Computed Tomography Imaging Of Abdomen Research Articles

Cross-modal Transfer Learning Based on an Improved CycleGAN Model for Accurate Kidney Segmentation in Ultrasound Images

ObjectiveDeep-learning algorithms have been widely applied in the field of automatic kidney ultrasound (US) image segmentation. However, obtaining a large number of accurate kidney labels clinically is very difficult and time-consuming. To solve this problem, we have proposed an efficient cross-modal transfer learning method to improve the performance of the segmentation network on a limited labeled kidney US dataset. MethodsWe aim to implement an improved image-to-image translation network called Seg-CycleGAN to generate accurate annotated kidney US data from labeled abdomen computed tomography images. The Seg-CycleGAN framework primarily consists of two structures: (i) a standard CycleGAN network to visually simulate kidney US from a publicly available labeled abdomen computed tomography dataset; (ii) and a segmentation network to ensure accurate kidney anatomical structures in US images. Based on the large number of simulated kidney US images and small number of real annotated kidney US images, we then aimed to employ a fine-tuning strategy to obtain better segmentation results. ResultsTo validate the effectiveness of the proposed method, we tested this method on both normal and abnormal kidney US images. The experimental results showed that the proposed method achieved a segmentation accuracy of 0.8548 in dice similarity coefficient on all testing datasets and 0.7622 on the abnormal testing dataset. ConclusionsCompared with existing data augmentation and transfer learning methods, the proposed method improved the accuracy and generalization of the kidney US image segmentation network on a limited number of training datasets. It therefore has the potential to significantly reduce annotation costs in clinical settings.

Assessing the inter-observer and intra-observer reliability of radiographic measurements for size-specific dose estimates

BackgroundCalculating size-specific dose estimates (SSDEs) requires measurement of the patient’s anteroposterior (AP) and lateral thickness based on computed tomography (CT) images. However, these measurements can be subject to variation due to inter-observer and intra-observer differences. This study aimed to investigate the impact of these variations on the accuracy of the calculated SSDE.MethodsFour radiographers with 1–10 years of experience were invited to measure the AP and lateral thickness on 30 chest, abdomen, and pelvic CT images. The images were sourced from an internet-based database and anonymized for analysis. The observers were trained to perform the measurements using MicroDicom software and asked to repeat the measurements 1 week later. The study was approved by the institutional review board at Taibah University, and written informed consent was obtained from the observers. Statistical analyses were performed using Python libraries Pingouin (version 0.5.3), Seaborn (version 0.12.2), and Matplotlib (version 3.7.1).ResultsThe study revealed excellent inter-observer agreement for the calculated effective diameter and AP thickness measurements, with Intraclass correlation coefficients (ICC) values of 0.95 and 0.96, respectively. The agreement for lateral thickness measurements was lower, with an ICC value of 0.89. The second round of measurements yielded nearly the same levels of inter-observer agreement, with ICC values of 0.97 for the effective diameter, 1.0 for AP thickness, and 0.88 for lateral thickness. When the consistency of the observer was examined, excellent consistency was found for the calculated effective diameter, with ICC values ranging from 0.91 to 1.0 for all observers. This was observed despite the lower consistency in the lateral thickness measurements, which had ICC values ranging from 0.78 to 1.0.ConclusionsThe study’s findings suggest that the measurements required for calculating SSDEs are robust to inter-observer and intra-observer differences. This is important for the clinical use of SSDEs to set diagnostic reference levels for CT scans.

Feature Extraction Based on Local Histogram with Unequal Bins and a Recurrent Neural Network for the Diagnosis of Kidney Diseases from CT Images.

Kidney disease remains one of the most common ailments worldwide, with cancer being one of its most common forms. Early diagnosis can significantly increase the good prognosis for the patient. The development of an artificial intelligence-based system to assist in kidney cancer diagnosis is crucial because kidney illness is a global health concern, and there are limited nephrologists qualified to evaluate kidney cancer. Diagnosing and categorising different forms of renal failure presents the biggest treatment hurdle for kidney cancer. Thus, this article presents a novel method for detecting and classifying kidney cancer subgroups in Computed Tomography (CT) images based on an asymmetric local statistical pixel distribution. In the first step, the input image is non-overlapping windowed, and a statistical distribution of its pixels in each cancer type is built. Then, the method builds the asymmetric statistical distribution of the image's gradient pixels. Finally, the cancer type is identified by applying the two built statistical distributions to a Deep Neural Network (DNN). The proposed method was evaluated using a dataset collected and authorised by the Dhaka Central International Medical Hospital in Bangladesh, which includes 12,446 CT images of the whole abdomen and urogram, acquired with and without contrast. Based on the results, it is possible to confirm that the proposed method outperformed state-of-the-art methods in terms of the usual correctness criteria. The accuracy of the proposed method for all kidney cancer subtypes presented in the dataset was 99.89%, which is promising.

