Current Medical Imaging Reviews最新文献

{"title":"Study on Improved Computed Tomography Scanning Parameters for Patients with Nasal Bone Fracture Based on ITK Three-dimensional Labelling.","authors":"Lei Yang, Cun Yang, Guo-Dong Gao, Ying Li, Liang Li, Xue-Fang Han, Jin-Hui Dong","doi":"10.2174/0115734056250334231214054003","DOIUrl":"https://doi.org/10.2174/0115734056250334231214054003","url":null,"abstract":"<p><strong>Objective: </strong>To investigate the influence of improved exposure parameters on the image quality of multi-slice spiral computed tomography in nasal bone fracture imaging.</p><p><strong>Methods: </strong>Fifty patients with optimised parameters combined with coronal scanning were allocated to the modified group and 50 patients with routine scanning parameters to the routine group. The image quality and nasal bone display of the two groups were assessed and statistically analysed, and the quality of scanned images before and after parameter optimisation was compared.</p><p><strong>Results: </strong>The optimised image quality was better than that of conventional scanning parameters. The parameters used were 120 kv, 180 mA, a layer thickness of 0.625 mm, a layer spacing of 0.312 mm, a pitch of 0.516:1, a frame speed of 1 s, a scanning field of 12 cm and a reconstructed layer thickness for scanning of 0.625 mm; the scanned image was clear, and the parameter optimisation was achieved. This ensures that the annotation data in ITK labelling is more accurate.</p><p><strong>Conclusion: </strong>The optimised parameters and scanned coronal plane show the nasal bone and its surrounding structures more comprehensively, which is of high diagnostic value for nasal bone fractures. The three-dimensional annotation data based on ITK is more standardised, laying a foundation for the subsequent research of artificial intelligence modelling.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139724994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SegEIR-Net: A Robust Histopathology Image Analysis Framework for Accurate Breast Cancer Classification.","authors":"Pritpal Singh, Rakesh Kumar, Meenu Gupta, Fadi Al-Turjman","doi":"10.2174/0115734056278974231211102917","DOIUrl":"https://doi.org/10.2174/0115734056278974231211102917","url":null,"abstract":"<p><strong>Background: </strong>Breast Cancer (BC) is a significant threat affecting women globally. An accurate and reliable disease classification method is required to get an early diagnosis. However, existing approaches lack accurate and robust classification.</p><p><strong>Objective: </strong>This study aims to design a model to classify BC Histopathology images accurately by leveraging segmentation techniques.</p><p><strong>Methods: </strong>This work proposes a combined segmentation and classification approach for classifying BC using histopathology images to address these issues. Chan-Vese algorithm is used for segmentation to accurately delineate regions of interest within the histopathology images, followed by the proposed SegEIR-Net (Segmentation using EfficientNet, InceptionNet, and ResNet) for classification. Bilateral Filtering is also employed for noise reduction. The proposed model uses three significant networks, ResNet, InceptionNet, and EfficientNet, concatenates the outputs from each block followed by Dense and Dropout layers. The model is trained on the breakHis dataset for four different magnifications and tested on BACH (BreAst Cancer Histology) and UCSB (University of California, Santa Barbara) datasets.</p><p><strong>Results: </strong>SegEIR-Net performs better than the existing State-of-the-Art (SOTA) methods in terms of accuracy on all three datasets, proving the robustness of the proposed model. The accuracy achieved on breakHis dataset are 98.66%, 98.39%, 97.52%, 95.22% on different magnifications, and 93.33% and 96.55% on BACH and UCSB datasets.</p><p><strong>Conclusion: </strong>These performance results indicate the robustness of the proposed SegEIR-Net framework in accurately classifying BC from histopathology images.