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Tunissen SAM, Smit EJ, Mikerov M, Prokop M, Sechopoulos I. Performance evaluation of a 4D similarity filter for dynamic CT angiography imaging of the liver. Med Phys 2024. [PMID: 39264288 DOI: 10.1002/mp.17394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Revised: 08/20/2024] [Accepted: 08/23/2024] [Indexed: 09/13/2024] Open
Abstract
BACKGROUND Dynamic computed tomography (CT) angiography of the abdomen provides perfusion information and characteristics of the tissues present in the abdomen. This information could potentially help characterize liver metastases. However, radiation dose has to be relatively low for the patient, causing the images to have very high noise content. Denoising methods are needed to increase image quality. PURPOSE The purpose of this study was to investigate the performance, limitations, and behavior of a new 4D filtering method, called the 4D Similarity Filter (4DSF), to reduce image noise in temporal CT data. METHODS The 4DSF averages voxels with similar time-intensity curves (TICs). Each phase is filtered individually using the information of all phases except for the one being filtered. This approach minimizes the bias toward the noise initially present in this phase. Since the 4DSF does not base similarity on spatial proximity, loss of spatial resolution is avoided. The 4DSF was evaluated on a 12-phase liver dynamic CT angiography acquisition of 52 digital anthropomorphic phantoms, each containing one hypervascular 1 cm lesion with a small necrotic core. The metrics used for evaluation were noise reduction, lesion contrast-to-noise ratio (CNR), CT number accuracy using peak-time and peak-intensity of the TICs, and resolution loss. The results were compared to those obtained by the time-intensity profile similarity (TIPS) filter, which uses the whole TIC for determining similarity, and the combination 4DSF followed by TIPS filter (4DSF + TIPS). RESULTS The 4DSF alone resulted in a median noise reduction by a factor of 6.8, which is lower than that obtained by the TIPS filter at 8.1, and 4DSF + TIPS at 12.2. The 4DSF increased the median CNR from 0. 44 to 1.85, which is less than the TIPS filter at 2.59 and 4DSF + TIPS at 3.12. However, the peak-intensity accuracy in the TICs was superior for the 4DSF, with a median intensity decrease of -34 HU compared to -88 and -50 HU for the hepatic artery when using the TIPS filter and 4DSF + TIPS, respectively. The median peak-time accuracy was inferior for the 4DSF filter and 4DSF + TIPS, with a time shift of -1 phases for the portal vein TIC compared to no shift in time when using the TIPS. The analysis of the full-width-at-half-maximum (FWHM) of a small artery showed significantly less spatial resolution loss for the 4DSF at 3.2 pixels, compared to the TIPS filter at 4.3 pixels, and 3.4 pixels for the 4DSF + TIPS. CONCLUSION The 4DSF can reduce noise with almost no resolution loss, making the filter very suitable for denoising 4D CT data for detection tasks, even in very low dose, i.e., very high noise level, situations. In combination with the TIPS filter, the noise reduction can be increased even further.
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Affiliation(s)
- Sjoerd A M Tunissen
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Ewoud J Smit
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Mikhail Mikerov
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Mathias Prokop
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Radiology, University Medical Center Groningen, Groningen, The Netherlands
| | - Ioannis Sechopoulos
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
- Dutch Expert Centre for Screening (LRCB), Nijmegen, The Netherlands
- Technical Medicine Centre, University of Twente, Enschede, The Netherlands
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Zhou Z, Gong H, Hsieh S, McCollough CH, Yu L. Image quality evaluation in deep-learning-based CT noise reduction using virtual imaging trial methods: Contrast-dependent spatial resolution. Med Phys 2024; 51:5399-5413. [PMID: 38555876 PMCID: PMC11321944 DOI: 10.1002/mp.17029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 02/19/2024] [Accepted: 02/26/2024] [Indexed: 04/02/2024] Open
Abstract
BACKGROUND Deep-learning-based image reconstruction and noise reduction methods (DLIR) have been increasingly deployed in clinical CT. Accurate image quality assessment of these methods is challenging as the performance measured using physical phantoms may not represent the true performance of DLIR in patients since DLIR is trained mostly on patient images. PURPOSE In this work, we aim to develop a patient-data-based virtual imaging trial framework and, as a first application, use it to measure the spatial resolution properties of a DLIR method. METHODS The patient-data-based virtual imaging trial framework consists of five steps: (1) insertion of lesions into projection domain data using the acquisition geometry of the patient exam to simulate different lesion characteristics; (2) insertion of noise into projection domain data using a realistic photon statistical model of the CT system to simulate different dose levels; (3) creation of DLIR-processed images from projection or image data; (4) creation of ensembles of DLIR-processed patient images from a large number of noise and lesion realizations; and (5) evaluation of image quality using ensemble DLIR images. This framework was applied to measure the spatial resolution of a ResNet based deep convolutional neural network (DCNN) trained on patient images. Lesions in a cylindrical shape and