Showing papers in "Journal of X-ray Science and Technology in 2019"

Deep CNN models for pulmonary nodule classification: Model modification, model integration, and transfer learning.

Abstract: Background Deep learning has made spectacular achievements in analysing natural images, but it faces challenges for medical applications partly due to inadequate images. Objective Aiming to classify malignant and benign pulmonary nodules using CT images, we explore different strategies to utilize the state-of-the-art deep convolutional neural networks (CNN). Methods Experiments are conducted using the Lung Image Database Consortium image collection (LIDC-IDRI), which is a public database containing 1018 cases. Three strategies are implemented including to 1) modify some state-of-the-art CNN architectures, 2) integrate different CNNs and 3) adopt transfer learning. Totally, 11 deep CNN models are compared using the same dataset. Results Study demonstrates that, for the model modification scheme, a concise CifarNet performs better than the other modified CNNs with more complex architectures, achieving an area under ROC curve of AUC = 0.90. Integrated CNN models do not significantly improve the classification performance, but the model complexity is reduced. Transfer learning outperforms the other two schemes and ResNet with fine-tuning leads to the best performance with an AUC = 0.94, as well as the sensitivity of 91% and an overall accuracy of 88%. Conclusions Model modification, model integration, and transfer learning can play important roles to identify and generate optimal deep CNN models in classifying pulmonary nodules based on CT images efficiently. Transfer learning is preferred when applying deep learning to medical imaging applications.

Expert knowledge-infused deep learning for automatic lung nodule detection

Abstract: Background Computer aided detection (CADe) of pulmonary nodules from computed tomography (CT) is crucial for early diagnosis of lung cancer. Self-learned features obtained by training datasets via deep learning have facilitated CADe of the nodules. However, the complexity of CT lung images renders a challenge of extracting effective features by self-learning only. This condition is exacerbated for limited size of datasets. On the other hand, the engineered features have been widely studied. Objective We proposed a novel nodule CADe which aims to relieve the challenge by the use of available engineered features to prevent convolution neural networks (CNN) from overfitting under dataset limitation and reduce the running-time complexity of self-learning. Methods The CADe methodology infuses adequately the engineered features, particularly texture features, into the deep learning process. Results The methodology was validated on 208 patients with at least one juxta-pleural nodule from the public LIDC-IDRI database. Results demonstrated that the methodology achieves a sensitivity of 88% with 1.9 false positives per scan and a sensitivity of 94.01% with 4.01 false positives per scan. Conclusions The methodology shows high performance compared with the state-of-the-art results, in terms of accuracy and efficiency, from both existing CNN-based approaches and engineered feature-based classifications.

A pilot study of radiomics technology based on X-ray mammography in patients with triple-negative breast cancer.

Abstract: PURPOSE To explore the radiomics features of triple negative breast cancer (TNBC) and non-triple negative breast cancer (non-TNBC) based on X-ray mammography, and to differentiate the two groups of cases. MATERIALS AND METHODS Preoperative mammograms of 120 patients with breast ductal carcinoma confirmed by surgical pathology were retrospectively analyzed, which include 30 TNBC and 90 non-TNBC patients. The manual segmentation of breast lesions was performed by ITK-SNAP software and 12 radiomics features were extracted by Omni-Kinetics software. The differences of these radiomics features between TNBC and non-TNBC groups were compared, and the receiver operating characteristic (ROC) curve was used to determine the optimal cutoff value of each radiomics parameter for differentiating TNBC from non-TNBC, and the corresponding area under the curve (AUC), sensitivity and specificity were obtained. RESULTS There were statistically significant differences for 4 radiomics features between TNBC and non-TNBC datasets (P < 0.05). They were the roundness, concavity, gray average and skewness of breast lesions. Compared with non-TNBC, TNBC cases have following characteristics of (1) more round with the roundness of 0.621 vs. 0.413 (P < 0.001), (2) more regular with the concavity of 0.087 vs. 0.141 (P < 0.01), (3) higher density or gray average (67.261 vs. 56.842, P < 0.05), and (4) lower skewness (- 0.837 vs.- 0.671, P = 0.034). AUCs of ROC curves computed using features of the roundness and concavity were both larger than 0.70. CONCLUSION Radiomics features based on X-ray mammography may be helpful to distinguish between TNBC and non-TNBC, which were associated with breast tumor histology.

Correlation between CT based radiomics features and gene expression data in non-small cell lung cancer.

Abstract: Objective Radiogenomics investigates radiographic imaging phenotypes associated with gene expression patterns. This study aims to explore relationships between CT imaging radiomics features and gene expression data in non-small cell lung cancer (NSCLC). Methods Eighty-nine NSCLC patients are included in the study. Radiomics features are extracted and selected to quantify the phenotype of tumors on CT-scans. Co-expressed genes are also clustered and the first principal component of the cluster is represented, which is defined as a metagene. Then, statistical analysis was performed to assess association of CT radiomics features with metagenes. In addition, predictive models are built and metagene enrichment are conducted to further evaluate performance of NSCLC radiogenomics statistically and biologically. Results There are 187 significant pairwise correlations between a CT radiomics feature and a metagene of NSCLC, where eighteen metagenes are annotated with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms. Metagenes are predicted in terms of radiomics features with an accuracy of 41.89% -89.93%. Conclusions This study reveals the associations between CT imaging radiomics features and NSCLC co-expressed gene sets. The findings suggest that CT radiomics features can reflect important biological information of NSCLC patients, which may have a significant clinical impact as CT is routinely used in clinical practice, assisting in improving medical decision-support at low cost.

