Background: Photon-counting computed tomography (CT) is an advanced imaging technique that enables multi-energy imaging from a single scan. However, the limited photon count assigned to narrow energy bins leads to increased quantum noise in the reconstructed spectral images. To address this issue, leveraging the prior information in the spectral images is essential. This study aimed to develop an efficient algorithm that enhances image reconstruction quality by reducing noise levels and preserving image details.
Methods: To improve image reconstruction quality for photon-counting CT, we propose an algorithm based on the subspace-assisted multi-prior information, including global, nonlocal, and local priors, for spectral CT reconstruction. Specifically, the algorithm first maps spectral CT images, which exhibit global low-rank characteristics, to low-dimensional eigenimages using subspace decomposition. Then, similar image patches are extracted based on the manifold structure distance from highly correlated eigenimages in both spectral and spatial domains. These patches are stacked to form a nonlocal full-channel tensor group. Subsequently, non-convex structural sparsity is applied to this tensor group through adaptive dictionary learning, exploiting nonlocal similarity. Finally, the alternating direction method of multipliers (ADMM) is applied to solve the optimization model iteratively.
Results: The simulated walnut and real mouse data were applied to validate the effectiveness of the proposed method. In the simulation experiments, the proposed method reduced the root mean square error (RMSE) by 87.74%, 86.88%, 67.01%, 46.42%, and 13.51% compared to the respective state-of-the-art five comparison methods. The time taken for one iteration of the proposed algorithm was as low as 32.57 seconds, which was 92.07% less than framelet tensor nuclear norm [framelet tensor sparsity with block-matching method (FTNN)] method and 74.13% less than total variation regularization [tensor nonlocal similarity and local TV sparsity method (ITS_TV)] method, the other two tensor block-matching (BM)-based comparison methods. The material decomposition results in real mouse data further validated the accuracy of the proposed method for different materials.
Conclusions: The experimental results indicate that the proposed algorithm effectively reduces computational costs while improving the accuracy of image reconstruction and material decomposition, showing promising advantages over the compared method.
Background and objective: Orthopedic prostheses have become increasingly prevalent in clinical practice in recent years. However, orthopedic prosthesis-associated infections (OPAI) have emerged as a serious complication associated with their use. Due to the variety of orthopedic implant types and the atypical clinical manifestations of OPAI, it is easy to cause delayed diagnosis and affect the prognosis of patients. The objective of this paper is to: (I) identify the potential imaging tools available to diagnose OPAI; (II) summarize the mechanisms and features by which each imaging modality can or cannot identify infection.
Methods: All the published papers were obtained from PubMed and Web of Science Core Collection on 1 February 2024. The study included original articles and reviews published in English between 2014 and 2024. EndNote was used to remove duplicates. Two independent authors conducted a comprehensive review of the titles and abstracts of the remaining literature to assess their eligibility for inclusion. Following this initial screening, the authors meticulously analyzed the abstracts and examined the full texts to confirm their suitability for final inclusion.
Key content and findings: A total of 55 articles were finally included for this narrative review. This review mainly summarized and analyzed the diagnostic value of ultrasound (US), X-ray, computed tomography (CT), magnetic resonance imaging (MRI), and nuclear medicine for OPAI, evaluated the advantages and disadvantages of each imaging technology, and tried to illuminate the future direction of diagnostic imaging methods development in this field.
Conclusions: Medical imaging has gained multidisciplinary recognition in the diagnosis of OPAI. Currently, there is an urgent need to establish large-scale, multicenter research projects. It is worth noting the key role of nuclear medicine diagnostic techniques and their unique metabolic information in the diagnosis of OPAI.
Background: Accurate segmentation of rib fractures represents a pivotal procedure within surgical interventions. This meticulous process not only mitigates the likelihood of postoperative complications but also facilitates expedited patient recuperation. However, rib fractures in computed tomography (CT) images exhibit an uneven morphology and are not fixed in position, posing difficulties in segmenting fractures. This study aims to enhance the accuracy of elongated rib fracture segmentation, ultimately improving the efficiency of clinical diagnosis.
