Non-Positive Corrections and Variance Models for Iterative Post-Log Reconstruction of Extremely Low-Dose CT Data

Gallium-68 prostate-specific membrane antigen positron emission tomography (68Ga-PSMA-PET) is a highly sensitive method to detect prostate cancer (PC) metastases. Visual discrimination between malignant and physiologic/unspecific tracer accumulation by a nuclear medicine (NM) specialist is essential for image interpretation. In the future, automated machine learning (ML)-based tools will assist physicians in image analysis. The aim of this work was to develop a tool for analysis of 68Ga-PSMA-PET images and to compare its efficacy to that of human readers. Five different ML methods were compared and tested on multiple positron emission tomography/computed tomography (PET/CT) data-sets. Forty textural features extracted from both PET- and low-dose CT data were analyzed. In total, 2419 hotspots from 72 patients were included. Comparing results from human readers to those of ML-based analyses, up to 98% area under the curve (AUC), 94% sensitivity (SE), and 89% specificity (SP) were achieved. Interestingly, textural features assessed in native low-dose CT increased the accuracy significantly. Thus, ML based on 68Ga-PSMA-PET/CT radiomics features can classify hotspots with high precision, comparable to that of experienced NM physicians. Additionally, the superiority of multimodal ML-based analysis considering all PET and low-dose CT features was shown. Morphological features seemed to be of special additional importance even though they were extracted from native low-dose CTs.

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Analysis of statistical models for iterative reconstruction of extremely low-dose CT data

2014 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) ◽

10.1109/nssmic.2014.7430762 ◽

2014 ◽

Author(s):

Soo Mee Kim ◽

Adam M. Alessio ◽

David S. Perlmutter ◽

Jean-Baptiste Thibault ◽

Bruno De Man ◽

...

Keyword(s):

Iterative Reconstruction ◽

Statistical Models ◽

Low Dose ◽

Low Dose Ct ◽

Ct Data

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Noise reduction with low dose CT data based on a modified ROF model

Optics Express ◽

10.1364/oe.20.017987 ◽

2012 ◽

Vol 20 (16) ◽

pp. 17987 ◽

Cited By ~ 26

Author(s):

Yining Zhu ◽

Mengliu Zhao ◽

Yunsong Zhao ◽

Hongwei Li ◽

Peng Zhang

Keyword(s):

Noise Reduction ◽

Low Dose ◽

Low Dose Ct ◽

Rof Model ◽

Ct Data

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A Fully Automatic CAD-CTC System Based on Curvature Analysis for Standard and Low-Dose CT Data

IEEE Transactions on Biomedical Engineering ◽

10.1109/tbme.2007.909506 ◽

2008 ◽

Vol 55 (3) ◽

pp. 888-901 ◽

Cited By ~ 17

Author(s):

T.A. Chowdhury ◽

P.F. Whelan ◽

O. Ghita

Keyword(s):

Low Dose ◽

Low Dose Ct ◽

Curvature Analysis ◽

Fully Automatic ◽

Ct Data

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Noise reduction method in low-dose CT data combining neural networks and an iterative reconstruction technique

International Forum on Medical Imaging in Asia 2019 ◽

10.1117/12.2521445 ◽

2019 ◽

Author(s):

Dahim Choi ◽

Juhee Kim ◽

Seung-Hoon Chae ◽

Jongduk Baek ◽

Andreas Maier ◽

...

Keyword(s):

Neural Networks ◽

Noise Reduction ◽

Iterative Reconstruction ◽

Low Dose ◽

Reduction Method ◽

Reconstruction Technique ◽

Low Dose Ct ◽

Iterative Reconstruction Technique ◽

Ct Data

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Pelvic bone CT: can tin-filtered ultra-low-dose CT and virtual radiographs be used as alternative for standard CT and digital radiographs?

European Radiology ◽

10.1007/s00330-021-07824-x ◽

2021 ◽

Author(s):

Christoph Stern ◽

Stefan Sommer ◽

Christoph Germann ◽

Julien Galley ◽

Christian W. A. Pfirrmann ◽

...

Keyword(s):

Dose Reduction ◽

Low Dose ◽

Projection Algorithm ◽

Pelvic Bone ◽

Low Dose Ct ◽

Digital Radiographs ◽

Pelvic Radiographs ◽

Tin Filtration ◽

Noise Ratio ◽

Ct Data

Abstract Objectives To compare ultra-low-dose CT (ULD-CT) of the osseous pelvis with tin filtration to standard clinical CT (CT), and to assess the quality of computed virtual pelvic radiographs (VRs). Methods CT protocols were optimized in a phantom and three pelvic cadavers. Thirty prospectively included patients received both standard CT (automated tube voltage selection and current modulation) and tin-filtered ULD-CT of the pelvis (Sn140kV/50mAs). VRs of ULD-CT data were computed using an adapted cone beam–based projection algorithm and were compared to digital radiographs (DRs) of the pelvis. CT and DR dose parameters and quantitative and qualitative measures (1 = worst, 4 = best) were compared. CT and ULD-CT were assessed for osseous pathologies. Results Dose reduction of ULD-CT was 84% compared to CT, with a median effective dose of 0.38 mSv (quartile 1–3: 0.37–0.4 mSv) versus 2.31 mSv (1.82–3.58 mSv; p < .001), respectively. Mean dose of DR was 0.37 mSv (± 0.14 mSv). The median signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of bone were significantly higher for CT (64.3 and 21.5, respectively) compared to ULD-CT (50.4 and 18.8; p ≤ .01), while ULD-CT was significantly more dose efficient (figure of merit (FOM) 927.6) than CT (FOM 167.6; p < .001). Both CT and ULD-CT were of good image quality with excellent depiction of anatomy, with a median score of 4 (4–4) for both methods (p = .1). Agreement was perfect between both methods regarding the prevalence of assessed osseous pathologies (p > .99). VRs were successfully calculated and were equivalent to DRs. Conclusion Tin-filtered ULD-CT of the pelvis at a dose equivalent to standard radiographs is adequate for assessing bone anatomy and osseous pathologies and had a markedly superior dose efficiency than standard CT. Key Points • Ultra-low-dose pelvic CT with tin filtration (0.38 mSv) can be performed at a dose of digital radiographs (0.37 mSv), with a dose reduction of 84% compared to standard CT (2.31 mSv). • Tin-filtered ultra-low-dose CT had lower SNR and CNR and higher image noise than standard CT, but showed clear depiction of anatomy and accurate detection of osseous pathologies. • Virtual pelvic radiographs were successfully calculated from ultra-low-dose CT data and were equivalent to digital radiographs.

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