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Dashtbani Moghari M, Sanaat A, Young N, Moore K, Zaidi H, Evans A, Fulton RR, Kyme AZ. Reduction of scan duration and radiation dose in cerebral CT perfusion imaging of acute stroke using a recurrent neural network. Phys Med Biol 2023; 68:165005. [PMID: 37327792 DOI: 10.1088/1361-6560/acdf3a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Accepted: 06/16/2023] [Indexed: 06/18/2023]
Abstract
Objective. Cerebral CT perfusion (CTP) imaging is most commonly used to diagnose acute ischaemic stroke and support treatment decisions. Shortening CTP scan duration is desirable to reduce the accumulated radiation dose and the risk of patient head movement. In this study, we present a novel application of a stochastic adversarial video prediction approach to reduce CTP imaging acquisition time.Approach. A variational autoencoder and generative adversarial network (VAE-GAN) were implemented in a recurrent framework in three scenarios: to predict the last 8 (24 s), 13 (31.5 s) and 18 (39 s) image frames of the CTP acquisition from the first 25 (36 s), 20 (28.5 s) and 15 (21 s) acquired frames, respectively. The model was trained using 65 stroke cases and tested on 10 unseen cases. Predicted frames were assessed against ground-truth in terms of image quality and haemodynamic maps, bolus shape characteristics and volumetric analysis of lesions.Main results. In all three prediction scenarios, the mean percentage error between the area, full-width-at-half-maximum and maximum enhancement of the predicted and ground-truth bolus curve was less than 4 ± 4%. The best peak signal-to-noise ratio and structural similarity of predicted haemodynamic maps was obtained for cerebral blood volume followed (in order) by cerebral blood flow, mean transit time and time to peak. For the 3 prediction scenarios, average volumetric error of the lesion was overestimated by 7%-15%, 11%-28% and 7%-22% for the infarct, penumbra and hypo-perfused regions, respectively, and the corresponding spatial agreement for these regions was 67%-76%, 76%-86% and 83%-92%.Significance. This study suggests that a recurrent VAE-GAN could potentially be used to predict a portion of CTP frames from truncated acquisitions, preserving the majority of clinical content in the images, and potentially reducing the scan duration and radiation dose simultaneously by 65% and 54.5%, respectively.
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Affiliation(s)
- Mahdieh Dashtbani Moghari
- School of Biomedical Engineering, Faculty of Engineering and Information Technologies, The University of Sydney, Sydney, Australia
| | - Amirhossein Sanaat
- Geneva University Hospitals, Division of Nuclear Medicine & Molecular Imaging, CH-1205 Geneva, Switzerland
| | - Noel Young
- Department of Radiology, Westmead Hospital, Sydney, Australia
- Medical imaging group, School of Medicine, Western Sydney University, Sydney, Australia
| | - Krystal Moore
- Department of Radiology, Westmead Hospital, Sydney, Australia
| | - Habib Zaidi
- Geneva University Hospitals, Division of Nuclear Medicine & Molecular Imaging, CH-1205 Geneva, Switzerland
| | - Andrew Evans
- Department of Aged Care & Stroke, Westmead Hospital, Sydney, Australia
- School of Health Sciences, University of Sydney, Sydney, Australia
| | - Roger R Fulton
- School of Health Sciences, University of Sydney, Sydney, Australia
- Department of Medical Physics, Westmead Hospital, Sydney, Australia
- The Brain & Mind Centre, The University of Sydney, Sydney, Australia
| | - Andre Z Kyme
- School of Biomedical Engineering, Faculty of Engineering and Information Technologies, The University of Sydney, Sydney, Australia
- The Brain & Mind Centre, The University of Sydney, Sydney, Australia
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Ghane B, Karimian A, Mostafapour S, Gholamiankhak F, Shojaerazavi S, Arabi H. Quantitative Analysis of Image Quality in Low-Dose Computed Tomography Imaging for COVID-19 Patients. JOURNAL OF MEDICAL SIGNALS & SENSORS 2023; 13:118-128. [PMID: 37448548 PMCID: PMC10336910 DOI: 10.4103/jmss.jmss_173_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 12/31/2021] [Accepted: 04/19/2022] [Indexed: 07/15/2023]