Multi-material decomposition of spectral CT images via Fully Convolutional DenseNets.

Abstract: Background Spectral computed tomography (CT) has the capability to resolve the energy levels of incident photons, which has the potential to distinguish different material compositions. Although material decomposition methods based on x-ray attenuation characteristics have good performance in dual-energy CT imaging, there are some limitations in terms of image contrast and noise levels. Objective This study focused on multi-material decomposition of spectral CT images based on a deep learning approach. Methods To classify and quantify different materials, we proposed a multi-material decomposition method via the improved Fully Convolutional DenseNets (FC-DenseNets). A mouse specimen was first scanned by spectral CT system based on a photon-counting detector with different energy ranges. We then constructed a training set from the reconstructed CT images for deep learning to decompose different materials. Results Experimental results demonstrated that the proposed multi-material decomposition method could more effectively identify bone, lung and soft tissue than the basis material decomposition based on post-reconstruction space in high noise levels. Conclusions The new proposed approach yielded good performance on spectral CT material decomposition, which could establish guidelines for multi-material decomposition approaches based on the deep learning algorithm.

Speckle noise reduction for ultrasound images by using speckle reducing anisotropic diffusion and Bayes threshold.

Abstract: Ultrasound imaging has been used for diagnosing lesions in the human body. In the process of acquiring ultrasound images, speckle noise may occur, affecting image quality and auto-lesion classification. Despite the efforts to resolve this, conventional algorithms exhibit poor speckle noise removal and edge preservation performance. Accordingly, in this study, a novel algorithm is proposed based on speckle reducing anisotropic diffusion (SRAD) and a Bayes threshold in the wavelet domain. In this algorithm, SRAD is employed as a preprocessing filter, and the Bayes threshold is used to remove the residual noise in the resulting image. Compared to the conventional filtering techniques, experimental results showed that the proposed algorithm exhibited superior performance in terms of peak signal-to-noise ratio (average = 28.61 dB) and structural similarity (average = 0.778).

The effects of mis-centering on radiation dose during CT head examination: A phantom study

Abstract: There are several factors that may contribute to the increase in radiation dose of CT including the use of unoptimized protocols and improper scanning technique. In this study, we aim to determine significant impact on radiation dose as a result of mis-centering during CT head examination. The scanning was performed by using Toshiba Aquilion 64 slices multi-detector CT (MDCT) scanner and dose were measured by using calibrated ionization chamber. Two scanning protocols of routine CT head; 120 kVp/ 180 mAs and 100 kVp/ 142 mAs were used represent standard and low dose, respectively. As reference measurement, the dose was first measured on standard cylindrical polymethyl methacrylate (PMMA) phantom that positioned at 104 cm from the floor (reference isocenter). The positions then were varied to simulate mis-centering by 5 cm from isocenter, superiorly and inferiorly at 109 cm, 114 cm, 119 cm, 124 cm and 99 cm, 94 cm, 89 cm, 84 cm, respectively. Scanning parameter and dose information from the console were recorded for the radiation effective dose (E) measurement. The highest mean CTDIvol value for MCS and MCI were 105.06 mGy (at +10 cm) and 105.51 mGy (at - 10 cm), respectively which differed significantly (p < 0.05) as compared to the isocenter. There were large significant different (p < 0.05) of mean Dose Length Product (DLP) recorded between isocenter to the MCS (85.8 mGy.cm) and MCI (93.1 mGy.cm). As the low dose protocol implemented, the volume CTDI (CTDIvol) were significantly increase (p < 0.05) for MCS (at +10 cm) and MCI (at - 10 cm) when compared to the isocenter. The phantom study revealed a noticeable different in radiation dose between isocenter and experimental groups due to degradation of the bowtie filter performance. It is anticipated that these noteworthy findings may emphasize the importance of accurate patient centering at the isocenter of CT gantry, so that CT optimization practice can be achieved.

Automatic prostate segmentation based on fusion between deep network and variational methods.

Abstract: BACKGROUND Segmentation of prostate from magnetic resonance images (MRI) is a critical process for guiding prostate puncture and biopsy. Currently, the best results are obtained by Convolutional Neural Network (CNN). However, challenges still exist when applying CNN to segment prostate, such as data distribution issue caused by insubstantial and inconsistent intensity levels and vague boundaries in MRI. OBJECTIVE To segment prostate gland from a MRI dataset including different prostate images with limited resolution and quality. METHODS We propose and apply a global histogram matching approach to make intensity distribution of the MRI dataset closer to uniformity. To capture the real boundaries and improve segmentation accuracy, we employ a module of variational models to help improve performance. RESULTS Using seven evaluation metrics to quantify improvements of our proposed fusion approach compared with the state of art V-net model resulted in increase in the Dice Coefficient (11.2%), Jaccard Coefficient (13.7%), Volumetric Similarity (12.3%), Adjusted Rand Index (11.1%), Area under ROC Curve (11.6%), and reduction of the Mean Hausdorff Distance (16.1%) and Mahalanobis Distance (2.8%). The 3D reconstruction also validates the advantages of our proposed framework, especially in terms of smoothness, uniformity, and accuracy. In addition, observations from the selected examples of 2D visualization show that our segmentation results are closer to the real boundaries of the prostate, and better represent the prostate shapes. CONCLUSIONS Our proposed approach achieves significant performance improvements compared with the existing methods based on the original CNN or pure variational models.

New one-step model of breast tumor locating based on deep learning.

