The addition of computer simulated noise to investigate radiation dose and image quality in images with spatial correlation of statistical noise: an example application to X-ray CT of the brain

A practical method to simulate realistic reduced-exposure CT images by the addition of computationally generated noise

Computed tomography (CT) scanning protocols should be optimized to minimize the radiation dose necessary for imaging. The addition of computationally generated noise to the CT images facilitates dose reduction. The objective of this study was to develop a noise addition method that reproduces the complexity of the noise texture present in clinical images with directionality that varies over images according to the underlying anatomy, requiring only Digital Imaging and Communications in Medicine (DICOM) images as input data and commonly available phantoms for calibration. The developed method is based on the estimation of projection data by forward projection from images, the addition of Poisson noise, and the reconstruction of new images. The method was validated by applying it to images acquired from cylindrical and thoracic phantoms using source images with exposures up to 49 mAs and target images between 39 and 5 mAs. 2D noise spectra were derived for regions of interest in the generated low-dose images and compared with those from the scanner-acquired low-dose images. The root mean square difference between the standard deviations of noise was 4%, except for very low exposures in peripheral regions of the cylindrical phantom. The noise spectra from the corresponding regions of interest exhibited remarkable agreement, indicating that the complex nature of the noise was reproduced. A practical method for adding noise to CT images was presented, and the magnitudes of noise and spectral content were validated. This method may be used to optimize CT imaging.

Modeling intracranial electrodes. A simulation platform for the evaluation of localization algorithms

Introduction

Intracranial electrodes are implanted in patients with drug-resistant epilepsy as part of their pre-surgical evaluation. This allows the investigation of normal and pathological brain functions with excellent spatial and temporal resolution. The spatial resolution relies on methods that precisely localize the implanted electrodes in the cerebral cortex, which is critical for drawing valid inferences about the anatomical localization of brain function. Multiple methods have been developed to localize the electrodes, mainly relying on pre-implantation MRI and post-implantation computer tomography (CT) images. However, they are hard to validate because there is no ground truth data to test them and there is no standard approach to systematically quantify their performance. In other words, their validation lacks standardization. Our work aimed to model intracranial electrode arrays and simulate realistic implantation scenarios, thereby providing localization algorithms with new ways to evaluate and optimize their performance.

Results

We implemented novel methods to model the coordinates of implanted grids, strips, and depth electrodes, as well as the CT artifacts produced by these. We successfully modeled realistic implantation scenarios, including different sizes, inter-electrode distances, and brain areas. In total, ∼3,300 grids and strips were fitted over the brain surface, and ∼850 depth electrode arrays penetrating the cortical tissue were modeled. Realistic CT artifacts were simulated at the electrode locations under 12 different noise levels. Altogether, ∼50,000 thresholded CT artifact arrays were simulated in these scenarios, and validated with real data from 17 patients regarding the coordinates' spatial deformation, and the CT artifacts' shape, intensity distribution, and noise level. Finally, we provide an example of how the simulation platform is used to characterize the performance of two cluster-based localization methods.

Conclusion

We successfully developed the first platform to model implanted intracranial grids, strips, and depth electrodes and realistically simulate thresholded CT artifacts and their noise. These methods provide a basis for developing more complex models, while simulations allow systematic evaluation of the performance of electrode localization techniques. The methods described in this article, and the results obtained from the simulations, are freely available via open repositories. A graphical user interface implementation is also accessible via the open-source iElectrodes toolbox.

PIMA-CT: Physical Model-Aware Cyclic Simulation and Denoising for Ultra-Low-Dose CT Restoration

A body of studies has proposed to obtain high-quality images from low-dose and noisy Computed Tomography (CT) scans for radiation reduction. However, these studies are designed for population-level data without considering the variation in CT devices and individuals, limiting the current approaches' performance, especially for ultra-low-dose CT imaging. Here, we proposed PIMA-CT, a physical anthropomorphic phantom model integrating an unsupervised learning framework, using a novel deep learning technique called Cyclic Simulation and Denoising (CSD), to address these limitations. We first acquired paired low-dose and standard-dose CT scans of the phantom and then developed two generative neural networks: noise simulator and denoiser. The simulator extracts real low-dose noise and tissue features from two separate image spaces (e.g., low-dose phantom model scans and standard-dose patient scans) into a unified feature space. Meanwhile, the denoiser provides feedback to the simulator on the quality of the generated noise. In this way, the simulator and denoiser cyclically interact to optimize network learning and ease the denoiser to simultaneously remove noise and restore tissue features. We thoroughly evaluate our method for removing both real low-dose noise and Gaussian simulated low-dose noise. The results show that CSD outperforms one of the state-of-the-art denoising algorithms without using any labeled data (actual patients' low-dose CT scans) nor simulated low-dose CT scans. This study may shed light on incorporating physical models in medical imaging, especially for ultra-low level dose CT scans restoration.

Cerebral CT Perfusion in Acute Stroke: The Effect of Lowering the Tube Load and Sampling Rate on the Reproducibility of Parametric Maps

The aim of this study was to define lower dose parameters (tube load and temporal sampling) for CT perfusion that still preserve the diagnostic efficiency of the derived parametric maps. Ninety stroke CT examinations from four clinical sites with 1 s temporal sampling and a range of tube loads (mAs) (100–180) were studied. Realistic CT noise was retrospectively added to simulate a CT perfusion protocol, with a maximum reduction of 40% tube load (mAs) combined with increased sampling intervals (up to 3 s). Perfusion maps from the original and simulated protocols were compared by: (a) similarity using a voxel-wise Pearson’s correlation coefficient r with in-house software; (b) volumetric analysis of the infarcted and hypoperfused volumes using commercial software. Pearson’s r values varied for the different perfusion metrics from 0.1 to 0.85. The mean slope of increase and cerebral blood volume present the highest r values, remaining consistently above 0.7 for all protocol versions with 2 s sampling interval. Reduction of the sampling rate from 2 s to 1 s had only modest impacts on a TMAX volume of 0.4 mL (IQR −1–3) (p = 0.04) and core volume of −1.1 mL (IQR −4–0) (p < 0.001), indicating dose savings of 50%, with no practical loss of diagnostic accuracy. The lowest possible dose protocol was 2 s temporal sampling and a tube load of 100 mAs.