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139724976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Immunoglobulin G4 (IgG4)-related Lymphadenopathy in Submandibular Space Mimicking Submandibular Malignant Tumor: A Case Report.","authors":"Go Eun Yang","doi":"10.2174/0115734056265681231126192308","DOIUrl":"https://doi.org/10.2174/0115734056265681231126192308","url":null,"abstract":"<p><strong>Background: </strong>Immunoglobulin G4 (Ig G4)-related disease is rare; however, it is a fibroinflammatory disease that has been studied a lot so far. Although the expression pattern varies depending on the organ affected, it usually manifests as organ hypertrophy and organ dysfunction.</p><p><strong>Case presentation: </strong>A 46-year-old man was referred to our otorhinolaryngology department for left submandibular swelling and tenderness that occurred 2 weeks ago. He was treated with antibiotics (augmentin 625mg, per oral) for 2 weeks, but his symptoms did not improve, and his white blood cell (WBC) count was 10,500 /μL (normal 3,800-10,000 /μL).</p><p><strong>Conclusion: </strong>A mass-like lesion of the submandibular space has been concluded and the laboratory findings have been satisfactory (IgG4 level); IgG4-related disease, which is rare, but recently often reported, can be included in the differential diagnosis.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139724975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"T1 Mapping and Amide Proton Transfer Weighted Imaging for Predicting Lymph Node Metastasis in Patients with Rectal Cancer.","authors":"Yue Wang, Anliang Chen, Wenjun Hu, Yuhui Liu, Jiazheng Wang, Liangjie Lin, Qingwei Song, Ailian Liu","doi":"10.2174/0115734056251952231012155314","DOIUrl":"https://doi.org/10.2174/0115734056251952231012155314","url":null,"abstract":"<p><strong>Background: </strong>Accurate preoperative judgment of lymph node (LN) metastasis is a critical step in creating a treatment strategy and evaluating prognosis in rectal cancer (RC) patients.</p><p><strong>Objective: </strong>This study aimed to explore the value of T1 mapping and amide proton transfer weighted (APTw) imaging in predicting LN metastasis in patients with rectal cancer.</p><p><strong>Methods: </strong>In a retrospective study, twenty-three patients with pathologically confirmed rectal adenocarcinoma who underwent MRI and surgery from August 2019 to August 2021 were selected. Then, 3.0T/MR sequences included conventional sequences (T1WI, T2WI, and DWI), APTw imaging, and T1 mapping. Patients were divided into LN metastasis (group A) and non-LN metastasis groups (group B). The intra-group correlation coefficient (ICC) was used to test the inter-observer consistency. Mann-Whitney U test was used to compare the differences between the two groups. Spearman correlation analysis was performed to evaluate the correlation between T1 and APT values. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the differential performance of each parameter and their combination. The difference between AUCs was compared using the DeLong test.</p><p><strong>Results: </strong>The APT value in patients with LN metastasis was significantly higher than in those without LN metastasis group (P=0.020). Also, similar results were observed for the T1 values (P=0.001). The area under the ROC curve of the APT value in the prediction of LN metastasis was 0.794; when the cutoff value was 1.73%, the sensitivity and specificity were 71.4% and 88.9%, respectively. The area under the ROC curve of the T1 value was 0.913; when the cutoff value was 1367.36 ms, the sensitivity and specificity were 78.6% and 100.0%, respectively. The area under the ROC curve of T1+APT was 0.929, with a sensitivity of 78.6% and specificity of 100.0%.</p><p><strong>Conclusion: </strong>APT and T1 values show great diagnostic efficiency in predicting LN metastasis in rectal cancer.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139724995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational Model for the Detection of Diabetic Retinopathy in 2-D Color Fundus Retina Scan.","authors":"Akshit Aggarwal, Shruti Jain, Himanshu Jindal","doi":"10.2174/0115734056248183231010111937","DOIUrl":"https://doi.org/10.2174/0115734056248183231010111937","url":null,"abstract":"<p><strong>Background: </strong>Diabetic Retinopathy (DR) is a growing problem in Asian countries. DR accounts for 5% to 7% of all blindness in the entire area. In India, the record of DR-affected patients will reach around 79.4 million by 2030.