Methods: In this study, we propose multi-stream and multi-scale fusion network based on efficient attention UXNet (M2SUXNet). It aims to enhance the segmentation accuracy of elongated rib fractures through multi-scale fusion attention enhancement. Firstly, we propose the multi-stream and multi-scale fusion (M2SF) module in the feature extraction stage. The module is designed with two parallel paths. Each path analyzes the image content using a different feature level. Then, the module effectively distinguishes the more critical feature information in the channel according to the feature weight ratio. The M2SF module integrates information from different scales to obtain comprehensive information on global and local features, achieving a more diverse feature representation. Secondly, the efficient attention (EA) module combines different channel information of input features to integrate channel and spatial features of different channels. The module better combines the context information, establishes the dependency between the space and the channel, enhances the focusing ability of the network on the fractures of different shapes, and improves the segmentation accuracy. Thirdly, the joint loss function of BCE with Logits Loss and Dice Loss is used to solve the sample imbalance problem.
Results: We verified the effectiveness of the proposed model on the public RibFrac dataset. The experimental results demonstrated that the model achieved a Dice coefficient of 75.34%, a joint intersection over union (IoU) of 60.44%, and a precision of 93.79%.
Conclusions: The proposed model for rib fracture segmentation has higher accuracy and feasibility than other existing models. Besides, the M2SUXNet can effectively improve the segmentation performance of elongated rib fractures.
Background: Skin lesion segmentation plays a significant role in skin cancer diagnosis. However, due to the complex shapes, varying sizes, and different color depths, precise segmentation of skin lesions is a challenging task. Therefore, the aim of this study was to design a customized deep learning (DL) model for the precise segmentation of skin lesions, particularly for complex shapes and small target lesions.
Methods: In this study, an adaptive deformable fusion convolutional network (Seg-SkiNet) was proposed. Seg-SkiNet integrated dual-channel convolution encoder (Dual-Conv encoder), Multi-Scale-Multi-Receptive Field Extraction and Refinement (Multi2ER) module, and local-global information interaction fusion decoder (LGI-FSN decoder). In the Dual-Conv encoder, a Dual-Conv module was proposed and cascaded with max pooling in each layer to capture the features of complex-shaped skin lesions. The design of the Dual-Conv module not only effectively captured edge features of the lesions but also learned deep internal features of the lesions. The Multi2ER module was composed of an Atrous Spatial Pyramid Pooling (ASPP) module and an Attention Refinement Module (ARM), and integrated multi-scale features of small target lesions by expanding the receptive field of the convolutional kernel, thereby improving the learning and accurately segmentation of small target lesions. In the LGI-FSN decoder, we integrated convolution and Local-Global Attention Fusion (LGAF) module in each layer to enable interactive fusion of local-global information in feature maps while eliminating redundant feature information. Additionally, we designed a densely connected architecture that fuses the feature maps from a specific layer of the Dual-Conv encoder and all of its preceding layers into the corresponding layer of the LGI-FSN decoder, preventing information loss caused by pooling operations.
Results: We validated the performance of Seg-SkiNet for skin lesion segmentation on three public datasets: International Skin Imaging Collaboration (ISIC)-2016, ISIC-2017, and ISIC-2018. The experimental results demonstrated that Seg-SkiNet achieved a Dice coefficient (DICE) of 93.66%, 89.44% and 92.29%, respectively.
Conclusions: The Seg-SkiNet model performed excellently in segmenting complex-shaped lesions and small target lesions.
Background: In recent years, stenting has been widely used to treat patients with idiopathic intracranial hypertension (IIH) and venous sinus stenosis (VSS); however, research comparing stenting and medical treatment (MT) remains scarce. This study aimed to evaluate the effectiveness of stenting and MT in treating patients with IIH and VSS.
Methods: In this single-center, retrospective, cohort study, the clinical data of patients diagnosed with IIH and VSS at The First Affiliated Hospital of Zhengzhou University from January 2018 to June 2023 were collected for analysis. Based on the treatment approaches, the patients were divided into the following two groups: the stenting group (Group S), and the MT group (Group M). The patients underwent 1:1 propensity score matching (PSM) to compare the improvement in papilledema Frisén grade, lumbar puncture opening pressure (LPOP), and clinical symptoms after treatment.