Efficient radiation dose reduction in whole-brain CT perfusion imaging using a 3D GAN: Performance and clinical feasibility

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.

Accurate Image Domain Noise Insertion in CT Images

Tools to simulate lower dose, noisy computed tomography (CT) images from existing data enable protocol optimization by quantifying the trade-off between patient dose and image quality. Many studies have developed and validated noise insertion techniques; however, most of these tools operate on proprietary projection data which can be difficult to access and can be time consuming when a large number of realizations is needed. In response, this work aims to develop and validate an image domain approach to accurately insert CT noise and simulate low dose scans. In this framework, information from the image is utilized to estimate the variance map and local noise power spectra (NPS). Normally distributed noise is filtered within small patches in the image domain using the inverse Fourier transform of the square root of the estimated local NPS to generate noise with the appropriate spatial correlation. The patches are overlapped and element-wise multiplied by the standard deviation map to produce locally varying, spatially correlated noise. The resulting noise image is scaled based on the relationship between the initial and desired dose and added to the original image. The results demonstrate excellent agreement between traditional projection domain methods and the proposed method, both for simulated and real data sets. This new framework is not intended to replace projection domain methods; rather, it fills a gap in CT noise simulation tools and is an accurate alternative when projection domain methods are not practical, for example, in large scale repeatability or detectability studies.

A general framework in single and multi-modality registration for lung imaging analysis using statistical prior shapes

BACKGROUND AND OBJECTIVE

A fusion of multi-slice computed tomography (MSCT) and single photon emission computed tomography (SPECT) represents a powerful tool for chronic obstructive pulmonary disease (COPD) analysis. In this paper, a novel and high-performance MSCT/SPECT non-rigid registration algorithm is proposed to accurately map the lung lobe information onto the functional imaging. Such a fusion can then be used to guide lung volume reduction surgery.

METHODS

The multi-modality fusion method proposed here is developed by a multi-channel technique which performs registration from MSCT scan to ventilation and perfusion SPECT scans simultaneously. Furthermore, a novel function with less parameters is also proposed to avoid the adjustment of the weighting parameter and to achieve a better performance in comparison with the exisitng methods in the literature.

RESULTS

A lung imaging dataset from a hospital and a synthetic dataset created by software are employed to validate single- and multi-modality registration results. Our method is demonstrated to achieve the improvements in terms of registration accuracy and stability by up to 23% and 54% respectively. Our multi-channel technique proposed here is also proved to obtain improved registration accuracy in comparison with single-channel method.

CONCLUSIONS

The fusion of lung lobes onto SPECT imaging is achievable by accurate MSCT/SPECT alignment. It can also be used to perform lobar lung activity analysis for COPD diagnosis and treatment.

STIR-Net: Deep Spatial-Temporal Image Restoration Net for Radiation Reduction in CT Perfusion

Computed Tomography Perfusion (CTP) imaging is a cost-effective and fast approach to provide diagnostic images for acute stroke treatment. Its cine scanning mode allows the visualization of anatomic brain structures and blood flow; however, it requires contrast agent injection and continuous CT scanning over an extended time. In fact, the accumulative radiation dose to patients will increase health risks such as skin irritation, hair loss, cataract formation, and even cancer. Solutions for reducing radiation exposure include reducing the tube current and/or shortening the X-ray radiation exposure time. However, images scanned at lower tube currents are usually accompanied by higher levels of noise and artifacts. On the other hand, shorter X-ray radiation exposure time with longer scanning intervals will lead to image information that is insufficient to capture the blood flow dynamics between frames. Thus, it is critical for us to seek a solution that can preserve the image quality when the tube current and the temporal frequency are both low. We propose STIR-Net in this paper, an end-to-end spatial-temporal convolutional neural network structure, which exploits multi-directional automatic feature extraction and image reconstruction schema to recover high-quality CT slices effectively. With the inputs of low-dose and low-resolution patches at different cross-sections of the spatio-temporal data, STIR-Net blends the features from both spatial and temporal domains to reconstruct high-quality CT volumes. In this study, we finalize extensive experiments to appraise the image restoration performance at different levels of tube current and spatial and temporal resolution scales.The results demonstrate the capability of our STIR-Net to restore high-quality scans at as low as 11% of absorbed radiation dose of the current imaging protocol, yielding an average of 10% improvement for perfusion maps compared to the patch-based log likelihood method.

Deep Evolutionary Networks with Expedited Genetic Algorithms for Medical Image Denoising

Deep convolutional neural networks offer state-of-the-art performance for medical image analysis. However, their architectures are manually designed for particular problems. On the one hand, a manual designing process requires many trials to tune a large number of hyperparameters and is thus quite a time-consuming task. On the other hand, the fittest hyperparameters that can adapt to source data properties (e.g., sparsity, noisy features) are not able to be quickly identified for target data properties. For instance, the realistic noise in medical images is usually mixed and complicated, and sometimes unknown, leading to challenges in applying existing methods directly and creating effective denoising neural networks easily. In this paper, we present a Genetic Algorithm (GA)-based network evolution approach to search for the fittest genes to optimize network structures automatically. We expedite the evolutionary process through an experience-based greedy exploration strategy and transfer learning. Our evolutionary algorithm procedure has flexibility, which allows taking advantage of current state-of-the-art modules (e.g., residual blocks) to search for promising neural networks. We evaluate our framework on a classic medical image analysis task: denoising. The experimental results on computed tomography perfusion (CTP) image denoising demonstrate the capability of the method to select the fittest genes for building high-performance networks, named EvoNets. Our results outperform state-of-the-art methods consistently at various noise levels.

