Bone-induced streak artifact suppression in sparse-view CT image reconstruction

ABSTRACT: BACKGROUND: In sparse-view CT imaging, strong streak artifacts may appear around bony structures and they often compromise the image readability. Compressed sensing (CS) or total variation (TV) minimization-based image reconstruction method has reduced the streak artifacts to a great extent, but, sparse-view CT imaging still suffers from residual streak artifacts. We introduce a new bone-induced streak artifact reduction method in the CS-based image reconstruction. METHODS: We firstly identify the high-intensity bony regions from the image reconstructed by the filtered backprojection (FBP) method, and we calculate the sinogram stemming from the bony regions only. Then, we subtract the calculated sinogram, which stands for the bony regions, from the measured sinogram before performing the CS-based image reconstruction. The image reconstructed from the subtracted sinogram will stand for the soft tissues with little streak artifacts on it. To restore the original image intensity in the bony regions, we add the bony region image, which has been identified from the FBP image, to the soft tissue image to form a combined image. Then, we perform the CS-based image reconstruction again on the measured sinogram using the combined image as the initial condition of the iteration. For experimental validation of the proposed method, we take images of a contrast phantom and a rat using a micro-CT and we evaluate the reconstructed images based on two figures of merit, relative mean square error and total variation caused by the streak artifacts. RESULTS: The images reconstructed by the proposed method have been found to have smaller streak artifacts than the ones reconstructed by the original CS-based method when visually inspected. The quantitative image evaluation studies have also shown that the proposed method outperforms the conventional CS-based method. CONCLUSIONS: The proposed method can effectively suppress streak artifacts stemming from bony structures in sparse-view CT imaging.     

Utility of fast non-local means (FNLM) filter for detection of pulmonary nodules in chest CT for pediatric patient

PurposeThis study was aimed to evaluate the utility based on imaging quality of the fast non-local means (FNLM) filter in diagnosing lung nodules in pediatric chest computed tomography (CT).MethodsWe retrospectively reviewed the chest CT reconstructed with both filtered back projection (FBP) and iterative reconstruction (IR) in pediatric patients with metastatic lung nodules. After applying FNLM filter with six h values (0.0001, 0.001, 0.01, 0.1, 1, and 10) to the FBP images, eight sets of images including FBP, IR, and FNLM were analyzed. The image quality of the lung nodules was evaluated objectively for coefficient of variation (COV), contrast to noise ratio (CNR), and point spread function (PSF), and subjectively for noise, sharpness, artifacts, and diagnostic acceptability.ResultsThe COV was lowest in IR images and decreased according to increasing h values and highest with FBP images (P < 0.001). The CNR was highest with IR images, increased according to increasing h values and lowest with FBP images (P < 0.001). The PSF was lower only in FNLM filter with h value of 0.0001 or 0.001 than in IR images (P < 0.001). In subjective analysis, only images of FNLM filter with h value of 0.0001 or 0.001 rarely showed unacceptable quality and had comparable results with IR images. There were less artifacts in FNLM images with h value of 0.0001 compared with IR images (p < 0.001).ConclusionFNLM filter with h values of 0.0001 allows comparable image quality with less artifacts compared with IR in diagnosing metastatic lung nodules in pediatric chest CT.     

Image quality characteristics for virtual monoenergetic images using dual-layer spectral detector CT: Comparison with conventional tube-voltage images