Abstract: Background Breast cancer has the highest cancer prevalence rate among the women worldwide. Early detection of breast cancer is crucial for successful treatment and reducing cancer mortality rate. However, tumor detection of breast ultrasound (US) image is still a challenging work in computer-aided diagnosis (CAD). Objective This study aims to develop a novel automated algorithm for breast tumor detection based on deep learning. Methods We proposed a new deep learning network named One-step model which have one input and two outputs, the first one was the segmentation result and the other one was used for false-positive reduction. The proposed One-step model includes three key components: Base-net, Seg-net, and Cls-net based on Anchor Box. The model chose DenseNet to construct Base-net, the decoder part of RefineNet as Seg-net, and connected several middle layers of Base-net and Seg-net to Cls-net. From the first output acquired by Base-net and Seg-net, the model detected a series of suspicious lesion regions. Then the second output from the Cls-net was used to recognize and reduce the false-positive regions. Results Experimental results showed that the new model achieved competitive detection result with 90.78% F1 score, which was 8.55% higher than Single Shot MultiBox Detector (SSD) method. In addition, running new model is also computational efficient and has comparative cost effect as SSD. Conclusions We established a novel One-step model which improves location accuracy by generating more precise bounding box via Seg-net and removing false targets by another object detection network (Cls-net). On the other hand, a real-time detection of tumor is achieved by sharing the common Base-net. The experimental results showed that the new model performed well on various irregular and blurred ultrasound images. As a result, this study demonstrated feasibility of applying deep learning scheme to detect breast lesions depicting on US image.

Tensor decomposition and non-local means based spectral CT image denoising.

Abstract: BACKGROUNDAs one type of the state-of-the-art detectors, photon counting detectors are used in spectral computed tomography (CT) to classify the received photons into several energy channels and generate multichannel projections simultaneously. However, FBP reconstructed images contain severe noise due to the low photon counts in each energy channel.OBJECTIVEA spectral CT image denoising method based on tensor-decomposition and non-local means (TDNLM) is proposed.METHODSIn a CT image, it is widely accepted that there exists self-similarity over the spatial domain. In addition, because a multichannel CT image is obtained from the same object at different energies, images among different channels are highly correlated. Motivated by these two characteristics of the spectral CT images, tensor decomposition and non-local means are employed to recover fine structures in spectral CT images. Moreover, images in all energy channels are added together to form a high signal-to-noise ratio image, which is applied to encourage the signal preservation of the TDNLM. The combination of TD, NLM and the guidance of a high-quality image enhances the low-dose spectral CT, and a parameter selection strategy is designed to achieve the optimal image quality.RESULTSThe effectiveness of the developed algorithm is validated on both numerical simulations and realistic preclinical applications. The root mean square error (RMSE) and the structural similarity (SSIM) are used to quantitatively assess the image quality. The proposed method successfully restored high-quality images (average RMSE=0.0217 cm-1 and SSIM=0.987) from noisy spectral CT images (average RMSE=0.225 cm-1 and SSIM=0.633). In addition, RMSE of each decomposed material component is also remarkably reduced. Compared to the state-of-the-art iterative spectral CT reconstruction algorithms, the proposed method achieves comparable performance with dramatically reduced computational cost, resulting in a speedup of >50.CONCLUSIONSThe outstanding denoising performance, the high computational efficiency and the adaptive parameter selection strategy make the proposed method practical for spectral CT applications.

The effectiveness of bismuth breast shielding with protocol optimization in CT Thorax examination.

Abstract: Background Numerous techniques had been proposed to reduce radiation exposure in computed tomography (CT) including the use of radiation shielding. Objective This study aims to evaluate efficacy of using a bismuth breast shield and optimized scanning parameter to reduce breast absorbed doses from CT thorax examination. Methods Five protocols comprising the standard CT thorax clinical protocol (CP1) and four modified protocols (CP2 to CP5) were applied in anthropomorphic phantom scans. The phantom was configured as a female by placing a breast component on the chest. The breast component was divided into four quadrants, where 2 thermoluminescence dosimeters (TLD-100) were inserted into each quadrant to measure the absorbed dose. The bismuth shield was placed over the breast component during CP4 and CP5 scans. Results The pattern of absorbed doses in each breast and quadrant were approximately the same for all protocols, where the 4th quadrant > 3rd quadrant > 2nd quadrant > 1st quadrant. The mean absorbed dose value in CP3 was reduced to almost 34% of CP1's mean absorbed dose. It was reduced even lower to 15% of CP1's mean absorbed dose when the breast shield was used in CP5. Conclusion This study showed that CT radiation exposure on the breast could be reduced by using a bismuth shield and low tube potential protocol without compromising the image quality.

Segmentation of pulmonary vascular tree by incorporating vessel enhancement filter and variational region-growing.

Abstract: Background Automatic segmentation of pulmonary vascular tree in the thoracic computed tomography (CT) image is a promising but challenging task with great clinical potential values. It is difficult to segment the whole vascular tree in reasonable time and acceptable accuracy. Objective To develop a novel pulmonary vessel segmentation approach by incorporating vessel enhancement filters and the anisotropic diffusion filter with the variational region growing. Methods First, the airway wall from the lung lobes is eliminated from CT images by using multi-scale morphological operations. Second, a Hessian-based multi-scale vesselness filter and medialness filter are applied to detect and enhance the potential vessel. Third, an anisotropic diffusion filter is used to remove noise and enhance the tube-like structures in CT images. Last, the vascular tree is segmented by applying variational region growing algorithm. Results Applying to the CT images collected from the entire dataset of VESSEL12 challenge, we achieved an average sensitivity of 92.9%, specificity of 91.6% and the area under the ROC curve of AUC = 0.972. Conclusions This study demonstrated feasibility of segmenting the pulmonary vessel effectively by incorporating vessel enhancement filters and the anisotropic diffusion filter with the variational region growing algorithm. Our method cannot only segment both large and peripheral vessels, but also distinguish the vessels from the adjacent tissues, especially the airway walls.

