Radiological Opinion Research Articles

Artificial intelligence-based graded training of pulmonary nodules for junior radiology residents and medical imaging students

BackgroundTo evaluate the efficiency of artificial intelligence (AI)-assisted diagnosis system in the pulmonary nodule detection and diagnosis training of junior radiology residents and medical imaging students.MethodsThe participants were divided into three groups. Medical imaging students of Grade 2020 in the Jinzhou Medical University were randomly divided into Groups 1 and 2; Group 3 comprised junior radiology residents. Group 1 used the traditional case-based teaching mode; Groups 2 and 3 used the ‘AI intelligent assisted diagnosis system’ teaching mode. All participants performed localisation, grading and qualitative diagnosed of 1,057 lung nodules in 420 cases for seven rounds of testing after training. The sensitivity and number of false positive nodules in different densities (solid, pure ground glass, mixed ground glass and calcification), sizes (less than 5 mm, 5–10 mm and over 10 mm) and positions (subpleural, peripheral and central) of the pulmonary nodules in the three groups were detected. The pathological results and diagnostic opinions of radiologists formed the criteria. The detection rate, diagnostic compliance rate, false positive number/case, and kappa scores of the three groups were compared.ResultsThere was no statistical difference in baseline test scores between Groups 1 and 2, and there were statistical differences with Group 3 (P = 0.036 and 0.011). The detection rate of solid, pure ground glass and calcified nodules; small-, medium-, and large-diameter nodules; and peripheral nodules were significantly different among the three groups (P<0.05). After seven rounds of training, the diagnostic compliance rate increased in all three groups, with the largest increase in Group 2. The average kappa score increased from 0.508 to 0.704. The average kappa score for Rounds 1–4 and 5–7 were 0.595 and 0.714, respectively. The average kappa scores of Groups 1,2 and 3 increased from 0.478 to 0.658, 0.417 to 0.757, and 0.638 to 0.791, respectively.ConclusionThe AI assisted diagnosis system is a valuable tool for training junior radiology residents and medical imaging students to perform pulmonary nodules detection and diagnosis.

Designing a Hybrid Method of Artificial Neural Network and Particle Swarm Optimization to Diagnosis Polyps from Colorectal CT Images.

Since colorectal cancer is one of the most important types of cancer in the world that often leads to death, computer-aided diagnostic (CAD) systems are a promising solution for early diagnosis of this disease with fewer side effects than conventional colonoscopy. Therefore, the aim of this research is to design a CAD system for processing colorectal Computerized Tomography (CT) images using a combination of an artificial neural network and a particle swarm optimizer. First, the data set of the research was created from the colorectal CT images of the patients of Loghman-e Hakim Hospitals in Tehran and Al-Zahra Hospitals in Isfahan who underwent colorectal CT imaging and had conventional colonoscopy done within a maximum period of one month after that. Then the steps of model implementation, including electronic cleansing of images, segmentation, labeling of samples, extraction of features, and training and optimization of the artificial neural network (ANN) with a particle swarm optimizer, were performed. A binomial statistical test and confusion matrix calculation were used to evaluate the model. The values of accuracy, sensitivity, and specificity of the model with a P value = 0.000 as a result of the McNemar test were 0.9354, 0.9298, and 0.9889, respectively. Also, the result of the P value of the binomial test of the ratio of diagnosis of the model and the radiologist from Loqman Hakim and Al-Zahra Hospitals was 0.044 and 0.021, respectively. The results of statistical tests and research variables show the efficiency of the CTC-CAD system created based on the hybrid of the ANN and particle swarm optimization compared to the opinion of radiologists in diagnosing colorectal polyps from CTC images.

The Results of Patients with Histopathological Diagnosed Xanthogranulomatous Cholecystitis, a Single-Center Experience

