Current Medical Imaging - Volume 19, Issue 10, 2023

Background: For almost three decades, computed tomography (CT) has been extensively used in medical diagnosis, which led researchers to conduct linking of CT dose exposure with image quality. Methods: In this study, a systematic review and a meta-analysis study were conducted on CT phantom for resolution study especially based on the low contrast detectability (LCD). Furthermore, the association between the CT parameter such as tube voltage and the type of reconstruction algorithm, the amount of phantom scanning affecting the image quality and the exposure dose were also investigated in this study. We utilize PubMed, ScienceDirect, Google Scholar and Scopus databases to search related published articles from the year 2011 until 2020. The notable keywords comprise “computed tomography”, “CT phantom”, and “low contrast detectability”. Of 52 articles, 20 articles are within the inclusion criteria in this systematic review. Results: The dichotomous outcomes were chosen to represent the results in terms of risk ratio as per meta-analysis study. Notably, the noise in iterative reconstruction (IR) reduced by 24%, 33% and 36% with the use of smooth, medium and sharp filters, respectively. Furthermore, adaptive iterative dose reduction (AIDR 3D) improved image quality and the visibility of smaller less dense objects compared to filtered back-projection. Most of the researchers used 120 kVp tube voltage to scan phantom for quality assurance study. Conclusion: Hence, optimizing primary factors such as tube potential reduces the dose exposure significantly, and the optimized IR technique could substantially reduce the radiation dose while maintaining the image quality.

Background: Liver and tumor segmentation from CT images is a complex and crucial step in achieving full-course adaptive radiotherapy and also plays an essential role in computer-aided clinical diagnosis systems. Deep learning-based methods play an important role in achieving automatic segmentation. Objective: This research aims to improve liver tumor detection performance by proposing a dual path feature extracting strategy and employing Swin-Transformer. Methods: The hierarchical Swin-Transformer is embedded into the encoder and decoder and combined with CNN to form a dual coding path structure incorporating long-range dependencies and multi-scale contextual connections to capture coarse-tuned features at different semantic scales. The features of the two encoding paths and the upsampling path are fused, tested and validated with LITS and in-house datasets. Results: The proposed method has a DG of 97.95% and a DC of 96.2% for liver segmentation; a DG of 80.6% and a DC of 68.1% for tumor segmentation; and a classification study of the tumor dataset shows a DG of 91.1% and a DC of 87.2% for large and continuous tumors and a DG of 71.7% and a DC of 66.4% for small and scattered tumors. Conclusion: Swin-Transformer can be used as a robust encoder for medical image segmentation networks and, combined with CNN networks, can better recover local spatial information and enhance feature representation. Accurate localization before segmentation can achieve better results for small and scattered tumors.

Background: In patients with systemic lupus erythematosus (SLE), myocardial involvement is the third leading course of death after lupus nephropathy (LN) and infections. Previous autopsy studies have demonstrated a high incidence of cardiovascular abnormalities in the myocardium. However, the patients with typical symptoms are far much fewer than expected from post-mortem examinations. Objectives: The current study aimed to evaluate the technetium-99m-sestamibi (^99mTc-MIBI) gated myocardial perfusion imaging (GMPI) characteristics of lupus patients without cardiovascular symptoms, and the relationships between GMPI characteristics and biochemical markers of myocardial injury, and to explore the role of GMPI in assessing myocardial involvement. Methods: Thirty patients were studied with rest myocardial perfusion imaging, and summed rest score (SRS), summed motion score (SMS), and summed thickening score (STS) were calculated automatically. Biomarkers, including N-terminal prohormone of brain natriuretic peptide (NT-proBNP) and creatine-kinase-MB (CK-MB), were detected simultaneously. GMPI parameters, LV functions and biomarkers were compared between two NT-proBNP groups. The relationships between these parameters were studied by correlation analysis. Results: SMS, STS, and glomerular filtration rate (eGFR) were the main influencing factors of NTproBNP level (p = 0.001, <0.001, 0.042, respectively). Thirteen patients with an evaluated concentration of NT-proBNP had the lower left ventricular ejection fraction (LVEF), peak filling rate (PFR), eGFR and higher levels of CK-MB (in all comparisons, p < 0.05), and SRS was the only influencing factor of NT-proBNP (p = 0.007). Within thirteen patients with SRS≥2, there was a significant correlation between SRS and NT-proBNP (p < 0.001). Conclusion: ^99mTc-MIBI GMPI could evaluate the left ventricular function and prompt the cardiomyocyte function at the cellular level. SMS and STS were the main influencers for plasma NT-proBNP, and SRS was the independent factor for elevated NT-proBNP. This radionuclide imaging method could provide additional diagnostic information on myocardial involvement in patients with SLE.