different contrast levels (-500, -100, -50, -20, -10 HU) were inserted to the lower right lobe of the liver in a patient case. Multiple dose levels were simulated (50%, 25%, 12.5%). Each lesion and dose condition had 600 noise realizations. Multiple reconstruction and denoising methods were used on all the noise realizations, including the original filtered-backprojection (FBP), iterative reconstruction (IR), and the DCNN method with three different strength setting (DCNN-weak, DCNN-medium, and DCNN-strong). Mean lesion signal was calculated by performing ensemble averaging of all the noise realizations for each lesion and dose condition and then subtracting the lesion-present images from the lesion absent images. Modulation transfer functions (MTFs) both in-plane and along the z-axis were calculated based on the mean lesion signals. The standard deviations of MTFs at each condition were estimated with bootstrapping: randomly sampling (with replacement) all the DLIR/FBP/IR images from the ensemble data (600 samples) at each condition. The impact of varying lesion contrast, dose levels, and denoising strengths were evaluated. Statistical analysis with paired t-test was used to compare the z-axis and in-plane spatial resolution of five algorithms for five different contrasts and three dose levels. RESULTS The in-plane and z-axis spatial resolution degradation of DCNN becomes more severe as the contrast or radiation dose decreased, or DCNN denoising strength increased. In comparison with FBP, a 59.5% and 4.1% reduction of in-plane and z-axis MTF (in terms of spatial frequencies at 50% MTF), respectively, was observed at low contrast (-10 HU) for DCNN with the highest denoising strength at 25% routine dose level. When the dose level reduces from 50% to 12.5% of routine dose, the in-plane and z-axis MTFs reduces from 92.1% to 76.3%, and from 98.9% to 95.5%, respectively, at contrast of -100 HU, using FBP as the reference. For most conditions of contrasts and dose levels, significant differences were found among the five algorithms, with the following relationship in both in-plane and cross-plane spatial resolution: FBP > DCNN-Weak > IR > DCNN-Medium > DCNN-Strong. The spatial resolution difference among algorithms decreases at higher contrast or dose levels. CONCLUSIONS A patient-data-based virtual imaging trial framework was developed and applied to measuring the spatial resolution properties of a DCNN noise reduction method at different contrast and dose levels using real patient data. As with other non-linear image reconstruction and post-processing techniques, the evaluated DCNN method degraded the in-plane and z-axis spatial resolution at lower contrast levels, lower radiation dose, and higher denoising strength.
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Affiliation(s)
| | - Hao Gong
- Department of Radiology, Mayo Clinic, Rochester, MN, US
| | - Scott Hsieh
- Department of Radiology, Mayo Clinic, Rochester, MN, US
| | | | - Lifeng Yu
- Department of Radiology, Mayo Clinic, Rochester, MN, US
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Ayala-Dominguez L, Medina LA, Aceves C, Lizano M, Brandan ME. Accuracy and Precision of Iodine Quantification in Subtracted Micro-Computed Tomography: Effect of Reconstruction and Noise Removal Algorithms. Mol Imaging Biol 2023; 25:1084-1093. [PMID: 37012518 PMCID: PMC10728260 DOI: 10.1007/s11307-023-01810-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 02/26/2023] [Accepted: 02/28/2023] [Indexed: 04/05/2023]
Abstract
PURPOSE To evaluate the effect of reconstruction and noise removal algorithms on the accuracy and precision of iodine concentration (CI) quantified with subtracted micro-computed tomography (micro-CT). PROCEDURES Two reconstruction algorithms were evaluated: a filtered backprojection (FBP) algorithm and a simultaneous iterative reconstruction technique (SIRT) algorithm. A 3D bilateral filter (BF) was used for noise removal. A phantom study evaluated and compared the image quality, and the accuracy and precision of CI in four scenarios: filtered FBP, filtered SIRT, non-filtered FBP, and non-filtered SIRT. In vivo experiments were performed in an animal model of chemically-induced mammary cancer. RESULTS Linear relationships between the measured and nominal CI values were found for all the scenarios in the phantom study (R2 > 0.95). SIRT significantly improved the accuracy and precision of CI compared to FBP, as given by their lower bias (adj. p-value = 0.0308) and repeatability coefficient (adj. p-value < 0.0001). Noise removal enabled a significant decrease in bias in filtered SIRT images only; non-significant differences were found for the repeatability coefficient. The phantom and in vivo studies showed that CI is a reproducible imaging parameter for all the scenarios (Pearson r > 0.99, p-value < 0.001). The contrast-to-noise ratio showed non-significant differences among the evaluated scenarios in the phantom study, while a significant improvement was found in the in vivo study when SIRT and BF algorithms were used. CONCLUSIONS SIRT and BF algorithms improved the accuracy and precision of CI compared to FBP and non-filtered images, which encourages their use in subtracted micro-CT imaging.
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Affiliation(s)
- Lízbeth Ayala-Dominguez
- Departamento de Física Experimental, Instituto de Física, Universidad Nacional Autónoma de México, Circuito de La Investigación Científica, Ciudad Universitaria UNAM, Mexico City, 04510, Mexico.
- Department of Medical Physics, University of Wisconsin, 1111 Highland Ave, WI, Madison, 53705, USA.
