Postopki za avtomatsko rekonstrukcijo in obdelavo CT slik v računalniškem programu syngo.via : diplomsko delo

Orlač, Alen (author), Alukić, Erna (mentor), Kaučič, Aleš (comentor), Žibert, Janez (reviewer)

Uvod: Računalniška tomografija (CT) je tehnika slikovne diagnostike, pri kateri snop rentgenske svetlobe potuje skozi izbrani anatomski predel, medtem ko se rentgenska cev in slikovni sprejemnik vrtita okrog pacienta. Rentgenska cev in detektorji se morajo zavrteti za vsaj 180 stopinj okrog pacienta, da dobimo dovolj atenuacijskih podatkov, iz katerih lahko računalnik s pomočjo algoritma rekonstruira sliko sprejemljive kakovosti. Rekonstrukcijo CT slik v osnovi delimo v dve večji kategoriji: analitična in iterativna rekonstrukcija. Poznamo več različnih tipov analitične rekonstrukcije slik, med njimi pa je najbolj uporabljena filtrirana povratna projekcija. Hiter tehnološki razvoj nam omogoča nove tehnike rekonstrukcije slik, med katerimi se največ pozornosti namenja uporabi umetne inteligence. Glaven namen je zmanjšati sevalno obremenitev za paciente, hkrati pa izboljšati kakovost CT slik. Namen: Namen diplomskega dela je predstavitev avtomatske rekonstrukcije CT slik ter vloge umetne inteligence pri CT slikanju. Predstavitev zajema uporabo umetne inteligence na področju rekonstrukcije, opis prednosti in slabosti, kje so meje uporabe umetne inteligence v slikovni diagnostiki, ter primerjava rekonstrukcije umetne inteligence in radiološkega inženirja. Metode dela: V diplomskem delu sem uporabil opisno metodo s pregledom literature, ki sem jo iskal v slovenskem in predvsem angleškem jeziku. Za izvedbo meritev in izdelavo rekonstrukcij CT slik sem uporabil program syngo.via na Zdravstveni fakulteti v Ljubljani. Rezultati: V rezultatih so prikazane funkcije, ki jih zmore programska oprema syngo.via. Prikazane so običajna transverzalna rekonstrukcija glave, samodejno označevanje reber in vretenc, ocena kalcinacije koronarnih arterij, meritve stenoze, lumna in dolžine torakalne aorte, prikaz arterij spodnjih okončin v celotnem poteku, avtomatska segmentacija pljuč ter avtomatsko odkrivanje pljučnih nodulov. Razprava in zaključek: Z diplomskim delom sem spoznal teoretični del algoritmov za rekonstrukcijo in obdelavo CT slik. Na podlagi obdelav CT slik, ki sem jih izvedel s programom syngo.via, sem ugotovil, da umetna inteligenca olajša delo radiološkemu inženirju, hkrati pa skrajša čas rekonstruiranja in obdelovanja slik. Umetna inteligenca je pri vseh izvedenih obdelavah dosegla pričakovane standarde in se lahko enakovredno primerja z obdelavami, ki jih izvede radiološki inženir. Na nekaterih področjih radioloških posegov se umetna inteligenca že rutinsko uporablja, bo pa še preteklo kar nekaj časa pred bo umetna inteligenca sposobna samostojno interpretirati slike in postaviti diagnozo.

• Language: Slovenian
• Keywords: diplomska dela, radiološka tehnologija, računalniška tomografija, rekonstrukcija slik, avtomatska rekonstrukcija slik, umetna inteligenca, algoritem in programska oprema
• Work type: Bachelor thesis/paper
• Typology: 2.11 - Undergraduate Thesis
• Organization: ZF - Faculty of Health Sciences
• Place of publishing: Ljubljana
• Publisher: [A. Orlač]
• Year: 2023
• Number of pages: 44 str.
• PID: 20.500.12556/RUL-150108
• UDC: 616-07
• COBISS.SI-ID: 164564739
• Publication date in RUL: 14.09.2023

Secondary language

• Language: English
• Title: Procedures for automatic reconstruction and processing of CT images in syngo.via : diploma work

Abstract

Introduction: Computed tomography (CT) is a diagnostic imaging technique in which a beam of X-ray light is passed through a selected anatomical region while the X-ray tube and the detectors rotate around the patient. The X-ray tube and detectors must be rotated at least 180 degrees around the patient to obtain sufficient attenuation data from which the computer can reconstruct an image of acceptable quality using an algorithm. CT image reconstruction is basically divided into two major categories: analytical and iterative reconstruction. There are several different types of analytical image reconstruction, the most widely used of which is filtered backprojection. Rapid technological developments are enabling new techniques for image reconstruction, with the most attention being paid to the use of artificial intelligence. The main aim is to reduce the radiation esposure on patients while improving the quality of CT images. Purpose: The purpose of this diploma work is to present the automatic reconstruction of CT images and the role of artificial intelligence in CT imaging. The presentation covers the use of AI in reconstruction, a description of the advantages and disadvantages, where the limits of AI in diagnostic imaging lie, and a comparison of reconstruction made by AI and by radiological engineer. Methods: In my diploma work I used a descriptive method with a literature review, which I searched in Slovene and mainly in English. I used the syngo.via software at the Faculty of Medicine in Ljubljana to perform the measurements and reconstruct the CT images. Results: In the results, I have presented functions that the syngo.via software can perform. The transverze head reconstruction, automatic rib and vertebral labeling, coronary artery calcification assessment, stenosis, length and diameter measurement of thoracic aorta, full course view of the lower limb arteries, automatic lung segmentation and automatic detection of pulmonary nodules are shown. Discussion and conclusion: My diploma work has introduced me to the theoretical part of algorithms for CT image reconstruction and postprocessing. Based on the CT image processing I have performed with syngo. via, I have found that artificial intelligence makes the radiology engineer's job easier, while reducing the time needed to reconstruct and process the images. The artificial intelligence performed all of the procedures to the expected standards and can be compared as equaly good as procedures performed by a radiological engineer. Artificial intelligence is already routinely used in some areas of radiology, but it will take some time before artificial intelligence will able to independently interpret images and establish a diagnosis.

• Keywords: diploma theses, radiologic technology, computed tomography, reconstruction of images, automatis reconstruction of images, artificial intelligence, algorithm and software