Background: For the accurate positioning of surgical tools, conventional intraoperative navigation systems have been developed to recognize the relationship between target positions and the tools. However, since an internal organ is deformed during the operation, registration between realtime two-dimensional (2D) ultrasound images and three-dimensional (3D) CT or MRI images is not always effective. Therefore, this study developed image registration between 2D and 3D ultrasound images considering deformation for tracking target vessel movement in the liver. Methods: 3D ultrasound image was obtained in advance with 3D coordinates, including the target vessel. Then real-time 2D images and ultrasound probe position were simultaneously acquired using a 3D position sensor. We applied multiple image resolution registration, where rapid and fine optimizations can be expected at higher and lower levels, respectively. Meanwhile, the gradient descent method was adopted for the optimization, which determines the relative arrangements to obtain maximum similarity between 2D and 3D images. We experimentally established resolution level parameters using a phantom before applying it to track liver blood vessel movements in a normal healthy subject. Results: Comparing the 2D images and the registered images, although the approach has some limitations in tracking large displacement, we confirmed that the cross-section of the target blood vessel was clearly visualized. Conclusion: This method has the potential for an ultrasound therapy targeting blood vessels under natural respiration conditions.

Background: 8% of women are diagnosed with breast cancer. (BC) BC is the second most common cause of death in both developed and undeveloped countries. BC is characterized by the mutation of genes, constant pain, changes in the size, color (redness), and skin texture of breasts. Classification of breast cancer leads pathologists to find a systematic and objective prognostic; generally, the most frequent classification is binary (benign/malignant). Introduction: Machine Learning (ML) techniques are broadly used in breast cancer classification. They provide high classification accuracy and effective diagnostic capabilities. Breast cancer remains one of the top diseases that lead to thousands of deaths in women yearly. Artificial intelligence (AI) has been utilized to rapidly and accurately identify breast tumors and for early diagnosis. This paper aims to research, determine and classify these tumors. Methods: Machine learning algorithm such as Random Forest (RF) is used to classify medical images into malignant and benign. Moreover, Machine learning has been employed recently for the same purpose. Results: The results showed that Random Forest achieved high accuracy; therefore, the researchers utilized various functions for this algorithm and added more features such as bagging and boosting to increase its efficacy. Conclusion: The random Forest algorithm achieved an enhanced accuracy of 98%.

Background: Adaptive radiotherapy (ART) has the potential to reduce the toxicities of radiotherapy and improve overall survival by considering variations in the patient's anatomy during the course of treatment. ART's first commercial solutions are now implemented in clinical radiotherapy departments. However, before they can be used safely with real patients, these solutions must be rigorously evaluated to precisely determine the limits of their use. Methods: In this paper, we evaluated an offline ART vendor system in 50 patients treated on tomotherapy- like systems for six months. Illustrated by numerous examples of head and neck, thoracic and abdominopelvic localizations, two limitations of image processing used in the ART workflow have been highlighted: deformable image registration (DIR) accuracy and the way the limited field of view (FOV) is compensated. This feedback from clinical experience makes it possible to identify topics of image processing research with strong clinical interest. Results: Current DIR method accuracy may be too weak for some clinical ART applications, and their improvement remains highly important, especially for multimodality registration. Improvements in contour propagation methods also remain crucial today. We showed that there is a need for the development of automatic DIR accuracy quantification methods to help streamline the ART process. Finally, the limited FOV of the onboard images may induce dose calculation errors, highlighting the need to develop new FOV extension methods. Conclusion: We have evaluated a vendor ART system, but some image processing pitfalls, such as DIR accuracy and the limited FOV of the onboard images, make its implementation into clinical practice difficult for the moment.