Abstract
Background Computed tomography (CT) scan is one of the main tools to diagnose and grade COVID-19 progression. To avoid the side effects of CT imaging, low-dose CT imaging is of crucial importance to reduce population absorbed dose. However, this approach introduces considerable noise levels in CT images. Methods In this light, we set out to simulate four reduced dose levels (60% dose, 40% dose, 20% dose, and 10% dose) of standard CT imaging using Beer-Lambert's law across 49 patients infected with COVID-19. Then, three denoising filters, namely Gaussian, bilateral, and median, were applied to the different low-dose CT images, the quality of which was assessed prior to and after the application of the various filters via calculation of peak signal-to-noise ratio, root mean square error (RMSE), structural similarity index measure, and relative CT-value bias, separately for the lung tissue and whole body. Results The quantitative evaluation indicated that 10%-dose CT images have inferior quality (with RMSE = 322.1 ± 104.0 HU and bias = 11.44% ± 4.49% in the lung) even after the application of the denoising filters. The bilateral filter exhibited superior performance to suppress the noise and recover the underlying signals in low-dose CT images compared to the other denoising techniques. The bilateral filter led to RMSE and bias of 100.21 ± 16.47 HU and - 0.21% ± 1.20%, respectively, in the lung regions for 20%-dose CT images compared to the Gaussian filter with RMSE = 103.46 ± 15.70 HU and bias = 1.02% ± 1.68% and median filter with RMSE = 129.60 ± 18.09 HU and bias = -6.15% ± 2.24%. Conclusions The 20%-dose CT imaging followed by the bilateral filtering introduced a reasonable compromise between image quality and patient dose reduction.
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Affiliation(s)
- Behrooz Ghane
- Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran
| | - Alireza Karimian
- Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran
| | - Samaneh Mostafapour
- Department of Radiology Technology, Faculty of Paramedical Sciences, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Faezeh Gholamiankhak
- Department of Medical Physics, Faculty of Medicine, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
| | - Seyedjafar Shojaerazavi
- Department of Cardiology, Ghaem Hospital Mashhad, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
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Horenko I, Pospíšil L, Vecchi E, Albrecht S, Gerber A, Rehbock B, Stroh A, Gerber S. Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed Tomography. J Imaging 2022; 8:jimaging8060156. [PMID: 35735955 PMCID: PMC9224620 DOI: 10.3390/jimaging8060156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 05/18/2022] [Accepted: 05/19/2022] [Indexed: 12/04/2022] Open
Abstract
We propose a pipeline for synthetic generation of personalized Computer Tomography (CT) images, with a radiation exposure evaluation and a lifetime attributable risk (LAR) assessment. We perform a patient-specific performance evaluation for a broad range of denoising algorithms (including the most popular deep learning denoising approaches, wavelets-based methods, methods based on Mumford−Shah denoising, etc.), focusing both on accessing the capability to reduce the patient-specific CT-induced LAR and on computational cost scalability. We introduce a parallel Probabilistic Mumford−Shah denoising model (PMS) and show that it markedly-outperforms the compared common denoising methods in denoising quality and cost scaling. In particular, we show that it allows an approximately 22-fold robust patient-specific LAR reduction for infants and a 10-fold LAR reduction for adults. Using a normal laptop, the proposed algorithm for PMS allows cheap and robust (with a multiscale structural similarity index >90%) denoising of very large 2D videos and 3D images (with over 107 voxels) that are subject to ultra-strong noise (Gaussian and non-Gaussian) for signal-to-noise ratios far below 1.0. The code is provided for open access.