PurposeTo investigate the image quality characteristics for virtual monoenergetic images compared with conventional tube-voltage image with dual-layer spectral CT (DLCT).MethodsHelical scans were performed using a first-generation DLCT scanner, two different sizes of acrylic cylindrical phantoms, and a Catphan phantom. Three different iodine concentrations were inserted into the phantom center. The single-tube voltage for obtaining virtual monoenergetic images was set to 120 or 140 kVp. Conventional 120- and 140-kVp images and virtual monoenergetic images (40–200-keV images) were reconstructed from slice thicknesses of 1.0 mm. The CT number and image noise were measured for each iodine concentration and water on the 120-kVp images and virtual monoenergetic images. The noise power spectrum (NPS) was also calculated.ResultsThe iodine CT numbers for the iodinated enhancing materials were similar regardless of phantom size and acquisition method. Compared with the iodine CT numbers of the conventional 120-kVp images, those for the monoenergetic 40-, 50-, and 60-keV images increased by approximately 3.0-, 1.9-, and 1.3-fold, respectively. The image noise values for each virtual monoenergetic image were similar (for example, 24.6 HU at 40 keV and 23.3 HU at 200 keV obtained at 120 kVp and 30-cm phantom size). The NPS curves of the 70-keV and 120-kVp images for a 1.0-mm slice thickness over the entire frequency range were similar.ConclusionVirtual monoenergetic images represent stable image noise over the entire energy spectrum and improved the contrast-to-noise ratio than conventional tube voltage using the dual-layer spectral detector CT.     

A Retrospective Respiratory Gating System Based on Epipolar Consistency Conditions

Motion artifacts of in vivo imaging, due to rapid respiration rate and respiration displacements of the mice while free-breathing, is a major challenge in micro computed tomography(micro-CT). The respiratory gating is often served for either projective images acquisition or per projection qualification, so as to eliminate the artifacts brought by in vivo motion. In this paper, we propose a novel respiratory gating method, which firstly divides one rotation cycle into a number of segments, and extracts the respiratory signal from the projective image series of current segment by the value of the epipolar consistency conditions (ECC), then in terms of the measured average respiratory period, sets next segment’s start-up time and rotation speed of the gantry for respiratory phase synchronization, and so on so forth. The gating procedure is through the whole projections of three cycles, only one among three projections at each angle is qualified by their phase value and is retained for future use for tomographic image reconstruction. In practical experiment, the ECC based gating method and the conventional hardware gating method are employed on micro CT imaging of C57BL/6 mice respectively. The result shows that, compared with the hardware based one, the proposed method not only achieve much better consistency in the projection images, but also suppresses the streak artifacts more effectively on the different parts like the breast, abdomen and head of in vivo mice.     

Improved visualization of X-ray phase contrast volumetric data through artifact-free integrated differential images

Artifacts arising when differential phase images are integrated is a common problem to several X-ray phase-based experimental techniques. The combination of noise and insufficient sampling of the high-frequency differential phase signal leads to the formation of streak artifacts in the projections, translating into poor image quality in the tomography slices. In this work, we apply a non-iterative integration algorithm proven to reduce streak artifacts in planar (2D) images to a differential phase tomography scan. We report on how the reduction of streak artifacts in the projections improves the quality of the tomography slices, especially in the directions different from the reconstruction plane. Importantly, the method is compatible with large tomography datasets in terms of computation time.     

宝石能谱CT GSI成像和常规成像在上腹部应用价值的对比研究

目的:探讨宝石能谱CT GSI扫描模式在上腹部检查中降低辐射剂量和优化图像质量的可行性及应用价值。方法:选择2016年9月至2016年12月期间我院40例拟行上腹部三期增强的患者,根据扫描模式将患者分为A组和B组,每组20例。A组患者采用宝石能谱CT常规扫描模式行螺旋扫描,管电压120 Kvp及自动毫安管电流,确定NI值为10。B组患者采用GSI模式行三期增强扫描收集门脉期图像。回顾性自适应统计迭代重建(ASIR)70kev单能量图像,应用ASIR Review工具收集0到100%ASIR的CT值、噪声值,计算图像信号噪声比(SNR)。记录各组剂量报告中CT剂量容积指数(CTDI vol)及剂量长度乘积(DLP),并计算有效剂量(ED),采用图像质量主观评分对图像进行评价。结果:B组CT值、噪声值及SNR均高于A组(P0.05),B组CTDIvol、DLP和ED均显著低于A组(P0.05);随着ASIR升高,SNR升高,但是图像质量主观评分先升高后降低。当ASIR为50%时,图像质量最高,不同ASIR的CT值、噪声值之间的差异无统计学意义(P0.05)。结论:宝石能谱CT GSI扫描模式的效果明显优于螺旋扫描,同时在降低图像噪声的前提下选择50%ASIR,可保障图像质量。     

