CT iterative reconstruction algorithms: a task-based image quality assessment

Greffier, Joël; Frandon, Julien; Larbi, Ahmed; Beregi, Jean‐Paul; Pereira, F.

doi:10.1007/s00330-019-06359-6

Cited by 108 publications

(94 citation statements)

References 25 publications

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“…This was especially successful for small and subtle features. Detectability was calculated utilizing a non-prewhitening matched filter with eye filter (NPWE) model as a surrogate for human perception that includes noise and resolution [32]. A potential for dose reduction was furthermore shown in the clinical context in a study from our institution in 2020.…”

Section: Deep Learning Reconstruction Algorithms In the Clinical Routinementioning

confidence: 99%

Deep Learning CT Image Reconstruction in Clinical Practice

Arndt

Güttler

Heinrich

et al. 2020

Rofo

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Background Computed tomography (CT) is a central modality in modern radiology contributing to diagnostic medicine in almost every medical subspecialty, but particularly in emergency services. To solve the inverse problem of reconstructing anatomical slice images from the raw output the scanner measures, several methods have been developed, with filtered back projection (FBP) and iterative reconstruction (IR) subsequently providing criterion standards. Currently there are new approaches to reconstruction in the field of artificial intelligence utilizing the upcoming possibilities of machine learning (ML), or more specifically, deep learning (DL). Method This review covers the principles of present CT image reconstruction as well as the basic concepts of DL and its implementation in reconstruction. Subsequently commercially available algorithms and current limitations are being discussed. Results and Conclusion DL is an ML method that utilizes a trained artificial neural network to solve specific problems. Currently two vendors are providing DL image reconstruction algorithms for the clinical routine. For these algorithms, a decrease in image noise and an increase in overall image quality that could potentially facilitate the diagnostic confidence in lesion conspicuity or may translate to dose reduction for given clinical tasks have been shown. One study showed equal diagnostic accuracy in the detection of coronary artery stenosis for DL reconstructed images compared to IR at higher image quality levels. Consequently, a lot more research is necessary and should aim at diagnostic superiority in the clinical context covering a broadness of pathologies to demonstrate the reliability of such DL approaches. Key Points: Citation Format

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Section: Deep Learning Reconstruction Algorithms In the Clinical Routinementioning

confidence: 99%

Deep Learning CT Image Reconstruction in Clinical Practice

Arndt

Güttler

Heinrich

et al. 2020

Rofo

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show abstract

“…Limitations of the present study include that the results only apply to contrast medium-enhanced neck imaging and the CT scanner and techniques used in this study. For example, IR algorithms from different vendors have been shown to have different effects on low-contrast detectability [31,32]. Also, due to the small number of phantoms investigated, signal locations were not completely random.…”

Section: Discussionmentioning

confidence: 99%

Task-based assessment of neck CT protocols using patient-mimicking phantoms—effects of protocol parameters on dose and diagnostic performance

