The effect of cobalt oxide nanoparticles on improving the quality of CT and PET scan medical imaging

COLORECTAL CANCER: Radiologic Staging

Pulse-coupled neural networks are a subpart of deep learning (DL) methodologies, which has vast number of applications. One of the preferred applications is multimodal medical image fusion, where different modality images such as MR scan images, CT scan images, and PET scan images are needed to be fused in one of the combinations to provide promising results to diagnose the abnormalities that were unable to detect in the individual images. When accomplishment a precise diagnosis or anatomy of the body, one type of medical imaging modality may not be adequate, imposing the fusion of images from several medical imaging modalities in order to deliver a final result in the immense popular of medical applications. In this work, MRI and PET scan images are fused by means of PCNN and shearlet transformation to yield better results.

Deep Inspiration Breath Hold [18F]FDG PET-CT on 4-rings scanners in evaluating lung lesions: Evidences from a phantom and a clinical study

Deep Inspiration Breath Hold [18F]FDG PET-CT on 4-rings scanners in evaluating lung lesions: Evidences from a phantom and a clinical study

Segmentation is one of the most significant parts of medical image processing. In image segmentation, the digital image is part of multiple sets of pixels. Magnetic Resonance Imaging, MRI and CT scanning is very important imaging techniques to explore the inner physiological constructions of the body noninvasively. A bone tumor is one of more life-threatening diseases, so exact detaching of the tumor regions is a pressing need. In this work, the K-means algorithm is employed on six MRI and CT scan images with different numbers of clusters. As well as many morphological operations like opening and dilation were applied after extract the fine tumor areas effectively. The results and the calculated surface areas of the separated tumor regions were compared to the radiologist delineation and the percent relative differences were found ranged from (0.63-1.75) % for MRI images and (0.34-1.51) % for CT scan images. This result indicates the high-quality performance of the adopted segmentation clustering-based method.

Brain cancer is one of the most deadly forms of cancer today, and its timely and accurate diagnosis can significantly impact the patient’s quality of life. A computerized tomography scan (CT) and magnetic resonance imaging (MRI) of the brain is required to diagnose this condition. In the past, several methods have been proposed as a means of diagnosing brain tumors through the use of medical images. However, due to the similarity between tumor tissue and other brain tissues, these methods have not proven to be accurate. A novel method for diagnosing brain tumors using MRI and CT scan images is presented in this paper. An architecture based on deep learning is used to extract the distinguishing characteristics of brain tissue from tumors. The use of fusion images allows for more accurate detection of tumor types. In comparison with other approaches, the proposed method has demonstrated superior results.

In order to explore the diagnostic value of spiral CT and magnetic resonance imaging scanning in gastric cancer and precancerous lesions, the author selected 56 gastric cancer patients treated in a medical center (group) as the experimental subjects, and all patients underwent MRI, multislice spiral CT scan, and enhanced CT scan two weeks before surgery; at the same time, the gastric cancer staging results of patients were diagnosed according to surgical pathology. All patients were examined under the condition of knowledge and performed breath-holding exercise before examination. The status, location, and extent of tumor lesions were evaluated. In comparison of T staging of gastric cancer patients with magnetic resonance scanning imaging and postoperative pathological examination results, among them, T staging of 3 patients did not match the results of pathological examination, the accuracy rates of T staging on magnetic resonance scanning imaging were T1 = 94.7%, T2 = 87.6%, T3 = 91.2%, and T4 = 94.7%, and the total accuracy was 92.1%. Comparison of helical CT scan imaging and postoperative pathological examination results is as follows. Among them, T staging of 8 patients did not match the results of pathological examination, 3 patients were ulcerative, 5 were protuberance, the accuracy rates of T staging in spiral CT scan imaging were T1 = 90.8%, T2 = 82.2%, T3 = 76.9%, and T4 = 87.6%, and the total accuracy was 84.5%. The total accuracy rate of imaging N staging was 77.5%, and the total accuracy rate of spiral CT scanning imaging N staging was 80.8%. The accuracy rates of M0 and M1 staging in magnetic resonance imaging were 100.0% and 96.5%, respectively, and the accuracy rates of M0 and M1 staging in spiral CT scan imaging were 96.5% and 96.5%, respectively. The accuracy rate of magnetic resonance imaging for the diagnosis of gastric cancer N staging was 77.4%, and the reason for the low accuracy rate was mainly because the enhancement effect was not obvious due to insufficient enhancement. The accuracy rate of spiral CT in diagnosing N staging of gastric cancer was 80.9%, which was mainly due to the small diameter of the middle celiac artery lymph nodes. Magnetic resonance and spiral CT scans have high value in the early diagnosis and staging of gastric cancer.

