Current Medical Imaging - Volume 16, Issue 1, 2020

Background: Automatic diagnostic systems in medical imaging provide useful information to support radiologists and other relevant experts. The systems that help radiologists in their analysis and diagnosis appear to be increasing. Discussion: Knee joints are intensively studied structures, as well. In this review, studies that automatically segment meniscal structures from the knee joint MR images and detect tears have been investigated. Some of the studies in the literature merely perform meniscus segmentation, while others include classification procedures that detect both meniscus segmentation and anomalies on menisci. The studies performed on the meniscus were categorized according to the methods they used. The methods used and the results obtained from such studies were analyzed along with their drawbacks, and the aspects to be developed were also emphasized. Conclusion: The work that has been done in this area can effectively support the decisions that will be made by radiology and orthopedics specialists. Furthermore, these operations, which were performed manually on MR images, can be performed in a shorter time with the help of computeraided systems, which enables early diagnosis and treatment.

Background: Lung cancer has become a major cause of cancer-related deaths. Detection of potentially malignant lung nodules is essential for the early diagnosis and clinical management of lung cancer. In clinical practice, the interpretation of Computed Tomography (CT) images is challenging for radiologists due to a large number of cases. There is a high rate of false positives in the manual findings. Computer aided detection system (CAD) and computer aided diagnosis systems (CADx) enhance the radiologists in accurately delineating the lung nodules. Objectives: The objective is to analyze CAD and CADx systems for lung nodule detection. It is necessary to review the various techniques followed in CAD and CADx systems proposed and implemented by various research persons. This study aims at analyzing the recent application of various concepts in computer science to each stage of CAD and CADx. Methods: This review paper is special in its own kind because it analyses the various techniques proposed by different eminent researchers in noise removal, contrast enhancement, thorax removal, lung segmentation, bone suppression, segmentation of trachea, classification of nodule and nonnodule and final classification of benign and malignant nodules. Results: A comparison of the performance of different techniques implemented by various researchers for the classification of nodule and non-nodule has been tabulated in the paper. Conclusion: The findings of this review paper will definitely prove to be useful to the research community working on automation of lung nodule detection.

Background: In this study, we used a convolutional neural network (CNN) to classify Alzheimer’s disease (AD), mild cognitive impairment (MCI), and normal control (NC) subjects based on images of the hippocampus region extracted from magnetic resonance (MR) images of the brain. Methods: The datasets used in this study were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). To segment the hippocampal region automatically, the patient brain MR images were matched to the International Consortium for Brain Mapping template (ICBM) using 3D-Slicer software. Using prior knowledge and anatomical annotation label information, the hippocampal region was automatically extracted from the brain MR images. Results: The area of the hippocampus in each image was preprocessed using local entropy minimization with a bi-cubic spline model (LEMS) by an inhomogeneity intensity correction method. To train the CNN model, we separated the dataset into three groups, namely AD/NC, AD/MCI, and MCI/NC. The prediction model achieved an accuracy of 92.3% for AD/NC, 85.6% for AD/MCI, and 78.1% for MCI/NC. Conclusion: The results of this study were compared to those of previous studies, and summarized and analyzed to facilitate more flexible analyses based on additional experiments. The classification accuracy obtained by the proposed method is highly accurate. These findings suggest that this approach is efficient and may be a promising strategy to obtain good AD, MCI and NC classification performance using small patch images of hippocampus instead of whole slide images.

Background: In this paper, a method for adaptive Pure Interpolation (PI) in the frequency domain, with gradient auto-regularization, is proposed. Methods: The input image is transformed into the frequency domain and convolved with the Fourier Transform (FT) of a 2D sampling array (interpolation kernel) of initial size L × M. The Inverse Fourier Transform (IFT) is applied to the output coefficients and the edges are detected and counted. To get a denser kernel, the sampling array is interpolated in the frequency domain and convolved again with the transform coefficients of the original image of low resolution and transformed back into the spatial domain. The process is repeated until a maximum number of edges is reached in the output image, indicating that a locally optimal magnification factor has been attained. Finally, a maximum ascend–descend gradient auto-regularization method is designed and the edges are sharpened. Results: For the gradient management, a new strategy is proposed, referred to as the Natural bi- Directional Gradient Field (NBGF). It uses a natural following of a pair of directional and orthogonal gradient fields. Conclusion: The proposed procedure is comparable to novel algorithms reported in the state of the art with good results for high scales of amplification.