Results: In total, 128 participants were included in the study. The participants had an average age of 40.0±11.1 years (range, 18-61 years) and a body mass index (BMI) of 27.5±3.3 kg/m2 (range, 20.0-40.0 kg/m2), and 73.43% were female (68 in Group S and 60 in Group M). Compared with the patients in Group M, those in Group S had a longer median time from onset to treatment (2 vs. 1 month, P=0.026), a higher proportion of papilledema (85.3% vs. 68.3%, P=0.033), a higher median pretreatment stenosis rate (80% vs. 70%, P=0.005), and a larger median pretreatment trans-stenotic pressure gradient (15.5 vs. 11.0 mmHg, P=0.001), and a larger median pretreatment LPOP (391.1 vs. 350.5 mmH2O, P=0.006). Following 1:1 PSM, both groups comprised 28 patients each, and there were no statistically significant differences between the two groups in terms of the covariates (all P>0.05). Compared with the patients in Group M, those in Group S had a lower median papilledema Frisén grade (1 vs. 2, P=0.002) and average LPOP (213.0 vs. 259.8 mmHg, P=0.003) at discharge, and showed more pronounced symptom improvement at the time of discharge (P=0.019), and at 6 months (P=0.011) and 12 months (P<0.001) post-discharge.
Conclusions: The research indicated that stenting was quicker and more effective in alleviating papilledema, LPOP, and corresponding symptoms and signs than MT.
Background: Clinical severity and progression of lung disease in cystic fibrosis (CF) are significantly influenced by the degree of lung inflammation. Non-invasive quantitative diagnostic tools are desirable to differentiate structural and inflammatory lung changes in order to help prevent chronic airway disease. This might also be helpful for the evaluation of longitudinal effects of novel therapeutics. Therefore, the present study assesses the quantification of inflammatory lung changes using positron emission tomography/magnetic resonance imaging (PET/MRI) of the lung in children and adolescents with CF and evaluates the possible impact of PET/MRI on individualized therapy management.
Methods: This monocentric, retrospective cohort study included 19 PET/MRI of the lung performed between 2014 and 2021 in 11 patients (16±4.5 years, 8-22 years; 7 females). PET acquisition was performed at least 20 minutes after i.v. application of a weight-adjusted dose of fluor-18-fluorodeoxyglucose (18F-FDG) of 1 MBq/kgBW (mean effective dose, 1.3±0.4 mSv). Lesions of increased uptake were quantified based on standardized uptake values (SUV) and compared to background activity, liver and blood pool. Pulmonary changes were assessed using the established magnetic resonance imaging-CF (MR-CF) score and correlated to inflammatory lesions. Results were correlated to changes in therapy (initiation, modification or discontinuation of therapy after baseline-PET/MRI) based on the electronic medical records.
Results: Uptake was highly increased in 5 cases, moderate in 4 cases, low in 7 cases, no uptake in 3 cases. Most MR-CF score points were assigned to peribronchitis (23%) and air trapping (23%). Metabolically increased lesions were mainly interpreted as consolidations (59%; P<0.001) and mucus plugging (19%, P=0.024). There was a decrease in mean number and volumes of inflammatory lesions (P=0.016 each) and MR-CF score (P=0.047) between baseline and follow-up. After PET/MRI, therapy changed in 18 cases (95%; new medication: 58%, n=11; termination of therapy: 16%, n=3; modification of therapy: 21%, n=4).
Conclusions: In selected cases, pulmonary FDG-PET/MRI can help guide therapeutic decision-making and provide complementary information on CF-related lung changes to conventional MRI at a low radiation exposure.
Background: Recently, deep learning has become a popular area of research, and has revolutionized the diagnosis and prediction of ocular diseases, especially fundus diseases. This study aimed to conduct a bibliometric analysis of deep learning in the field of ophthalmology to describe international research trends and examine the current research directions.
Methods: This cross-sectional bibliometric analysis examined the development of research on deep learning in the field of ophthalmology and its sub-topics from 2015 to 2024. Visualization of similarities (VOS)-viewer was used to analyze and evaluate 3,055 articles. Data from the articles were collected on a specific date (September 11, 2024) and downloaded from the Web of Science Core Collection (WOSCC) in plain-text format.
Results: A total of 3,055 relevant articles on the WOSCC published from 2015 to 2024 were included in this analysis. The first article on the application of deep learning to ophthalmology was published in 2015, and the number of articles on the subject has grown significantly since 2019. China was the most productive country (n=1,187), followed by the United States (n=673). Sun Yat-sen University was the institution with the most publications. Cheng and Bogunovic were the most frequently published authors. The following four different clusters were identified based on a co-occurrence cluster analysis of high-frequency keywords: (I) deep learning for the segmentation and feature extraction of ophthalmic images; (II) deep learning for the automatic detection and classification of ophthalmic images; (III) application of deep learning to ophthalmic imaging techniques; and (IV) deep learning for the diagnosis and management of ophthalmic diseases.