Feasibility of image quality improvement for high-speed CBCT imaging using deep convolutional neural network for image-guided radiotherapy in prostate cancer

PurposeHigh-speed cone-beam computed tomography (CBCT) scan for image-guided radiotherapy (IGRT) can reduce both the scan time and the exposure dose. However, it causes noise and artifacts in the reconstructed images due to the lower number of acquired projection data. The purpose of this study is to improve the image quality of high-speed CBCT using a deep convolutional neural network (DCNN).MethodsCBCT images of 36 prostate cancer patients were selected. The CBCT images acquired at normal scan speed were defined as CBCT_100%. Simulated high-speed CBCT images acquired at twofold and fourfold scan speed were created, which were defined as CBCT_50% and CBCT_25%, respectively. The image quality of the CBCT_50% was treated as the requirement for IGRT in this study because previous studies reported that its image is sufficient with respect to IGRT. The DCNN model was trained to learn direct mapping from CBCT_25% to the corresponding CBCT_100%. The performance of the DCNN model was evaluated using the sixfold cross-validation method. CBCT images generated by DCNN (CBCT_25%+DCNN) were evaluated for voxel value accuracy and image quality.ResultsThe DCNN model can process CBCT_25% of a new patient within 0.06 s/slice. The CBCT_25%+DCNN was comparable to the CBCT_50% in terms of both voxel value accuracy and image quality.ConclusionsWe developed a DCNN model to remove noise and artifacts from high-speed CBCT. We emphasize that it is possible to reduce exposure to one quarter and to increase the CBCT scan speed by a factor of four.     

Elimination of biological and physical artifacts in abdomen and brain computed tomography procedures using filtering techniques

IntroductionMedical images are usually affected by biological and physical artifacts or noise, which reduces image quality and hence poses difficulties in visual analysis, interpretation and thus requires higher doses and increased radiographs repetition rate.ObjectivesThis study aims at assessing image quality during CT abdomen and brain examinations using filtering techniques as well as estimating the radiogenic risk associated with CT abdomen and brain examinations.Materials and MethodsThe data were collected from the Radiology Department at Royal Care International (RCI) Hospital, Khartoum, Sudan. The study included 100 abdominal CT images and 100 brain CT images selected from adult patients. Filters applied are namely: Mean filter, Gaussian filter, Median filter and Minimum filter. In this study, image quality after denoising is measured based on the Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and the Structural Similarity Index Metric (SSIM).ResultsThe results show that the images quality parameters become higher after applications of filters. Median filter showed improved image quality as interpreted by the measured parameters: PSNR and SSIM, and it is thus considered as a better filter for removing the noise from all other applied filters.DiscussionThe noise removed by the different filters applied to the CT images resulted in enhancing high quality images thereby effectively revealing the important details of the images without increasing the patients’ risks from higher doses.ConclusionsFiltering and image reconstruction techniques not only reduce the dose and thus the radiation risks, but also enhances high quality imaging which allows better diagnosis.     

An iterative deconvolution algorithm for image recovery in clinical CT: A phantom study

PurposeTo study the feasibility of using an iterative reconstruction algorithm to improve previously reconstructed CT images which are judged to be non-diagnostic on clinical review. A novel rapidly converging, iterative algorithm (RSEMD) to reduce noise as compared with standard filtered back-projection algorithm has been developed.Materials and methodsThe RSEMD method was tested on in-silico, Catphan^®500, and anthropomorphic 4D XCAT phantoms. The method was applied to noisy CT images previously reconstructed with FBP to determine improvements in SNR and CNR. To test the potential improvement in clinically relevant CT images, 4D XCAT phantom images were used to simulate a small, low contrast lesion placed in the liver.ResultsIn all of the phantom studies the images proved to have higher resolution and lower noise as compared with images reconstructed by conventional FBP. In general, the values of SNR and CNR reached a plateau at around 20 iterations with an improvement factor of about 1.5 for in noisy CT images. Improvements in lesion conspicuity after the application of RSEMD have also been demonstrated. The results obtained with the RSEMD method are in agreement with other iterative algorithms employed either in image space or with hybrid reconstruction algorithms.ConclusionsIn this proof of concept work, a rapidly converging, iterative deconvolution algorithm with a novel resolution subsets-based approach that operates on DICOM CT images has been demonstrated. The RSEMD method can be applied to sub-optimal routine-dose clinical CT images to improve image quality to potentially diagnostically acceptable levels.     