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Affiliation(s)
- Illia Horenko
- Faculty of Mathematics, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany
- Correspondence: (I.H.); (S.G.)
| | - Lukáš Pospíšil
- Department of Mathematics, VSB Ostrava, Ludvika Podeste 1875/17, 708 33 Ostrava, Czech Republic;
| | - Edoardo Vecchi
- Institute of Computing, Faculty of Informatics, Universitá della Svizzera Italiana (USI), 6962 Viganello, Switzerland;
| | - Steffen Albrecht
- Institute of Physiology, University Medical Center of the Johannes Gutenberg—University Mainz, 55128 Mainz, Germany;
| | - Alexander Gerber
- Institute of Occupational Medicine, Faculty of Medicine, GU Frankfurt, 60590 Frankfurt am Main, Germany;
| | - Beate Rehbock
- Lung Radiology Center Berlin, 10627 Berlin, Germany;
| | - Albrecht Stroh
- Institute of Pathophysiology, University Medical Center of the Johannes Gutenberg—University Mainz, 55128 Mainz, Germany;
| | - Susanne Gerber
- Institute for Human Genetics, University Medical Center of the Johannes Gutenberg—University Mainz, 55128 Mainz, Germany
- Correspondence: (I.H.); (S.G.)
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Kulathilake KASH, Abdullah NA, Bandara AMRR, Lai KW. InNetGAN: Inception Network-Based Generative Adversarial Network for Denoising Low-Dose Computed Tomography. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:9975762. [PMID: 34552709 PMCID: PMC8452440 DOI: 10.1155/2021/9975762] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 08/18/2021] [Accepted: 08/27/2021] [Indexed: 12/24/2022]
Abstract
Low-dose Computed Tomography (LDCT) has gained a great deal of attention in clinical procedures due to its ability to reduce the patient's risk of exposure to the X-ray radiation. However, reducing the X-ray dose increases the quantum noise and artifacts in the acquired LDCT images. As a result, it produces visually low-quality LDCT images that adversely affect the disease diagnosing and treatment planning in clinical procedures. Deep Learning (DL) has recently become the cutting-edge technology of LDCT denoising due to its high performance and data-driven execution compared to conventional denoising approaches. Although the DL-based models perform fairly well in LDCT noise reduction, some noise components are still retained in denoised LDCT images. One reason for this noise retention is the direct transmission of feature maps through the skip connections of contraction and extraction path-based DL modes. Therefore, in this study, we propose a Generative Adversarial Network with Inception network modules (InNetGAN) as a solution for filtering the noise transmission through skip connections and preserving the texture and fine structure of LDCT images. The proposed Generator is modeled based on the U-net architecture. The skip connections in the U-net architecture are modified with three different inception network modules to filter out the noise in the feature maps passing over them. The quantitative and qualitative experimental results have shown the performance of the InNetGAN model in reducing noise and preserving the subtle structures and texture details in LDCT images compared to the other state-of-the-art denoising algorithms.
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Affiliation(s)
- K. A. Saneera Hemantha Kulathilake
- Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
- Department of Computing, Faculty of Applied Sciences, Rajarata University of Sri Lanka, Mihintale, Sri Lanka
| | - Nor Aniza Abdullah
- Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
| | | | - Khin Wee Lai
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
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Kulathilake KASH, Abdullah NA, Sabri AQM, Lai KW. A review on Deep Learning approaches for low-dose Computed Tomography restoration. COMPLEX INTELL SYST 2021; 9:2713-2745. [PMID: 34777967 PMCID: PMC8164834 DOI: 10.1007/s40747-021-00405-x] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 05/18/2021] [Indexed: 02/08/2023]
Abstract
Computed Tomography (CT) is a widely use medical image modality in clinical medicine, because it produces excellent visualizations of fine structural details of the human body. In clinical procedures, it is desirable to acquire CT scans by minimizing the X-ray flux to prevent patients from being exposed to high radiation. However, these Low-Dose CT (LDCT) scanning protocols compromise the signal-to-noise ratio of the CT images because of noise and artifacts over the image space. Thus, various restoration methods have been published over the past 3 decades to produce high-quality CT images from these LDCT images. More recently, as opposed to conventional LDCT restoration methods, Deep Learning (DL)-based LDCT restoration approaches have been rather common due to their characteristics of being data-driven, high-performance, and fast execution. Thus, this study aims to elaborate on the role of DL techniques in LDCT restoration and critically review the applications of DL-based approaches for LDCT restoration. To achieve this aim, different aspects of DL-based LDCT restoration applications were analyzed. These include DL architectures, performance gains, functional requirements, and the diversity of objective functions. The outcome of the study highlights the existing limitations and future directions for DL-based LDCT restoration. To the best of our knowledge, there have been no previous reviews, which specifically address this topic.
