Conventional Imaging Research Articles

Integral imaging systems using interleaved Fresnel lenses and transmissive display panels

Integral imaging reproduces the light-ray space of a scene to realize a 3D vision. In particular, a coarse integral imaging system, featuring elemental lenses large enough to cover more pixels than the number of views, holds promise in applications like interactive visualization systems and automobile head-up displays (HUDs) for 3D display. However, conventional coarse integral imaging and coarse integral volumetric imaging systems face challenges related to visible seams in the lens array and the emergence of moiré patterns caused by layered liquid crystal panels (LCDs), that significantly degrade image quality. This paper presents a solution to these problems by employing an integrated interleaved Fresnel lens sheet to achieve seamless 3D images with smooth motion parallax while using layered transmissive panels to generate volumetric images free from moiré.

Liquid Biopsy in Whole Blood for Identification of Gene Expression Patterns (mRNA and miRNA) Associated with Recurrence of Glioblastoma WHO CNS Grade 4.

GBM WHO CNS Grade 4 represents a major challenge for oncology due to its aggressive behavior. Conventional imaging has restrictions in detecting tumor recurrence. This prospective study aims to identify gene-based biomarkers in whole blood instead of isolating exosomes for the early detection of tumor recurrence. Blood samples (n = 33) were collected from seven GBM patients at time points before and after surgery as well as upon tumor recurrence. Four tumor tissue samples were assessed in parallel. Next-generation sequencing (NGS), including mRNA-seq and small RNA-seq, was used to analyze gene expression profiles in blood samples and tumor tissues. A novel filtering pipeline was invented to narrow down potential candidate genes. In total, between 6-93 mRNA and 1-19 small RNA candidates could be identified among the seven patients. The overlap of genes between the patients was minimal, indicating significant inter-individual variance among GBM patients. In summary, this prospective study supports the applicability of gene expression measurements in whole blood for the detection of tumor recurrence. It might provide an alternative to the challenging workflow of liquid biopsy after laborious exosome isolation from whole blood.

Ultra-High Contrast MRI: The Whiteout Sign Shown with Divided Subtracted Inversion Recovery (dSIR) Sequences in Post-Insult Leukoencephalopathy Syndromes (PILS).

Ultra-high contrast (UHC) MRI describes forms of MRI in which little or no contrast is seen on conventional MRI images but very high contrast is seen with UHC techniques. One of these techniques uses the divided subtracted inversion recovery (dSIR) sequence, which, in modelling studies, can produce ten times the contrast of conventional inversion recovery (IR) sequences. When used in cases of mild traumatic brain injury (mTBI), the dSIR sequence frequently shows extensive abnormalities in white matter that appears normal when imaged with conventional T2-fluid-attenuated IR (T2-FLAIR) sequences. The changes are bilateral and symmetrical in white matter of the cerebral and cerebellar hemispheres. They partially spare the anterior and posterior central corpus callosum and peripheral white matter of the cerebral hemispheres and are described as the whiteout sign. In addition to mTBI, the whiteout sign has also been seen in methamphetamine use disorder and Grinker's myelinopathy (delayed post-hypoxic leukoencephalopathy) in the absence of abnormalities on T2-FLAIR images, and is a central component of post-insult leukoencephalopathy syndromes. This paper describes the concept of ultra-high contrast MRI, the whiteout sign, the theory underlying the use of dSIR sequences and post-insult leukoencephalopathy syndromes.

Incidence and clinical analysis of asymptomatic intracranial hemorrhage in neonates with cerebral hypoxic-ischaemic risk based on multisequence MR images

The incidence and clinical distribution of intracranial haemorrhage (ICH) in neonates at risk of cerebral hypoxia–ischaemia have not been reported in specific studies. Based on conventional magnetic resonance imaging (MRI) versus susceptibility weighted imaging (SWI), this study aimed to analyse the occurrence of asymptomatic ICH in newborns with or without risk of cerebral hypoxia–ischaemia and to accumulate objective data for clinical evaluations of high-risk neonates and corresponding response strategies. 317 newborns were included. MRI revealed that the overall incidence of ICH was 59.31%. The most common subtype was intracranial extracerebral haemorrhage (ICECH) which included subarachnoid haemorrhage (SAH) and subdural haemorrhage (SDH). ICECH accounted for 92.02% of ICH. The positive detection rate of ICECH by SWI was significantly higher than that by T1WI. The incidence of total ICH, ICECH and SAH was greater among children who were delivered vaginally than among those who underwent caesarean delivery. Asymptomatic neonatal ICH may be a common complication of the neonatal birth process, and SWI may improve the detection rate. Transvaginal delivery and a weight greater than 2500 g were associated with a high incidence of ICECH in neonates. The impact of neonatal cerebral hypoxia–ischaemia risk factors on the occurrence of asymptomatic ICH may be negligible.

Efficient colorectal polyp segmentation using wavelet transformation and AdaptUNet: A hybrid U-Net

The prevalence of colorectal cancer, primarily emerging from polyps, underscores the importance of their early detection in colonoscopy images. Due to the inherent complexity and variability of polyp appearances, the task stands difficult despite recent advances in medical technology. To tackle these challenges, a deep learning model featuring a customized U-Net architecture, AdaptUNet is proposed. Attention mechanisms and skip connections facilitate the effective combination of low-level details and high-level contextual information for accurate polyp segmentation. Further, wavelet transformations are used to extract useful features overlooked in conventional image processing. The model achieves benchmark results with a Dice coefficient of 0.9104, an Intersection over Union (IoU) coefficient of 0.8368, and a Balanced Accuracy of 0.9880 on the CVC-300 dataset. Additionally, it shows exceptional performance on other datasets, including Kvasir-SEG and Etis-LaribDB. Training was performed using the Hyper Kvasir segmented images dataset, further evidencing the model's ability to handle diverse data inputs. The proposed method offers a comprehensive and efficient implementation for polyp detection without compromising performance, thus promising an improved precision and reduction in manual labour for colorectal polyp detection.