ContourGAN: Auto‐contouring of organs at risk in abdomen computed tomography images using generative adversarial network

AbstractAccurately identifying and contouring the organs at risk (OARs) is a crucial step in radiation treatment planning for precise dose calculation. This task becomes especially challenging in computed tomography (CT) images due to the irregular boundaries of the organs under study. The method currently employed in clinical practice is the manual contouring of CT images, which tends to be highly tedious and time‐consuming. The results are also prone to variations depending on the observer's skill level, environment, or equipment types. A deep learning‐based automatic contouring technique for segmenting OARs would help eliminate these problems and generate consistent results with minimal time and human effort. Our approach is to design a conditional generative adversarial network (GAN)‐based technique for the semantic segmentation of OARs in abdominal CT images. The residual blocks of the generator network have a multi‐scale context layer that explores more generic characteristics, greatly enhancing performance and lowering losses. A comparative analysis is undertaken based on various assessment measures widely employed in segmentation. The results show substantial improvement, with mean dice scores of 98.0%, 96.6%, 98.2%, and 86.1% for the respective organs—liver, kidney, spleen, and pancreas—in the abdominal CT. The proposed GAN‐based model could accurately segment the four abdominal organs, including the liver, kidney, spleen, and pancreas. The obtained results prove that the suggested model is able to compete with existing state‐of‐the‐art abdominal OAR segmentation techniques.

Abdomen CT multi-organ segmentation using token-based MLP-Mixer.

Manual contouring is very labor-intensive, time-consuming, and subject to intra- and inter-observer variability. An automated deep learning approach to fast and accurate contouring and segmentation is desirable during radiotherapy treatment planning. This work investigates an efficient deep-learning-based segmentation algorithm in abdomen computed tomography (CT) to facilitate radiation treatment planning. In this work, we propose a novel deep-learning model utilizing U-shaped multi-layer perceptron mixer (MLP-Mixer) and convolutional neural network (CNN) for multi-organ segmentation in abdomen CT images. The proposed model has a similar structure to V-net, while a proposed MLP-Convolutional block replaces each convolutional block. The MLP-Convolutional block consists of three components: an early convolutional block for local features extraction and feature resampling, a token-based MLP-Mixer layer for capturing global features with high efficiency, and a token projector for pixel-level detail recovery. We evaluate our proposed network using: (1) an institutional dataset with 60 patient cases and (2) a public dataset (BCTV) with 30 patient cases. The network performance was quantitatively evaluated in three domains: (1) volume similarity between the ground truth contours and the network predictions using the Dice score coefficient (DSC), sensitivity, and precision; (2) surface similarity using Hausdorff distance (HD), mean surface distance (MSD) and residual mean square distance (RMS); and (3) the computational complexity reported by the number of network parameters, training time, and inference time. The performance of the proposed network is compared with other state-of-the-art networks. In the institutional dataset, the proposed network achieved the following volume similarity measures when averaged over all organs: DSC = 0.912, sensitivity = 0.917, precision = 0.917, average surface similarities were HD = 11.95 mm, MSD = 1.90 mm, RMS = 3.86 mm. The proposed network achieved DSC = 0.786 and HD = 9.04 mm on the public dataset. The network also shows statistically significant improvement, which is evaluated by a two-tailed Wilcoxon Mann-Whitney U test, on right lung (MSD where the maximum p-value is 0.001), spinal cord (sensitivity, precision, HD, RMSD where p-value ranges from 0.001 to 0.039), and stomach (DSC where the maximum p-value is 0.01) over all other competing networks. On the public dataset, the network report statistically significant improvement, which is shown by the Wilcoxon Mann-Whitney test, on pancreas (HD where the maximum p-value is 0.006), left (HD where the maximum p-value is 0.022) and right adrenal glands (DSC where the maximum p-value is 0.026). In both datasets, the proposed method can generate contours in less than 5 s. Overall, the proposed MLP-Vnet demonstrates comparable or better performance than competing methods with much lower memory complexity and higher speed. The proposed MLP-Vnet demonstrates superior segmentation performance, in terms of accuracy and efficiency, relative to state-of-the-art methods. This reliable and efficient method demonstrates potential to streamline clinical workflows in abdominal radiotherapy, which may be especially important for online adaptive treatments.