VGG-COVIDNet: A Novel model for COVID detection from X-Ray and CT Scan images

Liver cancer being one of the most severe categories of cancerous diseases has lead to the death of numerous people. Images of Liver tumor by Computed Tomography (CT scan) have much noise in it, because of which diagnoses of tumor level becomes difficult. Changes in anatomy differ for different patients. This makes automatic recognition of tumor using CT scan images very challenging. To overcome this problem, in this paper, detection of liver cancer is done using fused images of CT scan and Magnetic Resonance Imaging (MRI) that plays a crucial role in the choice of different strategies for liver disease and also for treatment monitoring. This research article proposes a design for early detection and classification of liver cancer which consists of discrete wavelet transform image fusion technique, extraction of Speeding up robust features, feature selection using Cuckoo meta-heuristic approach and machine learning algorithms from contrast enhanced CT and MRI images. The proposed framework is fully automated which requires no user interaction.

Initially, the coronavirus infection has been diagnosed by using the Chest CT scan and x-ray images of the patients. An accurate representation of the victim’s respiratory system allows the medical practitioners to detect the covid-19 infection. The first step of the proposed approach is to preprocess the image in order to eliminate any undesirable noise that may be present in medical images. Following that, the intended features are retrieved from a processed image. Finally, Transfer Learning is used to categorize the data. The CT scan based representations are separated by using a U-net simulation, and the split representation is then used to train and analyze the data by using the v3 simulator, which helps to differentiate the coronavirus infection and pneumonia infection and securely protect the resulting documents.

7214 Background: Small cell lung cancer remains a major health concern. Prognosis, survival, and treatment options correlate with stage at diagnosis. We hypothesize PET scanning may have a role in the staging procedure of patients with small cell lung cancer. Methods: A retrospective chart review of all patients between 1998–2003 diagnosed with small cell lung cancer was performed. Those who received a PET scan as part of the initial evaluation were reviewed and comparison was made against CT scanning and MRI imaging. Follow-up imaging and clinical outcome were used to evaluate any areas of disagreement between imaging. Results: A total of 116 new patients with small cell lung cancer were reviewed. 44 patients (44/116) received a PET scan. Sensitivity of PET imaging was 100%. Agreement between PET imaging and standard imaging for limited and extensive stage disease was 80% (35/44). Five patients were diagnosed with limited stage disease instead of extensive disease based on PET results. CT scan suggested adrenal metastasis in 4 patients, and opposite lung lesions in another, but the PET scan was not hypermetabolic. All but one (who was lost to follow-up) were treated as limited stage disease with concurrent radiation and chemotherapy. The four who remain have had survival and response suggestive of limited stage disease (alive at 6 months, 18 months, 32 months, and 3 years). Two patients were increased from limited to extensive stage by PET scan results, one survived 9 months and the other is 5 months into therapy. One patient had a biopsy proven false positive PET finding of lymphoid hyperplasia in the nasopharynx that would have increased the stage to extensive. PET scan missed diffuse liver metastases in one patient (false negative), who was staged with limited disease. PET scan was insensitive to detect brain metastasis (40% sensitive), although this did not change the patients' stage. Conclusions: As in non-small cell lung cancer, changes in the adrenal glands seen on CT scan may be difficult to interpret. These results suggest PET can clarify incidental findings of CT scan imaging. A PET scan may be helpful to insure patients with small cell lung cancer are accurately staged and offered appropriate treatment. No significant financial relationships to disclose.

