Current Medical Imaging - Volume 15, Issue 6, 2019

Background: Huntington’s Disease is an irreversible neurodegenerative disease characterized by the progressive deterioration of specific brain nerve cells. The current evaluation of cellular and physiological events in patients with HD relies on the development of transgenic animal models. To explore such events in vivo, diffusion tensor imaging has been developed to examine the early macro and microstructural changes in brain tissue. However, the gap in diffusion tensor imaging findings between animal models and clinical studies and the lack of microstructural confirmation by histological methods has questioned the validity of this method. Objective: This review explores white and grey matter ultrastructural changes associated to diffusion tensor imaging, as well as similarities and differences between preclinical and clinical Huntington’s Disease studies. Methods: A comprehensive review of the literature using online-resources was performed (Pub- Med search). Results: Similar changes in fractional anisotropy as well as axial, radial and mean diffusivities were observed in white matter tracts across clinical and animal studies. However, comparative diffusion alterations in different grey matter structures were inconsistent between clinical and animal studies. Conclusion: Diffusion tensor imaging can be related to specific structural anomalies in specific cellular populations. However, some differences between animal and clinical studies could derive from the contrasting neuroanatomy or connectivity across species. Such differences should be considered before generalizing preclinical results into the clinical practice. Moreover, current limitations of this technique to accurately represent complex multicellular events at the single micro scale are real. Future work applying complex diffusion models should be considered.

Background: Studies on the influence of diabetes mellitus on the radiological presentation of pulmonary tuberculosis performed so far yielded inconsistent results. We aimed to summarize the relevant evidence on this topic systematically. Methods: We systematically searched PubMed/MEDLINE (1980–2016) and the references of related articles (English-language reports) for observational studies that compared the radiological presentation of pulmonary tuberculosis in diabetes and non-diabetes patients. Results: A total of fifteen studies that enrolled 2,020 diabetic patients and 5,280 controls were included in this systematic review. None of the included studies showed any significant difference in the upper lobe involvement and or in bilateral disease between diabetes and non-diabetes patients. However, lower lung field cavitary disease was found to be more frequent (relative risks ranging from 2.76, 95% CI 2.28-3.35 to 4.47, 95% CI 2.62-7.62) in patients with poor glycemic control (HbA1C >9%). Similarly, a significantly higher proportion of cavitary disease in diabetes patients was reported by 7 out of 15 studies, the meta-analysis of cavities of any size/site also showed the significantly higher risk of cavitary disease in diabetes patients (p-value = 0.0008). Three studies stratified the presence of cavities by diabetes control status, finding a higher proportion of cavities in uncontrolled diabetic patients (relative risks ranging from 1.85, 95%CI 1.34-2.55 to 3.59, 95%CI 2.53-5.11). One out of four studies found a significantly higher proportion of nodular infiltrations in diabetes versus non-diabetes patients. Conclusion: While there is no difference in localization of lung lesions between patients with diabetes and non-diabetes, our review found that the risk of cavitary disease is relatively higher in diabetes patients. It is essential for researchers to unify the criteria for diabetes diagnosis, patient selection, and radiographic severity and stratify the results by the potentially confounding factors.

Background: An accurate detection of tumor from the Magnetic Resonance Images (MRIs) is a critical and demanding task in medical image processing, due to the varying shape and structure of brain. So, different segmentation approaches such as manual, semi-automatic, and fully automatic are developed in the traditional works. Among them, the fully automatic segmentation techniques are increasingly used by the medical experts for an efficient disease diagnosis. But, it has the limitations of over segmentation, increased complexity, and time consumption. Objective: In order to solve these problems, this paper aims to develop an efficient segmentation and classification system by incorporating a novel image processing techniques. Methods: Here, the Distribution based Adaptive Median Filtering (DMAF) technique is employed for preprocessing the image. Then, skull removal is performed to extract the tumor portion from the filtered image. Further, the Neighborhood Differential Edge Detection (NDED) technique is implemented to cluster the tumor affected pixels, and it is segmented by the use of Intensity Variation Pattern Analysis (IVPA) technique. Finally, the normal and abnormal images are classified by using the Weighted Machine Learning (WML) technique. Results: During experiments, the results of the existing and proposed segmentation and classification techniques are evaluated based on different performance measures. To prove the superiority of the proposed technique, it is compared with the existing techniques. Conclusion: From the analysis, it is observed that the proposed IVPA-WML techniques provide the better results compared than the existing techniques.