Sarcopenia in children with chronic liver disease: Prevalence and impact on liver transplant outcomes.

Sarcopenia is a clinical condition characterized by a reduction in muscle mass, which typically affects adult patients; however, it has recently been recognized in pediatric literature. Few studies in children with chronic liver disease (CLD) undergoing liver transplantation (LT) have investigated the role of sarcopenia, with controversial results. The aim of our study was to assess the prevalence and impact of sarcopenia among children with CLD who are candidates for LT. We conducted a retrospective, single-center study at Bambino Gesù Children's Hospital (Rome, Italy) from July 2016 to July 2021, evaluating all children (0-16 years old) with CLD listed for LT with an abdomen computed tomography imaging available before LT. The total psoas muscle surface area (t-PMSA) was defined as the sum of left and right psoas muscle surface area measured at L4-L5 on axial images. The t-PMSA z-score was calculated according to reference data, and sarcopenia was defined as a t-PMSA z-score of ≤-2 (1-16 years) or a psoas muscle index [PMI; PMI = t-PMSA/(100 × BSA)] of <50th percentile of the population examined (<1 year). Clinical, laboratory, and LT outcome data were collected from all the patients with CLD. 27 out 48 (56%) of the patients aged 1-16 years were sarcopenic. No differences were noted in anthropometrics, nutritional support, liver function tests, model for ESLD (MELD), or pediatric ESLD (PELD) scores between patients with and without sarcopenia. The former showed a higher prevalence of respiratory complications (66.7% vs. 42.1%) and need for inotropes (40.7% vs. 10.8%) after LT. Among patients aged 0-1 years (n: 36), those with reduced muscle mass (50%) had a longer hospitalization time (44 vs. 24 days) and higher incidences of multi-organ failure syndrome (38.9% vs. 0%) and intensive care unit-related infections (61.1% vs. 27.8%) compared to those with greater muscle mass. t-PMSA and PMI were statistically significant predictors of LT outcomes. Sarcopenia is a reliable index of frailty in children with CLD, as its presence is associated with the risk of a more challenging LT. Future studies will have to investigate the functional aspects of sarcopenia and conceive preventive measures of muscle wasting in CLD patients.

Computed Tomography Diagnostic Reference Levels for Brain, Chest, and Abdominal/Pelvis Examinations

Radiation dosage variance is one of the topics that arise when dealing with computed tomography (CT) devices within medical imaging centers. In this article, a review was done to enlighten the causes of such dosage variance and the degree of variation for pediatric patients. The article focuses on the diagnostic reference levels (DRLs) for the brain, chest, and abdomen CT images. The reviewed studies were categorized depending on the type of the cases of pediatric patients, which include head, chest, and abdominal examinations. There were 9 studies using human data, 1 with phantom data, and 2 with combined human and phantom data. The dosage indices used in the studies were the DRLs, which were used as a key comparison between studies. The classification was likewise done at the expense of the radiation dose, with a secondary classification based on the patients' age, weight, and size. The type of scanner, differences in protocols, variations in patients, and variations in research design are all considered sources of variation. The following dosage indices were found in different combinations: volume CT dose index (CTDIvol), dose length product (DLP), and size-specific dose estimate (SSDE). The use of different dose indices limited the dose comparison between 11 studies.

LRFNet: A deep learning model for the assessment of liver reserve function based on Child‐Pugh score and CT image

