We have witnessed the unprecedented success of deep learning in virtually all areas of science and industry, with medical image analysis not being an exception here. Although there are a plethora of deep learning-powered techniques that established the state of the art in the field, e.g., in the context of automatic delineation of human organs and tumors in various image modalities, deploying...
Poor air quality and its negative impact on health is currently one of the civilizational problems in Poland. The aim of this study was an attempt to verify and examine, on the basis of data on the number and causes of deaths registered in Bielański Hospital in Warsaw, the increase in the number of deaths in Poland in January 2017 recorded by Statistics Poland.
We analysed the data on the...
Monte Carlo simulation of particle tracking in matter is the reference simulation method in the field of medical physics. It is heavily used in various applications such as
- patient dose distribution estimation in different therapy modalities (radiotherapy, protontherapy or ion therapy) or for radio-protection investigations of ionizing radiation-based imaging systems (CT, nuclear...
In PET medical imaging, the reconstruction of the spatial distribution
of the radiotracer in patient’s body is based on the photon pairs
grouped into time coincidences. Due to the limited resolution the selected
coincidences contain a fraction of events with a photon scattered in the
patient or detector material and photons accidentally registered in a coincidence.
Scatters and accidentals...
One of the biggest challenges in the deep learning application to the
medical imaging domain is the availability of training data. A promising
avenue to mitigate this problem is the usage of Generative Adversarial
Networks (GAN) to generate images to increase the size of training data
sets. A GAN is a class of unsupervised learning methods in which two
networks (generator and...
Radiotherapy aims at treating patients with cancer using ionising radiation. However, a key step is the optimization of the treatment. This is done using an inverse-planning approach where the treatment goals are encoded into a cost-function to minimize. The latter can be either non-convex or non-smooth with several local minima.
Quantum computers may efficiently solve this problem thanks to...