Objective: The study's goal was to diagnose the condition at an earlier stage by employing the optimization-based technique for image segmentation to find deformities in MRI and Aura images Methods: Our methodology was based on two case studies. The diseased data set of MRI images obtained from the UCI data set and Aura images from Bio-Well were taken into consideration. Using the Relevance Feedback Mechanism (RFM), the sick images that are most pertinent are determined. The optimization-based Cuckoo Search (CS) algorithm is used to find the best features. The resulting model utilising the Truncated Gaussian Mixture Model (TGMM) is used to compare the extracted characteristics. The most relevant images are chosen based on the likely hood estimation. Results: The suggested methodology is tested using 150 retrieved Aura images, 50 trained photos, and processing of the input image utilizing morphological techniques like dilation, erosion, opening, and closing to improve the image quality. Together with segmentation quality measurements including Global Consistency Error (GCE), Probability Random Index (PRI), and Volume of Symmetry(VOS), the results are assessed using image quality metrics such as Average Difference (AD), Maximum Difference (MD), and Image Fidelity (IF). Conclusion: The TGMM algorithm is used to conduct the experiment. The outcomes demonstrate the effectiveness of the suggested approaches in locating various injured tissues inside medical images obtained using MRI technology as well as in locating high-intensity energy zones in which a potential deformity is associated in Aura images. The outcomes reveal a respectable recognition accuracy of about 93%.

Background: Early and accurate diagnosis is vital for avoiding the development of nondisplaced fractures to displaced fractures. Dual-energy CT (Computed Tomography) can detect bone marrow edema (BME), which may help to detect non-displaced fractures. Aim: To evaluate the value of DECT (Dual-Energy Computed Tomography) VNCa (Virtual noncalcium) images for improving diagnostic performance and confidence in acute non-displaced knee fractures. Methods: 125 patients with clinical suspicion of knee fractures underwent both DECT and MR. Conventional linear-blended CT and VNCa images were obtained from DECT. First, five readers with varying levels of experience evaluated the presence of fractures on conventional linear-blended CT and graded their diagnostic confidence on a scale of 1 to 10. Then BME with VNCa images was evaluated and compared with MR. Finally, the VNCa images combined with conventional linear-blended CT images were used to reassess the presence of fractures and diagnostic confidence. Diagnostic performance and matched pair analyses were performed. Results: 20 non-displaced knee fractures were detected. The consistency test of VNCa images and MR by five radiologists showed Kappa values are 0.76, 0.79, 0.81,0.85,and 0.90,respectively. The diagnostic performance of all readers was improved when using VNCa images combined with conventional linear-blended CT compared with that with conventional linear-blended CT alone. Diagnostic confidence was improved with combined conventional linear-blended CT and VNCa images (median score:8,8,9,9, and 10, respectively) compared with conventional linear-blended CT alone (median score:7,7,8,9, and 9). Conclusion: DECT VNCa images could improve the radiologists' diagnostic performance and confidence with varying levels of experience in the detection of non-displaced knee fractures.

Objective: This study aimed to establish an academic basis for using a computed tomography (CT) model for predicting osteoporosis in the clinical setting by illustrating the effectiveness of morphometric texture analysis. We introduce texture analysis and quantitative approaches using CT Hounsfield units (HU) to screen osteoporosis. Methods: From March 6^th, 2013, to August 11^th, 2020, a total of 4,333 cases (1,766 patients) were included in the study. After applying exclusion criteria concerning the patient status and scan interval between CT and DXA, we selected only 1,647 samples (736 patients) and analyzed both their CT and DXA bone mineral density (BMD) results. BMD was measured in the femoral neck and L1 spine body. A region of interest (ROI) was extracted from each patient’s CT as the maximum trabecular area of the L1 spine body and femoral neck. A total of 45 texture features were extracted from every ROI using gray-level co-occurrence matrices. Machine-learning techniques, including linear regression (LR) and artificial neural network (ANN), were applied to predict BMD. Results: We assigned samples to (1) Set 1 (857 lumbar spine samples in chest model, L1 spine DXA BMD), (2) Set 2 (392 lumbar spine samples in lumbar spine CT model, L1 spine DXA BMD), (3) Set 3 (1,249 lumbar spine samples in both chest and lumbar spine CT model, L1 spine DXA BMD), (4) Set 4 (398 femoral neck samples in hip and pelvis CT model, femoral neck DXA BMD), and (5) Set 5 (a total of 1,647 samples). When we applied LR, the correlation coefficients between estimated and reference values for Sets 1, 2, 3, and 4 were 0.783, 0.784, 0.757, and 0.652, respectively. For total samples (Set 5), LR and ANN provided correlation coefficients of 0.707 and 0.782, respectively. Conclusion: The modality using morphometric texture analysis with CT HU can be an additional diagnostic tool for osteoporosis and an alternative for DXA.