Objective: Xanthogranulomatous cholecystitis (XGC) is a rare and benign gallbladder disorder. Unnecessary or additional surgical interventions were reported secondary to extended inflammation or suspicion of malignancy. This study aimed to examine patients diagnosed with XGC in our clinic. Materials and Methods: The data of patients were recruited who underwent cholecystectomy and diagnosed with XGC. We excluded patients with no radiologic examination, accompanying pancreas, biliary tree, and hepatic cancer. We recruited the data of patients' demographics, clinical symptoms, imaging studies, characteristics of surgery, and postoperative outcomes. Results: The mean age was 57 ±19.1 years, and there were 12 (31%) females. Abdominal pain was found in 38 (97%) patients as a significant complaint. Elective and emergent cholecystectomy were performed in 18 (46%) and 21 (54%) patients, respectively. Open surgery was performed on 17 (44%) patients, and laparoscopic surgery on 22 (56%). The surgery was completed as laparoscopic in 17 (77%) patients. According to radiologic examinations, suspicious gallbladder malignancy was reported in 4 (10%), 8 (20%), and 4 (10%) patients with USG, CT, and MRI, respectively. There was 1 (3%) patient diagnosed with XGC by USG, 4 (10%) by CT, and 1 (3%) by MRI. Common bile duct (CBD) stone was observed in 10 (26%) of 39 patients. Conversion to open surgery was found in 5 (13%) patients. Intraoperative frozen section analysis was performed in 8 (20%) patients. Extended surgery was performed to 7 (17%) patients. Conclusion: Preoperative diagnosis and surgical management of XGC can be challenging. If a surgeon has suspicion of XGC in preoperative period, expert radiologist opinion should be asked, and they should always keep in mind to perform frozen-section examination.

Assessment of encoder-decoder-based segmentation models for thyroid ultrasound images.

Encoder-decoder-based semantic segmentation models classify image pixels into the corresponding class, such as the ROI (region of interest) or background. In the present study, simple / dilated convolution / series / directed acyclic graph (DAG)-based encoder-decoder semantic segmentation models have been implemented, i.e., SegNet (VGG16), SegNet (VGG19), U-Net, mobileNetv2, ResNet18, ResNet50, Xception and Inception networks for the segment TTUS(Thyroid Tumor Ultrasound) images. Transfer learning has been used to train these segmentation networks using original and despeckled TTUS images. The performance of the networks has been calculated using mIoU and mDC metrics. Based on the exhaustive experiments, it has been observed that ResNet50-based segmentation model obtained the best results objectively with values 0.87 for mIoU, 0.94 for mDC, and also according to radiologist opinion on shape, margin, and echogenicity characteristics of segmented lesions. It is noted that the segmentation model, namely ResNet50, provides better segmentation based on objective and subjective assessment. It may be used in the healthcare system to identify thyroid nodules accurately in real time.

Pancreatic ductal adenocarcinoma (PDAC) regional nodal disease at standard lymphadenectomy: is MRI accurate for identifying node-positive patients?

To determine the accuracy of qualitative and quantitative MRI features for the diagnosis of pathologic regional lymph nodes at standard lymphadenectomy in patients with pancreatic ductal adenocarcinoma (PDAC). All adult patients with pancreatic MRI performed from 2011 to 2021 within 3months of a pancreaticoduodenectomy were eligible for inclusion in this single-center retrospective cohort study. Regional nodes at standard lymphadenectomy were independently reviewed by two fellowship-trained abdominal radiologists for the following qualitative features: heterogeneous T2 signal, round shape, indistinct margin, peri-nodal fat stranding, and restricted diffusion greater than the spleen. Quantitative characteristics including primary tumor size, largest node short- and long-axes length, number of regional nodes, absolute apparent diffusion coefficient (ADC) values, and ADC node-to-spleen signal index were assessed. Analysis was at the patient-level with surgical pathology as the reference standard. Of 75 patients, 85% (64/75) were positive for regional nodal disease on histopathology. None of the qualitative variables evaluated on MRI was associated with pathologic nodes. Median primary tumor maximum diameter was slightly larger for patients with pathologic nodes compared to those without (18mm (10-42mm) vs 16mm (9-22mm), p = 0.027). None of the other quantitative features was associated with pathologic nodes. Radiologist opinion was not associated with pathologic nodes (p = 0.520). Interobserver agreement was fair (kappa = 0.257). Lymph node morphologic features and radiologist opinion using MRI are of limited value for diagnosing PDAC regional nodal disease. Improved diagnostic techniques are needed given the prognostic implications of pathologic lymph nodes in these patients. • Multiple lymph node morphologic features routinely assessed on MRI for malignancies elsewhere in the body are likely not applicable when assessing for pancreatic ductal adenocarcinoma nodal disease. • Interobserver agreement for the presence or absence of pancreatic ductal adenocarcinoma lymph node morphologic features on MRI is fair (kappa = 0.257). • Many more lymph nodes are resected at PDAC standard lymphadenectomy than are detectable on MRI, median 25 vs 5 (p < 0.001), suggesting improved diagnostic techniques are needed to identify PDAC nodal disease.