TENDER: Tensor non-local deconvolution enabled radiation reduction in CT perfusion

Stroke is the leading cause of long-term disability and the second leading cause of mortality in the world, and exerts an enormous burden on the public health. Computed Tomography (CT) remains one of the most widely used imaging modality for acute stroke diagnosis. However when coupled with CT perfusion, the excessive radiation exposure in repetitive imaging to assess treatment response and prognosis has raised significant public concerns regarding its potential hazards to both short- and long-term health outcomes. Tensor total variation has been proposed to reduce the necessary radiation dose in CT perfusion without comprising the image quality by fusing the information of the local anatomical structure with the temporal blood flow model. However the local search in the TTV framework fails to leverage the non-local information in the spatio-temporal data. In this paper, we propose TENDER, an efficient framework of non-local tensor deconvolution to maintain the accuracy of the hemodynamic parameters and the diagnostic reliability in low radiation dose CT perfusion. The tensor total variation is extended using non-local spatio-temporal cubics for regularization, and an efficient algorithm is proposed to reduce the time complexity with speedy similarity computation. Evaluations on clinical data of patients subjects with cerebrovascular disease and normal subjects demonstrate the advantage of non-local tensor deconvolution for reducing radiation dose in CT perfusion.

A Simple Low-dose X-ray CT Simulation from High-dose Scan

Low-dose X-ray computed tomography (CT) simulation from high-dose scan is required in optimizing radiation dose to patients. In this study, we propose a simple low-dose CT simulation strategy in sinogram domain using the raw data from high-dose scan. Specially, a relationship between the incident fluxes of low- and high- dose scans is first determined according to the repeated projection measurements and analysis. Second, the incident flux level of the simulated low-dose scan is generated by properly scaling the incident flux level of high-dose scan via the determined relationship in the first step. Third, the low-dose CT transmission data by energy integrating detection is simulated by adding a statistically independent Poisson noise distribution plus a statistically independent Gaussian noise distribution. Finally, a filtered back-projection (FBP) algorithm is implemented to reconstruct the resultant low-dose CT images. The present low-dose simulation strategy is verified on the simulations and real scans by comparing it with the existing low-dose CT simulation tool. Experimental results demonstrated that the present low-dose CT simulation strategy can generate accurate low-dose CT sinogram data from high-dose scan in terms of qualitative and quantitative measurements.

Robust Low-Dose CT Perfusion Deconvolution via Tensor Total-Variation Regularization

Acute brain diseases such as acute strokes and transit ischemic attacks are the leading causes of mortality and morbidity worldwide, responsible for 9% of total death every year. "Time is brain" is a widely accepted concept in acute cerebrovascular disease treatment. Efficient and accurate computational framework for hemodynamic parameters estimation can save critical time for thrombolytic therapy. Meanwhile the high level of accumulated radiation dosage due to continuous image acquisition in CT perfusion (CTP) raised concerns on patient safety and public health. However, low-radiation leads to increased noise and artifacts which require more sophisticated and time-consuming algorithms for robust estimation. In this paper, we focus on developing a robust and efficient framework to accurately estimate the perfusion parameters at low radiation dosage. Specifically, we present a tensor total-variation (TTV) technique which fuses the spatial correlation of the vascular structure and the temporal continuation of the blood signal flow. An efficient algorithm is proposed to find the solution with fast convergence and reduced computational complexity. Extensive evaluations are carried out in terms of sensitivity to noise levels, estimation accuracy, contrast preservation, and performed on digital perfusion phantom estimation, as well as in vivo clinical subjects. Our framework reduces the necessary radiation dose to only 8% of the original level and outperforms the state-of-art algorithms with peak signal-to-noise ratio improved by 32%. It reduces the oscillation in the residue functions, corrects over-estimation of cerebral blood flow (CBF) and under-estimation of mean transit time (MTT), and maintains the distinction between the deficit and normal regions.

Tissue-specific sparse deconvolution for brain CT perfusion

Enhancing perfusion maps in low-dose computed tomography perfusion (CTP) for cerebrovascular disease diagnosis is a challenging task, especially for low-contrast tissue categories where infarct core and ischemic penumbra usually occur. Sparse perfusion deconvolution has been recently proposed to effectively improve the image quality and diagnostic accuracy of low-dose perfusion CT by extracting the complementary information from the high-dose perfusion maps to restore the low-dose using a joint spatio-temporal model. However the low-contrast tissue classes where infarct core and ischemic penumbra are likely to occur in cerebral perfusion CT tend to be over-smoothed, leading to loss of essential biomarkers. In this paper, we propose a tissue-specific sparse deconvolution approach to preserve the subtle perfusion information in the low-contrast tissue classes. We first build tissue-specific dictionaries from segmentations of high-dose perfusion maps using online dictionary learning, and then perform deconvolution-based hemodynamic parameters estimation for block-wise tissue segments on the low-dose CTP data. Extensive validation on clinical datasets of patients with cerebrovascular disease demonstrates the superior performance of our proposed method compared to state-of-art, and potentially improve diagnostic accuracy by increasing the differentiation between normal and ischemic tissues in the brain.

A multi-phased study of optimisation methodologies and radiation dose savings for head CT examinations

The impact of optimisation methods on dose reductions for head computerised tomography was undertaken in three phases for two manufacturer models. Phase 1: a Catphan(®)600 was employed to evaluate protocols where the impact of parameter manipulation on dose and image quality was gauged by psychophysical measurements of contrast and spatial resolution in terms of contrast discs and line pairs. mA, kV and pitch were systematically altered until the optimisation threshold was identified. Phantom studies provide dose comparisons during optimisation but lack anatomical detail. Phase 2: optimised protocols were tested on a porcine model permitting further dose reductions over phantom findings providing anatomical structures for image quality evaluation using relative visual grading analysis of anatomical criteria. Phase 3: patient images using pre- and post-optimised protocols were clinically audited using visual grading characteristic analysis and ordinal regression analysis providing a robust analysis of image quality data prior to clinical implementation.

Low-dose preview for patient-specific, task-specific technique selection in cone-beam CT

PURPOSE

A method is presented for generating simulated low-dose cone-beam CT (CBCT) preview images from which patient- and task-specific minimum-dose protocols can be confidently selected prospectively in clinical scenarios involving repeat scans.