Background: Using a classification network to generate class activation mapping (CAM) is a mainstream method for weakly supervised semantic segmentation. However, for brain tumor images, CAM cannot fit the boundary of the tumor well. Objective: To improve the performance of brain tumor CAM, we propose a weakly supervised learning strategy based on a multi-level sub-category and membership matrix. Methods: Firstly, a multi-level sub-category strategy is used to intensively classify the data set. It allows the convolutional network to learn the in-depth characteristics of the input for enhancing CAM. Secondly, the idea of fuzzy clustering is introduced into model learning. The membership matrix is combined with CAM to construct the loss function. Results: Exhaustive experiments on the brain tumor dataset BraTS2019 demonstrate that the proposed method can effectively improve the performance of CAM. Compared with the baseline method, our approach significantly improved by 17.1% using the common dice similarity coefficient evaluation approach, and compared with the recent study, our score also improved by almost 9%. Conclusion: The proposed methods train the network under image-level labels and help the convolutional network mine the target boundary information. They can help CAM fit the target border more accurately.

Introduction: Primary epiploic appendagitis, a relatively rare and self-limiting disease, often clinically mimics conditions of the acute abdomen such as acute appendicitis and acute diverticulitis. It is important to make accurate diagnoses because its treatment is conservative. Ultrasonography and computed tomographic studies enable a reliable diagnosis to prevent unnecessary invasive procedures. Herein, we report a case of primary epiploic appendagitis of the appendix vermiformis with clinical, laboratory and CT findings to improve awareness of this condition. Case Presentation: A 29-year-old female presented with acute abdominal pain in the right lower quadrant. Her medical history was not significant for surgery. She had no nausea, vomiting, diarrhea or fever. On physical examination, she had right lower quadrant tenderness with mild defense and rebound upon palpation. The leukocyte count (6300 mm-3) and other laboratory parameters, including urine tests, were unremarkable. With these findings, the provisional diagnosis of acute appendicitis was made, and a CT examination (Mx 8000 IDT 16, Philips, USA) was done upon the request of the referring physicians. The abdominal CT showed normal appendix vermiformis. However, a fat density lesion surrounding a hyperdense rim was seen adjacent to the appendix vermiformis. The diagnosis of PEA was thus established based on the characteristic radiologic findings. The patient was managed conservative treatment with pain medication as an outpatient. After a one-week follow-up, the patient was observed to be symptom-free and concluded to have recovered fully from their physician. Conclusion: To conclude, PEA needs to be considered by emergency clinicians and radiologists in the differential diagnosis of acute abdominal pain. With this in mind, it becomes easier for a substantive diagnosis to be made by ultrasound alone or combined with CT to prevent unnecessary surgical interventions, antibiotherapy and hospitalization.

Background: Schwannoma, also known as neurinoma, is the most common tumor of the peripheral nerves. Intrascrotal extratesticular schwannoma, which is not associated with schwannomatosis and neurofibromatosis-2, is a very rare entity, and few cases have been reported in the literature. In this paper, we have reported a case of extratesticular schwannoma, an extremely rare cause of scrotal mass, with ultrasound (US) and magnetic resonance imaging (MRI) findings. Case Report: A 22-year-old male presented with painless left scrotal swelling. Scrotal US showed an extra-testicular heterogeneous hypoechoic mass with the lobulated contour in the inferior part of the scrotum. Scrotal MRI demonstrated well-defined extratesticular mass, which showed heterogeneous hypointense T2-weighted images compared to testis parenchyma. On contrast-enhanced images, the mass showed mild-to-moderate enhancement. The patient underwent surgery with the preliminary diagnosis of benign intrascrotal extratesticular mass. The lesion was removed with preservation of the testicles by urologists, and the final diagnosis was made as scrotal schwannoma by histological and immunohistochemical examination. Conclusion: Although there is no specific imaging finding of scrotal schwannoma, MRI can be used as a guide to surgery by helping to accurately determine whether the lesion is intra- or extratesticular. In addition, radiologists and urologists should keep schwannoma in mind in the differential diagnosis in the presence of an intrascrotal extratesticular mass.

