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Deep Learning CT Image Reconstruction in Clinical Practice CT-Bildrekonstruktion Mit Deep Learning in Der Klinischen Praxis

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Deep Learning CT Image Reconstruction in Clinical Practice CT-Bildrekonstruktion Mit Deep Learning in Der Klinischen Praxis Published online: 2020-12-10 Review Deep Learning CT Image Reconstruction in Clinical Practice CT-Bildrekonstruktion mit Deep Learning in der klinischen Praxis Authors Clemens Arndt, Felix Güttler, Andreas Heinrich, Florian Bürckenmeyer, Ioannis Diamantis, Ulf Teichgräber Affiliation Ergebnis und Schlussfolgerung DL als Methode des Ma- Department of Radiology, Jena University Hospital, Jena, chine Learning nutzt im Allgemeinen ein trainiertes, künstli- Germany ches, neuronales Netzwerk zur Lösung von Problemen. Ak- tuell sind DL-Rekonstruktionsalgorithmen von 2 Herstellern Key words für die klinische Routine verfügbar. In den bisherigen Studien computed tomography, image reconstruction, deep neural konnte eine Reduktion des Bildrauschens und eine Verbesser- network, deep learning, artificial intelligence, image ung der Gesamtqualität gezeigt werden. Eine Studie zeigte, denoising bei höherer Qualität der mittels DL rekonstruierten Bilder, received 11.05.2020 eine vergleichbare diagnostische Genauigkeit zur IR für die accepted 20.08.2020 Detektion von Koronarstenosen. Weitere Studien sind not- published online 10.12.2020 wendig und sollten vor allem auch auf eine klinische Überle- genheit abzielen, während dabei ein breiter Umfang an Patho- Bibliography logien umfasst werden muss. Fortschr Röntgenstr 2021; 193: 252–261 DOI 10.1055/a-1248-2556 Kernaussagen: ▪ ISSN 1438-9029 Nach der aktuell verbreiteten iterativen Rekonstruktion © 2020. Thieme. All rights reserved. können CT-Schnittbilder im klinischen Alltag nun auch Georg Thieme Verlag KG, Rüdigerstraße 14, mittels Deep Learning (DL) als Methode der künstlichen 70469 Stuttgart, Germany Intelligenz rekonstruiert werden. ▪ Durch DL-Rekonstruktionen können Bildrauschen vermin- Correspondence dert, Bildqualität verbessert und möglicherweise Strah- Dr. Clemens Arndt lung reduziert werden. Institut für Diagnostische und Interventionelle Radiologie, ▪ Eine diagnostische Überlegenheit im klinischen Kontext Universitätsklinikum Jena, Am Klinikum 1, 07751 Jena, sollte in kommenden Studien gezeigt werden. Germany Tel.:++49/3641/9324839 ABSTRACT [email protected] Background Computed tomography (CT) is a central modal- ity in modern radiology contributing to diagnostic medicine in ZUSAMMENFASSUNG almost every medical subspecialty, but particularly in emer- Hintergrund DieComputertomografie(CT)isteinezentrale gency services. To solve the inverse problem of reconstructing Modalität der modernen Radiologie, die in nahezu allen medizi- anatomical slice images from the raw output the scanner nischen Fachdisziplinen, insbesondere aber in der Notfallmedi- measures, several methods have been developed, with fil- zin, einen wichtigen Bestandteil zur Gesundheitsversorgung tered back projection (FBP) and iterative reconstruction (IR) This document was downloaded for personal use only. Unauthorized distribution is strictly prohibited. liefert. Die Berechnung bzw. Rekonstruktion von Schnittbildern subsequently providing criterion standards. Currently there aus den rohen Messwerten der CT-Untersuchungen stellt are new approaches to reconstruction in the field of artificial mathematisch ein inverses Problem dar. Bisher waren die gefil- intelligence utilizing the upcoming possibilities of machine terte Rückprojektion und die iterative Rekonstruktion (IR) die learning (ML), or more specifically, deep learning (DL). Goldstandards, um schnell und zuverlässig Bilder zu berech- Method This review covers the principles of present CT image nen. Aktuell gibt es im Rahmen neuerer Entwicklungen im reconstruction as well as the basic concepts of DL and its imple- Bereich der künstlichen Intelligenz mit dem Deep Learning mentation in reconstruction. Subsequently commercially avail- (DL) einen weiteren Ansatzweg in der klinischen Routine. able algorithms and current limitations are being discussed. Methode Dieser Übersichtsartikel erläutert die bisherigen Results and Conclusion DL is an ML method that utilizes a Prinzipien der Bildrekonstruktion, das Konzept des DL und trained artificial neural network to solve specific problems. das Anwendungsprinzip zur Rekonstruktion. Anschließend Currently two vendors are providing DL image reconstruction werden kommerziell verfügbare Algorithmen und bisherige algorithms for the clinical routine. For these algorithms, a Studien diskutiert und Grenzen sowie Probleme dargestellt. decrease in image noise and an increase in overall image qual- 252 Arndt C et al. Deep Learning CT… Fortschr Röntgenstr 2021; 193: 252–261 | © 2020. Thieme. All rights reserved. ity that could potentially facilitate the diagnostic confidence ▪ DL image reconstruction algorithms decrease image noise, in lesion conspicuity or may translate to dose reduction for improve image quality, and have potential to reduce given clinical tasks have been shown. One study showed equal radiation dose. diagnostic accuracy in the detection of coronary artery steno- ▪ Diagnostic superiority in the clinical context should be sis for DL reconstructed images compared to IR at higher im- demonstrated in future trials. age quality levels. Consequently, a lot more research is neces- Citation Format sary and should aim at diagnostic superiority in the clinical ▪ Arndt C, Güttler F, Heinrich A et al. Deep Learning CT context covering a broadness of pathologies to demonstrate Image Reconstruction in Clinical Practice. Fortschr the reliability of such DL approaches. Röntgenstr 2021; 193: 252–261 Key Points: ▪ Following iterative reconstruction, there is a new approach to CT image reconstruction in the clinical routine using deep learning (DL) as a method of artificial intelligence. Background disadvantage of FBP is the strong relationship between radiation dose and noise, that is especially problematic in obese patients In the clinical routine, the crucial aim of CT imaging is to provide [7]. In the ongoing advancement in CT imaging from measured clinically relevant information or more specifically, information as data to algorithmic optimized data, FBP is closest to measured to whether a specific feature is found or not, with reasonable cer- data. As computational power for industrial purposes and graphi- tainty. However, the certainty of that radiological decision de- cal processing was advancing, FBP was gradually replaced by pends heavily on the image quality, e. g. contrast, resolution, iterative reconstruction [8]. noise, and artifacts [1]. Alongside image quality, radiation dose is an important ele- ment in CT protocol optimization. Furthermore, acquisition and Iterative reconstruction reconstruction time should be acceptable, especially in a high-lev- el workflow setting such as emergency radiology. Both image The IR method starts with the creation of an image estimate that quality and radiation dose are affected by acquisition parameters, is basically equal to FBP. Subsequently, the image estimate is pro- patient constitution, and positioning [2, 3]. Another important jected forward into an artificial sinogram and iteratively corrected element of the CT imaging process, influencing quality and recon- by comparison with the original raw data sinogram. When a pre- struction time, is the mathematical transformation of the raw defined endpoint condition in the algorithm is fulfilled, the itera- data into a three-dimensional volume viewable as an anatomical tive cycle is stopped, and the images are readily reconstructed ▶ slice image. To solve this transformation, several reconstruction ( Fig. 2). However, the iterative cycle can get implemented in algorithms have been developed [4]. Algorithms routinely used different steps, e. g. the raw data and the image domain. Addi- since the introduction of computer tomography are filtered back tionally, further statistic adjustments and modelling can be used. projection (FBP), which was used into the early 2010 s, and iterative Roughly iterative algorithms can be divided into methods without reconstruction (IR), which was subsequently used [5]. To under- statistics (e. g. ART, the first iterative algorithm), methods with stand the ongoing developments in CT image reconstruction, modeling of photon counting statistics (e. g. hybrid IR), and mod- the principles of FBP and IR are briefly reviewed, followed by an el-based methods beyond photon counting (MBIR) [9, 10]. With introduction to deep learning (DL). enhanced image quality, a reduction in patient dose was feasible. In a systematic review from 2015, the mean effective dose for contrast-enhanced chest CT with IR decreased by 50 % compared This document was downloaded for personal use only. Unauthorized distribution is strictly prohibited. Filtered back projection to FBP [11]. IR is now considered criterion standard for CT image reconstruction. FBP is the most commonly used analytical reconstruction method owing to its computational efficiency and stability creating ima- ges rapidly. The mathematical approach to FBP is primarily the Artificial neural networks and deep learning idea that a projection, consisting of measurements in multiple an- gles, can be back projected into a model of the scanned object by The recent rise in popularity of artificial intelligence is primarily an inverse radon transformation with a high-pass filter (▶ Fig. 1). due to the advances in the field of artificial neural networks Without a filter, smearing the projection values into the radiation (ANNs). ANNs are a subfield of machine learning (ML). In machine path would result in a blurry object morphology. This filter or ker- learning a model
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