METHODS

In clinical scenarios involving a series of CBCT images, the low-dose preview (LDP) method operates upon the first scan to create a projection dataset that accurately simulates the effects of dose reduction in subsequent scans by injecting noise of proper magnitude and correlation, including both quantum and electronic readout noise as important components of image noise in flat-panel detector CBCT. Experiments were conducted to validate the LDP method in both a head phantom and a cadaveric torso by performing CBCT acquisitions spanning a wide dose range (head: 0.8-13.2 mGy, body: 0.8-12.4 mGy) with a prototype mobile C-arm system. After injecting correlated noise to simulate dose reduction, the projections were reconstructed using both conventional filtered backprojection (FBP) and an iterative, model-based image reconstruction method (MBIR). The LDP images were then compared to real CBCT images in terms of noise magnitude, noise-power spectrum (NPS), spatial resolution, contrast, and artifacts.

RESULTS

For both FBP and MBIR, the LDP images exhibited accurate levels of spatial resolution and contrast that were unaffected by the correlated noise injection, as expected. Furthermore, the LDP image noise magnitude and NPS were in strong agreement with real CBCT images acquired at the corresponding, reduced dose level across the entire dose range considered. The noise magnitude agreed within 7% for both the head phantom and cadaveric torso, and the NPS showed a similar level of agreement up to the Nyquist frequency. Therefore, the LDP images were highly representative of real image quality across a broad range of dose and reconstruction methods. On the other hand, naïve injection ofuncorrelated noise resulted in strong underestimation of the true noise, which would lead to overly optimistic predictions of dose reduction.

CONCLUSIONS

Correlated noise injection is essential to accurate simulation of CBCT image quality at reduced dose. With the proposed LDP method, the user can prospectively select patient-specific, minimum-dose protocols (viz., acquisition technique and reconstruction method) suitable to a particular imaging task and to the user's own observer preferences for CBCT scans following the first acquisition. The method could provide dose reduction in common clinical scenarios involving multiple CBCT scans, such as image-guided surgery and radiotherapy.

Visual grading characteristics and ordinal regression analysis during optimisation of CT head examinations

OBJECTIVES

To evaluate visual grading characteristics (VGC) and ordinal regression analysis during head CT optimisation as a potential alternative to visual grading assessment (VGA), traditionally employed to score anatomical visualisation.

METHODS

Patient images (n = 66) were obtained using current and optimised imaging protocols from two CT suites: a 16-slice scanner at the national Maltese centre for trauma and a 64-slice scanner in a private centre. Local resident radiologists (n = 6) performed VGA followed by VGC and ordinal regression analysis.

RESULTS

VGC alone indicated that optimised protocols had similar image quality as current protocols. Ordinal logistic regression analysis provided an in-depth evaluation, criterion by criterion allowing the selective implementation of the protocols. The local radiology review panel supported the implementation of optimised protocols for brain CT examinations (including trauma) in one centre, achieving radiation dose reductions ranging from 24 % to 36 %. In the second centre a 29 % reduction in radiation dose was achieved for follow-up cases.

CONCLUSIONS

The combined use of VGC and ordinal logistic regression analysis led to clinical decisions being taken on the implementation of the optimised protocols. This improved method of image quality analysis provided the evidence to support imaging protocol optimisation, resulting in significant radiation dose savings.

MAIN MESSAGES

• There is need for scientifically based image quality evaluation during CT optimisation. • VGC and ordinal regression analysis in combination led to better informed clinical decisions. • VGC and ordinal regression analysis led to dose reductions without compromising diagnostic efficacy.

Tensor total-variation regularized deconvolution kegularlzea ueconvolution for efficient low-dose CT perfusion

Acute brain diseases such as acute stroke and transit ischemic attacks are the leading causes of mortality and morbidity worldwide, responsible for 9% of total death every year. 'Time is brain' is a widely accepted concept in acute cerebrovascular disease treatment. Efficient and accurate computational framework for hemodynamic parameters estimation can save critical time for thrombolytic therapy. Meanwhile the high level of accumulated radiation dosage due to continuous image acquisition in CT perfusion (CTP) raised concerns on patient safety and public health. However, low-radiation will lead to increased noise and artifacts which require more sophisticated and time-consuming algorithms for robust estimation. We propose a novel efficient framework using tensor total-variation (TTV) regularization to achieve both high efficiency and accuracy in deconvolution for low-dose CTP. The method reduces the necessary radiation dose to only 8% of the original level and outperforms the state-of-art algorithms with estimation error reduced by 40%. It also corrects over-estimation of cerebral blood flow (CBF) and under-estimation of mean transit time (MTT), at both normal and reduced sampling rate. An efficient computational algorithm is proposed to find the solution with fast convergence.

Impact of incremental increase in CT image noise on detection of low-contrast hypodense liver lesions

RATIONALE AND OBJECTIVES

To determine the impact of incremental increases in computed tomography (CT) image noise on detection of low-contrast hypodense liver lesions.

MATERIAL AND METHODS

We studied 50 CT examinations acquired at image noise index (NI) of 15 and hypodense liver lesions and 50 examinations with no lesions. Validation of a noise addition tool to be used in the evaluation of the CT examinations was performed with a liver phantom. Using this tool, three 100-image sets were assembled: an NI of 17.4 (simulating 75% of the original patient radiation dose), 21.2 (simulating 50% dose), and 29.7 (simulating 25%). Three readers scored certainty of lesion presence using a five-point Likert scale.

RESULTS

For original images (NI 15) plus images with NI of 17.4 and 21.2, sensitivity was >90% threshold (range, 95%-98%). For images with NI of 29.7, sensitivity was just below the threshold (89%). Reader Az values for receiver operating characteristic curves were good for original, NI 17.4, and NI 21.2 images (0.976, 0.973, and 0.96, respectively). For NI of 29.7, the Az decreased to 0.913. Detection sensitivity was <90% for both lesion size < 10 mm (85%) and lesion-to-liver contrast <60 Hounsfield units (85%) only at NI 29.7.

CONCLUSIONS

For low-contrast lesion detection in liver CT, image noise can be increased up to NI 21.2 (a 50% patient radiation dose reduction) without substantial reduction in sensitivity.

Validation of a low dose simulation technique for computed tomography images

PURPOSE

Evaluation of a new software tool for generation of simulated low-dose computed tomography (CT) images from an original higher dose scan.

