Characterization of Scattered X-Ray Photons in Dental Cone-Beam Computed Tomography

Purpose

Scatter is a very important artifact causing factor in dental cone-beam CT (CBCT), which has a major influence on the detectability of details within images. This work aimed to improve the image quality of dental CBCT through scatter correction.

Methods

Scatter was estimated in the projection domain from the low frequency component of the difference between the raw CBCT projection and the projection obtained by extrapolating the model fitted to the raw projections acquired with 2 different sizes of axial field-of-view (FOV). The function for curve fitting was optimized by using Monte Carlo simulation. To validate the proposed method, an anthropomorphic phantom and a water-filled cylindrical phantom with rod inserts simulating different tissue materials were scanned using 120 kVp, 5 mA and 9-second scanning time covering an axial FOV of 4 cm and 13 cm. The detectability of the CT image was evaluated by calculating the contrast-to-noise ratio (CNR).

Results

Beam hardening and cupping artifacts were observed in CBCT images without scatter correction, especially in those acquired with 13 cm FOV. These artifacts were reduced in CBCT images corrected by the proposed method, demonstrating its efficacy on scatter correction. After scatter correction, the image quality of CBCT was improved in terms of target detectability which was quantified as the CNR for rod inserts in the cylindrical phantom.

Conclusions

Hopefully the calculations performed in this work can provide a route to reach a high level of diagnostic image quality for CBCT imaging used in oral and maxillofacial structures whilst ensuring patient dose as low as reasonably achievable, which may ultimately make CBCT scan a reliable and safe tool in clinical practice.     

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.     

Comparison of ring artifact removal methods using flat panel detector based CT images

Background

Ring artifacts are the concentric rings superimposed on the tomographic images often caused by the defective and insufficient calibrated detector elements as well as by the damaged scintillator crystals of the flat panel detector. It may be also generated by objects attenuating X-rays very differently in different projection direction. Ring artifact reduction techniques so far reported in the literature can be broadly classified into two groups. One category of the approaches is based on the sinogram processing also known as the pre-processing techniques and the other category of techniques perform processing on the 2-D reconstructed images, recognized as the post-processing techniques in the literature. The strength and weakness of these categories of approaches are yet to be explored from a common platform.

Method

In this paper, a comparative study of the two categories of ring artifact reduction techniques basically designed for the multi-slice CT instruments is presented from a common platform. For comparison, two representative algorithms from each of the two categories are selected from the published literature. A very recently reported state-of-the-art sinogram domain ring artifact correction method that classifies the ring artifacts according to their strength and then corrects the artifacts using class adaptive correction schemes is also included in this comparative study. The first sinogram domain correction method uses a wavelet based technique to detect the corrupted pixels and then using a simple linear interpolation technique estimates the responses of the bad pixels. The second sinogram based correction method performs all the filtering operations in the transform domain, i.e., in the wavelet and Fourier domain. On the other hand, the two post-processing based correction techniques actually operate on the polar transform domain of the reconstructed CT images. The first method extracts the ring artifact template vector using a homogeneity test and then corrects the CT images by subtracting the artifact template vector from the uncorrected images. The second post-processing based correction technique performs median and mean filtering on the reconstructed images to produce the corrected images.

Results

The performances of the comparing algorithms have been tested by using both quantitative and perceptual measures. For quantitative analysis, two different numerical performance indices are chosen. On the other hand, different types of artifact patterns, e.g., single/band ring, artifacts from defective and mis-calibrated detector elements, rings in highly structural object and also in hard object, rings from different flat-panel detectors are analyzed to perceptually investigate the strength and weakness of the five methods. An investigation has been also carried out to compare the efficacy of these algorithms in correcting the volume images from a cone beam CT with the parameters determined from one particular slice. Finally, the capability of each correction technique in retaining the image information (e.g., small object at the iso-center) accurately in the corrected CT image has been also tested.