| | - Luis-Alberto Medina
- Departamento de Física Experimental, Instituto de Física, Universidad Nacional Autónoma de México, Circuito de La Investigación Científica, Ciudad Universitaria UNAM, Mexico City, 04510, Mexico
- Unidad de Investigación Biomédica en Cáncer INCan-UNAM, Instituto Nacional de Cancerología, Av. San Fernando 22, Tlalpan, Mexico City, 14080, Mexico
| | - Carmen Aceves
- Departamento de Neurobiología Celular Y Molecular, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Boulevard Juriquilla 3001, Querétaro, Juriquilla, 76230, Mexico
| | - Marcela Lizano
- Unidad de Investigación Biomédica en Cáncer INCan-UNAM, Instituto Nacional de Cancerología, Av. San Fernando 22, Tlalpan, Mexico City, 14080, Mexico
- Departamento de Medicina Genómica Y Toxicología Ambiental, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Circuito Exterior S/N, Ciudad Universitaria UNAM, Mexico City, 04510, Mexico
| | - Maria-Ester Brandan
- Departamento de Física Experimental, Instituto de Física, Universidad Nacional Autónoma de México, Circuito de La Investigación Científica, Ciudad Universitaria UNAM, Mexico City, 04510, Mexico
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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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Sliwicka O, Swiderska-Chadaj Z, Snoeren M, Brink M, Salah K, Peters-Bax L, Stille T, van Amerongen MJ, Sechopoulos I, Habets J. Multireader image quality evaluation of dynamic myocardial computed tomography perfusion imaging with a novel four-dimensional noise reduction filter. Acta Radiol 2023; 64:999-1006. [PMID: 35765201 DOI: 10.1177/02841851221108804] [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] [Indexed: 11/16/2022]
Abstract
BACKGROUND Dynamic myocardial computed tomography perfusion (CTP) is a novel technique able to depict cardiac ischemia. PURPOSE To evaluate the impact of a four-dimensional noise reduction filter (similarity filter [4D-SF]) on image quality in dynamic CTP imaging, allowing for substantial radiation dose reduction. MATERIAL AND METHODS Dynamic CTP datasets of 30 patients (16 women) with suspected coronary artery disease, acquired with a 320-slice CT system, were retrieved, reconstructed with the deep learning-based algorithm of the system (DLR), and filtered with the 4D-SF. For each case, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in six regions of interest (33-38mm2) were calculated before and after filtering, in four-chamber and short-axis views, and t-tested. Furthermore, six radiologists of different expertise evaluated subjective image preference by answering five visual grading analysis-type questions (regarding acceptable level of noise, absence of artifacts, natural appearance, cardiac contour sharpness, diagnostic acceptability) using a 5-point scale. The results were analyzed using visual grade characteristics (VGC) and intraclass correlation coefficient (ICC). RESULTS Mean SNR in four-chamber view (unfiltered vs. filtered) were: septum=4.1 ± 2.1 versus 7.6 ± 5.6; lateral wall=4.5 ± 2.0 versus 8.0 ± 4.9; CNRseptum=16.6 ± 8.9 versus 31.7 ± 28; lateral wall=16.2 ± 8.9 versus 31.3 ± 28.9. Similar results were obtained in short-axis view. The perceived filtered image quality indicated decreased noise (VGCAUC=0.96) and artifacts (0.65), improved natural appearance (0.59), cardiac contour sharpness (0.74), and diagnostic acceptability (0.78). The inter-observer variability was excellent (ICC=0.79). All results were statistically significant (P < 0.05). CONCLUSION Similarity filtering after DLR improves image quality, possibly enabling dose reduction in dynamic CTP imaging in patient with suspected chronic coronary syndrome.
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Affiliation(s)
- Olga Sliwicka
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
- Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Miranda Snoeren
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Monique Brink
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Khibar Salah
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Liesbeth Peters-Bax
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Tip Stille
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Ioannis Sechopoulos
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jesse Habets
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
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Diagnostic Performance in Low- and High-Contrast Tasks of an Image-Based Denoising Algorithm Applied to Radiation Dose-Reduced Multiphase Abdominal CT Examinations. AJR Am J Roentgenol 2023; 220:73-85. [PMID: 35731096 DOI: 10.2214/ajr.22.27806] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
BACKGROUND. Anatomic redundancy between phases can be used to achieve denoising of multiphase CT examinations. A limitation of iterative reconstruction (IR) techniques is that they generally require use of CT projection data. A frequency-split multi-band-filtration algorithm applies denoising to the multiphase CT images themselves. This method does not require knowledge of the acquisition process or integration into the reconstruction system of the scanner, and it can be implemented as a supplement to commercially available IR algorithms. OBJECTIVE. The purpose of the present study is to compare radiologists' performance for low-contrast and high-contrast diagnostic tasks (i.e., tasks for which differences in CT attenuation between the imaging target and its anatomic background are subtle or large, respectively) evaluated on multiphase abdominal CT between routine-dose images and radiation dose-reduced images processed by a frequency-split multiband-filtration denoising algorithm. METHODS. This retrospective single-center study included 47 patients who underwent multiphase contrast-enhanced CT for known or suspected liver metastases (a low-contrast task) and 45 patients who underwent multiphase contrast-enhanced CT for pancreatic cancer staging (a high-contrast task). Radiation dose-reduced images corresponding to dose reduction of 50% or more were created using a validated noise insertion technique and then underwent denoising using the frequency-split multi-band-filtration algorithm. Images were independently evaluated in multiple sessions by different groups of abdominal radiologists for each task (three readers in the low-contrast arm and four readers in the high-contrast arm). The noninferiority of denoised radiation dose-reduced images to routine-dose images was assessed using the jackknife alternative free-response ROC (JAFROC) figure-of-merit (FOM; limit of noninferiority, -0.10) for liver metastases detection and using the Cohen kappa statistic and reader confidence scores (100-point scale) for pancreatic cancer vascular invasion. RESULTS. For liver metastases detection, the JAFROC FOM for denoised radiation dose-reduced images was 0.644 (95% CI, 0.510-0.778), and that for routine-dose images was 0.668 (95% CI, 0.543-0.792; estimated difference, -0.024 [95% CI, -0.084 to 0.037]). Intraobserver agreement for pancreatic cancer vascular invasion was substantial to near perfect when the two image sets were compared (κ = 0.53-1.00); the 95% CIs of all differences in confidence scores between image sets contained zero. CONCLUSION. Multiphase contrast-enhanced abdominal CT images with a radiation dose reduction of 50% or greater that undergo denoising by a frequency-split multiband-filtration algorithm yield performance similar to that of routine-dose images for detection of liver metastases and vascular staging of pancreatic cancer. CLINICAL IMPACT. The image-based denoising algorithm facilitates radiation dose reduction of multiphase examinations for both low- and high-contrast diagnostic tasks without requiring manufacturer-specific hardware or software.