7214 Background: Small cell lung cancer remains a major health concern. Prognosis, survival, and treatment options correlate with stage at diagnosis. We hypothesize PET scanning may have a role in the staging procedure of patients with small cell lung cancer. Methods: A retrospective chart review of all patients between 1998–2003 diagnosed with small cell lung cancer was performed. Those who received a PET scan as part of the initial evaluation were reviewed and comparison was made against CT scanning and MRI imaging. Follow-up imaging and clinical outcome were used to evaluate any areas of disagreement between imaging. Results: A total of 116 new patients with small cell lung cancer were reviewed. 44 patients (44/116) received a PET scan. Sensitivity of PET imaging was 100%. Agreement between PET imaging and standard imaging for limited and extensive stage disease was 80% (35/44). Five patients were diagnosed with limited stage disease instead of extensive disease based on PET results. CT scan suggested adrenal metastasis in 4 patients, and opposite lung lesions in another, but the PET scan was not hypermetabolic. All but one (who was lost to follow-up) were treated as limited stage disease with concurrent radiation and chemotherapy. The four who remain have had survival and response suggestive of limited stage disease (alive at 6 months, 18 months, 32 months, and 3 years). Two patients were increased from limited to extensive stage by PET scan results, one survived 9 months and the other is 5 months into therapy. One patient had a biopsy proven false positive PET finding of lymphoid hyperplasia in the nasopharynx that would have increased the stage to extensive. PET scan missed diffuse liver metastases in one patient (false negative), who was staged with limited disease. PET scan was insensitive to detect brain metastasis (40% sensitive), although this did not change the patients' stage. Conclusions: As in non-small cell lung cancer, changes in the adrenal glands seen on CT scan may be difficult to interpret. These results suggest PET can clarify incidental findings of CT scan imaging. A PET scan may be helpful to insure patients with small cell lung cancer are accurately staged and offered appropriate treatment. No significant financial relationships to disclose.

Contrast EUS Versus EUS Sono-Elastography in the Differentiation of Atypical Pancreatic Masses

Introduction: The novel coronavirus (COVID-19) originated in Wuhan, China, in December 2019. To date, the virus has infected more than 110 million people worldwide and claimed 2.5 million lives. With the rapid increase in the number of infected cases, some countries face a shortage of testing resources. Computational methods such as deep learning algorithms can help in such a situation to expedite and automate the diagnosis of COVID-19. Methods: In this research, we trained eight convolutional neural network models to automatically detect and diagnose COVID-19 from medical imaging, including X-ray and CT scan images. Those deep learning networks have a predefined structure in which we re-train on medical images to serve our purpose, which is called transfer learning. Results: We used two different medical images known as X-ray and CT scan. The experimental results show that CT scan achieved better performance than X-ray. Specifically, the Xception network model has achieved an overall performance on CT scan of 84%, 91%, and 77% for accuracy, sensitivity, and specificity, respectively. That was the highest in all models that we trained. On the other hand, the same network model (Xception) was applied on X-ray and performed 69%, 83%, and 55% for accuracy, sensitivity, and specificity, respectively. Conclusion: The performance of our proposed model to detect COVID-19 from CT scan is acceptable and promising to start in the field. We target the medical sectors to help them by providing rapid and accurate diagnosis of COVID-19 cases using an alternative detection approach to the traditional ones.

Medical images play a crucial role in the diagnosis of diseases. To make the diagnosis more accurate, the image should usually be enhanced first using image processing methods such as segmentation and edge detection stages. However, the complexity and noise that may arise in these images pose challenges in edge detection. Therefore, to portray the characteristics of edge detection operators, this research presents a systematic literature review of the performance of various edge detection operators in medical images, focusing on literature published between 2019 and 2023. After the selection process, 41 papers out of the initial 112 collected papers were chosen for further review. The study evaluates edge detection operators e.g., Canny, Sobel, Prewitt, Roberts, and Laplacian of Gaussian (LOG) on medical images such as X-rays, MRI, CT scans, ultrasound, Pap smears, and others. In the analysis, the accuracy, computational time, and response to noise of each operator are compared. The results indicate that despite longer computational times, Canny emerges as the most accurate operator, especially in Pap smear and CT scan images. The LOG operator offers high accuracy in MRI images with more efficient computational time. Evaluation of operator reliability against noise confirms the superiority of Canny. Furthermore, the future potential of edge detection in medical services is also reviewed. For instance, Canny, known for accurate and noise-resistant edges, enhances detection in complex CT-Scan and X-ray images. Meanwhile, LOG, handling artifacts with lower computational time, improves edge clarity in medical images. Potential applications include enhanced diagnosis, efficient patient monitoring, and improved image clarity in future medical services.