Current Medical Imaging Reviews最新文献

Background: 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.

Objective: 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).

Methods: 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.

Results: 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.

Conclusion: 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.

Background: 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.

Objective: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

Background: A clinical medical image provides vital information about a person's health and bodily condition. Typically, doctors monitor and examine several types of medical images individually to gather supplementary information for illness diagnosis and treatment. As it is arduous to analyze and diagnose from a single image, multi-modality images have been shown to enhance the precision of diagnosis and evaluation of medical conditions.

Objective: Several conventional image fusion techniques strengthen the consistency of the information by combining varied image observations; nevertheless, the drawback of these techniques in retaining all crucial elements of the original images can have a negative impact on the accuracy of clinical diagnoses. This research develops an improved image fusion technique based on fine-grained saliency and an anisotropic diffusion filter to preserve structural and detailed information of the individual image.

Method: In contrast to prior efforts, the saliency method is not executed using a pyramidal decomposition, but rather an integral image on the original scale is used to obtain features of superior quality. Furthermore, an anisotropic diffusion filter is utilized for the decomposition of the original source images into a base layer and a detail layer. The proposed algorithm's performance is then contrasted to those of cutting-edge image fusion algorithms.

Results: The proposed approach cannot only cope with the fusion of medical images well, both subjectively and objectively, according to the results obtained, but also has high computational efficiency.

Conclusion: Furthermore, it provides a roadmap for the direction of future research.

Purpose: The purpose of this article is to combine the functional information of CT images with the anatomical and soft tissue information of MRI through image fusion technology, providing more detailed information for rehabilitation treatment and thus providing a scientific basis for clinical applications and better training plans.

Methods: In this paper, functional brain imaging technology combining CT (computed tomography) and MRI (magnetic resonance imaging) was used for image fusion, and SURF (accelerated robust feature) feature points of images were extracted. In this study, 40 patients with mild and moderate closed traumatic brain injury admitted to the rehabilitation department of a rehabilitation center from 2018 to 2022 were selected as the research objects.

Results: Compared with using only CT images and MRI images for brain injury diagnosis, the fusion image had a higher detection rate of abnormal brain injury diagnosis, with a detection rate of 97.5%. When using fused images for the diagnosis of abnormal brain injury, the patient's exercise rehabilitation effect was better.

Conclusion: CT and MRI image fusion technology had a high diagnostic accuracy for brain injury, which could timely guide doctors in determining exercise rehabilitation plans and help improve the effectiveness of patient exercise rehabilitation.

Background: Patients with cancer can develop bone metastasis when a solid tumor invades the bone, which is the third most commonly affected site by metastatic cancer, after the lung and liver. The early detection of bone metastases is crucial for making appropriate treatment decisions and increasing survival rates. Deep learning, a mainstream branch of machine learning, has rapidly become an effective approach to analyzing medical images.

Objective: To automatically diagnose bone metastasis with bone scintigraphy, in this work, we proposed to cast the bone metastasis diagnosis problem into automated image classification by developing a deep learning-based automated classification model.

Methods: A self-defined convolutional neural network consisting of a feature extraction sub-network and feature classification sub-network was proposed to automatically detect lung cancer bone metastasis, with a feature extraction sub-network extracting hierarchal features from SPECT bone scintigrams and feature classification sub-network classifying high-level features into two categories (i.e., images with metastasis and without metastasis).

Results: Using clinical data of SPECT bone scintigrams, the proposed model was evaluated to examine its detection accuracy. The best performance was achieved if the two images (i.e., anterior and posterior scans) acquired from each patient were fused using pixel-wise addition operation on the bladder-excluded images, obtaining the best scores of 0.8038, 0.8051, 0.8039, 0.8039, 0.8036, and 0.8489 for accuracy, precision, recall, specificity, F-1 score, and AUC value, respectively.

Conclusion: The proposed two-class classification network can predict whether an image contains lung cancer bone metastasis with the best performance as compared to existing classical deep learning models. The high accumulation of ^99mTc MDP in the urinary bladder has a negative impact on automated diagnosis of bone metastasis. It is recommended to remove the urinary bladder before automated analysis.

Background: The purpose of this work was to identify which Glioblastoma (GBM) problems can be handled by Magnetic Resonance Imaging (MRI) and Machine Learning (ML) techniques. Results, limitations, and trends through a review of the scientific literature in the last 5 years were performed. Google Scholar, PubMed, Elsevier databases, and forward and backward citations were used for searching articles applying ML techniques in GBM. The 50 most relevant papers fulfilling the selection criteria were deeply analyzed. The PRISMA statement was followed to structure our report.

Methods: A partial taxonomy of the GBM problems tackled with ML methods was formulated with 15 subcategories grouped into four categories: extraction of characteristics from tumoral regions, differentiation, characterization, and problems based on genetics.

Results: The dominant techniques in solving these problems are: Radiomics for feature extraction, Least Absolute Shrinkage and Selection Operator for feature selection, Support Vector Machines and Random Forest for classification, and Convolutional Neural Networks for characterization. A noticeable trend is that the application of Deep Learning on GBM problems is growing exponentially. The main limitations of ML methods are their interpretability and generalization.

Conclusion: The diagnosis, treatment, and characterization of GBM have advanced with the aid of ML methods and MRI data, and this improvement is expected to continue. ML methods are effective in solving GBM-related problems with different precisions, Overall Survival being the hardest problem to solve with accuracies ranging from 57%-71%, and GBM differentiation the one with the highest accuracy ranging from 80%-97%.