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Zeng D, Zeng C, Zeng Z, Li S, Deng Z, Chen S, Bian Z, Ma J. Basis and current state of computed tomography perfusion imaging: a review. Phys Med Biol 2022; 67. [PMID: 35926503 DOI: 10.1088/1361-6560/ac8717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 08/04/2022] [Indexed: 12/30/2022]
Abstract
Computed tomography perfusion (CTP) is a functional imaging that allows for providing capillary-level hemodynamics information of the desired tissue in clinics. In this paper, we aim to offer insight into CTP imaging which covers the basics and current state of CTP imaging, then summarize the technical applications in the CTP imaging as well as the future technological potential. At first, we focus on the fundamentals of CTP imaging including systematically summarized CTP image acquisition and hemodynamic parameter map estimation techniques. A short assessment is presented to outline the clinical applications with CTP imaging, and then a review of radiation dose effect of the CTP imaging on the different applications is presented. We present a categorized methodology review on known and potential solvable challenges of radiation dose reduction in CTP imaging. To evaluate the quality of CTP images, we list various standardized performance metrics. Moreover, we present a review on the determination of infarct and penumbra. Finally, we reveal the popularity and future trend of CTP imaging.
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Affiliation(s)
- Dong Zeng
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Cuidie Zeng
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Zhixiong Zeng
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Sui Li
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Zhen Deng
- Department of Neurology, Nanfang Hospital, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Sijin Chen
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Zhaoying Bian
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
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Zhou X, Nan Y, Ju J, Zhou J, Xiao H, Wang S. Comparison of Two Software Packages for Perfusion Imaging: Ischemic Core and Penumbra Estimation and Patient Triage in Acute Ischemic Stroke. Cells 2022; 11:2547. [PMID: 36010624 PMCID: PMC9406974 DOI: 10.3390/cells11162547] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 07/20/2022] [Accepted: 08/12/2022] [Indexed: 11/16/2022] Open
Abstract
Purpose: Automated postprocessing packages have been developed for managing acute ischemic stroke (AIS). These packages identify ischemic core and penumbra using either computed tomographic perfusion imaging (CTP) data or magnetic resonance imaging (MRI) data. Measurements of abnormal tissues and treatment decisions derived from different vendors can vary. The purpose of this study is to investigate the agreement of volumetric and decision-making outcomes derived from two software packages. Methods: A total of 594 AIS patients (174 underwent CTP and 420 underwent MRI) were included. Imaging data were accordingly postprocessed by two software packages: RAPID and RealNow. Volumetric outputs were compared between packages by performing intraclass correlation coefficient (ICC), Wilcoxon paired test and Bland-Altman analysis. Concordance of selecting patients eligible for mechanical thrombectomy (MT) was assessed based on neuroimaging criteria proposed in DEFUSE3. Results: In the group with CTP data, mean ischemic core volume (ICV)/penumbral volume (PV) was 14.9/81.1 mL via RAPID and 12.6/83.2 mL via RealNow. Meanwhile, in the MRI group, mean ICV/PV were 52.4/68.4 mL and 48.9/61.6 mL via RAPID and RealNow, respectively. Reliability, which was measured by ICC of ICV and PV in CTP and MRI groups, ranged from 0.87 to 0.99. The bias remained small between measurements (CTP ICV: 0.89 mL, CTP PV: -2 mL, MRI ICV: 3.5 mL and MRI PV: 6.8 mL). In comparison with CTP ICV with follow-up DWI, the ICC was 0.92 and 0.94 for RAPID and Realnow, respectively. The bias remained small between CTP ICV and follow-up DWI measurements (Rapid: -4.65 mL, RealNow: -3.65 mL). Wilcoxon paired test showed no significant difference between measurements. The results of patient triage were concordant in 159/174 cases (91%, ICC: 0.90) for CTP and 400/420 cases (95%, ICC: 0.93) for MRI. Conclusion: The CTP ICV derived from RealNow was more accurate than RAPID. The similarity in volumetric measurement between packages did not necessarily relate to equivalent patient triage. In this study, RealNow showed excellent agreement with RAPID in measuring ICV and PV as well as patient triage.