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Affiliation(s)
- K. A. Saneera Hemantha Kulathilake
- Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
| | - Nor Aniza Abdullah
- Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
| | - Aznul Qalid Md Sabri
- Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
| | - Khin Wee Lai
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
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El Ketara S, Ford NL. Time-course study of a gold nanoparticle contrast agent for cardiac-gated micro-CT imaging in mice. Biomed Phys Eng Express 2020; 6:035025. [PMID: 33438670 DOI: 10.1088/2057-1976/ab8741] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Although micro-computed tomography (micro-CT) images have high contrast for bone or air, between soft tissues the contrast is typically low. To overcome this inherent issue, attenuating exogenous contrast agents are used to provide contrast enhancement in the vasculature and abdominal organs. The aim of this study is to measure the contrast enhancement time course for a gold nanoparticle blood-pool contrast agent and use it to perform cardiac-gated 4D micro-CT scans of the heart. Six healthy female C57BL/6 mice were anesthetized and imaged after receiving an injected dose of MVivo gold nanoparticle blood-pool contrast agent. Following the injection, we performed micro-CT scans at 0, 0.25, 0.5, 0.75, 1, 2, 4, 8, 24, 48 and 72 h. The mean CT number was measured for 7 different organs. No contrast enhancement was noticed in the bladder, kidneys or muscle during the time-course study. However, it clearly appears that the contrast enhancement is high in both right ventricle and vena cava. To perform cardiac-gated imaging, either the gold nanoparticle agent (n = 3) or an iodine-based (n = 3) contrast agent was introduced and images representing 9 phases of the cardiac cycle were obtained in 6 additional mice. A few typical cardiac parameters were measured or calculated, with similar accuracy between the gold and iodinated agents, but better visualization of structures with the gold agent. The MVivo Au contrast agent can be used for investigations of cardiac or vascular disease with a single bolus injection, with an optimal cardiac imaging window identified during the first hour after injection, demonstrating similar image quality to iodinated contrast agents and excellent measurement accuracy. Furthermore, the long-lasting contrast enhancement of up to 8 h can be very useful for scanning protocols that require longer acquisition times.
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Affiliation(s)
- Samir El Ketara
- Oral Biological and Medical Sciences, The University of British Columbia, Vancouver, Canada. Université Grenobles Alpes, Grenoble, France
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Mahmood F, Shahid N, Vandergheynst P, Skoglund U. Graph-based sinogram denoising for tomographic reconstructions. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2016:3961-3664. [PMID: 28269152 DOI: 10.1109/embc.2016.7591594] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Limited data and low-dose constraints are common problems in a variety of tomographic reconstruction paradigms, leading to noisy and incomplete data. Over the past few years, sinogram denoising has become an essential preprocessing step for low-dose Computed Tomographic (CT) reconstructions. We propose a novel sinogram denoising algorithm inspired by signal processing on graphs. Graph-based methods often perform better than standard filtering operations since they can exploit the signal structure. This makes the sinogram an ideal candidate for graph based denoising since it generally has a piecewise smooth structure. We test our method with a variety of phantoms using different reconstruction methods. Our numerical study shows that the proposed algorithm improves the performance of analytical filtered back-projection (FBP) and iterative methods such as ART (Kaczmarz), and SIRT (Cimmino). We observed that graph denoised sinograms always minimize the error measure and improve the accuracy of the solution, compared to regular reconstructions.
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