A dosimetric comparison of Volumetric Modulated Arc Therapy (VMAT) and High Dose Rate (HDR) brachytherapy in localized cervical cancer radiotherapy.

Abstract: BACKGROUND Cervical cancer radiotherapy is usually administrated through 3-Dimensional Conformal Radiation Therapy (3DCRT) followed by a brachytherapy (BT) boost. PURPOSE To investigate whether Volumetric Modulated Arc Therapy (VMAT) can replace High Dose Rate (HDR) intracavitary BT boost for patients undergoing cervical cancer radiotherapy. MATERIALS AND METHODS Computed Tomography (CT) images for ten patients with tandem and ovoids were included in this study. Target volumes, rectum, bladder, sigmoid, small bowel and both femoral heads were delineated. Two plans were carried out including (a) a BT plan optimized manually by modifying dwell time and Ir-192 source positions, (b) a VMAT plan generated using two partial arcs with 10 MV photon beam. The prescribed dose was 7 Gy. The relevant dose volume parameters (DVPs) of target volumes and OARs for the two plans were analyzed statistically using SPSS Wilcoxon Signed Rank test. RESULTS VMAT plan showed a significant reduction of 9.1%, 9.3%, 15.4%, 14.4% and 13.1% in rectum maximum dose, rectum D2cc, bladder maximum dose, bladder D2cc and sigmoid maximum dose (P < 0.05). VMAT and BT plans showed comparable D2cc of sigmoid and small bowel maximum doses (P = 0.333 and P = 0.646). On the other hand, VMAT showed significantly higher small bowel D2cc and maximum point dose for both femoral heads comparing to BT plan (P < 0.05). Also, VMAT plan yielded greater homogeneous target coverage compared to BT plan (P < 0.05). CONCLUSION The study demonstrated that VMAT plan achieves significant dose reduction of rectum, bladder and sigmoid, as well as superior homogeneous target coverage compared to BT plan. On the other hand, VMAT delivers more radiation exposures to small bowel and femoral heads.

Application of two-dimensional fractional-order convolution and bounding box pixel analysis for rapid screening of pleural effusion.

Abstract: Pleural effusion is a pathologic symptom in which there is accumulation of body fluids around the lungs. A chest radiograph is a rapid examination technique and does not require complex setup for making a preliminary diagnosis of lung and heart diseases. In radiographic visualization, the symptom patterns appear as light or dark areas in the lung cavity. Computer-aided diagnosis is an automatic manner that can rapidly highlight the object region by preanalyzing medical images. It can improve the problems of manual inspection and allow diagnosis in remote medical facilities. Based on the ratios of lung anatomy, the automatic screening manner based on pattern recognition can be viewed as pixel value detection in the bilateral lung cavities. In this study, a fractional-order convolution (FOC) process is used to enhance the original image for an accurate extrapolation of the desired object in an image. The specific object image feature can be improved, and an accurate quantification of the pleural effusion region can be obtained using the suitable ranges of fractional-order parameters. Based on the boundaries of homogeneous regions, the pixel ratios of the lung anatomy between normal and abnormal conditions can be computed. The pleural effusion sizes and volumes can be rapidly estimated through the number of pixel changes. The experimental results reveal that the feature maps are similar and stable on image enhancement and segmentation with two fractional-order enhancement masks, as fractional-order v = 0.05 to 0.20 for mask 1# and v = 0.80 to 0.95 for mask 2#, respectively. The results also demonstrate the feasibility of the study on combining two-dimensional image FOC-process and bounding box pixel analysis to estimate the moderate and large effusion sizes from 500-2,000 mL.

Dosimetric verification of lung phantom calculated by collapsed cone convolution: A Monte Carlo and experimental evaluation.

Abstract: Objective To evaluate the dose calculation accuracy in the Prowess Panther treatment planning system (TPS) using the collapsed cone convolution (CCC) algorithm. Methods The BEAMnrc Monte Carlo (MC) package was used to predict the dose distribution of photon beams produced by the Oncor® linear accelerator (linac). The MC model of an 18 MV photon beam was verified by measurement using a p-type diode dosimeter. Percent depth dose (PDD) and dose profiles were used for comparison based on three field sizes: 5×5, 10×10, and 20×20cm2. The accuracy of the CCC dosimetry was also evaluated using a plan composed of a simple parallel-opposed field (11×16cm2) in a lung phantom comprised of four tissue simulating media namely, lung, soft tissue, bone and spinal cord. The CCC dose calculation accuracy was evaluated by MC simulation and measurements according to the dose difference and 3D gamma analysis. Gamma analysis was carried out through comparison of the Monte Carlo simulation and the TPS calculated dose. Results Compared to the dosimetric results measured by the Farmer chamber, the CCC algorithm underestimated dose in the planning target volume (PTV), right lung and lung-tissue interface regions by about -0.11%, -1.6 %, and -2.9%, respectively. Moreover, the CCC algorithm underestimated the dose at the PTV, right lung and lung-tissue interface regions in the order of -0.34%, -0.4% and -3.5%, respectively, when compared to the MC simulation. Gamma analysis results showed that the passing rates within the PTV and heterogeneous region were above 59% and 76%. For the right lung and spinal cord, the passing rates were above 80% for all gamma criteria. Conclusions This study demonstrates that the CCC algorithm has potential to calculate dose with sufficient accuracy for 3D conformal radiotherapy within the thorax where a significant amount of tissue heterogeneity exists.