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Affiliation(s)
- Xiang Zhou
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, 389 Xincun Rd., Shanghai 200065, China
| | - Yashi Nan
- YIWEI Medical Technology Co., Ltd., Room 1001, MAI KE LONG Building, Shenzhen 518000, China
| | - Jieyang Ju
- The Second Affiliated Hospital of Nanjing Medical University, 121 Jiangjiayuan Rd., Nanjing 210011, China
| | - Jingyu Zhou
- YIWEI Medical Technology Co., Ltd., Room 1001, MAI KE LONG Building, Shenzhen 518000, China
| | - Huanhui Xiao
- YIWEI Medical Technology Co., Ltd., Room 1001, MAI KE LONG Building, Shenzhen 518000, China
| | - Silun Wang
- YIWEI Medical Technology Co., Ltd., Room 1001, MAI KE LONG Building, Shenzhen 518000, China
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Intelligent Algorithm-Based Ultrasound Image for Evaluating the Effect of Comprehensive Nursing Scheme on Patients with Diabetic Kidney Disease. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:6440138. [PMID: 35309831 PMCID: PMC8930247 DOI: 10.1155/2022/6440138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Revised: 02/20/2022] [Accepted: 02/22/2022] [Indexed: 11/17/2022]
Abstract
This study was aimed at exploring the effect of ultrasound image evaluation of comprehensive nursing scheme based on artificial intelligence algorithms on patients with diabetic kidney disease (DKD). 44 patients diagnosed with DKD were randomly divided into two groups: group A (no nursing intervention) and group B (comprehensive nursing). In the same period, 32 healthy volunteers were selected as the control group. Ultrasonographic images based on the
non-local-means (KNL-Means) filtering algorithm were used to perform imaging examinations in healthy people and DKD patients before and after care. The results suggested that compared with those of the SAE reconstruction algorithm and KAVD reconstruction algorithm, the PSNR value of artificial bee colony algorithm reconstruction of image was higher and the MSE value was lower. The resistant index (RI) of DKD patients in group B after nursing was
, apparently distinct from the RI of the healthy people (controls) in the same group (
) and the RI of DKD patients in group A (
) (
). The incidence rate of complications in DKD patients in group B was apparently inferior to that in group A. After comprehensive nursing intervention (CNI), the scores of all dimensions of quality of life (QoL) in DKD patients in group B were obviously superior versus those in DKD patients in group A. It suggests that implementation of nursing intervention for DKD patients can effectively help patients improve and control the level of renal function, while ultrasound images based on intelligent algorithm can dynamically detect the changes in the level of renal function in patients, which has the value of clinical promotion.
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10
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Sun T, Fulton R, Hu Z, Sutiono C, Liang D, Zheng H. Inferring CT perfusion parameters and uncertainties using a Bayesian approach. Quant Imaging Med Surg 2022; 12:439-456. [PMID: 34993092 DOI: 10.21037/qims-21-338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 06/24/2021] [Indexed: 11/06/2022]
Abstract
BACKGROUND Computed tomography perfusion imaging is commonly used for the rapid assessment of patients presenting with symptoms of acute stroke. Maps of perfusion parameters, such as cerebral blood volume (CBV), cerebral blood flow (CBF), and mean transit time (MTT) derived from the perfusion scan data, provide crucial information for stroke diagnosis and treatment decisions. Most CT scanners use singular value decomposition (SVD)-based methods to calculate these parameters. However, some known problems are associated with conventional methods. METHODS In this work, we propose a Bayesian inference algorithm, which can derive both the perfusion parameters and their uncertainties. We apply the variational technique to the inference, which then becomes an expectation-maximization problem. The probability distribution (with Gaussian mean and variance) of each estimated parameter can be obtained, and the coefficient of variation is used to indicate the uncertainty. We perform evaluations using both simulations and patient studies. RESULTS In a simulation, we show that the proposed method has much less bias than conventional methods. Then, in separate simulations, we apply the proposed method to evaluate the impacts of various scan conditions, i.e., with different frame intervals, truncated measurement, or motion, on the parameter estimate. In one patient study, the method produced CBF and MTT maps indicating an ischemic lesion consistent with the radiologist's report. In a second patient study affected by patient movement, we showed the feasibility of applying the proposed method to motion corrected data. CONCLUSIONS The proposed method can be used to evaluate confidence in parameter estimation and the scan protocol design. More clinical evaluation is required to fully test the proposed method.
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Affiliation(s)
- Tao Sun
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Roger Fulton
- Faculty of Medicine and Health and School of Physics, University of Sydney, Sydney, Australia.,Department of Medical Physics, Westmead Hospital, Sydney, Australia
| | - Zhanli Hu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Christina Sutiono
- Radiology Department, Western Sydney Local Health District, Westmead Hospital, Sydney, Australia
| | - Dong Liang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Hairong Zheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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11
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Wu D, Ren H, Li Q. Self-Supervised Dynamic CT Perfusion Image Denoising With Deep Neural Networks. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2021. [DOI: 10.1109/trpms.2020.2996566] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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12
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Dashtbani Moghari M, Zhou L, Yu B, Young N, Moore K, Evans A, Fulton RR, Kyme AZ. Efficient radiation dose reduction in whole-brain CT perfusion imaging using a 3D GAN: Performance and clinical feasibility. Phys Med Biol 2021; 66. [PMID: 33621965 DOI: 10.1088/1361-6560/abe917] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 02/23/2021] [Indexed: 02/08/2023]
Abstract
Dose reduction in cerebral CT perfusion (CTP) imaging is desirable but is accompanied by an increase in noise that can compromise the image quality and the accuracy of image-based haemodynamic modelling used for clinical decision support in acute ischaemic stroke. The few reported methods aimed at denoising low-dose CTP images lack practicality by considering only small sections of the brain or being computationally expensive. Moreover, the prediction of infarct and penumbra size and location - the chief means of decision support for treatment options - from denoised data has not been explored using these approaches. In this work, we present the first application of a 3D generative adversarial network (3D GAN) for predicting normal-dose CTP data from low-dose CTP data. Feasibility of the approach was tested using real data from 30 acute ischaemic stroke patients in conjunction with low dose simulation. The 3D GAN model was applied to 64^3 voxel patches extracted from two different configurations of the CTP data- frame-based and stacked. The method led to whole-brain denoised data being generated for haemodynamic modelling within 90 seconds. Accuracy of the method was evaluated using standard image quality metrics and the extent to which the clinical content and lesion characteristics of the denoised CTP data were preserved. Results showed an average improvement of 5.15-5.32 dB PSNR and 0.025-0.033 SSIM for CTP images and 2.66-3.95 dB PSNR and 0.036-0.067 SSIM for functional maps at 50% and 25% of normal dose using GAN model in conjunction with a stacked data regime for image synthesis. Consequently, the average lesion volumetric error reduced significantly (p-value < 0.05) by 18-29% and dice coefficient improved significantly by 15-22%. We conclude that GAN-based denoising is a promising practical approach for reducing radiation dose in CTP studies and improving lesion characterisation.