Background and objectiveLiver reserve function should be accurately evaluated in patients with hepatic cellular cancer before surgery to evaluate the degree of liver tolerance to surgical methods. Meanwhile, liver reserve function is also an important indicator for disease analysis and prognosis of patients. Child-Pugh score is the most widely used liver reserve function evaluation and scoring system. However, this method also has many shortcomings such as poor accuracy and subjective factors. To achieve comprehensive evaluation of liver reserve function, we developed a deep learning model to fuse bimodal features of Child-Pugh score and computed tomography (CT) image. Methods1022 enhanced abdomen CT images of 121 patients with hepatocellular carcinoma and impaired liver reserve function were retrospectively collected. Firstly, CT images were pre-processed by de-noising, data amplification and normalization. Then, new branches were added between the dense blocks of the DenseNet structure, and the center clipping operation was introduced to obtain a lightweight deep learning model liver reserve function network (LRFNet) with rich liver scale features. LRFNet extracted depth features related to liver reserve function from CT images. Finally, the extracted features are input into a deep learning classifier composed of fully connected layers to classify CT images into Child-Pugh A, B and C. Precision, Specificity, Sensitivity, and Area Under Curve are used to evaluate the performance of the model. ResultsThe AUC by our LRFNet model based on CT image for Child-Pugh A, B and C classification of liver reserve function was 0.834, 0.649 and 0.876, respectively, and with an average AUC of 0.774, which was better than the traditional clinical subjective Child-Pugh classification method. ConclusionDeep learning model based on CT images can accurately classify Child-Pugh grade of liver reserve function in hepatocellular carcinoma patients, provide a comprehensive method for clinicians to assess liver reserve function before surgery.

Renal tumors segmentation in abdomen CT Images using 3D-CNN and ConvLSTM

Renal tumor is one of the common tumors with high incidence, and accurate segmentation of renal tumors is helpful for preoperative evaluation. Computed Tomography (CT) plays an important role in the treatment of renal tumors and accurate segmentation of tumors in CT images may provide critical information for surgery. In this paper, a segmentation approach based on deep learning with limited computation cost is proposed to improve the segmentation accuracy for kidneys and renal tumors. Firstly, a pre-trained restruction network is presented to alleviate small samples problems, which utilizes abdominal CT data to transfer network model effectively; Then, prior contour-assisted channel is introduced in two-dimensional network to segment the region of interest which contains kidneys and renal tumors and act as the input of the subsequente fine segmentation network; Finally, convolutional long short-term memory (ConvLSTM) is employed to extract spatial correlation information between slices and combined with a three-dimensional convolutional neural networks for fine segmentation. Several experiments on the 2019 renal tumor segmentation challenge(Kits19) dataset are designed to evaluate the performance of the proposed method, and the mean segmentation accuracy for kidneys and renal tumors are 96.39% and 78.91% for cross validation tests, which outperforms the other neural network algorithms, including 3D Res-Unet with 95.4% and 72.35%.

Removal of computed tomography ring artifacts via radial basis function artificial neural networks

Ring artifacts in computed tomography (CT) images are caused by the undesirable response of detector pixels, which leads to the degradation of CT images. Accordingly, it affects the image interpretation, post-processing, and quantitative analysis. In this study, a radial basis function neural network (RBFNN) was used to remove ring artifacts. The proposed method employs polar coordinate transformation. First, ring artifacts were transformed into linear artifacts by polar coordinate transformation. Then, smoothing operators were applied to locate these artifacts exactly. Subsequently, RBFNN was operated on each linear artifact. The neuron numbers of the input, hidden, and output layers of the neural network were 8, 40, and 1, respectively. Neurons in the input layer were selected according to the characteristics of the artifact itself and its relationship with the surrounding normal pixels. For the training of the neural network, a hybrid of adaptive gradient descent algorithm (AGDA) and gravitational search algorithm (GSA) was adopted. After the corrected image was obtained using the updated neural network, the inverse coordinate transformation was implemented. The experimental data were divided into simulated ring artifacts and real ring artifacts, which were based on brain and abdomen CT images. Compared with current artifact removal methods, the proposed method removed ring artifacts more effectively and retained the maximum detail of normal tissues. In addition, for index analysis, the performance of proposed method was superior to that of the other methods.

Biological fingerprint using scout computed tomographic images for positive patient identification.