</p><p><strong>Aims: </strong>The main objective of the investigation is to utilize 2-D colored fundus retina scans to determine if an individual possesses DR or not. In this regard, Engineering-based techniques such as deep learning and neural networks play a methodical role in fighting against this fatal disease.</p><p><strong>Methods: </strong>In this research work, a Computational Model for detecting DR using Convolutional Neural Network (DRCNN) is proposed. This method contrasts the fundus retina scans of the DR-afflicted eye with the usual human eyes. Using CNN and layers like Conv2D, Pooling, Dense, Flatten, and Dropout, the model aids in comprehending the scan's curve and color-based features. For training and error reduction, the Visual Geometry Group (VGG-16) model and Adaptive Moment Estimation Optimizer are utilized.</p><p><strong>Results: </strong>The variations in a dataset like 50%, 60%, 70%, 80%, and 90% images are reserved for the training phase, and the rest images are reserved for the testing phase. In the proposed model, the VGG-16 model comprises 138M parameters. The accuracy is achieved maximum rate of 90% when the training dataset is reserved at 80%. The model was validated using other datasets.</p><p><strong>Conclusion: </strong>The suggested contribution to research determines conclusively whether the provided OCT scan utilizes an effective method for detecting DRaffected individuals within just a few moments.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139708537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Diagnostic Value of Ultrasound-guided Attenuation Parameter (UGAP) in metabolic fatty liver disease.","authors":"Hailan Wu, Yinyan Zeng, Fangqun Chen, Juan Peng, Li Chen","doi":"10.2174/0115734056275504231126033905","DOIUrl":"https://doi.org/10.2174/0115734056275504231126033905","url":null,"abstract":"<p><strong>Objectives: </strong>To evaluate the diagnostic value of ultrasound-guided attenuation parameter (UGAP) in metabolic fatty liver disease (MAFLD) and to explore the correlation between the attenuation coefficient (AC) value of UGAP and commonly used clinical obesity indicators.</p><p><strong>Methods: </strong>A total of 121 subjects who had physical examinations from November 2021 to March 2022 were prospectively selected; the height, weight, and waist circumference (WC) of all subjects were collected, and conventional ultrasound and UGAP examinations for all subjects.</p><p><strong>Results: </strong>Under the standard of conventional ultrasound, among the 121 subjects, 53 had normal liver, 42 had mild fatty liver, 21 had moderate fatty liver, and 5 had severe fatty liver. The mean AC value of 121 patients was 0.66 ± 0.13 dB/cm/MHz. The best cut-off values for diagnosing mild, moderate, and severe fatty liver were 0.65dB/cm/MHz, 0.72dB/cm/MHz, and 0.83dB/cm/MHz, respectively. The area under the curve (AUC) values were 0.891, 0.929, and 0.914, respectively. When grouped by WC, there was a statistically significant difference in AC value between the normal group and the obese group (t=-4.675, P<0.001). Overall WC and within group WC were moderately correlated with the AC value of UGAP (P<0.001).</p><p><strong>Conclusions: </strong>UGAP has a good diagnostic value in the quantitative evaluation of liver steatosis in MAFLD, and the change of WC can reflect the occurrence of liver steatosis to a certain extent.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139708551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research Progress in Tumor Diagnosis Based on Raman Spectroscopy.","authors":"Zhichao Wang, Huanghao Shi, Litao Zhou, Jian Yin, Huancai Yin, Liangxu Xie, Shan Chang","doi":"10.2174/1573405620666230811142737","DOIUrl":"https://doi.org/10.2174/1573405620666230811142737","url":null,"abstract":"<p><strong>Background: </strong>Cancer is a major disease that threatens human life and health. Raman spectroscopy can provide an effective detection method.</p><p><strong>Objective: </strong>The study aimed to introduce the application of Raman spectroscopy to tumor detection. We have introduced the current mainstream Raman spectroscopy technology and related application research.