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Affiliation(s)
- Mahdieh Dashtbani Moghari
- Biomedical Engineering, Faculty of Engineering and Computer Science, Darlington Campus, The University of Sydney, NSW, 2006, AUSTRALIA
| | - Luping Zhou
- The University of Sydney, Sydney, 2006, AUSTRALIA
| | - Biting Yu
- University of Wollongong, Wollongong, New South Wales, AUSTRALIA
| | - Noel Young
- Radiology, Westmead Hospital, Sydney, New South Wales, AUSTRALIA
| | - Krystal Moore
- Westmead Hospital, Sydney, New South Wales, AUSTRALIA
| | - Andrew Evans
- Aged Care & Stroke, Westmead Hospital, Sydney, New South Wales, AUSTRALIA
| | - Roger R Fulton
- Faculty of Health Sciences, University of Sydney, 94 Mallett Street, Camperdown, Sydney, New South Wales, 2050, AUSTRALIA
| | - Andre Z Kyme
- Brain & Mind Research Institute, University of Sydney, Sydney, NSW 2006, Sydney, New South Wales, AUSTRALIA
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13
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Booij R, Budde RPJ, Dijkshoorn ML, van Straten M. Technological developments of X-ray computed tomography over half a century: User's influence on protocol optimization. Eur J Radiol 2020; 131:109261. [PMID: 32937253 DOI: 10.1016/j.ejrad.2020.109261] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 08/11/2020] [Accepted: 08/27/2020] [Indexed: 12/14/2022]
Abstract
Since the introduction of Computed Tomography (CT), technological improvements have been impressive. At the same time, the number of adjustable acquisition and reconstruction parameters has increased substantially. Overall, these developments led to improved image quality at a reduced radiation dose. However, many parameters are interrelated and part of automated algorithms. This makes it more complicated to adjust them individually and more difficult to comprehend their influence on CT protocol adjustments. Moreover, the user's influence in adapting protocol parameters is sometimes limited by the manufacturer's policy or the user's knowledge. As a consequence, optimization can be a challenge. A literature search in Embase, Medline, Cochrane, and Web of Science was performed. The literature was reviewed with the objective to collect information regarding technological developments in CT over the past five decades and the role of the associated acquisition and reconstruction parameters in the optimization process.
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Affiliation(s)
- Ronald Booij
- Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, P.O. Box 2240, 3000 CA, The Netherlands.
| | - Ricardo P J Budde
- Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, P.O. Box 2240, 3000 CA, The Netherlands.
| | - Marcel L Dijkshoorn
- Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, P.O. Box 2240, 3000 CA, The Netherlands.
| | - Marcel van Straten
- Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, P.O. Box 2240, 3000 CA, The Netherlands.
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14
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Yuan G, Liu Y, Huang W, Hu B. Differentiating Grade in Breast Invasive Ductal Carcinoma Using Texture Analysis of MRI. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2020; 2020:6913418. [PMID: 32328154 PMCID: PMC7166276 DOI: 10.1155/2020/6913418] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Revised: 11/20/2019] [Accepted: 03/14/2020] [Indexed: 12/03/2022]
Abstract
PURPOSE The objective of this study is to investigate the use of texture analysis (TA) of magnetic resonance image (MRI) enhanced scan and machine learning methods for distinguishing different grades in breast invasive ductal carcinoma (IDC). Preoperative prediction of the grade of IDC can provide reference for different clinical treatments, so it has important practice values in clinic. METHODS Firstly, a breast cancer segmentation model based on discrete wavelet transform (DWT) and K-means algorithm is proposed. Secondly, TA was performed and the Gabor wavelet analysis is used to extract the texture feature of an MRI tumor. Then, according to the distance relationship between the features, key features are sorted and feature subsets are selected. Finally, the feature subset is classified by using a support vector machine and adjusted parameters to achieve the best classification effect. RESULTS By selecting key features for classification prediction, the classification accuracy of the classification model can reach 81.33%. 3-, 4-, and 5-fold cross-validation of the prediction accuracy of the support vector machine model is 77.79%~81.94%. CONCLUSION The pathological grading of IDC can be predicted and evaluated by texture analysis and feature extraction of breast tumors. This method can provide much valuable information for doctors' clinical diagnosis. With further development, the model demonstrates high potential for practical clinical use.