Background: Skin cancer is one of the most common forms of cancers among humans. It can be classified as non-melanoma and melanoma. Although melanomas are less common than non-melanomas, the former is the most common cause of mortality. Therefore, it becomes necessary to develop a Computer-aided Diagnosis (CAD) aiming to detect this kind of lesion and enable the diagnosis of the disease at an early stage in order to augment the patient’s survival likelihood. Aims: This paper aims to develop a simple method capable of detecting and classifying skin lesions using dermoscopy images based on ABCD rules. Methods: The proposed approach follows four steps. 1) The preprocessing stage consists of filtering and contrast enhancing algorithms. 2) The segmentation stage aims at detecting the lesion. 3) The feature extraction stage based on the calculation of the four parameters which are asymmetry, border irregularity, color and diameter. 4) The classification stage based on the summation of the four extracted parameters multiplied by their weights yields the total dermoscopy value (TDV); hence, the lesion is classified into benign, suspicious or malignant. The proposed approach is implemented in the MATLAB environment and the experiment is based on PH2 database containing suspicious melanoma skin cancer. Results and Conclusion: Based on the experiment, the accuracy of the developed approach is 90%, which reflects its reliability.

Introduction: Positron-emission tomography (PET)/computerized tomography (CT) with 18F-fludeoxyglucose (FDG) has been come into use for risk assessment of Hodgkin lymphoma (HL) patients in recent years. The aim of our study is to evaluate the reliability of interim PET results according to Deauville score (DS), and also to compared PET findings with tumor reduction on CT. Methods: Forty-two HL patients (median 39, range 19-75 y, 27 M, 15 F) were retrospectively evaluated with pre, interim and post-treatment PET/CT imaging. PET/CT imaging was obtained 60 min after the intravenous administration of 3.7-5.2 MBq/kg 18F-FDG. Results: The negative predictive value of the interim PET was 89%. Four (10.5%) of the 38 interim PET-negative patients became post-treatment PET-positive. According to CT, 15 patients were in complete remission (CR), 27 (64.6%) patients were in partial remission (PR) or stable disease (SD). Conclusion: The negative predictive value of interim PET was not satisfactory considering the treatment rate of over 80% of HL. Additionally, high rate of interim PET-negative patients’ conversion to PET-positive post-treatment state was considered as unexpected.

Background: Esophageal motility studies are performed in patients who have dysphagia that is not explained by stenosis. Diagnosis can be challenging and requires expertise in the interpretation of tests and symptoms. Aims: Our aim is to investigate the diagnostic value of videofluoroscopic swallowing study (VFSS) in combination with esophageal manometry. Study Design: This study has a prospective study design. Methods: 73 patients with dysphagia underwent videofluoroscopy in a standing position. Each subject swallowed barium boluses and findings were correlated with manometry findings. Results: The study cohort was categorized into five groups according to their disease as achalasia (31.1%), presbyesophagus (4.1%), scleroderma (5.5%), neurogenic dysphagia (6.8%), and other diseases (54.4%), which included gastroesophageal reflux, diffuse esophageal spasm, cricopharyngeal achalasia, and diseases with nonspecific VFSS patterns. When evaluating VFSS, the perfect agreement was observed between two observers in the final diagnosis. (kappa: 0.91, p<0,001). Conclusion: Although it does not replace manometry, VFSS is important as an additional useful imaging method in EMDs.