Conclusions

The results show that the performances of the algorithms are limited and none is fully suitable for correcting different types of ring artifacts without introducing processing distortion to the image structure. To achieve the diagnostic quality of the corrected slices a combination of the two approaches (sinogram- and post-processing) can be used. Also the comparing methods are not suitable for correcting the volume images from a cone beam flat-panel detector based CT.     

Artifacts in magnetic resonance imaging and computed tomography caused by dental materials

Background

Artifacts caused by dental restorations, such as dental crowns, dental fillings and orthodontic appliances, are a common problem in MRI and CT scans of the head and neck. The aim of this in-vitro study was to identify and evaluate the artifacts produced by different dental restoration materials in CT and MRI images.

Methods

Test samples of 44 materials (Metal and Non-Metal) commonly used in dental restorations were fabricated and embedded with reference specimens in gelatin moulds. MRI imaging of 1.5T and CT scan were performed on the samples and evaluated in two dimensions. Artifact size and distortions were measured using a digital image analysis software.

Results

In MRI, 13 out of 44 materials produced artifacts, while in CT 41 out of 44 materials showed artifacts. Artifacts produced in both MRI and CT images were categorized according to the size of the artifact.

Significance

Metal based restoration materials had strong influence on CT and less artifacts in MRI images. Rare earth elements such as Ytterbium trifluoride found in composites caused artifacts in both MRI and CT. Recognizing these findings would help dental materials manufacturers and developers to produce materials which can cause less artifacts in MRI and CT images.     

Four Dimensional Digital Tomosynthesis Using on-Board Imager for the Verification of Respiratory Motion

Purpose

To evaluate respiratory motion of a patient by generating four-dimensional digital tomosynthesis (4D DTS), extracting respiratory signal from patients'' on-board projection data, and ensuring the feasibility of 4D DTS as a localization tool for the targets which have respiratory movement.

Methods and Materials

Four patients with lung and liver cancer were included to verify the feasibility of 4D-DTS with an on-board imager. CBCT acquisition (650–670 projections) was used to reconstruct 4D DTS images and the breath signal of the patients was generated by extracting the motion of diaphragm during data acquisition. Based on the extracted signal, the projection data was divided into four phases: peak-exhale phase, mid-inhale phase, peak-inhale phase, and mid-exhale phase. The binned projection data was then used to generate 4D DTS, where the total scan angle was assigned as ±22.5° from rotation center, centered on 0° and 180° for coronal “half-fan” 4D DTS, and 90° and 270° for sagittal “half-fan” 4D DTS. The result was then compared with 4D CBCT which we have also generated with the same phase distribution.

Results

The motion of the diaphragm was evident from the 4D DTS results for peak-exhale, mid-inhale, peak-inhale and mid-exhale phase assignment which was absent in 3D DTS. Compared to the result of 4D CBCT, the view aliasing effect due to arbitrary angle reconstruction was less severe. In addition, the severity of metal artifacts, the image distortion due to presence of metal, was less than that of the 4D CBCT results.

Conclusion

We have implemented on-board 4D DTS on patients data to visualize the movement of anatomy due to respiratory motion. The results indicate that 4D-DTS could be a promising alternative to 4D CBCT for acquiring the respiratory motion of internal organs just prior to radiotherapy treatment.     

Metal artifact reduction on cervical CT images by deep residual learning

Background

Cervical cancer is the fifth most common cancer among women, which is the third leading cause of cancer death in women worldwide. Brachytherapy is the most effective treatment for cervical cancer. For brachytherapy, computed tomography (CT) imaging is necessary since it conveys tissue density information which can be used for dose planning. However, the metal artifacts caused by brachytherapy applicators remain a challenge for the automatic processing of image data for image-guided procedures or accurate dose calculations. Therefore, developing an effective metal artifact reduction (MAR) algorithm in cervical CT images is of high demand.