MATERIALS AND METHODS

Original CT scan data (100 mAs, 80 mAs, 60 mAs, 40 mAs, 20 mAs, 10 mAs; 100 kV) of a swine were acquired (approved by the regional governmental commission for animal protection). Simulations of CT acquisition with a lower dose (simulated 10-80 mAs) were calculated using a low-dose simulation algorithm. The simulations were compared to the originals of the same dose level with regard to density values and image noise. Four radiologists assessed the realistic visual appearance of the simulated images.

RESULTS

Image characteristics of simulated low dose scans were similar to the originals. Mean overall discrepancy of image noise and CT values was -1.2% (range -9% to 3.2%) and -0.2% (range -8.2% to 3.2%), respectively, p>0.05. Confidence intervals of discrepancies ranged between 0.9-10.2 HU (noise) and 1.9-13.4 HU (CT values), without significant differences (p>0.05). Subjective observer evaluation of image appearance showed no visually detectable difference.

CONCLUSION

Simulated low dose images showed excellent agreement with the originals concerning image noise, CT density values, and subjective assessment of the visual appearance of the simulated images. An authentic low-dose simulation opens up opportunity with regard to staff education, protocol optimization and introduction of new techniques.

Tissue-specific sparse deconvolution for low-dose CT perfusion

Sparse perfusion deconvolution has been recently proposed to effectively improve the image quality and diagnostic accuracy of low-dose perfusion CT by extracting the complementary information from the high-dose perfusion maps to restore the low-dose using a joint spatio-temporal model. However the low-contrast tissue classes where infarct core and ischemic penumbra usually occur in cerebral perfusion CT tend to be over-smoothed, leading to loss of essential biomarkers. In this paper, we extend this line of work by introducing tissue-specific sparse deconvolution to preserve the subtle perfusion information in the low-contrast tissue classes by learning tissue-specific dictionaries for each tissue class, and restore the low-dose perfusion maps by joining the tissue segments reconstructed from the corresponding dictionaries. Extensive validation on clinical datasets of patients with cerebrovascular disease demonstrates the superior performance of our proposed method with the advantage of better differentiation between abnormal and normal tissue in these patients.

Improving low-dose blood-brain barrier permeability quantification using sparse high-dose induced prior for Patlak model

Blood-brain barrier permeability (BBBP) measurements extracted from the perfusion computed tomography (PCT) using the Patlak model can be a valuable indicator to predict hemorrhagic transformation in patients with acute stroke. Unfortunately, the standard Patlak model based PCT requires excessive radiation exposure, which raised attention on radiation safety. Minimizing radiation dose is of high value in clinical practice but can degrade the image quality due to the introduced severe noise. The purpose of this work is to construct high quality BBBP maps from low-dose PCT data by using the brain structural similarity between different individuals and the relations between the high- and low-dose maps. The proposed sparse high-dose induced (shd-Patlak) model performs by building a high-dose induced prior for the Patlak model with a set of location adaptive dictionaries, followed by an optimized estimation of BBBP map with the prior regularized Patlak model. Evaluation with the simulated low-dose clinical brain PCT datasets clearly demonstrate that the shd-Patlak model can achieve more significant gains than the standard Patlak model with improved visual quality, higher fidelity to the gold standard and more accurate details for clinical analysis.

A low dose simulation tool for CT systems with energy integrating detectors

PURPOSE

This paper introduces a new strategy for simulating low-dose computed tomography (CT) scans using real scans of a higher dose as an input. The tool is verified against simulations and real scans and compared to other approaches found in the literature.

METHODS

The conditional variance identity is used to properly account for the variance of the input high-dose data, and a formula is derived for generating a new Poisson noise realization which has the same mean and variance as the true low-dose data. The authors also derive a formula for the inclusion of real samples of detector noise, properly scaled according to the level of the simulated x-ray signals.

RESULTS

The proposed method is shown to match real scans in number of experiments. Noise standard deviation measurements in simulated low-dose reconstructions of a 35 cm water phantom match real scans in a range from 500 to 10 mA with less than 5% error. Mean and variance of individual detector channels are shown to match closely across the detector array. Finally, the visual appearance of noise and streak artifacts is shown to match in real scans even under conditions of photon-starvation (with tube currents as low as 10 and 80 mA). Additionally, the proposed method is shown to be more accurate than previous approaches (1) in achieving the correct mean and variance in reconstructed images from pure-Poisson noise simulations (with no detector noise) under photon-starvation conditions, and (2) in simulating the correct noise level and detector noise artifacts in real low-dose scans.

CONCLUSIONS

The proposed method can accurately simulate low-dose CT data starting from high-dose data, including effects from photon starvation and detector noise. This is potentially a very useful tool in helping to determine minimum dose requirements for a wide range of clinical protocols and advanced reconstruction algorithms.

Effects of increased image noise on image quality and quantitative interpretation in brain CT perfusion

BACKGROUND AND PURPOSE

There is a desire within many institutions to reduce the radiation dose in CTP examinations. The purpose of this study was to simulate dose reduction through the addition of noise in brain CT perfusion examinations and to determine the subsequent effects on quality and quantitative interpretation.

MATERIALS AND METHODS

A total of 22 consecutive reference CTP scans were identified from an institutional review board-approved prospective clinical trial, all performed at 80 keV and 190 mAs. Lower-dose scans at 188, 177, 167, 127, and 44 mAs were generated through the addition of spatially correlated noise to the reference scans. A standard software package was used to generate CBF, CBV, and MTT maps. Six blinded radiologists determined quality scores of simulated scans on a Likert scale. Quantitative differences were calculated.

RESULTS

For qualitative analysis, the correlation coefficients for CBF (-0.34; P < .0001), CBV (-0.35; P < .0001), and MTT (-0.44; P < .0001) were statistically significant. Interobserver agreements in quality for the simulated 188-, 177-, 167-, 127-, and 44-mAs scans for CBF were 0.95, 0.98, 0.98, 0.95, and 0.52, respectively. Interobserver agreements in quality for the simulated CBV were 1, 1, 1, 1, and 0.83, respectively. For MTT, the interobserver agreements were 0.83, 0.86, 0.88, 0.74, and 0.05, respectively. For quantitative analysis, only the lowest simulated dose of 44 mAs showed statistically significant differences from the reference scan values for CBF (-1.8; P = .04), CBV (0.07; P < .0001), and MTT (0.46; P < .0001).