Background: Soft tissue attenuation artifacts are the most common cause of misinterpretation in myocardial perfusion Imaging (MPI). Few studies assessing the value of prone imaging in women have been published. Breast attenuation artifacts can be present in up to 40% of the MPI studies in women. Objectives: This study aimed at evaluating the potential impact of prone MPI on breast attenuation, with a critical analysis of activity optimization and breast size influence. Methods: MPI of an Anthropomorphic Torso Phantom with silicone breast prostheses and equivalent adipose tissue was compared to a standard MPI database. Results: A medical qualitative and semiquantitative analysis demonstrated higher uptake in the LV anterior segments in the prone position for all injected activities. An artificial myocardium lesion was diagnosable in the right segment in all images, which shows that prone positioning would not mask a true lesion and it assists the cardiologist with a more accurate analysis. These results showed that it is possible to optimize the activity to be injected by up to 55.6% when using combined supine-prone images. Conclusion: Prone position has a high impact on the interpretation of MPI in female patients since it reduces the breast attenuation artifacts, and optimizes the radiation protection of the patient and all staff involved in the procedure, making it more cost-effective.

Background: Our aim was to detect the role of radiological abdominal fat parameters by tomography and stone density by plain X-ray on extracorporeal Shock Wave Lithotripsy (SWL) stone-free rate. Methods: The patients who had undergone SWL for a single opaque renal stone < 2 cm in diameter and proximal ureteric stone < 1 cm in diameter were collected retrospectively. The characteristics of patients and stones were recorded. The stone attitude, HU, abdominal fat parameters, paraperirenal fat area, perirenal infiltration and severity of hydronephrosis with pre-treatment Non- Contrast Computed Tomography (NCCT) and stone density with radiography were evaluated by a radiologist. Four weeks after the last SWL; all patients were evaluated by plain X-ray and categorized as Stone Free (SF) and Residual Fragment (RF) group. Results: 51 patients with renal stones and 88 patients with proximal ureteral stones were included in the study. 24 (47%) and 63 (71%) patients were classified as SFfor renal and ureteral stones respectively. Only stone size was an independent predictor for stone-free rates after SWL for renal and proximal ureteral stones on multivariate analysis. The Receiver Operating Characteristic (ROC) curves for renal calculi revealed that creatinine level, stone size, stone attitude, Houns-Field Unit (HU) and Skin-to-Stone Distance (SSD) were the predictive factors for the SWL outcome (p< 0.05). The ROC curve for ureteral calculi demonstrated that HU, stone size and attitude were the predictive factors (p< 0.05). Conclusion: All abdominal fat parameters and the stone density were not related to SWL failure. A large follow-up with more patients is essential to confirm the role of radiological parameters on the outcome of SWL.

Background: Wandering spleen (WS) is a rare clinical condition which may cause fatal complication like torsion with subsequent infarction. Determination of splenic parenchyma viability is very important in deciding whether splenopexy rather than splenectomy is an option. Contrast- enhanced computed tomography (CECT) is important for the diagnosis of WS and assessment of the viability of spleen. Discussion: We reviewed the CT studies of four cases with WS. We measured the mean splenic and liver density and calculated liver-to-spleen attenuation ratio (LSAR). We also assessed the CT findings for each patient. Mean splenic density was measured as 40.77 Hounsfield Unit (HU) in cases with infarction, 127.1 HU in case without infarction. LSAR was calculated as 2.55 in cases with infarction, 0.99 in case without infarction. We detected whirlpool sign, intraperitoneal free fluid, splenic arterial enhancement in all patient, parenchymal and splenic vein enhancement in one patient without infarction, fat rim sign in three patients with infarction, capsular rim sign in one patient with infarction. Conclusion: CECT should be obtained for the diagnosis of WS and assessment of the viability of spleen. CECT could suggest the diagnosis of infarction of the spleen with following findings; absence of parenchymal enhancement, very low density of spleen (<45 HU), and LSAR which is greater than 2.