Methods

A novel residual learning method based on convolutional neural network (RL-ARCNN) is proposed to reduce metal artifacts in cervical CT images. For MAR, a dataset is generated by simulating various metal artifacts in the first step, which will be applied to train the CNN. This dataset includes artifact-insert, artifact-free, and artifact-residual images. Numerous image patches are extracted from the dataset for training on deep residual learning artifact reduction based on CNN (RL-ARCNN). Afterwards, the trained model can be used for MAR on cervical CT images.

Results

The proposed method provides a good MAR result with a PSNR of 38.09 on the test set of simulated artifact images. The PSNR of residual learning (38.09) is higher than that of ordinary learning (37.79) which shows that CNN-based residual images achieve favorable artifact reduction. Moreover, for a 512?×?512 image, the average removal artifact time is less than 1 s.

Conclusions

The RL-ARCNN indicates that residual learning of CNN remarkably reduces metal artifacts and improves critical structure visualization and confidence of radiation oncologists in target delineation. Metal artifacts are eliminated efficiently free of sinogram data and complicated post-processing procedure.

     

Ring Artifacts Reduction in CBCT: Pixels Detection and Patch Based Correction

The ring artifacts introduced by the defective pixels with non-linear responses in the high-resolution detector, have a great impact on subsequent processing and quantitative analysis of the reconstructed images. In this paper, a multistep method is proposed to suppress the ring artifacts of micro CT images, which firstly locates the positions of the defective pixels in the sinogram, and then corrects the corresponding value in the projections. Since the defective pixels always appear as vertical stripes in the sinogram, a horizontal curve is derived by summing the pixel values along vertical direction, thus the abrupt segments related to the defective stripes are enhanced notably, and a proportion coefficient based on the second derivative of the curve is taken as the indicator for the position and the severity of the defective pixels. Then, the detected defective pixels in the sinogram are transferred and relocated in the projections, an improved 3D block matching filtering (BM3D) algorithm is applied to restore the defective pixels in corresponding projection images. In the end, the tomographic images are reconstructed from the corrected projections. In the experiment, a small piece of the motherwort’s rhizome and a part of a mouse’s lung are imaged by micro-CT, and the result shows that, compared with the other four state-of-art methods, the proposed method has a great reduction on the ring artifacts of the reconstructed images, and makes less impact in spatial resolution and contrast in the same time.     

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.     

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%.     

Enhancement of soft-tissue contrast in cone-beam CT using an anti-scatter grid with a sparse sampling approach

PurposeAnti-scatter grids suppress the scatter substantially thus improving image contrast in radiography. However, its active use in cone-beam CT for the purpose of improving contrast-to-noise ratio (CNR) has not been successful mainly due to the increased noise related to Poisson statistics of photons. This paper proposes a sparse-view scanning approach to address the above issue.MethodCompared to the conventional cone-beam CT imaging framework, the proposed method reduces the number of projections and increases exposure in each projection to enhance image quality without an additional cost of radiation dose to patients. For image reconstruction from sparse-view data, an adaptive-steepest-descent projection-onto-convex-sets (ASD POCS) algorithm regularized by total-variation (TV) minimization was adopted. Contrast and CNR with various scattering conditions were evaluated in projection domain by a simulation study using GATE. Then we evaluated contrast, resolution, and image uniformity in CT image domain with Catphan phantom. A head phantom with soft-tissue structures was also employed for demonstrating a realistic application. A virtual grid-based estimation and reduction of scatter has also been implemented for comparison with the real anti-scatter grid.ResultsIn the projection domain evaluation, contrast and CNR enhancement was observed when using an anti-scatter grid compared to the virtual grid. In the CT image domain, the proposed method produced substantially higher contrast and CNR of the low-contrast structures with much improved image uniformity.ConclusionWe have shown that the proposed method can provide high-quality CBCT images particularly with an increased contrast of soft-tissue at a neutral dose for image-guidance.     