Collision-avoiding imaging trajectories for linac mounted cone-beam CT.

Abstract: Background Some patients cannot be imaged with cone-beam CT for image-guided radiation therapy because their size, pose, or fixation devices cause collisions with the machine. Objective To investigate imaging trajectories that avoid such collisions by using virtual isocenter and variable magnification during acquisition while yielding comparable image quality. Methods The machine components most likely to collide are the gantry and kV detector. A virtual isocenter trajectory continuously moves the patient during gantry rotation to maintain an increased separation between the two. With dynamic magnification, the kV detector is dynamically moved to increase clearance for an angular range around the potential collision point while acquiring sufficient data to maintain the field-of-view. Both strategies were used independently and jointly with the resultant image quality evaluated against the standard circular acquisition. Results Collision avoiding trajectories show comparable contrast and resolution to standard techniques. For an anthropomorphic phantom, the RMSE is 0.97, and visual image fidelity is >0.96 for all trajectories when compared to a standard circular scan. Conclusions The proposed trajectories avoid machine-patient collisions while providing comparable image quality to the current standard thereby enabling CBCT imaging for patients that could not otherwise be scanned.

Extending the dynamic range of biomedical micro-computed tomography for application to geomaterials.

Abstract: BACKGROUND X-ray computed tomography (CT) can non-destructively examine objects by producing three-dimensional images of their internal structure. Although the availability of biomedical micro-CT offers the increased access to scanners, CT images of dense objects are susceptible to artifacts particularly due to beam hardening. OBJECTIVE This study proposes and evaluates a simple semi-empirical correction method for beam hardening and scatter that can be applied to biomedical scanners. METHODS Novel calibration phantoms of varying diameters were designed and built from aluminum and poly[methyl-methacrylate]. They were imaged using two biomedical micro-CT scanners. Absorbance measurements made through different phantom sections were fit to polynomial and inversely exponential functions and used to determine linearization parameters. Corrections based on the linearization equations were applied to the projection data before reconstruction. RESULTS Correction for beam hardening was achieved when applying both scanners with the correction methods to all test objects. Among them, applying polynomial correction method based on the aluminum phantom provided the best improvement. Correction of sample data demonstrated a high agreement of percent-volume composition of dense metallic inclusions between using the Bassikounou meteorite from the micro-CT images (13.7%) and previously published results using the petrographic thin sections (14.6% 8% metal and 6.6% troilite). CONCLUSIONS Semi-empirical linearization of X-ray projection data with custom calibration phantoms allows accurate measurements to be obtained on the radiodense samples after applying the proposed correction method on biomedical micro-CT images.

Iterative reconstruction for image enhancement and dose reduction in diagnostic cone beam CT imaging.

Abstract: Background Iterative reconstruction is well-established in diagnostic multidetector computed tomography (MDCT) for dose reduction and image quality enhancement. Its application to diagnostic cone beam computed tomography (CBCT) is only emerging and warrants a quantitative evaluation. Methods Several phantoms and a canine head specimen were imaged using a commercially available small-field CBCT scanner. Raw projection data were reconstructed using the Feldkamp-Davis-Kress (FDK) method with different filters, including denoising via total variation (TV) minimization (FDK-TV). Iterative reconstruction was carried out using the TV-regularized ordered subsets convex technique (OSC-TV). Signal-to-noise ratio (SNR), noise power spectrum (NPS) and spatial resolution of images were estimated. Dose levels were measured via the weighted computed tomography dose index, while low-dose image quality degradation was estimated via structural similarity (SSIM). Results OSC-TV and FDK-TV were shown to significantly improve image signal-to-noise ratio (SNR) compared to FDK with a standard filter, 5.8 and 4.0 times, respectively. Spatial resolution attained with different algorithms varied moderately across different experiments. For low-dose acquisitions, image quality decreased dramatically for FDK but not for FDK-TV nor OSC-TV. For low-dose canine head images acquired using about 1/5 of the dose compared to a reference image, SSIM dropped to about 0.3 for FDK, while remaining at 0.92 for FDK-TV and 0.96 for OSC-TV. Conclusion OSC-TV was shown to improve image quality compared to FDK and FDK-TV. Moreover, this iterative approach allowed for significant dose reduction while maintaining image quality.

Complete resection of adult prostatic rhabdomyosarcoma with 3D video and printing model: A case report and literature review.

Abstract: Prostatic rhabdomyosarcoma (RMS) is a subtype of prostate sarcoma which is rarely reported in adults and usually huge in size. Although there is no consensus on the standard therapy to prostatic RMS, complete resection with negative margin is identified as the best way for maximum survival time. However, to remove a much enlarged prostate completely from a RMS patient is still a very difficult task for a skilled urologist so far. As three-dimension (3D) technology becomes more widely used in medicine, surgeons have the opportunity to challenge previously impossible surgery. In this paper, we reported a 36-year-old male patient with a 9.6*5.3*7.6 cm prostatic RMS. With the aid of 3D reconstructed video and printing model, the giant tumor was entirely removed without surgery complications and adjacent organs injury. The patient was alive and had no recurrence after 18 months from surgery. This case revealed that 3D reconstruction technology could help in the preoperative assessment and gave benefits to both patients and surgeons.

Few-view CT image reconstruction using improved total variation regularization.