Management of patient identification is an important issue that should be addressed to ensure patient safety while using modern healthcare systems. Patient identification errors can be mainly attributed to human errors or system problems. An error-tolerant system, such as a biometric system, should be able to prevent or mitigate potential misidentification occurrences. Herein, we propose the use of scout computed tomography (CT) images for biometric patient identity verification and present the quantitative accuracy outcomes of using this technique in a clinical setting. Scout CT images acquired from routine examinations of the chest, abdomen, and pelvis were used as biological fingerprints. We evaluated the resemblance of the follow-up with the baseline image by comparing the estimates of the image characteristics using local feature extraction and matching algorithms. The verification performance was evaluated according to the receiver operating characteristic (ROC) curves, area under the ROC curves (AUC), and equal error rates (EER). The closed-set identification performance was evaluated according to the cumulative match characteristic curves and rank-one identification rates (R1). A total of 619 (383 males, 236 females, age range 21-92years) patients who underwent baseline and follow-up chest-abdomen-pelvis CT scans on the same CT system were analyzed for verification and closed-set identification. The highest performances of AUC, EER, and R1 were 0.998, 1.22%, and 99.7%, respectively, in the considered evaluation range. Furthermore, to determine whether the performance decreased in the presence of metal artifacts, the patients were classified into two groups, namely scout images with (255 patients) and without (364 patients) metal artifacts, and the significance test was performed for two ROC curves using the unpaired Delong's test. No significant differences were found between the ROC performances in the presence and absence of metal artifacts when using a sufficient number of local features. Our proposed technique demonstrated that the performance was comparable to that of conventional biometrics methods when using chest, abdomen, and pelvis scout CT images. Thus, this method has the potential to discover inadequate patient information using the available chest, abdomen, and pelvis scout CT image; moreover, it can be applied widely to routine adult CT scans where no significant body structure effects due to illness or aging are present. Our proposed method can obtain accurate patient information available at the point-of-care and help healthcare providers verify whether a patient's identity is matched accurately. We believe the method to be a key solution for patient misidentification problems.

18F-Fludeoxyglucose PET/CT in SCLC: Analysis of the CONVERT Randomized Controlled Trial

IntroductionWe used phase-3 CONVERT trial data to investigate the impact of fludeoxyglucose F 18 (18F-FDG) positron emission tomography (PET)/computed tomography (CT) in SCLC. MethodsCONVERT randomized patients with limited-stage SCLC to twice-daily (45 Gy in 30 fractions) or once-daily (66 Gy in 33 fractions) chemoradiotherapy. Patients were divided into two groups in this unplanned analysis: those staged with conventional imaging (contrast-enhanced thorax and abdomen CT and brain imaging with or without bone scintigraphy) and those staged with 18F-FDG PET/CT in addition. ResultsData on a total of 540 patients were analyzed. Compared with patients who underwent conventional imaging (n = 231), patients also staged with 18F-FDG PET/CT (n = 309) had a smaller gross tumor volume (p = 0.003), were less likely to have an increased pretreatment serum lactate dehydrogenase level (p = 0.035), and received more chemotherapy (p = 0.026). There were no significant differences in overall (hazard ratio = 0.87, 95% confidence interval: 0.70–1.08, p = 0.192) and progression-free survival (hazard ratio = 0.87, 95% confidence interval: 0.71–1.07], p = 0.198) between patients staged with or without 18F-FDG PET/CT. In the conventional imaging group, we found no survival difference between patients staged with or without bone scintigraphy. Although there were no differences in delivered radiotherapy dose, 18F-FDG PET/CT–staged patients received lower normal tissue (lung, heart, and esophagus) radiation doses. Apart from a higher incidence of late esophagitis in patients staged with conventional imaging (for grade ≥1, 19% versus 11%; [p = 0.012]), the incidence of acute and late radiotherapy-related toxicities was not different between the two groups. ConclusionIn CONVERT, survival outcomes were not significantly different in patients staged with or without 18F-FDG PET/CT. However, this analysis cannot support the use or omission of 18F-FDG PET/CT owing to study limitations.

VARIATION IN THE SIZE OF ABDOMINAL AORTA MEASURED ON COMPUTED TOMOGRAPHY

Objective: The normal size of the abdominal aorta is expected to be &lt;3 cm though the size changes with age as well as workload. There is a need to know the variation in size of the abdominal aorta to avoid the formation of aneurysm and further complications.Materials and Methods: This study was carried out at Kasturba Medical College after taking 130 patients considering the inclusion and exclusion criteria. 130 patients who all came for abdomen computed tomography (CT) were divided into 65 patients in each age group under 20–40 years and 40–80 years. The measurements of abdominal aorta were anteroposterior (AP) and right left (RL) measured at T12-L1 level on plain axial abdomen CT images on Extended Brilliance Workstation.Result: We observed that there was a statistical significant difference in the average AP diameter between the two age groups (p&lt;0.001). Another finding was a statistical significant difference in the average RL diameter between the two age groups (p&lt;0.001), and, hence the size of abdominal aorta varies with age.Conclusion: The proposed study concluded that there was variation in the size of abdominal aorta with age. These findings would be helpful in early detecting of aneurysm and in avoiding aortic dissection.