USC-ENet: a high-efficiency model for the diagnosis of liver tumors combining B-mode ultrasound and clinical data.

Ultrasound image acquisition has the advantages of being low cost, rapid, and non-invasive, and it does not produce radiation. Currently, ultrasound is widely used in the diagnosis of liver tumors. However, owing to the complex presentation and diverse features of benign and malignant liver tumors, accurate diagnosis of liver tumors using ultrasound is difficult even for experienced radiologists. In recent years, artificial intelligence-assisted diagnosis has proven to provide effective support to radiologists. However, there is room for further improvement in the existing ultrasound artificial intelligence diagnostic model of liver tumor. First, the image diagnostic model may not fully consider relevant clinical data in the decision-making process. Second, owing to the difficulty in collecting biopsy pathology and physician-labeled ultrasound data of liver tumors, training datasets are usually small, and commonly used large neural networks tend to overfit on small datasets, which seriously affects the generalization of the model. In this study, we propose a deep learning-assisted diagnosis model called USC-ENet, which integrates B-mode ultrasound features of liver tumors and clinical data of patients, and we design a small neural network specifically for small-scale medical images combined with an attention mechanism. Real data from 542 patients with liver tumors (N = 2168 images) are used during model training and validation. Experiments show that USC-ENet can achieve a good classification effect (area under the curve = 0.956, sensitivity = 0.915, and specificity = 0.880) after small-scale data training, and it has certain interpretability, showing good potential for clinical adoption. In conclusion, our model provides not only a reliable second opinion for radiologists but also a reference for junior radiologists who lack clinical experience.

A clinical and radiological profile of short stature in Central India

Background: Stature is methodically described as height vertex. Short stature can be defined as an individual in whom height is two standard deviations below the standard mean height of a given sex, age and population. Methods: A cross sectional study was conducted in 200 children attending endocrine OPD over 12 months from October 2019 to September 2020 with complaints of short stature to study the clinical and radiological profile of children between 2-15 years of age. Results: Totally 200 children with short stature were studied in age group less than 15 years. Total no. of males were 105 (52.5%) and females were 95 (47.5%). The highest percentage of patients i.e., 38.5% belonged to 5-10 years followed by 36% who were of 10-15 years of age group while, the least i.e., only 25.5% belonged to &lt;5 years of age group. Conclusions: In our study endocrine causes are most common including hypothyroid (27.5%), GHD (2.5%) and adrenal insufficiency (1%). 2nd most common are normal variants CDGP (11.5%) and FSS (9%). After endocrine causes this dominance of normal variants of growth is in accordance with other worldwide studies. Thus, it is very important to remember that many cases of short stature in general population may be normal, as determined by meticulous measurements, and determination of bone age using standard charts and expert’s radiological opinion.

Amyloidoma Presenting as Compressive Myelopathy.

Spinal cord compression is a common problem in clinical practice. Sometimes puzzling symptoms may mislead the clinician in the initial days. We present a 59 year old lady who presented with progressive numbness of both feet and abdomen, walking difficulties for which the initial evaluation did not yield any result. The work up at our facility showed collapsed D3 vertebra, prevertebral collection and cord compression. She was tentatively started on antituberculous treatment, based on the radiologist opinion. Two months later, the patient had further progression of symptoms and had to undergo D3 laminectomy and surgical decompression. She also developed hypertension and diabetes in the meantime. Histopathology did not favour tuberculous process, but showed amyloid deposition. Antituberculous treatment was halted and she was evaluated for plasma cell dyscrasias. The serum protein electrophoresis with immunofixation was in favour of IgG Kappa light chain monoclonal gammopathy. She was further subjected to bone marrow aspiration and biopsy at a Cancer institute. The result was negative for plasma cell malignancy, and was suggestive of only reactive lymphocytosis. On the advice of oncologist, antituberculous treatment was started again and continued as RH- two drug regimen for 9 months along with supportive care. She improved symptomatically, able to walk independently and attended regular follow up. Three years later, a repeat MRI scan showed enlargement of the lesion to involve the posterior mediastinum. The oncology service decided to irradiate the lesion, only if she develops new symptoms. This outcome suggests the unusual presentation of the disease, and the so litary amyloidoma is probably a primary disease in itself or secondary to a neoplastic process.