Image reconstruction of optical computed tomography by using the algebraic reconstruction technique for dose readouts of polymer gel dosimeters

Optical computed tomography (optical CT) has been proven to be a useful tool for dose readouts of polymer gel dosimeters. In this study, the algebraic reconstruction technique (ART) for image reconstruction of gel dosimeters was used to improve the image quality of optical CT. Cylindrical phantoms filled with N-isopropyl-acrylamide polymer gels were irradiated using a medical linear accelerator. A circular dose distribution and a hexagonal dose distribution were produced by applying the VMAT technique and the six-field dose delivery, respectively. The phantoms were scanned using optical CT, and the images were reconstructed using the filtered back-projection (FBP) algorithm and the ART. For the circular dose distribution, the ART successfully reduced the ring artifacts and noise in the reconstructed image. For the hexagonal dose distribution, the ART reduced the hot spots at the entrances of the beams and increased the dose uniformity in the central region. Within 50% isodose line, the gamma pass rates for the 2 mm/3% criteria for the ART and FBP were 99.2% and 88.1%, respectively. The ART could be used for the reconstruction of optical CT images to improve image quality and provide accurate dose conversion for polymer gel dosimeters.     

A Novel Weighted Total Difference Based Image Reconstruction Algorithm for Few-View Computed Tomography

In practical applications of computed tomography (CT) imaging, due to the risk of high radiation dose imposed on the patients, it is desired that high quality CT images can be accurately reconstructed from limited projection data. While with limited projections, the images reconstructed often suffer severe artifacts and the edges of the objects are blurred. In recent years, the compressed sensing based reconstruction algorithm has attracted major attention for CT reconstruction from a limited number of projections. In this paper, to eliminate the streak artifacts and preserve the edge structure information of the object, we present a novel iterative reconstruction algorithm based on weighted total difference (WTD) minimization, and demonstrate the superior performance of this algorithm. The WTD measure enforces both the sparsity and the directional continuity in the gradient domain, while the conventional total difference (TD) measure simply enforces the gradient sparsity horizontally and vertically. To solve our WTD-based few-view CT reconstruction model, we use the soft-threshold filtering approach. Numerical experiments are performed to validate the efficiency and the feasibility of our algorithm. For a typical slice of FORBILD head phantom, using 40 projections in the experiments, our algorithm outperforms the TD-based algorithm with more than 60% gains in terms of the root-mean-square error (RMSE), normalized root mean square distance (NRMSD) and normalized mean absolute distance (NMAD) measures and with more than 10% gains in terms of the peak signal-to-noise ratio (PSNR) measure. While for the experiments of noisy projections, our algorithm outperforms the TD-based algorithm with more than 15% gains in terms of the RMSE, NRMSD and NMAD measures and with more than 4% gains in terms of the PSNR measure. The experimental results indicate that our algorithm achieves better performance in terms of suppressing streak artifacts and preserving the edge structure information of the object.     

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.     