et al. 2020

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Objectives To assess how modifying multiple protocol parameters affects the dose and diagnostic performance of a neck CT protocol using patient-mimicking phantoms and task-based methods. Methods Six patient-mimicking neck phantoms containing hypodense lesions of 1 cm diameter and 30 HU contrast and one non-lesion phantom were examined with 36 CT protocols. All possible combinations of the following parameters were investigated: 100- and 120-kVp tube voltage; tube current modulation (TCM) noise levels of SD 7.5, 10, and 14; pitches of 0.637, 0.813, and 1.388; filtered back projection (FBP); and iterative reconstruction (AIDR 3D). Dose-length products (DLPs) and lesion detectability (assessed by 14 radiologists) were compared with the clinical standard protocol (120 kVp, TCM SD 7.5, 0.813 pitch, AIDR 3D). Results The DLP of the standard protocol was 25 mGy•cm; the area under the curve (AUC) was 0.839 (95%CI: 0.790–0.888). Combined effects of tube voltage reduction to 100 kVp and TCM noise level increase to SD 10 optimized protocol performance by improving dose (7.3 mGy•cm) and detectability (AUC 0.884, 95%CI: 0.844–0.924). Diagnostic performance was significantly affected by the TCM noise level at 120 kVp (AUC 0.821 at TCM SD 7.5 vs. 0.776 at TCM SD 14, p = 0.003), but not at 100-kVp tube voltage (AUC 0.839 at TCM SD 7.5 vs. 0.819 at TCM SD 14, p = 0.354), the reconstruction method at 100 kVp (AUC 0.854 for AIDR 3D vs. 0.806 for FBP, p < 0.001), but not at 120-kVp tube voltage (AUC 0.795 for AIDR 3D vs. 0.793 for FBP, p = 0.822), and the tube voltage for AIDR 3D reconstruction (p < 0.001), but not for FBP (p = 0.226). Conclusions Combined effects of 100-kVp tube voltage, TCM noise level of SD 10, a pitch of 0.813, and AIDR 3D resulted in an optimal neck protocol in terms of dose and diagnostic performance. Protocol parameters were subject to complex interactions, which created opportunities for protocol improvement. Key Points • A task-based approach using patient-mimicking phantoms was employed to optimize a CT system for neck imaging through systematic testing of protocol parameters. • Combined effects of 100-kVp tube voltage, TCM noise level of SD 10, a pitch of 0.813, and AIDR 3D reconstruction resulted in an optimal protocol in terms of dose and diagnostic performance. • Interactions of protocol parameters affect diagnostic performance and should be considered when optimizing CT techniques.

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“…(2) As for reconstruction of images and XAS, Filtered Back-Projection (FBP) was employed in this experiment because it is the most widely used algorithm for commercial and medical CT applications [28]. In contrast to FBP on the quality of image reconstruction and the classification difference of reconstructed XAS, we also adopted ART and ML-EM as representative of iterative algorithm, which has been proven it is capable of reducing noise and obtaining agreeable imaging quality at low doses [29].…”

Section: Tube Voltage (Kv)mentioning

confidence: 99%

Effect of Reconstruction Algorithm on the Identification of 3D Printing Polymers Based on Hyperspectral CT Technology Combined with Artificial Neural Network

Zheng

Wang

et al. 2020

Materials

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Hyperspectral X-ray CT (HXCT) technology provides not only structural imaging but also the information of material components therein. The main purpose of this study is to investigate the effect of various reconstruction algorithms on reconstructed X-ray absorption spectra (XAS) of components shown in the CT image by means of HXCT. In this paper, taking 3D printing polymer as an example, seven kinds of commonly used polymers such as thermoplastic elastomer (TPE), carbon fiber reinforced polyamide (PA-CF), acrylonitrile butadiene styrene (ABS), polylactic acid (PLA), ultraviolet photosensitive resin (UV9400), polyethylene terephthalate glycol (PETG), and polyvinyl alcohol (PVA) were selected as samples for hyperspectral CT reconstruction experiments. Seven kinds of 3D printing polymer and two interfering samples were divided into a training set and test sets. First, structural images of specimens were reconstructed by Filtered Back-Projection (FBP), Algebra Reconstruction Technique (ART) and Maximum-Likelihood Expectation-Maximization (ML-EM). Secondly, reconstructed XAS were extracted from the pixels of region of interest (ROI) compartmentalized in the images. Thirdly, the results of principal component analysis (PCA) demonstrated that the first four principal components contain the main features of reconstructed XAS, so we adopted Artificial Neural Network (ANN) trained by the reconstructed XAS expressed by the first four principal components in the training set to identify that the XAS of corresponding polymers exist in both of test sets from the training set. The result of ANN displays that FBP has the best performance of classification, whose ten-fold cross-validation accuracy reached 99%. It suggests that hyperspectral CT reconstruction is a promising way of getting image features and material features at the same time, which can be used in medical imaging and nondestructive testing.

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CT iterative reconstruction algorithms: a task-based image quality assessment

Cited by 108 publications

References 25 publications

Deep Learning CT Image Reconstruction in Clinical Practice

Deep Learning CT Image Reconstruction in Clinical Practice

Task-based assessment of neck CT protocols using patient-mimicking phantoms—effects of protocol parameters on dose and diagnostic performance

Effect of Reconstruction Algorithm on the Identification of 3D Printing Polymers Based on Hyperspectral CT Technology Combined with Artificial Neural Network

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