Classification of Multiclass Histopathological Breast Images Using Residual Deep Learning.

Pathologists need a lot of clinical experience and time to do the histopathological investigation. AI may play a significant role in supporting pathologists and resulting in more accurate and efficient histopathological diagnoses. Breast cancer is one of the most diagnosed cancers in women worldwide. Breast cancer may be detected and diagnosed using imaging methods such as histopathological images. Since various tissues make up the breast, there is a wide range of textural intensity, making abnormality detection difficult. As a result, there is an urgent need to improve computer-assisted systems (CAD) that can serve as a second opinion for radiologists when they use medical images. A self-training learning method employing deep learning neural network with residual learning is proposed to overcome the issue of needing a large number of labeled images to train deep learning models in breast cancer histopathology image classification. The suggested model is built from scratch and trained.

COVID-19 classification using chest X-ray images: A framework of CNN-LSTM and improved max value moth flame optimization.

Coronavirus disease 2019 (COVID-19) is a highly contagious disease that has claimed the lives of millions of people worldwide in the last 2 years. Because of the disease's rapid spread, it is critical to diagnose it at an early stage in order to reduce the rate of spread. The images of the lungs are used to diagnose this infection. In the last 2 years, many studies have been introduced to help with the diagnosis of COVID-19 from chest X-Ray images. Because all researchers are looking for a quick method to diagnose this virus, deep learning-based computer controlled techniques are more suitable as a second opinion for radiologists. In this article, we look at the issue of multisource fusion and redundant features. We proposed a CNN-LSTM and improved max value features optimization framework for COVID-19 classification to address these issues. The original images are acquired and the contrast is increased using a combination of filtering algorithms in the proposed architecture. The dataset is then augmented to increase its size, which is then used to train two deep learning networks called Modified EfficientNet B0 and CNN-LSTM. Both networks are built from scratch and extract information from the deep layers. Following the extraction of features, the serial based maximum value fusion technique is proposed to combine the best information of both deep models. However, a few redundant information is also noted; therefore, an improved max value based moth flame optimization algorithm is proposed. Through this algorithm, the best features are selected and finally classified through machine learning classifiers. The experimental process was conducted on three publically available datasets and achieved improved accuracy than the existing techniques. Moreover, the classifiers based comparison is also conducted and the cubic support vector machine gives better accuracy.

1041 Impact of Spectral Contamination and Anatomical Location on Diagnostic Accuracy of Single-Voxel Spectroscopy in Pre-Operative Glioma Characterisation

Abstract Aim Single-voxel spectroscopy (SVS) discriminates amongst brain tumours by measuring metabolite variations reflective of malignant transformation. However, spectral contamination may affect diagnostic accuracy, impacting radiological opinion of regions of interest (ROI) required to determine extent of surgical resection. Method A single-centre, retrospective, observational study, including 93 SVS meaningfully interpretable examinations between 2012 and 2018. Accuracy of lesion classification was assessed by comparison of histological diagnosis as reference standards against discrimination between entities and tumour grades by SVS, with consideration for voxel location, volume, and proximity to the calvaria or ventricular system. Quantification of metabolites was recorded as ratios, and visual inspection, qualitative and descriptive analysis were undertaken. Results SVS demonstrated high entity accuracy (100%) but lower grade accuracy (87%), ranging from periventricular (73%) vs non-periventricular (90%), and subcalvarial (86%) vs subcortical (88%) voxels. CSF contamination caused metabolite dilution and/or enhanced lactate signals from sub-arachnoid or ventricular CSF, especially in areas of increased brain-skull distance; lipid contamination from peri-calvarial fat and subcutaneous tissue resulted in enhanced lipid signals neighbouring areas of increased brain-scalp distance. Misclassified lesions were located regions of increased cranial concavity, reduced cerebral cortical thickness and inappropriate outer volume suppression (OVS). Voxel volume variation had no impact across cerebral coordinates (p=0.605), calvaria proximity (p=0.127) or ventricular proximity (p=0.367). Conclusion Spectral contamination significantly impacted grade accuracy, with metabolite dilution and enhance lactate and lipids signals leading to radiological under- and overestimation of glioma grade, respectively, thus indicating that avoidance of contaminating tissues and OVS are pivotal to adequate spectral quality for diagnostic accuracy.