Iterative image reconstruction using modified non-local means filtering for limited-angle computed tomography

PurposeLimited-angle CT imaging is an effective technique to reduce radiation. However, existing image reconstruction methods can effectively reduce streak artifacts but fail to suppress those artifacts around edges due to incomplete projection data. Thus, a modified NLM (mNLM) based reconstruction method is proposed.MethodsSince the artifacts around edges mainly exist in local position, it is possible to restore the true pixels in artifacts using pixels located in artifacts-free regions. In each iteration, mNLM is performed on image reconstructed by ART followed by positivity constraint. To solve the problem caused by ART-mNLM that there is undesirable information that may appear in the image, ART-TV is then utilized in the following iterative process after ART-mNLM iterates for a number of iterations. The proposed algorithm is named as ART-mNLM/TV.ResultsSimulation experiments are performed to validate the feasibility of algorithm. When the scanning range is [0, 150°], our algorithm outperforms the ART-NLM and ART-TV with more than 40% and 29% improvement in terms of SNR and with more than 58% and 49% reduction in terms of MAE. Consistently, reconstructed images from real projection data also demonstrate the effectiveness of presented algorithm.ConclusionThis paper uses mNLM which benefits from redundancy of information across the whole image, to recover the true value of pixels in artifacts region by utilizing pixels from artifact-free regions, and artifacts around the edges can be mitigated effectively. Experiments show that the proposed ART-mNLM/TV is able to achieve better performances compared to traditional methods.     

Simulation of scanner- and patient-specific low-dose CT imaging from existing CT images

PurposeSimulating low-dose Computed Tomography (CT) facilitates in-silico studies into the required dose for a diagnostic task. Conventionally, low-dose CT images are created by adding noise to the projection data. However, in practice the raw data is often simply not available. This paper presents a new method for simulating patient-specific, low-dose CT images without the need of the original projection data.MethodsThe low-dose CT simulation method included the following: (1) computation of a virtual sinogram from a high dose CT image through a radon transform; (2) simulation of a ‘reduced’-dose sinogram with appropriate amounts of noise; (3) subtraction of the high-dose virtual sinogram from the reduced-dose sinogram; (4) reconstruction of a noise volume via filtered back-projection; (5) addition of the noise image to the original high-dose image. The required scanner-specific parameters, such as the apodization window, bowtie filter, the X-ray tube output parameter (reflecting the photon flux) and the detector read-out noise, were retrieved from calibration images of a water cylinder. The low-dose simulation method was evaluated by comparing the noise characteristics in simulated images with experimentally acquired data.ResultsThe models used to recover the scanner-specific parameters fitted accurately to the calibration data, and the values of the parameters were comparable to values reported in literature. Finally, the simulated low-dose images accurately reproduced the noise characteristics in experimentally acquired low-dose-volumes.ConclusionThe developed methods truthfully simulate low-dose CT imaging for a specific scanner and reconstruction using filtered backprojection. The scanner-specific parameters can be estimated from calibration data.     

Algorithm-based artifact reduction in patients with arms-down positioning in computed tomography

PurposeArm-artifact, a type of streak artifact frequently observed in computed tomography (CT) images obtained at arms-down positioning in polytrauma patients, is known to degrade image quality. This study aimed to develop a novel arm-artifact reduction algorithm (AAR) applied to projection data.MethodsA phantom resembling an adult abdomen with two arms was scanned using a 16-row CT scanner. The projection data were processed by AAR, and CT images were reconstructed. The artifact reduction for the same phantom was compared with that achieved by two latest iterative reconstruction (IR) techniques (IR1 and IR2) using a normalized artifact index (nAI) at two locations (ventral and dorsal side). Image blurring as a processing side effect was compared with IR2 of the model-based IR using a plastic needle phantom. Additionally, the projection data of two clinical cases were processed using AAR, and the image noise was evaluated.ResultsAAR and IR2 significantly reduced nAI by 87.5% and 74.0%, respectively at the ventral side and 84.2% and 69.6%, respectively, at the dorsal side compared with each filtered back projection (P < 0.01), whereas IR1 did not. The proposed algorithm mostly maintained the original spatial resolution, compared with IR2, which yielded apparent image blurring. The image noise in the clinical cases was also reduced significantly (P < 0.01).ConclusionsAAR was more effective and superior than the latest IR techniques and is expected to improve the image quality of polytrauma CT imaging with arms-down positioning.     