Abstract: X-ray radiation is harmful to human health. Thus, obtaining a better reconstructed image with few projection view constraints is a major challenge in the computed tomography (CT) field to reduce radiation dose. In this study, we proposed and tested a new algorithm that combines penalized weighted least-squares using total generalized variation (PWLS-TGV) and dictionary learning (DL), named PWLS-TGV-DL to address this challenge. We first presented and tested this new algorithm and evaluated it through both data simulation and physical experiments. We then analyzed experimental data in terms of image qualitative and quantitative measures, such as the structural similarity index (SSIM) and the root mean square error (RMSE). The experiments and data analysis indicated that applying the new algorithm to CT data recovered images more efficiently and yielded better results than the traditional CT image reconstruction approaches.

Comparison of vaporization using 120-W GreenLight laser versus 2-micrometer continuous laser for treating benign prostatic hyperplasia: A 24-month follow-up study of a single center

Abstract: Objective To evaluate safety, efficacy, and long-term outcomes of photoselective vaporization of prostate using 120-W HPS GreenLight KTP laser and compare the results with those obtained with 2-micrometer continuous-wave (2 um CW) laser for treatment of patients with benign prostatic hyperplasia (BPH). Materials and methods One group of 216 patients diagnosed with BPH underwent 120-W KTP laser vaporization of the prostate, while another group of 198 BPH patients underwent 2 um CW laser vaporization. The relevant pre-, peri-, and post-operative parameters were compared between the two therapy groups. Functional results in terms of improvement of International Prostate Symptom Score (IPSS), maximum flow rate (Qmax), and post-void residual (PVR) urine were assessed at 3, 6, 12, and 24 months. Results BPH was successfully treated with 120-W HPS KTP laser and 2 um CW laser in all patients. There were no significant difference between two patient groups in the baseline characteristics (such as PSA, IPSS, QoL, and Qmax). No major complications occurred intraoperatively (capsule perforation and TUR syndrome) or postoperatively (electric unbalance), and no blood transfusions were required in both groups. Average catheterization time was 1.9±1.3 days for the 120-W PVP and 2.2±1.9 days for the 2 um CW laser treatment. In addition, the hospitalization times were 3.8±1.2days (120-W PVP) and 4.8±1.5 days (2 um CW laser), respectively. The incidence of dysuria and urge incontinence was higher in the 2 um CW laser group (35/198, 24/198) than in the 120 W PVP group (15/216, 10/216). Dramatic improvement was observed in Qmax, IPSS, Qol, and PVR as compared with the respective pre-operative values. The degree of improvement during the follow-up period was comparable in both groups. No significant differences were observed in terms of re-operation rates, bladder neck stricture, and urethral stricture. Conclusions Both 120-W HPS laser and 2 um CW laser vaporization present effective treatment options in patients with BPH, but 120-W PVP provides safer therapy with less post-operative complications within the 2-year follow-up period.

Comparison of geometrical uncertainties in the radiotherapy for various treatment sites with two different immobilization marking methods.

Abstract: OBJECTIVE The skin marking method (SMM) and bow-form-ruler marking method (BFRM) are two commonly used patient marking methods in mainland China. This study aims to evaluate SMM and BFRM by comparing the inter-fraction setup errors from using these two methods together with vacuum cushion immobilization in patients underwent radiotherapy for different treatment sites. MATERIALS AND METHODS Eighteen patients diagnosed with pelvic, abdominal and thoracic malignant tumors (with 6 patients per treatment site) were enrolled in this prospective study. All patients were immobilized with vacuum cushion. Each patient was marked by both SMM and BFRM before computed tomography (CT) simulation. Target location was verified by cone beam CT images with displacements assessed prior to each sampled treatment session. The localization errors in three translational and three rotational directions were recorded and analyzed. RESULTS Images from 108 fractions in 18 patients produced 324 translational and 324 rotational comparisons for SMM and BFRM. The setup errors of all treatment sites showed no difference in two marking methods in any directions (p > 0.05). In subgroups of treatment site analysis, SMM significantly lessened the lateral and yaw setup errors compared to BFRM in the pelvic sites (0.39±1.85 mm vs -1.28±1.13 mm, p < 0.01 and -0.19±0.59° vs -0.61±0.59°, p < 0.05). However, in the abdominal subgroup, BFRM was superior to SMM for reduced vertical errors (0.17±2.73 mm vs 2.28±3.16 mm, p < 0.05). For the underweight or obese patients (with Body Mass Index, BMI < 18.5 or BMI≥24), SMM resulted in less yaw errors compared to BFRM (-0.05±0.38° vs -0.43±0.48°, p < 0.05). No significant difference between SMM and BFRM in setup errors of normal weighted patients (18.5≤BMI < 24) was observed for all three studied treatment sites. CONCLUSIONS This study shows no significant difference in patient setup errors for various treatment sites between SMM and BFRM in general. SMM may be suitable for the pelvic tumor and patients with BMI < 18.5 or BMI≥24, while BFRM is recommended for the abdominal tumor sites.

Investigation of AAA dose calculation algorithm accuracy in surface and buildup region for 6MV photon beam using markus parallel-plate ion chamber.