CONCLUSIONS

From a reference CTP study performed at 80 keV and 190 mAs, this simulation study demonstrates the potential of a 33% reduction in tube current and dose while maintaining image quality and quantitative interpretations. This work can be used to inform future studies by using true, nonsimulated scans.

Towards robust deconvolution of low-dose perfusion CT: sparse perfusion deconvolution using online dictionary learning

Computed tomography perfusion (CTP) is an important functional imaging modality in the evaluation of cerebrovascular diseases, particularly in acute stroke and vasospasm. However, the post-processed parametric maps of blood flow tend to be noisy, especially in low-dose CTP, due to the noisy contrast enhancement profile and the oscillatory nature of the results generated by the current computational methods. In this paper, we propose a robust sparse perfusion deconvolution method (SPD) to estimate cerebral blood flow in CTP performed at low radiation dose. We first build a dictionary from high-dose perfusion maps using online dictionary learning and then perform deconvolution-based hemodynamic parameters estimation on the low-dose CTP data. Our method is validated on clinical data of patients with normal and pathological CBF maps. The results show that we achieve superior performance than existing methods, and potentially improve the differentiation between normal and ischemic tissue in the brain.

Sparsity-based deconvolution of low-dose perfusion CT using learned dictionaries

Computational tomography perfusion (CTP) is an important functional imaging modality in the evaluation of cerebrovascular diseases, such as stroke and vasospasm. However, the post-processed parametric maps of blood flow tend to be noisy, especially in low-dose CTP, due to the noisy contrast enhancement profile and the oscillatory nature of the results generated by the current computational methods. In this paper, we propose a novel sparsity-base deconvolution method to estimate cerebral blood flow in CTP performed at low-dose. We first built an overcomplete dictionary from high-dose perfusion maps and then performed deconvolution-based hemodynamic parameters estimation on the low-dose CTP data. Our method is validated on a clinical dataset of ischemic patients. The results show that we achieve superior performance than existing methods, and potentially improve the differentiation between normal and ischemic tissue in the brain.

Quantifying the margin sharpness of lesions on radiological images for content-based image retrieval

PURPOSE

To develop a method to quantify the margin sharpness of lesions on CT and to evaluate it in simulations and CT scans of liver and lung lesions.

METHODS

The authors computed two attributes of margin sharpness: the intensity difference between a lesion and its surroundings, and the sharpness of the intensity transition across the lesion boundary. These two attributes were extracted from sigmoid curves fitted along lines automatically drawn orthogonal to the lesion margin. The authors then represented the margin characteristics for each lesion by a feature vector containing histograms of these parameters. The authors created 100 simulated CT scans of lesions over a range of intensity difference and margin sharpness, and used the concordance correlation between the known parameter and the corresponding computed feature as a measure of performance. The authors also evaluated their method in 79 liver lesions (44 patients: 23 M, 21 F, mean age 61) and 58 lung nodules (57 patients: 24 M, 33 F, mean age 66). The methodology presented takes into consideration the boundary of the liver and lung during feature extraction in clinical images to ensure that the margin feature do not get contaminated by anatomy other than the normal organ surrounding the lesions. For evaluation in these clinical images, the authors created subjective independent reference standards for pairwise margin sharpness similarity in the liver and lung cohorts, and compared rank orderings of similarity used using our sharpness feature to that expected from the reference standards using mean normalized discounted cumulative gain (NDCG) over all query images. In addition, the authors compared their proposed feature with two existing techniques for lesion margin characterization using the simulated and clinical datasets. The authors also evaluated the robustness of their features against variations in delineation of the lesion margin by simulating five types of deformations of the lesion margin. Equivalence across deformations was assessed using Schuirmann's paired two one-sided tests.

RESULTS

In simulated images, the concordance correlation between measured gradient and actual gradient was 0.994. The mean (s.d.) and standard deviation NDCG score for the retrieval of K images, K = 5, 10, and 15, were 84% (8%), 85% (7%), and 85% (7%) for CT images containing liver lesions, and 82% (7%), 84% (6%), and 85% (4%) for CT images containing lung nodules, respectively. The authors' proposed method outperformed the two existing margin characterization methods in average NDCG scores over all K, by 1.5% and 3% in datasets containing liver lesion, and 4.5% and 5% in datasets containing lung nodules. Equivalence testing showed that the authors' feature is more robust across all margin deformations (p < 0.05) than the two existing methods for margin sharpness characterization in both simulated and clinical datasets.

CONCLUSIONS

The authors have described a new image feature to quantify the margin sharpness of lesions. It has strong correlation with known margin sharpness in simulated images and in clinical CT images containing liver lesions and lung nodules. This image feature has excellent performance for retrieving images with similar margin characteristics, suggesting potential utility, in conjunction with other lesion features, for content-based image retrieval applications.

A CT acquisition technique to generate images at various dose levels for prospective dose reduction studies

OBJECTIVE

The purpose of this article is to determine whether the average of N CT images acquired at a particular dose (D) has image noise equivalent to that of a single image acquired at a dose of N × D.

MATERIALS AND METHODS

An electron density phantom, an image quality phantom, and an adult anthropomorphic phantom were scanned multiple times on a 16-MDCT scanner at five effective tube current-rotation time product (mAs) settings (130 kVp; 12, 24, 48, 72, and 144 mAs). Lower-mAs images were averaged to simulate higher-mAs images. Differences in CT number and image noise between simulated and acquired images were quantified using the electron density phantom. Image quality phantom images were scored by three physicists to investigate differences in low- and high-contrast resolution. A forced-choice observer study was performed with three radiologists using anthropomorphic phantom images to evaluate differences in overall image quality.

RESULTS

The CT number was, on average, reproduced to within 1 HU, and image noise was reproduced to within 4%, which is below the threshold for visibly perceptible differences in noise. Low- and high-contrast resolution were not degraded, and simulated images were visually indistinguishable from acquired images.