MfdcModel: A Novel Classification Model for Classification of Benign and Malignant Breast Tumors in Ultrasound Images

Automatic classification of benign and malignant breast ultrasound images is an important and challenging task to improve the efficiency and accuracy of clinical diagnosis of breast tumors and reduce the rate of missed and misdiagnosis. The task often requires a large amount of data to train. However, it is difficult to obtain medical images, which contradicts the large amount of data needed to obtain good diagnostic models for training. In this paper, a novel classification model for the classification of breast tumors is proposed to improve the performance of diagnosis models trained by small datasets. The method integrates three features from medical features extracted from segmented images, features selected from the pre-trained ResNet101 output by principal component analysis (PCA), and texture features. Among the medical features that are used to train the naive Bayes (NB) classifier, and the PCA-selected features are used to train the support vector machine (SVM) classifier. Subsequently, the final results of boosting are obtained by weighting the classifiers. A five-fold cross-validation experiment yields an average accuracy of 89.17%, an average precision of 90.00%, and an average AUC value of 0.95. According to the experimental results, the proposed method has better classification accuracy compared to the accuracy obtained by other models trained on only small datasets. This approach can serve as a reliable second opinion for radiologists, and it can also provide useful advice for junior radiologists who do not have sufficient clinical experience.

Deployment and validation of an AI system for detecting abnormal chest radiographs in clinical settings.

BackgroundThe purpose of this paper is to demonstrate a mechanism for deploying and validating an AI-based system for detecting abnormalities on chest X-ray scans at the Phu Tho General Hospital, Vietnam. We aim to investigate the performance of the system in real-world clinical settings and compare its effectiveness to the in-lab performance.MethodThe AI system was directly integrated into the Hospital's Picture Archiving and Communication System (PACS) after being trained on a fixed annotated dataset from other sources. The system's performance was prospectively measured by matching and comparing the AI results with the radiology reports of 6,285 chest X-ray examinations extracted from the Hospital Information System (HIS) over the last 2 months of 2020. The normal/abnormal status of a radiology report was determined by a set of rules and served as the ground truth.ResultsOur system achieves an F1 score—the harmonic average of the recall and the precision—of 0.653 (95% CI 0.635, 0.671) for detecting any abnormalities on chest X-rays. This corresponds to an accuracy of 79.6%, a sensitivity of 68.6%, and a specificity of 83.9%.ConclusionsComputer-Aided Diagnosis (CAD) systems for chest radiographs using artificial intelligence (AI) have recently shown great potential as a second opinion for radiologists. However, the performances of such systems were mostly evaluated on a fixed dataset in a retrospective manner and, thus, far from the real performances in clinical practice. Despite a significant drop from the in-lab performance, our result establishes a reasonable level of confidence in applying such a system in real-life situations.

Extract roi from mammogram images using morphological and k-means clustering

Cancer is leading cause of death in worldwide. Cancer cells damages all cells surrounded by it and hence covers the complete area of the body. Amongst women breast cancer is most common deadly disease as compared to men. In any type of cancer early-stage detection plays vital role as it can save the patient’s life. If cancer is diagnosed early by using breast self-examination (BSE) and clinical breast examination (CBE) at 40-49 years of age, the survival rate of breast cancer reaches 100 per cent. New strategies named CAD (computer-assisted diagnosis) programs for early detection utilizing multiple mammogram datasets, such as mini-mias, DDSM, etc., CAD (Computer Aided Diagnosis) systems are mostly used for the second opinion for radiologists. Many researchers are already developing different CAD systems. Different databases are available for the researchers to study and detect the cancerous tumors. In this paper Mini-Mias database is used. Mammogram images are low contrast and may contain noise. Filtering or pre-processing is required to remove any noises present in the image, here Weiner filter is used. Two methodologies are compared here. First using morphological operated segmentation and second is by using k-means clustering algorithm.