Routine CT scanning of patients retrieved to a tertiary centre on veno-venous extracorporeal membrane oxygenation: a retrospective risk benefit analysis.

Comprehensive clinical examination can be compromised in patients on veno-venous extracorporeal membrane oxygenation (VV-ECMO). Adjunctive diagnostic imaging strategies range from bedside imaging only to routine computed tomography (CT). The risk-benefit of either approach remains to be evaluated. Patients retrieved to the Royal Brompton Hospital (RBH) on VV-ECMO routinely undergo admission CT imaging of head, chest, abdomen and pelvis. This study aimed to identify how frequently changes in therapy or adverse events could be attributed to routine CT scanning. Demographic and clinical data were gathered retrospectively from patients retrieved to RBH on VV-ECMO (January 2014-2016). Scans were categorized as 'routine' or requested to clarify a specific clinical uncertainty. Clinical records were reviewed to identify attributable management changes and CT- related adverse events. Seventy-two patients were retrieved on VV-ECMO (median age 44 years) and 65 scanned on admission (mean radiation dose 2344mGy-cm). Routine head CT head yielded novel clinical information in 11 patients, 10 of whom had unexpected intracranial haemorrhage and, subsequently, had their anticoagulation withheld. Routine thoracic CT identified unexpected positive findings in three patients (early fibrosis, pulmonary vasculitis, pneumomediastinum), eliciting management variation in one (steroid administration). Routine abdomen/pelvis CT identified new information in three patients (adrenal haemorrhage, hepatosteatosis, splenic infarction), changing the management in one (withholding anticoagulation). CT scanning was not associated with consequential adverse events (e.g. accidental decannulation, gas entrainment into the circuit, hypoxia, hypotension). Median transfer/scan time was 78 minutes, requiring five ITU staff-members. In our cohort, a policy of routine head CT changed the management in 17% of patients; the yield from routine chest, abdomen and pelvis CT was modest. CT transfer was safe, but resource intensive. Prospective studies should evaluate whether routine CT impacts outcome.

Organ Location Determination and Contour Sparse Representation for Multiorgan Segmentation.

Organ segmentation on computed tomography (CT) images is of great importance in medical diagnoses and treatment. This paper proposes organ location determination and contour sparse representation methods (OLD-CSR) for multiorgan segmentation (liver, kidney, and spleen) on abdomen CT images using an extreme learning machine classifier. First, a location determination method is designed to obtain location information of each organ, which is used for coarse segmentation. Second, for coarse-to-fine segmentation, a contour gradient and rate change based feature point extraction method is proposed. A sparse optimization model is developed for refining the contour feature points. Experimentations with 153 CT images demonstrate the performance advantages of OLD-CSR as compared with related work.

Computer-aided abdomen disease diagnosis using curvelet-based texture analysis with progressive classification

Designing Computer-Aided Diagnostic (CAD) system for diagnosis diseases of various sizes is a difficult task. The abdomen diseases are of different sizes varying from medium to very small. Most CAD systems fail in classifying abdomen diseases of variable sizes. Hence, a method for classification of abdomen diseases of all sizes in computed tomography (CT) images is presented. The idea is to use features extracted using curvelet transform progressively for the classification of the abdomen diseases that leads to the accurate texture analysis and classification method. Experiments have been performed on 120 CT images of abdomen comprising of 30 images, each of normal tissues, cysts, tumours and stones. Three different sets of query images of variable sizes are used as input to the proposed system. Experimental result shows that the proposed method performs better than the conventional texture analysis and classification methods for diagnosis abdomen diseases of various sizes.

Computer aided diagnosis system for abdomen diseases in computed tomography images