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MESH Headings
- Algorithms
- Breast Neoplasms/diagnostic imaging
- Breast Neoplasms/pathology
- Carcinoma, Ductal, Breast/diagnostic imaging
- Carcinoma, Ductal, Breast/pathology
- Computational Biology
- Diagnosis, Computer-Assisted/methods
- Diagnosis, Computer-Assisted/statistics & numerical data
- Female
- Humans
- Image Interpretation, Computer-Assisted/methods
- Image Interpretation, Computer-Assisted/statistics & numerical data
- Machine Learning
- Magnetic Resonance Imaging/methods
- Magnetic Resonance Imaging/statistics & numerical data
- Models, Statistical
- Neoplasm Grading/methods
- Neoplasm Grading/statistics & numerical data
- Neural Networks, Computer
- Support Vector Machine
- Wavelet Analysis
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Affiliation(s)
- Gaoteng Yuan
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
| | - Yihui Liu
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
| | - Wei Huang
- Department VI of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250117, China
| | - Bing Hu
- School of Medicine and Life Sciences, University of Jinan, Jinan 250022, China
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15
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Li J, Chen XY, Xu K, Zhu L, He M, Sun T, Zhang WJ, Flohr TG, Jin ZY, Xue HD. Detection of insulinoma: one-stop pancreatic perfusion CT with calculated mean temporal images can replace the combination of bi-phasic plus perfusion scan. Eur Radiol 2020; 30:4164-4174. [PMID: 32189051 DOI: 10.1007/s00330-020-06657-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2019] [Revised: 12/12/2019] [Accepted: 01/07/2020] [Indexed: 10/24/2022]
Abstract
OBJECTIVE To evaluate the feasibility of one-stop pancreatic perfusion CT with mean temporal (MT) imaging replacing the combination of a bi-phasic scan plus a perfusion scan to detect insulinoma. MATERIAL AND METHODS Forty-five patients with suspected insulinoma, who underwent both biphasic and perfusion CT, were enrolled in this retrospective study. MT datasets including images for different delineation purposes were generated by averaging 3 dynamic datasets from perfusion CT, which are MTA for arterial, MTPV for portal vein and MTO for lesions. Two readers assessed the image quality and diagnostic performance separately for biphasic and MT datasets. Radiation doses were also assessed. Paired t tests, Wilcoxon signed-rank tests and McNemar's tests were applied for comparison. RESULTS Compared with bi-phasic CT images, image noise, SNR and CNR of the MTA and MTPV datasets were all non-inferior (noise and CNR of the portal vein, p = 0.565 and p = 0.227, respectively) or superior (p ≤ 0.001). The subjective image quality was better in the MTA and MTPV images (p < 0.001 to p = 0.004). The sensitivity and NPV of MT images were also better (95% vs 75% and 75% vs 37.5% for reader 1; 97.5% vs 72.5% and 85.7% vs 35.3% for reader 2). Omitting the bi-phasic scan resulted in a dose reduction of 25% ± 4%. CONCLUSION MT imaging can allow pancreatic perfusion CT to be used alone without the need for an additional bi-phasic CT in the detection of insulinoma. KEY POINTS • Mean temporal images reconstructed from perfusion CT with an averaging technique reproduce usual bi-phasic images (arterial and portal phases). • The image quality of mean temporal images is non-inferior or superior to native bi-phasic CT. The sensitivity and NPV for the diagnosis of insulinoma are better for mean temporal images than for traditional bi-phasic CT. • Mean temporal imaging can allow pancreatic perfusion CT to be used alone without the need for an additional bi-phasic CT in the detection of insulinoma. Radiation dose saving is important.
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Affiliation(s)
- Juan Li
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No. 1, Dongcheng District, Beijing, 100730, China
| | - Xin-Yue Chen
- CT Collaboration, Siemens-Healthineers, Beijing, China
| | - Kai Xu
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No. 1, Dongcheng District, Beijing, 100730, China
| | - Liang Zhu
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No. 1, Dongcheng District, Beijing, 100730, China
| | - Ming He
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No. 1, Dongcheng District, Beijing, 100730, China
| | - Ting Sun
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No. 1, Dongcheng District, Beijing, 100730, China
| | - Wen-Jia Zhang
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No. 1, Dongcheng District, Beijing, 100730, China
| | - Thomas G Flohr
- Department of Computed Tomography, Siemens Healthcare GmbH, Forchheim, Germany
| | - Zheng-Yu Jin
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No. 1, Dongcheng District, Beijing, 100730, China.
| | - Hua-Dan Xue
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No. 1, Dongcheng District, Beijing, 100730, China.
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16
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Wu H, Eck BL, Levi J, Fares A, Li Y, Wen D, Bezerra HG, Muzic RF, Wilson DL. SLICR super-voxel algorithm for fast, robust quantification of myocardial blood flow by dynamic computed tomography myocardial perfusion imaging. J Med Imaging (Bellingham) 2019; 6:046001. [PMID: 31720314 PMCID: PMC6833456 DOI: 10.1117/1.jmi.6.4.046001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Accepted: 09/18/2019] [Indexed: 11/14/2022] Open
Abstract
We created and evaluated a processing method for dynamic computed tomography myocardial perfusion imaging (CT-MPI) of myocardial blood flow (MBF), which combines a modified simple linear iterative clustering algorithm (SLIC) with robust perfusion quantification, hence the name SLICR. SLICR adaptively segments the myocardium into nonuniform super-voxels with similar perfusion time attenuation curves (TACs). Within each super-voxel, an α-trimmed-median TAC was computed to robustly represent the super-voxel and a robust physiological model (RPM) was implemented to semi-analytically estimate MBF. SLICR processing was compared with another voxel-wise MBF preprocessing approach, which included a spatiotemporal bilateral filter (STBF) for noise reduction prior to perfusion quantification. Image data from a digital CT-MPI phantom and a porcine ischemia model were evaluated. SLICR was ∼ 50 -fold faster than voxel-wise RPM and other model-based methods while retaining sufficient resolution to show clinically relevant features, such as a transmural perfusion gradient. SLICR showed markedly improved accuracy and precision, as compared with other methods. At a simulated MBF of 100 mL/min-100 g and a tube current-time product of 100 mAs (50% of nominal), the MBF estimates were 101 ± 12 , 94 ± 56 , and 54 ± 24 mL / min - 100 g for SLICR, the voxel-wise Johnson-Wilson model, and a singular value decomposition-model independent method with STBF, respectively. SLICR estimated MBF precisely and accurately ( 103 ± 23 mL / min - 100 g ) at 25% nominal dose, while other methods resulted in larger errors. With the porcine model, the SLICR results were consistent with the induced ischemia. SLICR simultaneously accelerated and improved the quality of quantitative perfusion processing without compromising clinically relevant distributions of perfusion characteristics.
