models do not yet
support matrix problem formulations. Current approaches are
based on SymPy [ 2]–> slow for matrix problems


Speed up matrix operations for problem formulations



Development of a comprehensive SPECT phantom dataset using Monte Carlo Simulation


Single Photon Emission Computed Tomography (SPECT) [1] is a medical imaging technique used
to study the biological function and detection of various diseases in humans and animals. Due
to the low amount of radioactivity typically used in SPECT scans, we have a lot of noise in our
SPECT acquired images, and because it is an inverse problem we do not have an exact ground truth.
For this reason we simulate objects with numerical ground truth, that will be used to create our
simulated dataset. The created dataset can then be used to train a Neural Network, analyze noise,
test multiple reconstruction techniques or evaluate the effects of acquisition geometry.
The objective of this research laboratory is to generate a large dataset of SPECT images, that will
be useful in the applications of deep learning in medical image processing.


We simulate 100 phantoms with different shapes and properties e.g. attenuation and activity maps.
Simulating simple geometric phantoms such as spheres, cubes and cylinders is the first step of
this research laboratory. In the following step we generate alphabetic letters phantoms. Last we
simulate more realistic physical phantoms like the Shepp-Logan or XCAT phantoms. To simulate
measurements of these phantoms, we use SIMIND, a Monte Carlo based simulation program [2].
SIMIND can describe different scintillation cameras, that can be used to obtain sets of projection
images of the simulated phantom. SIMIND allows the adjustment of different acquisition parameters
e.g. photon energy, number of projections, detector size, energy resolution, allowing the creation of a
comprehensive database of SPECT acquisitions in terms of geometry, and acquisition configuration.
After postprocessing the projection data, we obtain the reconstructed 3D images from the data by
applying iterative reconstruction techniques like Ordered Subset Expectation Maximization (OSEM)
and Ordered Subset Conjugate Gradient Minimization (OSCGM).

Expected Results

At the end of this research laboratory, the student shall have a deeper knowledge of Monte Carlo
Simulation (MCS) and reconstruction for SPECT imaging. Further, the student shall have created
a dataset that will be available for future projects, including denoising, reconstruction and other
image processing related tasks. Additionally, the student shall summarize their findings in a short
report and write a documentation about the database and how to use it.


[1] Miles N Wernick and John N Aarsvold. Emission tomography: the fundamentals of PET and
SPECT. Elsevier, 2004.
[2] Michael Ljungberg and Sven-Erik Strand. A monte carlo program for the simulation of scintillation
camera characteristics. Computer methods and programs in biomedicine, 29(4):257–272,

A Review of Diagnosis Rheumatoid Arthritis, with Evaluating Parameters of Micro-CT Scanner and Laboratory Measurements

Simulation of Spike Artifact Obstructed MR Images for Machine Learning Methods

Deep Learning-based XRM Projection Super Resolution

Self-supervised XRM Super Resolution

Evaluation of an Attention U-Net for Glacier Segmentation

Evaluation of an Optimized U-Net for Glacier Segmentation

Evaluation of a Bayesian U-Net for Glacier Segmentation

CT Projection Inpainting Using Denoising Diffusion Probabilistic Models