Assessing clinical applicability of COVID-19 detection in chest radiography with deep learning

The coronavirus disease 2019 (COVID-19) pandemic has impacted healthcare systems across the world. Chest radiography (CXR) can be used as a complementary method for diagnosing/following COVID-19 patients. However, experience level and workload of technicians and radiologists may affect the decision process. Recent studies suggest that deep learning can be used to assess CXRs, providing an important second opinion for radiologists and technicians in the decision process, and super-human performance in detection of COVID-19 has been reported in multiple studies. In this study, the clinical applicability of deep learning systems for COVID-19 screening was assessed by testing the performance of deep learning systems for the detection of COVID-19. Specifically, four datasets were used: (1) a collection of multiple public datasets (284.793 CXRs); (2) BIMCV dataset (16.631 CXRs); (3) COVIDGR (852 CXRs) and 4) a private dataset (6.361 CXRs). All datasets were collected retrospectively and consist of only frontal CXR views. A ResNet-18 was trained on each of the datasets for the detection of COVID-19. It is shown that a high dataset bias was present, leading to high performance in intradataset train-test scenarios (area under the curve 0.55–0.84 on the collection of public datasets). Significantly lower performances were obtained in interdataset train-test scenarios however (area under the curve > 0.98). A subset of the data was then assessed by radiologists for comparison to the automatic systems. Finetuning with radiologist annotations significantly increased performance across datasets (area under the curve 0.61–0.88) and improved the attention on clinical findings in positive COVID-19 CXRs. Nevertheless, tests on CXRs from different hospital services indicate that the screening performance of CXR and automatic systems is limited (area under the curve < 0.6 on emergency service CXRs). However, COVID-19 manifestations can be accurately detected when present, motivating the use of these tools for evaluating disease progression on mild to severe COVID-19 patients.

Multi-Class Breast Cancer Classification Using Ensemble of Pretrained models and Transfer Learning.

Early detection of breast cancer has reduced many deaths. Earlier CAD systems used to be the second opinion for radiologists and clinicians. Machine learning and deep learning have brought tremendous changes in medical diagnosis and imagining. Breast cancer is the most commonly occurring cancer in women and it is the second most common cancer overall. According to the 2018 statistics, there were over 2million cases all over the world. Belgium and Luxembourg have the highest rate of cancer. A method for breast cancer detection has been proposed using Ensemble learning. 2- class and 8-class classification is performed. To deal with imbalance classification, the authors have proposed an ensemble of pretrained models. 98.5% training accuracy and 89% of test accuracy are achieved on 8-class classification. Moreover, 99.1% and 98% train and test accuracy are achieved on 2 class classification. it is found that there are high misclassifications in class DC when compared to the other classes, this is due to the imbalance in the dataset. In the future, one can increase the size of the datasets or use different methods. In implement this research work, authors have used 2 Nvidia Tesla V100 GPU's in google cloud platform.

Renal inflammatory myofibroblastic tumour presenting with a large retroperitoneal abscess

Inflammatory myofibroblastic tumour arising from the kidney is a rare occurrence. In this case report, we present a rare case of inflammatory myofibroblastic tumour arising from the kidney diagnosed after the presentation with a large retroperitoneal abscess for the first time in literature. A 55-year-old woman with diabetes mellitus presented to us with painful lump in the left lumbar region of the back for 1-week duration. On examination, there was a firm, diffuse lump in the left lumbar region of the back. Her inflammatory markers were high, but the serum creatinine and blood urea were within the normal range. Abdominal ultrasonography showed a distorted left kidney with a heterogeneous mass consisting cystic and solid components measuring approximately 7 × 9 × 8 cm in size. A contrast-enhanced computed tomography scan showed an 11 × 9 × 9 cm-sized low-density mass posterior to the left kidney with multiple contrast-enhancing septations which appeared posterior to but separate from the left kidney within the left renal fascia. There was breeching of the Gerota’s fascia with extension posteriorly up to subcutaneous tissue. Since the radiological opinion was in favour of a complex perinephric abscess, open drainage of the abscess was performed after failed attempts of ultrasound-guided drainage. The biopsy of the abscess wall was suggestive of a renal cell carcinoma and radical nephrectomy was planned. Due to tumour invasion, the radical nephrectomy was combined with a splenectomy and the specimen sent for histology. It showed an inflammatory myofibroblastic tumour or pseudotumour with the periphery showing ulceration and abscess formation. The patient had an uneventful recovery following surgery. Thus, we report the first case of renal inflammatory myofibroblastic tumour presenting with a large retroperitoneal abscess extending to the subcutaneous tissue plane. Final diagnosis was made only after radical surgery which was curative.