CONCLUSION

For the dose range studied, it was concluded that the image quality of a CT image produced by averaging multiple low-mAs CT images is identical to that of a high-mAs image acquired at equivalent effective dose, when all other acquisition and reconstruction parameters are held constant. Prospective CT dose-reduction studies may be feasible by acquiring multiple low-dose scans instead of a single high-dose scan. Simulated high-dose images could be interpreted clinically, whereas lower-dose images would be available for an observer study.

Patient dose considerations in computed tomography examinations

Ionizing radiation is extensively used in medicine and its contribution to both diagnosis and therapy is undisputable. However, the use of ionizing radiation also involves a certain risk since it may cause damage to tissues and organs and trigger carcinogenesis. Computed tomography (CT) is currently one of the major contributors to the collective population radiation dose both because it is a relatively high dose examination and an increasing number of people are subjected to CT examinations many times during their lifetime. The evolution of CT scanner technology has greatly increased the clinical applications of CT and its availability throughout the world and made it a routine rather than a specialized examination. With the modern multislice CT scanners, fast volume scanning of the whole human body within less than 1 min is now feasible. Two dimensional images of superb quality can be reconstructed in every possible plane with respect to the patient axis (e.g. axial, sagital and coronal). Furthermore, three-dimensional images of all anatomic structures and organs can be produced with only minimal additional effort (e.g. skeleton, tracheobronchial tree, gastrointestinal system and cardiovascular system). All these applications, which are diagnostically valuable, also involve a significant radiation risk. Therefore, all medical professionals involved with CT, either as referring or examining medical doctors must be aware of the risks involved before they decide to prescribe or perform CT examinations. Ultimately, the final decision concerning justification for a prescribed CT examination lies upon the radiologist. In this paper, we summarize the basic information concerning the detrimental effects of ionizing radiation, as well as the CT dosimetry background. Furthermore, after a brief summary of the evolution of CT scanning, the current CT scanner technology and its special features with respect to patient doses are given in detail. Some numerical data is also given in order to comprehend the magnitude of the potential radiation risk involved in comparison with risk from exposure to natural background radiation levels.

Evaluation of image-enhanced paediatric computed tomography brain examinations

The aim of this study was to evaluate the possibility of reducing the radiation dose to paediatric patients undergoing computed tomography (CT) brain examination by using image-enhancing software. Artificial noise was added to the raw data collected from 20 patients aged between 1 and 10 y to simulate tube current reductions of 20, 40 and 60 mA. All images were created in duplicate; one set of images remained unprocessed whereas the other was processed with image-enhancing software. Three paediatric radiologists assessed the image quality based on their ability to visualise the high- and low-contrast structures and their overall impression of the diagnostic value of the image. For patients aged 6-10 y, it was found that dose reductions from 27 mGy (CTDI(vol)) to 23 mGy (15 %) in the upper brain and from 32 to 28 mGy (13 %) in the lower brain were possible for standard diagnostic CT examinations when using the image-enhancing filter. For patients 1-5 y, the results for standard diagnostics in the upper brain were inconclusive, for the lower brain no dose reductions were found possible.

Development and validation of a low dose simulator for computed tomography

Purpose

To develop and validate software for facilitating observer studies on the effect of radiation exposure on the diagnostic value of computed tomography (CT).

Methods

A low dose simulator was developed which adds noise to the raw CT data. For validation two phantoms were used: a cylindrical test object and an anthropomorphic phantom. Images of both were acquired at different dose levels by changing the tube current of the acquisition (500 mA to 20 mA in five steps). Additionally, low dose simulations were performed from 500 mA downwards to 20 mA in the same steps. Noise was measured within the cylindrical test object and in the anthropomorphic phantom. Finally, noise power spectra (NPS) were measured in water.

Results

The low dose simulator yielded similar image quality compared with actual low dose acquisitions. Mean difference in noise over all comparisons between actual and simulated images was 5.7 ± 4.6% for the cylindrical test object and 3.3 ± 2.6% for the anthropomorphic phantom. NPS measurements showed that the general shape and intensity are similar.

Conclusion

The developed low dose simulator creates images that accurately represent the image quality of acquisitions at lower dose levels and is suitable for application in clinical studies.

Pediatric MDCT: towards assessing the diagnostic influence of dose reduction on the detection of small lung nodules

RATIONALE AND OBJECTIVES

The purpose of this study was to evaluate the effect of reduced tube current (dose) on lung nodule detection in pediatric multidetector array computed tomography (MDCT).

MATERIALS AND METHODS

The study included normal clinical chest MDCT images of 13 patients (aged 1-7 years) scanned at tube currents of 70 to 180 mA. Calibrated noise addition software was used to simulate cases as they would have been acquired at 70 mA (the lowest original tube current), 35 mA (50% reduction), and 17.5 mA (75% reduction). Using a validated nodule simulation technique, small lung nodules of 3 to 5 mm in diameter were inserted into the cases, which were then randomized and rated independently by three experienced pediatric radiologists for nodule presence on a continuous scale ranging from zero (definitely absent) to 100 (definitely present). The observer data were analyzed to assess the influence of dose on detection accuracy using the Dorfman-Berbaum-Mets method for multiobserver, multitreatment receiver-operating characteristic (ROC) analysis and the Williams trend test.

RESULTS

The areas under the ROC curves were 0.95, 0.91, and 0.92 at 70, 35, and 17.5 mA, respectively, with standard errors of 0.02 and interobserver variability of 0.02. The Dorfman-Berbaum-Mets method and the Williams trend test yielded P values for the effect of dose of .09 and .05, respectively.

CONCLUSION

Tube current (dose) has a weak effect on the detection accuracy of small lung nodules in pediatric MDCT. The effect on detection accuracy of a 75% dose reduction was comparable to interobserver variability, suggesting a potential for dose reduction.