Single wall carbon nanotubes enter cells by endocytosis and not membrane penetration

Background

Quantitative analysis of nanoparticle uptake at the cellular level is critical to nanomedicine procedures. In particular, it is required for a realistic evaluation of their effects. Unfortunately, quantitative measurements of nanoparticle uptake still pose a formidable technical challenge. We present here a method to tackle this problem and analyze the number of metal nanoparticles present in different types of cells. The method relies on high-lateral-resolution (better than 30 nm) transmission x-ray microimages with both absorption contrast and phase contrast -- including two-dimensional (2D) projection images and three-dimensional (3D) tomographic reconstructions that directly show the nanoparticles.

Results

Practical tests were successfully conducted on bare and polyethylene glycol (PEG) coated gold nanoparticles obtained by x-ray irradiation. Using two different cell lines, EMT and HeLa, we obtained the number of nanoparticle clusters uptaken by each cell and the cluster size. Furthermore, the analysis revealed interesting differences between 2D and 3D cultured cells as well as between 2D and 3D data for the same 3D specimen.

Conclusions

We demonstrated the feasibility and effectiveness of our method, proving that it is accurate enough to measure the nanoparticle uptake differences between cells as well as the sizes of the formed nanoparticle clusters. The differences between 2D and 3D cultures and 2D and 3D images stress the importance of the 3D analysis which is made possible by our approach.     

A C-GAN Denoising Algorithm in Projection Domain for Micro-CT

Micro-CT provides a high-resolution 3D imaging of micro-architecture in a non-invasive way, which becomes a significant tool in biomedical research and preclinical applications. Due to the limited power of micro-focus X-ray tube, photon starving occurs and noise is inevitable for the projection images, resulting in the degradation of spatial resolution, contrast and image details. In this paper, we propose a C-GAN (Conditional Generative Adversarial Nets) denoising algorithm in projection domain for Micro-CT imaging. The noise statistic property is utilized directly and a novel variance loss is developed to suppress the blurry effects during denoising procedure. Conditional Generative Adversarial Networks (C-GAN) is employed as a framework to implement the denoising task. To guarantee the pixelwised accuracy, fully convolutional network is served as the generator structure. During the alternative training of the generator and the discriminator, the network is able to learn noise distribution automatically. Moreover, residual learning and skip connection architecture are applied for faster network training and further feature fusion. To evaluate the denoising performance, mouse lung, milkvetch root and bamboo stick are imaged by micro-CT in the experiments. Compared with BM3D, CNN-MSE and CNN-VGG, the proposed method can suppress noise effectively and recover image details without introducing any artifacts or blurry effect. The result proves that our method is feasible, efficient and practical.     

Quantifying the effect of soil moisture content on segmenting root system architecture in X-ray computed tomography images

Aims

A commonly accepted challenge when visualising plant roots in X-ray micro Computed Tomography (μCT) images is the similar X-ray attenuation of plant roots and soil phases. Soil moisture content remains a recognised, yet currently uncharacterised source of segmentation error. This work sought to quantify the effect of soil moisture content on the ability to segment roots from soil in μCT images.

Methods

Rice (Oryza sativa) plants grown in contrasting soils (loamy sand and clay loam) were μCT scanned daily for nine days whilst drying from saturation. Root volumes were segmented from μCT images and compared with volumes derived by root washing.

Results

At saturation the overlapping attenuation values of root material, water-filled soil pores and soil organic matter significantly hindered segmentation. However, in dry soil (ca. six days of drying post-saturation) the air-filled pores increased image noise adjacent to roots and impeded accurate visualisation of root material. The root volume was most accurately segmented at field capacity.

Conclusions

Root volumes can be accurately segmented from μCT images of undisturbed soil without compromising the growth requirements of the plant providing soil moisture content is kept at field capacity. We propose all future studies in this area should consider the error associated with scanning at different soil moisture contents.     

A New Method of Detecting Pulmonary Nodules with PET/CT Based on an Improved Watershed Algorithm

Background

Integrated ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) is widely performed for staging solitary pulmonary nodules (SPNs). However, the diagnostic efficacy of SPNs based on PET/CT is not optimal. Here, we propose a method of detection based on PET/CT that can differentiate malignant and benign SPNs with few false-positives.