Abstract: In radiotherapy, dose distributions are obtained by using dose calculation algorithms that are implanted in treatment planning systems (TPS). This study aims to compare the surface doses of separate field sizes calculated by different version of The Analytical Anisotropic Algorithm (AAA) and measured by the parallel-plate ion chamber that is admitted as the most reliable dosimetry system for the surface region dose measurements. In order to measure the near surface dose, water equivalent solid phantom was used and measurements were made for 6MV photon beam at 100 cm source-detector distance for 5×5, 10×10, and 20×20 cm2 field sizes. AAA 8.9 and AAA 15.1 versions of the Varian Eclipse TPS were used for surface dose calculations by generating beams with separate field sizes. The doses were read by considering the effective buildup thickness of Markus parallel-plate ion chamber. The surface doses using 6 MV photon beams for 10×10 cm2 field size at 0.07 mm were found to be 11.04%, 26.25%, and 19.69% for AAA v8.9, AAA v15.1 and Markus chamber, respectively. It was seen that for both of the AAA versions and Markus parallel-plate ion chamber, increasing field sizes also increase surface dose. For all field sizes, surface dose was lowest by using AAA v8.9 at 0.07 mm. The different versions of the same TPS algorithms may calculate the surface doses distinctively. After upgrading of TPS algorithms, surface doses should be calculated and compared by measurements with different dosimetry systems to better understand their calculation behaviors in the near surface region.

On the relevance of denoising and artefact reduction in 3D segmentation and classification within complex computed tomography imagery.

Abstract: We evaluate the impact of denoising and Metal Artefact Reduction (MAR) on 3D object segmentation and classification in low-resolution, cluttered dual-energy Computed Tomography (CT). To this end, we present a novel 3D materials-based segmentation technique based on the Dual-Energy Index (DEI) to automatically generate subvolumes for classification. Subvolume classification is performed using an extension of Extremely Randomised Clustering (ERC) forest codebooks, constructed using dense feature-point sampling and multiscale Density Histogram (DH) descriptors. Within this experimental framework, we evaluate the impact on classification accuracy and computational expense of pre-processing by intensity thresholding, Non-Local Means (NLM) filtering, Linear Interpolation-based MAR (LIMar) and Distance-Driven MAR (DDMar) in the domain of 3D baggage security screening. We demonstrate that basic NLM filtering, although removing fewer artefacts, produces state-of-the-art classification results comparable to the more complex DDMar but at a significant reduction in computational cost - bringing into question the importance (in terms of automated CT analysis) of computationally expensive artefact reduction techniques. Overall, it was found that the use of MAR pre-processing approaches produced only a marginal improvement in classification performance ( 10×) when compared to NLM pre-processing.

Assessment of radiation dose and iodine load reduction in head-neck CT angiography using two scan protocols with wide-detector.

Abstract: OBJECTIVE To compare image quality, radiation dose, and iodine intake of head-neck CT angiography (CTA) acquired by wide-detector with the gemstone spectral imaging (GSI) combination with low iodine intake or routine scan protocol. METHODS Three hundred patients who had head-neck CTA were enrolled and divided into three groups according to their BMI values: group A (18.5 kg/m2 ≦ BMI <24.9 kg/m2), group B (24.9 kg/m2 ≦ BMI <29.9 kg/m2) and group C (29.9 kg/m2 ≦ BMI ≦ 34.9 kg/m2) with 100 patients in each group. Patients in each group were randomly divided into two subgroups (n = 50) namely, A1, A2, B1, B2, C1 and C2. The patients in subgroups A1, B1 and C1 underwent GSI with low iodine intake (270 mgI/ml, 50 ml) and combined with the ASiR-V algorithm. Other patients underwent three dimensional (3D) smart mA modulation with routine iodine intake (350 mgI/ml, 60 ml). Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of all images were calculated after angiography. Images were then subjectively assessed using a 5-point scale. CT dose index of volume and dose-length product (DLP) was converted to the effective dose (ED) and then compared. RESULTS The mean CT values, SNR, CNR and subjective image quality in subgroups A2, B2 and C2 are significantly lower than in subgroups A1, B1, and C1 (P < 0.01), respectively. The ED values in subgroup A1, B1, and C1 are 55.18%, 61.89%, and 69.64% lower than those in A2, B2, and C2, respectively (P < 0.01). The total iodine intakes in subgroups A1, B1, and C1 are 35.72% lower than those in subgroups A2, B2, and C2. CONCLUSIONS The gemstone spectral imaging with monochromatic images at 53-57 keV combined with ASiR-V algorithm allows significant reduction in iodine load and radiation dose in head-neck CT angiography than those yielded in routine scan protocol. It also enhances signal intensity of head-neck CTA and maintains image quality.

Radio-protective potential of lipoic acid free and nano-capsule against 99mTc-MIBI induced injury in cardio vascular tissue.

Abstract: Background SPECT MPI (Single photon emission computed tomography myocardial perfusion imaging) is an essential tool for diagnosis of cardiovascular disease, but it also involves considerable exposure to ionizing radiation. Objective To determine the radioprotective potential of lipoic acid free and nano-capsule against 99mTc-MIBI-induced injury in cardiovascular tissue. Methods The radioprotective ability was assessed by blood count, histopathology and heart enzymes in different groups of mice. Hearts of mice from all groups were dissected and prepared for oxidative stress analysis of superoxide dismutase (SOD) and malondialdehyde (MDA). Furthermore, levels of DNA damage in heart and bone marrow cells were evaluated by alkaline comet assay technique. The same measurements were estimated after treating the mice with lipoic acid. Results Comparing mice injected by radiopharmaceutics with control group showed a significant depression in the count of white blood cells (WBC) by about 40 % at 24 &72 hrs post-radiopharmaceutical administration. Moreover, platelets count was decreased by 27% at 72 hrs post-radiopharmaceutical administration. Radiation also dropped in super oxide dismutase (SOD) and increased in activity of heart enzymes and level of MDA (Malondialdehyde). Additionally, histopathological observation was characterized by focal necrosis of cardiac myocytes. 99mTc-MIBI induced DNA damage had significant increase. Nevertheless, pretreatment with free and lipoic acid nano-capsules (LANC's) prevented the reduction induced in WBCs and platelets, and improved their counts significantly. Conversely pre-treatment with lipoic acid free and nano-capsule significantly increased the activity of SOD and decreased the level of MDA and therefore protected the cardiovascular tissues and reduced DNA strand-break, consequently and enhanced the body weight of the mice. Conclusions These findings highlight the efficacy of lipoic acid free and nano-capsule as a radio protector.