Conclusions: The analysis of fundus images and the clinical application of deep learning techniques have emerged as prominent research areas in the field of ophthalmology. The substantial increase in publications and citations signifies the expanding impact and global collaboration in the application of deep learning research to ophthalmology. By identifying four distinct clusters representing sub-topics in deep learning ophthalmology research, this study contributes to the understanding of current trends and potential future advancements in the field.
Background: Preliminary scientific evidence suggests that freezing of gait (FoG) in patients with Parkinson disease (PD) is linked to noradrenergic dysfunction in the locus coeruleus (LC). However, definitive findings regarding the correlation between FoG occurrence and the LC are lacking. This study thus aimed to investigate the relationship between the FoG occurrence and LC degeneration in patients with PD by analyzing the signal characteristics of the LC in neuromelanin-sensitive magnetic resonance imaging (NM-MRI).
Methods: This study enrolled 22 patients with PD and FoG, 24 patients with PD without FoG, and 13 matched healthy controls (HCs). All participants underwent magnetic resonance imaging (MRI) scanning and clinical assessments. The contrast-to-noise ratio (CNR) of LC was measured on NM-MRI images. We used two statistical models (model 1 and model 2) to screen and adjust for potential confounding factors and evaluated the independent relationship between LC's CNR and FoG.
Results: The statistical models showed that except for the target factor FoG [model 1: β=0.127, 95% confidence interval (CI): 0.019-0.236, P=0.023; model 2: β=0.153, 95% CI: 0.019-0.287, P=0.026], rapid-eye-movement sleep behavior disorder (RBD) (model 1: β=0.182, 95% CI: 0.073-0.291, P=0.002; model 2: β=0.171, 95% CI: 0.048-0.294, P=0.008), and gender (model 1: β=0.150, 95% CI: 0.042-0.257, P=0.007) were independent factors associated with the CNR of the left LC. Among these, RBD had the greatest influence, followed by gender and FoG.
Conclusions: Our findings indicated that the FoG is associated with noradrenergic dysfunction caused by LC degeneration.
Background: Breast cancer (BC) is a common cancer among women worldwide, and although the use of neoadjuvant therapy (NAT) for BC has become more widespread, there is no standardized prediction of the efficacy of NAT for BC. This study aimed to evaluate the value of quantitative parameters of dual-layer detector spectral computed tomography (DLCT) in predicting whether BC patients can achieve pathological complete response (pCR) after NAT.
Methods: Patients who were first diagnosed with BC in Shandong Cancer Hospital and Institute and received only NAT before surgery were selected for participation in this study. All breast computed tomography (CT) imaging examinations were performed using DLCT, within 1 week before initiating NAT. The gold standard for evaluating the effect of NAT is pathologic response established at surgery. The Miller-Payne grading system was applied to assess the response to NAT. Quantitative parameters were extracted from DLCT, including CT value, normalized CT value, iodine concentration (IC), normalized iodine concentration (NIC), the slope of the spectral Hounsfield unit (HU) curve, effective atomic number, and the normalized effective atomic number. The Mann-Whitney U test was used to compare the distribution differences of DLCT quantitative parameters between the pCR group and the non-pCR group. The diagnostic performance of the quantitative parameters was analyzed by receiver operating characteristic curve.
Results: In the neoadjuvant chemotherapy group (n=80), compared with the non-pCR group, the slope of the spectral HU curve, IC, effective atomic number, and NIC of arterial phase in the pCR group were higher, and the difference was statistically significant (P<0.05); area under the curve (AUC): 0.768, 0.791, 0.834, and 0.770, respectively. In the neoadjuvant targeted therapy group (n=40), compared with the pCR group, the CT value, IC, effective atomic number, and NIC of the arterial phase in the non-pCR group were higher, and the difference was statistically significant (P<0.05); AUC: 0.844, 0.813, 0.802, and 0.766, respectively. There was no significant difference (P>0.05) in DLCT venous phase quantitative parameters between pCR and non-pCR in 70 patients treated with NAT.
Conclusions: The study suggested a possibility that DLCT provided a potential tool to develop a model for predicting pCR to NAT in BC.
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