SIRM\u2013SIC appropriateness criteria for the use of Cardiac Computed Tomography. Part 1: Congenital heart diseases, primary prevention, risk assessment before surgery, suspected CAD in\xa0symptomatic patients, plaque and epicardial adipose tissue characterization, and functional assessment of stenosis

In the past 20 years, Cardiac Computed Tomography (CCT) has become a pivotal technique for the noninvasive diagnostic work-up of coronary and cardiac diseases. Continuous technical and methodological improvements, combined with fast growing scientific evidence, have progressively expanded the clinical role of CCT. Recent large multicenter randomized clinical trials documented the high prognostic value of CCT and its capability to increase the cost-effectiveness of the management of patients with suspected CAD. In the meantime, CCT, initially perceived as a simple non-invasive technique for studying coronary anatomy, has transformed into a multiparametric “one-stop-shop” approach able to investigate the heart in a comprehensive way, including functional, structural and pathophysiological biomarkers. In this complex and revolutionary scenario, it is urgently needed to provide an updated guide for the appropriate use of CCT in different clinical settings. This manuscript, endorsed by the Italian Society of Medical and Interventional Radiology (SIRM) and by the Italian Society of Cardiology (SIC), represents the first of two consensus documents collecting the expert opinion of Radiologists and Cardiologists about current appropriate use of CCT.

Multi-class Classification of Thorax Diseases Using Neural Networks

Chest radiograph or chest X-ray (CXR) is one of the most conducted radiology examinations, it is also considered difficult to interpret. Increasing availability of imaging equipment has also increased the demand for highly trained staff. Doctors are absolutely good at putting forward the diagnosis, but some minor details can be overlooked. In this paper, deep learning method is used to predict Thorax disease categories using CXR and its metadata using Convolutional Neural Network (CNN) and MobileNets neural network architecture. The huge NIH (National Institutes of Health, Maryland) dataset of chest X-rays available is used for making the predictions of a multiclass image classification problem with 15 different labels. This model will provide a sanity test in the form of second opinion for radiologists and doctors to achieve more confidence in predicting accurate diagnosis. The accuracy of this model is appreciable as compared to the current practice of predicting diseases by radiologists. This paper successfully classifies different categories of Thorax disease with 94.68% accuracy, which concludes that this model trained with deep learning neural network has a real-world application and can be used in the prediction of Thorax diseases. The scope of this model would increase along with the accuracy when more data is collected from some other lab tests, clinical notes, or some other scans.

A deep cascade of ensemble of dual domain networks with gradient-based T1 assistance and perceptual refinement for fast MRI reconstruction.

Deep learning networks have shown promising results in fast magnetic resonance imaging (MRI) reconstruction. In our work, we develop deep networks to further improve the quantitative and the perceptual quality of reconstruction. To begin with, we propose reconsynergynet (RSN), a network that combines the complementary benefits of independently operating on both the image and the Fourier domain. For a single-coil acquisition, we introduce deep cascade RSN (DC-RSN), a cascade of RSN blocks interleaved with data fidelity (DF) units. Secondly, we improve the structure recovery of DC-RSN for T2 weighted Imaging (T2WI) through assistance of T1 weighted imaging (T1WI), a sequence with short acquisition time. T1 assistance is provided to DC-RSN through a gradient of log feature (GOLF) fusion. Furthermore, we propose perceptual refinement network (PRN) to refine the reconstructions for better visual information fidelity (VIF), a metric highly correlated to radiologist's opinion on the image quality. Lastly, for multi-coil acquisition, we propose variable splitting RSN (VS-RSN), a deep cascade of blocks, each block containing RSN, multi-coil DF unit, and a weighted average module. We extensively validate our models DC-RSN and VS-RSN for single-coil and multi-coil acquisitions and report the state-of-the-art performance. We obtain a SSIM of 0.768, 0.923, and 0.878 for knee single-coil-4x, multi-coil-4x, and multi-coil-8x in fastMRI, respectively. We also conduct experiments to demonstrate the efficacy of GOLF based T1 assistance and PRN.