</p><p><strong>Methods: </strong>This article has first introduced the grim situation of malignant tumors in the world. The advantages of tumor diagnosis based on Raman spectroscopy have also been analyzed. Secondly, various Raman spectroscopy techniques applied in the medical field are introduced. Several studies on the application of Raman spectroscopy to tumors in different parts of the human body are discussed. Then the advantages of combining deep learning with Raman spectroscopy in the diagnosis of tumors are discussed. Finally, the related problems of tumor diagnosis methods based on Raman spectroscopy are pointed out. This may provide useful clues for future work.</p><p><strong>Conclusion: </strong>Raman spectroscopy can be an effective method for diagnosing tumors. Moreover, Raman spectroscopy diagnosis combined with deep learning can provide more convenient and accurate detection results.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139708538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Medical Imaging and Analysis of Thermal Necrosis During Bone Grinding: Implementation of Non-dominated Sorting Genetic Algorithm (NSGA-III) in Healthcare.","authors":"Atul Babbar, Vivek Jain, Dheeraj Gupta, Vidyapati Kumar, Bhargav Prajwal Pathri, Ankit Sharma","doi":"10.2174/0115734056284074231222042746","DOIUrl":"https://doi.org/10.2174/0115734056284074231222042746","url":null,"abstract":"<p><strong>Background: </strong>Medical imaging plays a key role in neurosurgery; thereby, imaging and analysis of the soft and hard tissues during bone grinding is of paramount importance for neurosurgeons. Bone grinding, a minimally invasive operation in the field of neurosurgery amid osteotomy, has been used during brain cancer surgery.</p><p><strong>Aims and objectives: </strong>With increasing attention to neural tissue damage in machining operations, imaging of these neural tissues becomes vital and reducing temperature is imperative.</p><p><strong>Method: </strong>In the present study, a novel attempt has been made to perform the imaging of bone tissues during the bone grinding procedure and further investigate the relationship between rotational speed, feed rate, depth of cut with cutting forces, and temperature. The role of cutting forces and temperature has been addressed as per the requirements of neurosurgeons. Firstly, a three-factor, three-level design was constructed with a full factorial design. Regression models were employed to construct the models between input parameters and response characteristics. Medical imaging techniques were used to perform a thorough analysis of thermal necrosis and damage to the bone. Subsequently, the non-dominated sorting genetic algorithm (NSGA-III) was used to optimize the parameters for reduction in the cutting forces and temperature during bone grinding while reducing neural tissue damage.</p><p><strong>Results: </strong>The results revealed that the maximum value of tangential force was 21.32 N, thrust force was 9.25 N, grinding force ratio was 0.453, torque was 4.55 N-mm, and temperature was 59.3°C. It has been observed that maximum temperature was generated at a rotational speed of 55000 rpm, feed rate of 60 mm/min, and depth of cut of 1.0 mm. Histopathological imaging analysis revealed the presence of viable lacunas, empty lacunas, haversian canals, and osteocytes in the bone samples. Furthermore, the elemental composition of the bone highlights the presence of carbon (c) 59.49%, oxygen (O) 35.82%, sodium (Na) 0.11%, phosphorous 1.50%, sulphur 0.33%, chlorine 0.98%, and calcium 1.77%.</p><p><strong>Conclusion: </strong>The study revealed that compared to the initial scenario, NSGA-III can produce better results without compromising the trial results. According to a statistical study, the rise in temperature during bone grinding was significantly influenced by rotating speed. The density of osteocytes in the lacunas was higher at lower temperatures. Furthermore, the results of surface electron microscopy and energy dispersive spectroscopy revealed the presence of bone over the surface of the grinding burr, which resulted in the loading of the grinding burr. The results of the present investigation will be beneficial for researchers and clinical practitioners worldwide.