Method

Our proposed method combines the features of positron-emission tomography (PET) and computed tomography (CT). A dynamic threshold segmentation method was used to identify lung parenchyma in CT images and suspicious areas in PET images. Then, an improved watershed method was used to mark suspicious areas on the CT image. Next, the support vector machine (SVM) method was used to classify SPNs based on textural features of CT images and metabolic features of PET images to validate the proposed method.

Results

Our proposed method was more efficient than traditional methods and methods based on the CT or PET features alone (sensitivity 95.6%; average of 2.9 false positives per scan).     

Infant Cardiac CT Angiography with 64-Slice and 256-Slice CT: Comparison of Radiation Dose and Image Quality Using a Pediatric Phantom

Background

The aims of this study were to investigate the image quality and radiation exposure of pediatric protocols for cardiac CT angiography (CTA) in infants under one year of age.

Methodology/Principal Findings

Cardiac CTA examinations were performed using an anthropomorphic phantom representing a 1-year-old child scanned with non-electrocardiogram-gated (NG), retrospectively electrocardiogram-gated helical (RGH) and prospectively electrocardiogram-gated axial (PGA) techniques in 64-slice and 256-slice CT scanners. The thermoluminescent dosimeters (TLD) were used for direct organ dose measurement, while dose-length product and effective mAs were also used to estimate the patient dose. For image quality, noise and signal-to-noise-ratio (SNR) were assessed based on regions-of-interest drawn on the reconstructed CT images, and were compared with the proposed cardiac image quantum index (CIQI). Estimated dose results were in accordant to the measured doses. The NG scan showed the best image quality in terms of noise and SNR. The PGA scan had better image quality than the RGH scan with 83.70% dose reduction. Noise and SNR were also corresponded to the proposed CIQI.

Conclusions/Significance

The PGA scan protocol was a good choice in balancing radiation exposure and image quality for infant cardiac CTA. We also suggested that the effective mAs and the CIQI were suitable in assessing the tradeoffs between radiation dose and image quality for cardiac CTA in infants. These results are useful for future implementation of dose reduction strategies in pediatric cardiac CTA protocols.     

Respiratory Motion Reduction in PET/CT Using Abdominal Compression for Lung Cancer Patients

Purpose

Respiratory motion causes substantial artifacts in reconstructed PET images when using helical CT as the attenuation map in PET/CT imaging. In this study, we aimed to reduce the respiratory artifacts in PET/CT images of patients with lung tumors using an abdominal compression device.

Methods

Twelve patients with lung cancer located in the middle or lower lobe of the lung were recruited. The patients were injected with 370 MBq of ¹⁸F-FDG. During PET, the patients assumed two bed positions for 1.5 min/bed. After conducting free-breathing imaging, we obtained images of the patients with abdominal compression by applying the same setup used in the free-breathing scan. The differences in the standardized uptake value (SUV)_max, SUV_mean, tumor volume, and the centroid of the tumors between PET and various CT schemes were measured.

Results

The SUV_max and SUV_mean derived from PET/CT imaging using an abdominal compression device increased for all the lesions, compared with those obtained using the conventional approach. The percentage increases were 18.1% ±14% and 17% ±16.8% for SUV_max and SUV_mean, respectively. PET/CT imaging combined with abdominal compression generally reduced the tumor mismatch between CT and the corresponding attenuation corrected PET images, with an average decrease of 1.9±1.7 mm over all the cases.

Conclusions

PET/CT imaging combined with abdominal compression reduces respiratory artifacts and PET/CT misregistration, and enhances quantitative SUV in tumor. Abdominal compression is easy to set up and is an effective method used in PET/CT imaging for clinical oncology, especially in the thoracic region.     

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.     