Background: To compare the integrity, clarity, conciseness, etc., of the structured report (SR) versus free-text report (FTR) for computed tomography enterography of Crohn's disease (CD).

Methods: FTRs and SRs were generated for 30 patients with CD. The integrity, clarity, conciseness etc., of SRs versus FTRs, were compared. In this study, an evidence-based medicine practice model was utilized on 92 CD patients based on SR in order to evaluate its clinical value. Then, the life quality of the patients in two groups was evaluated before and after three months of intervention using an Inflammatory Bowel Disease Questionnaire (IBDQ).

Results: SRs received higher ratings for satisfaction with integrity (median rating 4.27 vs. 3.75, P=0.008), clarity (median rating 4.20 vs. 3.43, P=0.003), conciseness (median rating 4.23 vs. 3.20, P=0.003), the possibility of contacting a radiologist to interpret (median rating 4.17 vs. 3.20, P<0.001), and overall clinical impact (median rating 4.23 vs. 3.27, P<0.001) than FTRs. Besides, research group had higher score of IBDQ intestinal symptom dimension (median score 61.13 vs. 58.02, P=0.003), IBDQ systemic symptom dimension (median score 24.48 vs. 20.67, P<0.001), IBDQ emotional capacity dimension (median score 65.65 vs. 61.74, P<0.001), IBDQ social ability dimension (median score 26.80 vs. 22.37, P<0.001), and total IBDQ score (median score 178.07 vs. 162.80, P<0.001) than control group.

Conclusion: The SR of CTE in CD patients was conducive to improving the quality and readability of the report, and CD patients' life quality could significantly improve after the intervention of an evidence-based medicine model based on SR.

Objective: In this work, we have used histopathology as the gold standard for the diagnosis, calculated the sensitivity and positive predictive value (PPV) of computed tomography (CT), and analyzed the CT and clinical characteristics of pathologically proven elastofibromas.

Methods: A systematic retrospective analysis was performed on all patients with infrascapular lesions who were treated in the hospital from 2006 to 2018. CT and histopathological examinations were performed for all cases, and the CT sensitivity and PPV for the diagnosis of elastofibroma were calculated. 12 of 53 cases (20 lesions) underwent enhanced CT scan after CT plain scan, and the related clinical and CT features of elastofibromas have been discussed.

Results: Of the 54 patients treated during the study, CT diagnosis was consistent with histopathology in 53 cases. One was a false-positive patient. The PPV and sensitivity of the CT in the diagnosis of elastofibroma were 93.3% (95% CI 68.0%-99.8%) and 100%, respectively. The CT values of 12 patients with 20 lesions on plain and enhanced scans were statistically significant (P=0.001). The prevalence of elastofibromas in males and females was statistically significant (P=.000). There was no statistically significant difference in the incidence of left and right elastofibromas (P=0.752). There was no significant difference in the volume of left and right lesions (P=0.209) and the volume of elastofibromas between males and females (P=.474).

Conclusion: CT is the most practical tool for the evaluation of elastofibromas in the infrascapular region.

Dual-energy computed tomography (DECT) applies two energy spectra distributions to collect raw data based on traditional CT imaging. The application of hepatobiliary imaging, has the advantages of optimizing the scanning scheme, improving the imaging quality, highlighting the disease characterization, and increasing the detection rate of lesions. In order to summarize the clinical application value of DECT in hepatobiliary diseases, we searched the technical principles of DECT and its existing studies, case reports, and clinical guidelines in hepatobiliary imaging from 2010 to 2023 (especially in the past 5 years) through PubMed and CNKI, focusing on the clinical application of DECT in hepatobiliary diseases, including liver tumors, diffuse liver lesions, and biliary system lesions. The first part of this article briefly describes the basic concept and technical advantages of DECT. The following will be reviewed:the detection of lesions, diagnosis and differential diagnosis of lesions, hepatic steatosis, quantitative analysis of liver iron, and analyze the advantages and disadvantages of DECT in hepatobiliary imaging. Finally, the contents of this paper are summarized and the development prospect of DECT in hepatobiliary imaging is prospected.

Background: Lung cancer patients with post-chemotherapy may have disconnected or weakened function connections within brain networks.

Objective: This study aimed to explore the abnormality of brain functional networks in lung cancer patients with post-chemotherapy by modular edge analysis.

Methods: Resting-state functional magnetic resonance imaging (rs-fMRI) scans were performed on 40 patients after chemotherapy, 40 patients before chemotherapy and 40 normal controls. Patients in all three groups were age and sex well-matched. Then, modular edge analysis was applied to assess brain functional network alterations.

Results: Post-chemotherapy patients had the worst MoCA scores among the three groups (p < 0.001). In intra-modular connections, compared with normal controls, the patients after chemotherapy had decreased connection strengths in the occipital lobe module (p < 0.05). Compared with the nonchemotherapy group, the patients after chemotherapy had decreased connection strengths in the subcortical module (p < 0.05). In inter-modular connections, compared with normal controls, the patients after chemotherapy had decreased connection strength in the frontal-temporal lobe modules (p < 0.05). Compared with the non-chemotherapy group, the patients after chemotherapy had decreased connection strength in the subcortical-temporal lobe modules (p < 0.05).

Conclusion: The results reveal that chemotherapy can disrupt connections in brain functional networks. As far as we know, the use of modular edge analysis to report changes in brain functional brain networks associated with chemotherapy was rarely reported. Modular edge analysis may play a crucial part in predicting the clinical outcome for the patients after chemotherapy.