Correlation between 18F-FDG PET CT SUV and symptomatic or asymptomatic pulmonary tuberculosis.

Abstract: OBJECTIVE To explore the difference of 18F-FDG PET/CT images between the symptomatic and asymptomatic pulmonary tuberculosis, as well as the correlation between the standard uptake value (SUV) and the symptomatic/asymptomatic pulmonary tuberculosis. METHODS A study dataset of 57 pulmonary tuberculosis cases was retrospectively assembled and analyzed. Among these cases, 30 were diagnosed having symptomatic pulmonary tuberculosis and 27 were asymptomatic pulmonary tuberculosis. PET/CT was performed in all 57 cases. The clinical data, CT images and PET/CT radioactive uptake data were analyzed using statistical data analysis software. RESULTS All 57 cases showed radioactively high uptake, with the maximum standard uptake value (SUVmax) of the lesion ranging from 1.60 to 27.30 and a mean value of 6.63±4.82. The symptomatic cases had an SUVmax of 8.76±4.97 and the asymptomatic cases had an SUVmax of 4.27±3.39. The SUVmax as well as singular or multiple lesions showed statistical differences between symptomatic and asymptomatic cases. CONCLUSION The symptomatic pulmonary tuberculosis cases show significantly higher SUVmax than the asymptomatic cases. Based on the criteria of SUVmax greater than 2.0 to define active lesions, 100% of symptomatic cases might have active lesions while 70.4% of asymptomatic cases might have active lesions. Therefore, focused attention should be clinically paid on the asymptomatic cases of pulmonary tuberculosis to avoid miss diagnosis and delayed treatment.

Projection data smoothing through noise-level weighted total variation regularization for low-dose computed tomography.

Abstract: Reducing radiation dose while maintaining the quality of the reconstructed images is a major challenge in the computed tomography (CT) community. In light of the non-stationary Gaussian noise distribution, we developed a model that incorporates a noise-level weighted total variation (NWTV) regularization term for denoising the projection data. Contrary to the well-known edge-weighted total variation method, which aims for better edge preserving, the proposed NWTV tries to adapt the regularization with the spatially varying noise levels. Experiments on simulated data as well as the real imaging data suggest that the proposed NWTV regularization could achieve quite competitive results. For sinograms with sharp edges, the NWTV could do a better job at balancing noise reduction and edge preserving, such that noise is removed in a more uniform manner. Another conclusion from our experiments is that the well-recognized stair-casing artifacts of TV regularization play little role in the reconstructed images when the NWTV method is applied to low-dose CT imaging data.

3D reconstruction of CT scans aid in preoperative planning for sarcomatoid renal cancer: A case report and mini-review

Abstract: Contrast-enhanced multi-slice computed tomography (MSCT) is commonly used in the diagnosis of complex malignant tumours. This technology provides comprehensive and accurate information about tumour size and shape in relation to solid tumours and the affected adjacent organs and tissues. This case report demonstrates the benefit of using MSCT 3D imaging for preoperative planning in a patient with late-stage (T4) sarcomatoid renal cell carcinoma, a rare renal malignant tumour. The surgical margin on the liver was negative, and no metastases to veins, lungs or other organs were detected by abdominal and chest contrast-enhanced CT. Although sarcomatoid histology is considered to be a poor prognostic factor, the patient is alive and well 17 months after surgery. The MSCT imaging modality enables 3D rendering of an area of interest, which assists surgical decision-making in cases of advanced renal tumours. In this case, as a result of MSCT 3D reconstruction, the patient received justified surgical treatment without compromising oncological principles.

The accuracy assessment of ultrasound for the diagnosis of ischiofemoral space - A Validation Study.

Abstract: OBJECTIVE To investigate and evaluate the accuracy of ultrasound (US) imaging for measurement of ischiofemoral space (IFS) compared with magnetic resonance imaging (MRI). METHODS Twenty-five hips of 16 patients with hip pain and ipsilateral quadratus femoris muscle (QFM) edema were recruited to the IFI group, and 19 hips of 19 patients without hip pain and QFM edema were recruited as the control group. IFS of both groups was measured by US and MRI. The correlations and differences between US and MRI measurements were then assessed. Last, the receiver operating characteristic (ROC) data analysis was performed. RESULTS The US results revealed a decreased IFS in IFI hips compared with controls (P < 0.001), suggesting that US were valuable in identifying IFI. IFS measured by US and MRI showed positive correlations in both the IFI group (r = 0.409, P = 0.042) and control group (r = 0.575, P = 0.01). There were no statistically significant differences between IFS measured by US and MRI in the control group (P = 0.657), while IFS measurements in the IFI group performed with US were significantly greater than those with MRI (P < 0.001). ROC curve analysis revealed that the cutoff value of IFS measured with US was 2.14 cm, with a sensitivity of 92.0% and specificity of 68.4%, while measured by MRI was 1.87 cm, with a sensitivity of 96.0% and specificity of 84.2%. CONCLUSIONS IFS measurements obtained with US are very similar to those obtained with MRI. Therefore, US provides a low-cost and easy-to-operate alternative imaging modality to measure IFS.