Background: Osteoarthritis (OA) is a degenerative disease of joint cartilage affecting the elderly people around the world. Visualization and quantification of cartilage is very much essential for the assessment of OA and rehabilitation of the affected people. Magnetic Resonance Imaging (MRI) is the most widely used imaging modality in the treatment of knee joint diseases. But there are many challenges in proper visualization and quantification of articular cartilage using MRI. Volume rendering and 3D visualization can provide an overview of anatomy and disease condition of knee joint. In this work, cartilage is segmented from knee joint MRI, visualized in 3D using Volume of Interest (VOI) approach. Methods: Visualization of cartilage helps in the assessment of cartilage degradation in diseased knee joints. Cartilage thickness and volume were quantified using image processing techniques in OA affected knee joints. Statistical analysis is carried out on processed data set consisting of 110 of knee joints which include male (56) and female (54) of normal (22) and different stages of OA (88). The differences in thickness and volume of cartilage were observed in cartilage in groups based on age, gender and BMI in normal and progressive OA knee joints. Results: The results show that size and volume of cartilage are found to be significantly low in OA as compared to normal knee joints. The cartilage thickness and volume is significantly low for people with age 50 years and above and Body Mass Index (BMI) equal and greater than 25. Cartilage volume correlates with the progression of the disease and can be used for the evaluation of the response to therapies. Conclusion: The developed methods can be used as helping tool in the assessment of cartilage degradation in OA affected knee joint patients and treatment planning.

Introduction: Radiotherapy after Breast-Conserving Surgery (BCS) is a standard treatment for breast cancer. Currently, surgical clips are used to determine the tumour bed before radiotherapy planning. This study aimed to evaluate the migration of these clips on mammograms. Methods: The study was conducted on 121 females who were treated with radiotherapy after BCS at their first radiologic control examination 6 months after the end of treatment. MLO and CC views of all cases were evaluated regarding the clips. The distance between the surgical scar centre and the centre of the area covered by the clips was measured on both MLO and CC projections and recorded separately. This distance was determined as the clip displacement. A displacement ≤10 mm was recorded as no displacement. Results: The clips were out of the images and were not evaluated in 45 cases (37.2%) on CC and in 9 cases (7.4%) on MLO projections. There were no clip displacements in 37 (30.6%) cases on CC and in 43 (35.5%) cases on MLO views. The amount of displacement ranged from 11 to 56 mm with a mean of 24.38 mm on CC views, while on MLO projections, displacement ranged from 11 to 66 mm with a mean of 24.42 mm. Conclusion: A clip displacement of greater than 10 mm was found in 64.5% of cases on MLO views. Therefore, we believe that the reliability of these clips for accurate delineation of the tumour bed in radiotherapy planning is controversial and other methods must be added.

Background: Schwannomas are benign slow-growing tumors most often associated with the cranial nerves. Schwannomas often originate from the eighth cranial nerve. They may also originate from the peripheral nervous system of the neck and extremities. However extracranial peripheral schwannomas are considered a rare entity. Objectives: The knowledge of rare localizations and typical imaging findings will lead to a successfulradiological diagnosis. Therefore, in this study, we present the clinical findings and MRI characteristics of schwannomas with a rare localization involving the peripheral, lower and upper extremity and intramuscular regions. Materials and Methods: The hospital database was screened for patients with an extracranial soft tissue mass. Twenty-one cases of schwannomas were found in rare localization. We analyzed the MR images of these patients retrospectively. The MR images were evaluated in terms of tumor location, signal intensity, and enhancement pattern. The histological examination of all the patients confirmed the diagnosis of schwannoma. Results: In 21 patients, the schwannomas were peripheral, localized to upper (n = 6) and lower extremities (n = 11). The remaining four patients had intramuscular schwannomas. The patients diagnosed with intramuscular schwannomas had schwannomas in sternocleidomastoid, gastrocnemius, triceps muscle and lateral wall of the abdomen. The average long-axis diameter of the tumor was 27.7 mm and the average short-axis diameter was 16.4 mm. The contrast pattern was diffused in eight tumors and peripheral in 13. Conclusion: In this study, we present clinical findings and MRI characteristics of schwannomas with a rare localization involving the peripheral, lower and upper extremity and intramuscular regions.

Objective: To investigate the characteristics of aortic elasticity in type 1 diabetes mellitus (DM1) patients with hypertension using dual-source computed tomography (DSCT). Methods: Subjects were divided into four groups: 31 with hypertension (EH group), 28 with DM1 (DM1 group), 30 with hypertension and DM1 (DM1 + EH group), and 30 healthy volunteers (control group, CG). ECG-gated DSCT was used to measure the cross-sectional area of the aorta in diastole and systole and calculate the aortic elasticity (D). Analysis of variance and multiple linear regression were used for statistical analysis. Results: Compared with the CG, aortic elasticity (D) in the DM1 group patients was not significantly decreased. Compared with the EH group, the DM1 + EH group showed no significant change. The aortic elasticity (D) in the EH and DM1 + EH groups was significantly lower than that in the CG and DM1 group. Conclusion: DM1 slightly affects the aortic elasticity of the descending aorta, whereas hypertension exerts the main effect.