In this paper, a computer aided diagnostic (CAD) system for classification of abdomen diseases from computed tomography (CT) images is presented. The methodology used in this paper is to select the most appropriate machine learning technique of segmentation, feature extraction and classification for each module of proposed CAD. The methodology of selecting appropriate machine learning technique for each module of CAD results in accurate and efficient system. Regions of interest are segmented from CT images of tumor, cyst, calculi and normal liver using active contour models, region growing and thresholding. The CAD presented in this research work exploits the discriminating power of features for classifying abdominal diseases. Therefore, feature extraction module extracts statistical texture descriptors using three kinds of feature extraction methods i.e. Gray-Level co-occurrence matrices (GLCM), Discrete Wavelet Transform (DWT) and Discrete Curvelet Transform (DCT). At the next stage, effective and optimum features of ROIs are selected using Genetic Algorithm (GA). Further, Support Vector Machine (SVM) and Artificial Neural Network (ANN) are used to assess the capability of features for classification of diseases of abdomen. The study is performed on 120 CT images of abdomen (30 normal, 30 tumor, 30 cyst and 30 calculi). It is observed from the results that proposed CAD consists of edge based active contour model combined with optimized statistical texture descriptors using DCT along with ANN as classifier achieves the best diagnostic performance of 95.1%. It is also shown in results that proposed CAD achieves highest sensitivity, specificity of 95% and 98% respectively.

Cardiac rhabdomyosarcoma of the left atrium.

Cardiac rhabdomyosarcoma of the left atrium.

Novel connecting tube for saline chaser in contrast-enhanced CT: the effect of spiral flow of saline on contrast enhancement

To evaluate the effect of a newly developed connecting tube, which generates a spiral flow of saline, on aortic and hepatic contrast enhancement during hepatic-arterial phase (HAP) and portal venous phase (PVP) computed tomography (CT). Eighty patients were randomly assigned to one of two protocols: with a new or a conventional tube. The contrast material (600 mgI/kg) was delivered over 30 s; this was followed by the administration of 25 ml saline solution delivered at the same injection rate as the contrast material. Unenhanced and contrast-enhanced CT images of the upper abdomen were obtained. We calculated the changes in the CT number (∆HU) for the aorta during HAP and PVP, and for the liver during PVP. We compared ∆HU between protocols. The mean ∆HU for the abdominal aorta during HAP was significantly higher with the new tube protocol than with the conventional tube protocol (322 ± 53 vs. 290 ± 53, P < 0.01). There were no significant differences in the mean ∆HU for the abdominal aorta and liver during PVP between the two protocols (P > 0.05). The new connecting tube increased the effect of a saline chaser and significantly improved aortic enhancement during HAP. • Optimal administration of intravenous contrast material is essential for optimal CT quality. • A new connecting tube can generate spiral flow, which improves intravenous administration. • The new connecting tube improved aortic contrast enhancement during the hepatic-arterial phase. • The new connecting tube increased the effect of a saline chaser.

Predicting the fidelity of JPEG2000 compressed CT images using DICOM header information

To propose multiple logistic regression (MLR) and artificial neural network (ANN) models constructed using digital imaging and communications in medicine (DICOM) header information in predicting the fidelity of Joint Photographic Experts Group (JPEG) 2000 compressed abdomen computed tomography (CT) images. Our institutional review board approved this study and waived informed patient consent. Using a JPEG2000 algorithm, 360 abdomen CT images were compressed reversibly (n = 48, as negative control) or irreversibly (n = 312) to one of different compression ratios (CRs) ranging from 4:1 to 10:1. Five radiologists independently determined whether the original and compressed images were distinguishable or indistinguishable. The 312 irreversibly compressed images were divided randomly into training (n = 156) and testing (n = 156) sets. The MLR and ANN models were constructed regarding the DICOM header information as independent variables and the pooled radiologists' responses as dependent variable. As independent variables, we selected the CR (DICOM tag number: 0028, 2112), effective tube current-time product (0018, 9332), section thickness (0018, 0050), and field of view (0018, 0090) among the DICOM tags. Using the training set, an optimal subset of independent variables was determined by backward stepwise selection in a four-fold cross-validation scheme. The MLR and ANN models were constructed with the determined independent variables using the training set. The models were then evaluated on the testing set by using receiver-operating-characteristic (ROC) analysis regarding the radiologists' pooled responses as the reference standard and by measuring Spearman rank correlation between the model prediction and the number of radiologists who rated the two images as distinguishable. The CR and section thickness were determined as the optimal independent variables. The areas under the ROC curve for the MLR and ANN predictions were 0.91 (95% CI; 0.86, 0.95) and 0.92 (0.87, 0.96), respectively. The correlation coefficients of the MLR and ANN predictions with the number of radiologists who responded as distinguishable were 0.76 (0.69, 0.82, p < 0.001) and 0.78 (0.71, 0.83, p < 0.001), respectively. The MLR and ANN models constructed using the DICOM header information offer promise in predicting the fidelity of JPEG2000 compressed abdomen CT images.