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Affiliation(s)
- Hao Wu
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States
| | - Brendan L. Eck
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States
| | - Jacob Levi
- Case Western Reserve University, Department of Physics, Cleveland, Ohio, United States
| | - Anas Fares
- University Hospitals Cleveland Medical Center, Harrington Heart and Vascular Institute, Cardiovascular Imaging Core Laboratory, Cleveland, Ohio, United States
| | - Yuemeng Li
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States
| | - Di Wen
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States
| | - Hiram G. Bezerra
- University Hospitals Cleveland Medical Center, Harrington Heart and Vascular Institute, Cardiovascular Imaging Core Laboratory, Cleveland, Ohio, United States
| | - Raymond F. Muzic
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States
- Case Western Reserve University, Department of Radiology, Cleveland, Ohio, United States
| | - David L. Wilson
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States
- Case Western Reserve University, Department of Radiology, Cleveland, Ohio, United States
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17
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Liu C, Huang H. Noise reduction in dual‐energy computed tomography virtual monoenergetic imaging. J Appl Clin Med Phys 2019; 20:104-113. [PMID: 31390137 PMCID: PMC6753738 DOI: 10.1002/acm2.12694] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Revised: 06/05/2019] [Accepted: 07/23/2019] [Indexed: 12/15/2022] Open
Abstract
Purpose Virtual monoenergetic images (VMIs) derived from dual‐energy computed tomography (DECT) have been explored for several clinical applications in recent years. However, VMIs at low and high keVs have high levels of noise. The aim of this study was to reduce image noise in VMIs by using a two‐step noise reduction technique. Methods VMI was first denoised using a modified highly constrained backprojection (HYPR) method. After the first‐step denoising, a general‐threshold filtering method was performed. Two sets of anthropomorphic phantoms were scanned with a clinical dual‐source DECT system. DECT data (80/140Sn kV) were reconstructed as VMI series at 12 different energy levels (range, 40‐150 keV, interval, 10 keV). For comparison, the averaged VMIs obtained from 10 repeated DECT scans were used as the reference standard. The signal‐to‐noise ratio (SNR), contrast‐to‐noise ratio (CNR) and root‐mean‐square error (RMSE) were used to evaluate the quality of VMIs. Results Compared to the original HYPR method, the proposed two‐step image denoising method could provide better performance in terms of SNR, CNR, and RMSE. In addition, the proposed method could achieve effective noise reduction while preserving edges and small structures, especially for low‐keV VMIs. Conclusion The proposed two‐step image denoising method is a feasible method for reducing noise in VMIs obtained from a clinical DECT scanner. The proposed method can also reduce edge blurring and the loss of intensity in small lesions.
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Affiliation(s)
- Chi‐Kuang Liu
- Department of Medical Imaging Changhua Christian Hospital Changhua City Taiwan
| | - Hsuan‐Ming Huang
- Institute of Medical Device and Imaging, College of Medicine National Taiwan University Taipei Taiwan
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18
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Lukas S, Feger S, Rief M, Zimmermann E, Dewey M. Noise reduction and motion elimination in low-dose 4D myocardial computed tomography perfusion (CTP): preliminary clinical evaluation of the ASTRA4D algorithm. Eur Radiol 2019; 29:4572-4582. [DOI: 10.1007/s00330-018-5899-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Revised: 10/15/2018] [Accepted: 11/20/2018] [Indexed: 12/20/2022]
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19
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Pisana F, Haubenreisser H, Henzler T, Schönberg S, Kachelrieß M. Singular value-guided similarity filter improves detection of vessels in low-dose dynamic CT angiography: application to DIEP flap studies. ACTA ACUST UNITED AC 2018; 63:165003. [DOI: 10.1088/1361-6560/aad477] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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20
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Sun Y, Liu X, Cong P, Li L, Zhao Z. Digital radiography image denoising using a generative adversarial network. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2018; 26:523-534. [PMID: 29889095 PMCID: PMC6130336 DOI: 10.3233/xst-17356] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Revised: 03/31/2018] [Accepted: 04/28/2018] [Indexed: 05/15/2023]
Abstract
Statistical noise may degrade the x-ray image quality of digital radiography (DR) system. This corruption can be alleviated by extending exposure time of detectors and increasing the intensity of radiation. However, in some instances, such as the security check and medical imaging examination, the system demands rapid and low-dose detection. In this study, we propose and test a generative adversarial network (GAN) based x-ray image denoising method. Images used in this study were acquired from a digital radiography (DR) imaging system. Promising results have been obtained in our experiments with x-ray images for the security check application. The Experiment results demonstrated that the proposed new image denoising method was able to effectively remove the statistical noise from x-ray images, while kept sharp edge and clear structure. Thus, comparing with the traditional convolutional neural network (CNN) based method, the proposed new method generates more plausible-looking images, which contains more details.
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Affiliation(s)
- Yuewen Sun
- Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China
- Beijing Key Laboratory of Nuclear Detection, Beijing, China
| | - Ximing Liu
- Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China
- Beijing Key Laboratory of Nuclear Detection, Beijing, China
| | - Peng Cong
- Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China
- Beijing Key Laboratory of Nuclear Detection, Beijing, China
| | - Litao Li
- Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China
- Beijing Key Laboratory of Nuclear Detection, Beijing, China
| | - Zhongwei Zhao
- Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China
- Beijing Key Laboratory of Nuclear Detection, Beijing, China
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