Validation of CT dose-reduction simulation

The objective of this research was to develop and validate a custom computed tomography dose-reduction simulation technique for producing images that have an appearance consistent with the same scan performed at a lower mAs (with fixed kVp, rotation time, and collimation). Synthetic noise is added to projection (sinogram) data, incorporating a stochastic noise model that includes energy-integrating detectors, tube-current modulation, bowtie beam filtering, and electronic system noise. Experimental methods were developed to determine the parameters required for each component of the noise model. As a validation, the outputs of the simulations were compared to measurements with cadavers in the image domain and with phantoms in both the sinogram and image domain, using an unbiased root-mean-square relative error metric to quantify agreement in noise processes. Four-alternative forced-choice (4AFC) observer studies were conducted to confirm the realistic appearance of simulated noise, and the effects of various system model components on visual noise were studied. The "just noticeable difference (JND)" in noise levels was analyzed to determine the sensitivity of observers to changes in noise level. Individual detector measurements were shown to be normally distributed (p > 0.54), justifying the use of a Gaussian random noise generator for simulations. Phantom tests showed the ability to match original and simulated noise variance in the sinogram domain to within 5.6% +/- 1.6% (standard deviation), which was then propagated into the image domain with errors less than 4.1% +/- 1.6%. Cadaver measurements indicated that image noise was matched to within 2.6% +/- 2.0%. More importantly, the 4AFC observer studies indicated that the simulated images were realistic, i.e., no detectable difference between simulated and original images (p = 0.86) was observed. JND studies indicated that observers' sensitivity to change in noise levels corresponded to a 25% difference in dose, which is far larger than the noise accuracy achieved by simulation. In summary, the dose-reduction simulation tool demonstrated excellent accuracy in providing realistic images. The methodology promises to be a useful tool for researchers and radiologists to explore dose reduction protocols in an effort to produce diagnostic images with radiation dose "as low as reasonably achievable".

Effect of tube current on diagnostic image quality in paediatric cerebral multidetector CT images

The aim of this study was to investigate the effect of tube current on diagnostic image quality in paediatric cerebral multidetector CT (MDCT) images in order to identify the minimum radiation dose required to reproduce acceptable levels of different diagnostic image qualities. Original digital scanning data (raw data) were selected retrospectively from routine MDCT brain examinations of 25 paediatric patients. All examinations had been performed using axial scanning on an eight-slice MDCT (LightSpeed Ultra, GE Healthcare). Their ages ranged from newborn to 15 years. Quantum noise was added artificially to the raw data representing dose reductions equivalent to steps of 20 mA. Patient identification information was removed. Three experienced radiologists blindly and randomly assessed the resulting images from two different levels of the brain with regard to reproduction of structures and overall image quality. Final data were evaluated using the non-parametric statistical approach of inter-scale concordance. The minimum value of tube current-time product (mAs) required to reproduce an image of sufficient diagnostic quality was established in relation to the age of the patient. The corresponding CT dose index values by volume (CTDI(vol) (mGy)) were also established. In conclusion, acceptable reproduction of low-contrast structures was possible at CTDI(vol) values down to 20 mGy (patients 1-5 years old). For acceptable reproduction of high-contrast structures, CTDI(vol) values down to 10 mGy were considered possible (patients 1-5 years old). The original image quality for patients under 6 months of age (15 mGy) was found to be inadequate for acceptable reproduction of low-contrast structures.

Radiation dose reduction in chest CT: a review

OBJECTIVE

This article aims to summarize the available data on reducing radiation dose exposure in routine chest CT protocols. First, the general aspects of radiation dose in CT and radiation risk are discussed, followed by the effect of changing parameters on image quality. Finally, the results of previous radiation dose reduction studies are reviewed, and important information contributing to radiation dose reduction will be shared.

CONCLUSION

A variety of methods and techniques for radiation dose reduction should be used to ensure that radiation exposure is kept as low as is reasonably achievable.

Analysis of CR dose reduction in pediatric patients, based on computer-simulated noise addition

This paper validates a technique to add statistical noise to a Computed Radiography (CR) in order to simulate accurately how the same image would appear if taken at a reduced tube current. To that, a noise addition software has been developed to create lower dose CR using existing pediatric radiographies based on the selection of lower X-ray tube current. The effect of different milliAmpere-seconds (mAs) setting on image quality has been evaluated using the CDMAM 3.4 phantom and the obtained results show good agreements between the simulated and real images in terms of noise measurement. The new CR images allow medical researchers to study how lower dose affects the patient diagnosis.

Appendicitis in Children: Low-Dose CT with a Phantom-based Simulation Technique—Initial Observations

PURPOSE

To retrospectively determine the accuracy of low-dose (20-mAs) computed tomography (CT) in the diagnosis of acute appendicitis in children by using a technique that enables the simulation of human CT scans acquired at a lower tube current given the image acquired at a standard dose.

MATERIALS AND METHODS

Institutional review board approval was obtained, informed consent was not required, and the study was HIPAA compliant. The authors reviewed 100 standard-dose pediatric abdominal-pelvic CT scans (50 positive and 50 negative scans) obtained in 100 patients and corresponding simulated low-dose (20-mAs) scans. The standard-dose scans were obtained for evaluation in patients suspected of having appendicitis. Scans were reviewed in randomized order by four experienced pediatric radiologists. The patients with positive findings included 21 girls (mean age, 9.2 years) and 29 boys (mean age, 8.4 years). The patients with negative findings included 28 girls (mean age, 9.2 years) and 22 boys (mean age, 8.4 years). Simulation was achieved by adding noise patterns from repeated 20-mAs scans of a pediatric pelvis phantom to the original scans obtained with a standard tube current. Observers recorded their confidence in the diagnosis of appendicitis by using a six-point scale. Dose-related changes were analyzed with generalized estimating equations and the nonparametric sign test.

RESULTS

There was a statistically significant (P < .001, sign test) decrease in both sensitivity and accuracy with a lower tube current, from 91.5% with the original tube current to 77% with the lower tube current. A low dose was the only statistically significant (P < .001) risk factor for a false-negative result. The specificity was unchanged at 94% for both the images obtained with the original tube current and the simulated low-dose images. The overall accuracy decreased from 92% with the original dose to 86% with the low dose.

CONCLUSION

Preliminary findings indicate that it is feasible to optimize the CT dose used to evaluate appendicitis in children by using phantom-based computer simulations.