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139572083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Sentinel Node and Occult Lesion Localization (SNOLL) Technique Using a Single Radiopharmaceutical in Non-palpable Breast Lesions.","authors":"Berna Okudan, Bedri Seven, Pelin Arıcan","doi":"10.2174/0115734056275326231210193544","DOIUrl":"https://doi.org/10.2174/0115734056275326231210193544","url":null,"abstract":"<p><strong>Background: </strong>In order to perform a full surgical resection on non-palpable breast lesions, a current method necessitates correct intraoperative localization. Additionally, because it is an important prognostic factor for these patients, the examination of the lymph node status is crucial.</p><p><strong>Objective: </strong>The aim of this study was to evaluate the efficiency of the sentinel node and occult lesion localization (SNOLL) technique in localizing nonpalpable breast lesions together with sentinel lymph node (SLN) using a single radiotracer, that is, nanocolloid particles of human serum albumin (NC) labeled with technetium-99m (99mTc).</p><p><strong>Methods: </strong>39 patients were included, each having a single non-palpable breast lesion and clinically no evidence of axillary disease. Patients received 99mTc- NC intratumorally on the same day as surgery under the guidance of ultrasound. Planar and single-photon emission computed tomography/computed tomography lymphoscintigraphy were performed to localize the breast lesion and the SLN. The occult breast lesion and SLN were both localized using a hand-held gamma-probe, which was also utilized to determine the optimal access pathway for surgery. In order to ensure a radical treatment in a single surgical session and reduce the amount of normal tissue that would need to be removed, the surgical field was checked with the gamma probe after the specimen was removed to confirm the lack of residual sources of considerable radioactivity.</p><p><strong>Results: </strong>Breast lesions were successfully localized and removed in all patients. Pathological findings revealed breast carcinoma in 11/39 patients (28%) and benign lesions in 28 (72%). Axillary SLNs were detected in 31/39 (79.5%) patients. The metastatic involvement of SLN was only seen in two cases.</p><p><strong>Conclusion: </strong>While the identification rate of the SNOLL technique performed with an intratumoral injection of 99mTc-NC as the sole radiotracer in non-palpable breast lesions was great, it was not fully satisfactory in SLNs.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139572089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Empirical Curvelet-ridgelet Wavelet Transform for Multimodal Fusion of Brain Images.","authors":"Anupama Jamwal, Shruti Jain","doi":"10.2174/0115734056269529231205101519","DOIUrl":"https://doi.org/10.2174/0115734056269529231205101519","url":null,"abstract":"<p><strong>Background: </strong>Empirical curvelet and ridgelet image fusion is an emerging technique in the field of image processing that aims to combine the benefits of both transforms.</p><p><strong>Objective: </strong>The proposed method begins by decomposing the input images into curvelet and ridgelet coefficients using respective transform algorithms for Computerized Tomography (CT) and magnetic Resonance Imaging (MR) brain images.</p><p><strong>Methods: </strong>An empirical coefficient selection strategy is then employed to identify the most significant coefficients from both domains based on their magnitude and directionality. These selected coefficients are coalesced using a fusion rule to generate a fused coefficient map. To reconstruct the image, an inverse curvelet and ridgelet transform was applied to the fused coefficient map, resulting in a high-resolution fused image that incorporates the salient features from both input images.</p><p><strong>Results: </strong>The experimental outcomes on real-world datasets show how the suggested strategy preserves crucial information, improves image quality, and outperforms more conventional fusion techniques. For CT Ridgelet-MR Curvelet and CT Curvelet-MR Ridgelet, the authors' maximum PSNRs were 58.97 dB and 55.03 dB, respectively. Other datasets are compared with the suggested methodology.</p><p><strong>Conclusion: </strong>The proposed method's ability to capture fine details, handle complex geometries, and provide an improved trade-off between spatial and spectral information makes it a valuable tool for image fusion tasks.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139572125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}