能谱CT在去除腰椎术后金属伪影中的应用进展

多层计算机断层扫描(Multi-slice computed tomography,MSCT)具有时间及空间分辨率高的特点已经被广泛应用于脊柱术后评价,然而,因为术中植入金属植入物,CT图像由于其成像原理的限制,其图像质量受到金属植入物的严重影响,金属伪影大多来源于量子噪声、散射线和射束硬化效应,降低图像的对比度,使细微解剖结构显示不清,从而使得感兴趣区域的检测能力降低,其图像质量明显下降,甚至使临床医师做出错误的诊断,因此,为了做出更准确的腰椎术后并发症诊断和术后评估,使金属伪影最小化成为关键。能谱CT及其扫描及重建技术可以有效的去除腰椎术后产生的金属伪影,改善图像质量,本文能谱CT在腰椎术后金属伪影去除方面的应用进行综述。     

Kalman estimator- and general linear model-based on-line brain activation mapping by near-infrared spectroscopy

Background

Methods of manual cell localization and outlining are so onerous that automated tracking methods would seem mandatory for handling huge image sequences, nevertheless manual tracking is, astonishingly, still widely practiced in areas such as cell biology which are outside the influence of most image processing research. The goal of our research is to address this gap by developing automated methods of cell tracking, localization, and segmentation. Since even an optimal frame-to-frame association method cannot compensate and recover from poor detection, it is clear that the quality of cell tracking depends on the quality of cell detection within each frame.

Methods

Cell detection performs poorly where the background is not uniform and includes temporal illumination variations, spatial non-uniformities, and stationary objects such as well boundaries (which confine the cells under study). To improve cell detection, the signal to noise ratio of the input image can be increased via accurate background estimation. In this paper we investigate background estimation, for the purpose of cell detection. We propose a cell model and a method for background estimation, driven by the proposed cell model, such that well structure can be identified, and explicitly rejected, when estimating the background.

Results

The resulting background-removed images have fewer artifacts and allow cells to be localized and detected more reliably. The experimental results generated by applying the proposed method to different Hematopoietic Stem Cell (HSC) image sequences are quite promising.

Conclusion

The understanding of cell behavior relies on precise information about the temporal dynamics and spatial distribution of cells. Such information may play a key role in disease research and regenerative medicine, so automated methods for observation and measurement of cells from microscopic images are in high demand. The proposed method in this paper is capable of localizing single cells in microwells and can be adapted for the other cell types that may not have circular shape. This method can be potentially used for single cell analysis to study the temporal dynamics of cells.     

Three Dimensional Imaging of Paraffin Embedded Human Lung Tissue Samples by Micro-Computed Tomography

Background

Understanding the three-dimensional (3-D) micro-architecture of lung tissue can provide insights into the pathology of lung disease. Micro computed tomography (µCT) has previously been used to elucidate lung 3D histology and morphometry in fixed samples that have been stained with contrast agents or air inflated and dried. However, non-destructive microstructural 3D imaging of formalin-fixed paraffin embedded (FFPE) tissues would facilitate retrospective analysis of extensive tissue archives of lung FFPE lung samples with linked clinical data.

Methods

FFPE human lung tissue samples (n = 4) were scanned using a Nikon metrology µCT scanner. Semi-automatic techniques were used to segment the 3D structure of airways and blood vessels. Airspace size (mean linear intercept, Lm) was measured on µCT images and on matched histological sections from the same FFPE samples imaged by light microscopy to validate µCT imaging.

Results

The µCT imaging protocol provided contrast between tissue and paraffin in FFPE samples (15mm x 7mm). Resolution (voxel size 6.7 µm) in the reconstructed images was sufficient for semi-automatic image segmentation of airways and blood vessels as well as quantitative airspace analysis. The scans were also used to scout for regions of interest, enabling time-efficient preparation of conventional histological sections. The Lm measurements from µCT images were not significantly different to those from matched histological sections.

Conclusion

We demonstrated how non-destructive imaging of routinely prepared FFPE samples by laboratory µCT can be used to visualize and assess the 3D morphology of the lung including by morphometric analysis.