能谱CT优化胃肿瘤扫描辐射剂量对肾上腺嗜铬细胞瘤的诊断价值

目的:探讨能谱CT优化胃肿瘤扫描辐射剂量对肾上腺嗜铬细胞瘤的诊断价值。方法:采用回顾性、抽样、随机研究方法选择2012年9月到2017年2月在我院诊治的肾上腺嗜铬细胞瘤患者59例作为研究对象,所有患者都给予常规CT扫描与能谱CT优化胃肿瘤扫描,记录和比较辐射剂量与图像质量。结果:所有病例包膜均完整,边缘清楚,肿瘤内见单发或多发低密度区,肿瘤实质区呈不均匀显著强化。常规CT与能谱CT的图像质量主观评分分别为3.89±0.45分和4.54±0.34分;常规CT与能谱CT图像的胃肿瘤CT值分别为31.94±6.39HU和35.29±5.19HU,对比都有显著差异(P0.05)。能谱CT图像的膀胱和皮下脂肪图像噪声值都显著低于常规CT图像,对比差异都有统计学意义(P0.05);能谱CT扫描的CTDIvol和DLP分别为12.39±3.48mGy和624.10±39.19mGy.cm,都显著低于常规CT扫描的14.09±4.13mGy和653.92±56.29mGy.cm(P0.05)。结论:能谱CT优化胃肿瘤扫描在肾上腺嗜铬细胞瘤诊断中的应用能有效减少辐射剂量与图像噪声,提高图像CT值与主观质量,临床应用价值更高。     

Simultaneous achievement of accurate CT number and image quality improvement for myocardial perfusion CT at 320-MDCT volume scanning

PurposeTo investigate differences in image-to-image variations between full- and half-scan reconstruction on myocardial CT perfusion (CTP) study.MethodsUsing a cardiac phantom we performed ECG-gated myocardial CTP on a second-generation 320-multidetector CT volume scanner. The heart rate was set at 60 bpm; once per second for a total of 24 s were performed. CT images were acquired at 80- and 120 kVp and subjected to full- and half-scan reconstruction. On images acquired at the same slice level we then measured image-to-image variations, coefficients of variance (CV), and image noise.ResultsThe image-to-image variations with full- and half-scan reconstruction were 1.3 HU vs. 27.2 HU at 80 kVp (p < 0.001) and 0.70 HU vs. 9.3 HU at 120 kVp (p < 0.001) even though the mean HU value was almost the same for both reconstruction methods. The CV of 80- and 120-kVp images of the left ventricular cavity decreased by 0.16% and 0.17%, respectively, with full-scan reconstruction; with half-scan reconstruction it decreased by 3.34% and 2.30%, respectively. Compared with half-scan reconstruction, the image noise was reduced by 27.2% at 80 kVp and by 28.0% at 120 kVp with full-scan reconstruction.ConclusionMyocardial CTP with full-scan reconstruction substantially decreased image-to-image variations and provided accurate CT attenuation.     

Statistical learning methods as a preprocessing step for survival analysis: evaluation of concept using lung cancer data

Background

Despite its superb lateral resolution, flat-panel-detector (FPD) based tomosynthesis suffers from low contrast and inter-plane artifacts caused by incomplete cancellation of the projection components stemming from outside the focal plane. The incomplete cancellation of the projection components, mostly due to the limited scan angle in the conventional tomosynthesis scan geometry, often makes the image contrast too low to differentiate the malignant tissues from the background tissues with confidence.

Methods

In this paper, we propose a new method to suppress the inter-plane artifacts in FPD-based tomosynthesis. If 3D whole volume CT images are available before the tomosynthesis scan, the CT image data can be incorporated into the tomosynthesis image reconstruction to suppress the inter-plane artifacts, hence, improving the image contrast. In the proposed technique, the projection components stemming from outside the region-of-interest (ROI) are subtracted from the measured tomosynthesis projection data to suppress the inter-plane artifacts. The projection components stemming from outside the ROI are calculated from the 3D whole volume CT images which usually have lower lateral resolution than the tomosynthesis images. The tomosynthesis images are reconstructed from the subtracted projection data which account for the x-ray attenuation through the ROI. After verifying the proposed method by simulation, we have performed both CT scan and tomosynthesis scan on a phantom and a sacrificed rat using a FPD-based micro-CT.