Objective: The aim of this study was to compare images from Tc-99m MDP bone scan (BS) and Ga-68 PSMA PET/CT of patients with prostate cancer in terms of bone metastases. Methods: Overall, 34 patients exhibited a mean age of 66 ± 9.5 (50-88) years, mean PSA of 51 ± 159ng/ml (0-912), and mean Gleason score of 8 (6-9). BS and Ga-68 PSMA PET/CT were applied to 34 patients within 30 days, and the results were evaluated, retrospectively. In both tests, radiopharmaceutical uptake in bones were compared. Results: In 7 patients (20.5%), uptake was not significant on BS and Ga-68 PSMA PET / CT images, which is related to metastasis. In 14 (41%) patients, bone metastases were observed in both examinations. However, more metastatic lesions were observed in the Ga-68 PSMA PET/CT of 3 patients and in the bone scintigraphy of 2 patients. PSMA expression was not observed on Ga-68 PSMA PET / CT in 13 (38%) patients with increased activity in bone scintigraphy. Two (6%) of these patients were thought to be metastatic, 2 (6%) were suspicious for metastasis, and 9 (26%) had no metastasis. When a lesion-based evaluation was performed, a total of 480 activities were evaluated: increased activity uptake was found in 305 BS, and 427 PSMA expression activity was detected. Furthermore, 435 of these activities were evaluated as metastatic. Conclusion: Ga-68 PSMA PET/CT provides an additional contribution to the BS evaluation of activity areas because of the presence of PSMA expression and anatomical lesions. In 6% of the patients, activity on BS and metastatic appearance in CT images were observed and the presence of lesions in the absence of PSMA was determined. This suggests that bone metastases without PSMA expression may also be present.

Background: Brain tumor is the leading cause of death worldwide. It is obvious that the chances of survival can be increased if the tumor is identified and properly classified at an initial stage. MRI (Magnetic Resonance Imaging) is one source of brain tumors detection tool and is extensively used in the diagnosis of brain to detect blood clots. In the past, many researchers developed Computer-Aided Diagnosis (CAD) systems that help the radiologist to detect the abnormalities in an efficient manner. Objective: The aim of this research is to improve the brain tumor detection performance by proposing a multimodal feature extracting strategy and employing machine learning techniques. Methods: In this study, we extracted multimodal features such as texture, morphological, entropybased, Scale Invariant Feature Transform (SIFT), and Elliptic Fourier Descriptors (EFDs) from brain tumor imaging database. The tumor was detected using robust machine learning techniques such as Support Vector Machine (SVM) with kernels: polynomial, Radial Base Function (RBF), Gaussian; Decision Tree (DT), and Naïve Bayes. Most commonly used Jack-knife 10-fold Cross- Validation (CV) was used for testing and validation of dataset. Results: The performance was evaluated in terms of specificity, sensitivity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), False Positive Rate (FPR), Total Accuracy (TA), Area under the receiver operating Curve (AUC), and P-value. The highest performance of 100% in terms of Specificity, Sensitivity, PPV, NPV, TA, AUC using Naïve Bayes classifiers based on entropy, morphological, SIFT and texture features followed by Decision Tree classifier with texture features (TA=97.81%, AUC=1.0) and SVM polynomial kernel with texture features (TA=94.63%). The highest significant p-value was obtained using SVM polynomial with texture features (P-value 2.65e-104) followed by SVM RB with texture features (P-value 1.96e-98). Conclusion: The results reveal that Naïve Bayes followed by Decision Tree gives highest detection accuracy based on entropy, morphological, SIFT and texture features.

Background: High-pressure water jet injuries are an uncommon cause of trauma. Highpressure water injuries are usually seen at the extremities and less often in the thorax and abdominal areas. A review of the literature reveals a few case reports describing intraabdominal injuries due to penetrating trauma. İn this paper, we present the radiological findings of a rare case of abdominal wall injury without penetration of peritoneum caused by high-pressure water jet due to the Coanda effect. The Coanda effect is the tendency of a fluid jet to stay attached to a curved. In our case, the peritoneum also functions as flat and curved surface, causing non-penetrant abdominal wall injury. Case Report: A 28-year-old male working at a hydroelectric terminal presented to the emergency department with abdominal pain and a two-centimeter cutaneous gap in the left lower quadrant of the abdomen caused by high-pressure water. The CT findings revealed a small entrance to left lower quadrant of the abdominal wall and extensive subcutaneous injury. There was no internal injury. The cutaneous gap was managed by primary surgical closure. Subcutaneous emphysema was diminished on the follow up physical examination. The patient was recovered without sequelae. Conclusion: The water jet can cause a wide subcutaneous injury by following the peritoneum even though there is a small entrance. Therefore radiological imaging can help to determine their severity and planning treatment.