Introduction: Emphysematous Osteomyelitis (EO) is an extremely rare bone infection caused by gas-forming bacteria with few documented cases in the literature. Our study aims to highlight characteristic imaging features, including the novel use of positron emission tomographymagnetic resonance imaging (PET-MRI) in diagnosing this potentially fatal entity. Case: Radiography and computed tomography (CT) of the pelvis were performed due to complaints of persistent back pain in a 36-year-old male with a history of recent abdominal aorta surgery. Sacroiliac joint aspiration was performed, and a follow-up PET-MRI was subsequently performed. Results: Radiography and CT demonstrated bilateral sacroiliitis, osteonecrosis and EO in the bony pelvis. Left sacroiliac joint aspiration identified Staphylococcus aureus as the causative organism. PET-MRI revealed EO with left iliopsoas abscess and abdominal aortic graft infection. The patient's symptoms resolved following antibiotic therapy and image-guided abscess drainage. Conclusion: EO is a lethal variant of osteomyelitis with a dearth of published cases. Pertinent imaging characteristics of EO on radiography, CT and PET-MRI are discussed here, along with a review of the literature surrounding this rare condition.

Non-Hodgkin's lymphoma and acute myeloid leukemia are both hematological malignancies that rarely coexist at the time of initial diagnosis. We present a case of non-Hodgkin lymphoma and acute myeloid leukemia diagnosed on the first admission. Background: Lymphoma and leukemia, both malignant hematological cancers, are primarily different diseases, with a majority of cases originating independently. The co-occurrence of lymphoma and leukemia at the time of the first diagnosis is extremely rare, and few relevant reports exist in the medical literature. We describe a case of a patient with non-Hodgkin's lymphoma and acute myeloid leukemia, a very rare occurrence. Case Report: A 57-year-old man complained of fatigue and neck tumors. A physical examination revealed several enlarged superficial lymph nodes throughout the body. On admission, routine blood tests revealed anemia, thrombocytopenia, and normal counts of white blood cells. Cytology of two cervical lymph nodes indicated non- Hodgkin's lymphoma, ¹⁸F-PET/CT: multiple enlarged lymph nodes with hypermetabolism, diffuse hypermetabolism of the bone marrow, suggesting lymphoma infiltration in the bone marrow, and a bone marrow biopsy revealed acute myeloid leukemia. Ultimately, the patient was diagnosed with non-Hodgkin’s lymphoma and acute myeloid leukemia. Conclusion: Primary bilineage hematological malignancies are rare, and the mechanism underlying their incidence is unknown. Infiltration of the bone marrow by lymphoma or leukemia can result in diffuse hypermetabolism, mostly diagnosed via bone marrow biopsy.

Background: Peritoneal loose body (PLB) which is also called "peritoneal mouse" is a rare disease, with only a few cases reported. PLB is mostly found due to unplanned examination, surgery, or autopsy. With the increase in abdominal imaging examinations, it was gradually recognized by us. The diagnosis of PLB has three major characteristics: free movement, no blood supply, and pathological exclusion of other tumors. Case Presentation: We reported a 66-year-old male patient with an asymptomatic freely moving peritoneal loose body. The size of the lesion was about 4*4cm. The imaging data were complete including Ultrasound, Magnetic resonance (MR), Computed tomography (CT) plain scan and enhancement. Interestingly, changes in the location of the lesion were found on follow-up CT. The lesion showed concentric circles changes on CT without obvious enhancement. MR also appeared as a multi-layer signal of concentric circles. In addition, the imaging manifestations were compared with histological pathology in this article to explain imaging characteristics. Conclusion: PLB is an uncommon disease. Through the complete imaging examination of this case, we can further understand the mobility of the lesion by changing the position. The CT and MR have characteristic concentric circle changes without obvious enhancement. The diagnosis could be confirmed before surgery, which prompted the clinical early diagnosis of the disease, and an appropriate choice between surgery and conservative treatment.