Results

We have measured contrast-to-noise ratio (CNR) from the tomosynthesis images which is an indicator of the residual inter-plane artifacts on the focal-plane image. In both cases of the simulation and experimental imaging studies of the contrast evaluating phantom, CNRs have been significantly improved by the proposed method. In the rat imaging also, we have observed better visual contrast from the tomosynthesis images reconstructed by the proposed method.

Conclusions

The proposed tomosynthesis technique can improve image contrast with aids of 3D whole volume CT images. Even though local tomosynthesis needs extra 3D CT scanning, it may find clinical applications in special situations in which extra 3D CT scan is already available or allowed.     

Technical Note: Quality assessment of virtual monochromatic spectral images on a dual energy CT scanner

PurposeTo assess the quality of images obtained on a dual energy computed tomography (CT) scanner.MethodsImage quality was assessed on a 64 detector-row fast kVp-switching dual energy CT scanner (Revolution GSI, GE Medical Systems). The Catphan phantom and a low contrast resolution phantom were employed. Acquisitions were performed at eight different radiation dose levels that ranged from 9 mGy to 32 mGy. Virtual monochromatic spectral images (VMI) were reconstructed in the 40–140 keV range using all available kernels and iterative reconstruction (IR) at four different blending levels. Modulation Transfer Function (MTF) curves, image noise, image contrast, noise power spectrum and contrast to noise ratio were assessed.ResultsIn-plane spatial resolution at the 10% of the MTF curve was 0.60 mm⁻¹. In-plane spatial resolution was not modified with VMI energy and IR blending level. Image noise was reduced from 16.6 at 9 mGy to 6.7 at 32 mGy, while peak frequency remained within 0.14 ± 0.01 mm⁻¹. Image noise was reduced from 14.3 at IR 10% to 11.5 at IR 50% at a constant peak frequency. The lowest image noise and maximum peak frequency were recorded at 70 keV.ConclusionsOur results have shown how objective image quality is varied when different levels of radiation dose and different settings in IR are applied. These results provide CT operators an in depth understanding of the imaging performance characteristics in dual energy CT.     

Adaptive Iterative Dose Reduction Using Three Dimensional Processing (AIDR3D) Improves Chest CT Image Quality and Reduces Radiation Exposure

Objective

To assess the advantages of Adaptive Iterative Dose Reduction using Three Dimensional Processing (AIDR3D) for image quality improvement and dose reduction for chest computed tomography (CT).

Methods

Institutional Review Boards approved this study and informed consent was obtained. Eighty-eight subjects underwent chest CT at five institutions using identical scanners and protocols. During a single visit, each subject was scanned using different tube currents: 240, 120, and 60 mA. Scan data were converted to images using AIDR3D and a conventional reconstruction mode (without AIDR3D). Using a 5-point scale from 1 (non-diagnostic) to 5 (excellent), three blinded observers independently evaluated image quality for three lung zones, four patterns of lung disease (nodule/mass, emphysema, bronchiolitis, and diffuse lung disease), and three mediastinal measurements (small structure visibility, streak artifacts, and shoulder artifacts). Differences in these scores were assessed by Scheffe''s test.

Results

At each tube current, scans using AIDR3D had higher scores than those without AIDR3D, which were significant for lung zones (p<0.0001) and all mediastinal measurements (p<0.01). For lung diseases, significant improvements with AIDR3D were frequently observed at 120 and 60 mA. Scans with AIDR3D at 120 mA had significantly higher scores than those without AIDR3D at 240 mA for lung zones and mediastinal streak artifacts (p<0.0001), and slightly higher or equal scores for all other measurements. Scans with AIDR3D at 60 mA were also judged superior or equivalent to those without AIDR3D at 120 mA.

Conclusion

For chest CT, AIDR3D provides better image quality and can reduce radiation exposure by 50%.