Restoring lung CT images from photographs for AI ap- plications

Type: MA thesis

Status: finished

Date: April 26, 2021 - October 26, 2021

Supervisors: Daniel Stromer, Andreas Maier

Motivation: Interstitial lung diseases (ILD) describe a group of acute or chronic diseases
of the interstitium or the alveoli [1]. The diagnosis of ILD is very challenging since there are
more than 200 di erent diseases with each of them occurring only rarely. The modality of
choice for diagnosing ILD is computed tomography (CT), even though the di erent diseases
cause similar or sometimes even identical imaging signs in the lung. Therefore, the results of
the CT-scan have to be combined with additional information like the history of the patient,
the symptoms and the laboratory values [2]. Approaches to assist doctors by including
machine learning algorithms like a similar patient search (SPS) already exist [3]. The idea
is to develop an app to take a photograph of the CT-scan and process the image in order
to start a SPS. The main focus of this work will be on the processing of the photograph in
order to restore the CT-properties of the original scan.
Methods: Taking photographs of a CT-scan on a screen leads to a loss of the Houns eld
Units and introduces artifacts like moire patterns, light and mirroring artifacts and imbalanced
illumination. To restore the lung CT image from a photograph, a traditional
approach using lters in contrast to a deep learning approach will be investigated. The
new approach subtracts the screen pixel array in order to avoid moire patterns, removes
the other most critical artifacts from the photograph and restores the lung CT window by
converting the pixel values of the photograph back into Houns eld Units. The processed
photograph can then be send to the SPS tool in order to help doctors nd the right diagnosis.
The Master’s thesis covers the following aspects:
1. Identi cation of the most critical artifacts appearing in photographs
2. Investigation of traditional and deep learning based approaches for artifact reduction
3. Determination of reading room conditions
4. Determination of an adequate framework and test criteria
5. Implementation of an image processing algorithm based on a literature research and
the identi ed artifacts
6. Evaluation of the proposed method
Supervisors: Dr. Daniel Stromer, Dr. Christian Tietjen, Dr. Christoph Speier,
Dr. med. Johannes Haubold, Prof. Dr.-Ing. habil. Andreas Maier

[1] B. Schonhofer and M. Kreuter, \Interstitielle lungenerkrankungen,” in Referenz Inten-
sivmedizin (G. Marx, K. Zacharowski, and S. Kluge, eds.), pp. 287{293, Stuttgart: Georg
Thieme Verlag, 2020.
[2] M. Kreuter, U. Costabel, F. Herth, and D. Kirsten, eds., Seltene Lungenerkrankungen.
Berlin and Heidelberg: Springer, 2015.
[3] Siemens Healthcare GmbH, \Similar patient search: syngo.via: Va20a,” 2021.