Survival Patterns Based on First-site–specific Visceral Metastatic Prostate Cancer: Are Outcomes of Visceral Metastases the Same?

Background and objectiveVisceral metastatic disease in prostate cancer patients conveys a poor prognosis. Using advanced imaging techniques, studies have demonstrated increasing detection rates of visceral metastasis. Visceral metastases are now seen in up to 30–60% of prostate cancer patients. Survival patterns of site-specific visceral metastasis are described poorly in the literature. Here, we sought to investigate survival patterns in prostate cancer patients according to their first detected site of visceral metastasis. MethodsRetrospectively, we identified 203 prostate cancer patients with visceral metastases from the Mayo Clinic Advanced Prostate Cancer Registry. Patients were divided into three groups according to the first site of visceral metastases detected: lung, brain, or liver. Visceral metastases were detected primarily on either metabolic imaging (C-11 choline) or prostate-specific membrane antigen positron emission tomography computed tomography (CT) scan. Confirmation of visceral metastasis diagnosis was established with either biopsy when feasible or focused conventional imaging, including focused CT or magnetic resonance imaging. Overall survival and cancer-specific survival were estimated using the Kaplan-Meier method. Univariate and multivariate Cox regression model was conducted to assess different variables that affect overall and cancer-specific survival. Key findings and limitationsOver a median (interquartile range) follow-up duration of 16.2 (3.9–49.8) mo, the overall and cancer-specific survival of the entire cohort suggests better survival patterns in patients with first-site lung metastases than in patients with first-site brain or liver metastases (p < 0.0001). In univariate and multivariate analyses of factors impacting patients’ overall and cancer-specific survival, a high prostate-specific antigen level at diagnosis of visceral metastasis, concomitant bone and lymph node disease, and more than four visceral metastases were associated with poor overall and cancer-specific survival (p < 0.05). On the contrary, first-site lung metastasis was associated with improved overall and cancer-specific survival, compared with first-site liver and brain metastases (p < 0.001). Conclusions and clinical implicationsThese data suggest that prostate cancer patients with visceral metastatic disease have varying survival patterns according to first-site detected visceral metastasis. In our cohort, patients with first-site lung metastasis demonstrated better survival outcomes than patients with first-site brain or liver metastasis. Patient summaryOur study explored the survival outcomes among patients with visceral metastatic prostate cancer employing cutting-edge imaging methods. Prostate cancer patients with metastases to different organs have different survival rates. Patients with cancer spreading to the lungs first showed better survival than those with cancer spreading to the brain or liver first.

Choledochal cancer region detection in hyperspectral images using U-Net based models

Cholangiocarcinoma (CCA) is a type of cancer that forms in the bile duct that carry digestive fluid from the liver. CCA is the primary form of liver cancer that affects population ranging from age 60 to 69 years. CCA is difficult to diagnose at an early stage. Hyperspectral (HS) imaging is an advanced imaging technique that combines spectroscopy with conventional imaging. HS imaging is an emerging field of study which can be used for early CCA detection. HS imaging involves capturing images across various spectral bands, which forms a three-dimensional data cube often called as hyperspectral data cube. In this study, we have utilized U-Net based models, namely U-Net and DenseUNet were used to perform semantic segmentation on the HS images of CCA tissues. A band selective approach was employed to derive a subset of meaningful bands based on the spectrum plot from the HS image. The HS images are further preprocessed with Principal Component Analysis (PCA). The models were further evaluated by computing the accuracy, AUC (Area under the ROC curve), sensitivity and specificity metrics. The proposed models, namely, U-Net and DenseUNet reported an overall accuracy of 73.47% and 77.09% respectively. The DenseUNet models outperforms the U-Net model on every evaluation metric. The proposed models were also compared with other state-of-the-art (SOTA) models trained on various HS dataset. This study explores the application of HS imaging in carcinoma detection. The findings of this study could be used for further enhancement of the approach.

Dynamic off-resonance correction improves functional image analysis in fMRI of awake behaving non-human primates.

Use of functional MRI in awake non-human primate (NHPs) has recently increased. Scanning animals while awake makes data collection possible in the absence of anesthetic modulation and with an extended range of possible experimental designs. Robust awake NHP imaging however is challenging due to the strong artifacts caused by time-varying off-resonance changes introduced by the animal's body motion. In this study, we sought to thoroughly investigate the effect of a newly proposed dynamic off-resonance correction method on brain activation estimates using extended awake NHP data. We correct for dynamic B0 changes in reconstruction of highly accelerated simultaneous multi-slice EPI acquisitions by estimating and correcting for dynamic field perturbations. Functional MRI data were collected in four male rhesus monkeys performing a decision-making task in the scanner, and analyses of improvements in sensitivity and reliability were performed compared to conventional image reconstruction. Applying the correction resulted in reduced bias and improved temporal stability in the reconstructed time-series data. We found increased sensitivity to functional activation at the individual and group levels, as well as improved reliability of statistical parameter estimates. Our results show significant improvements in image fidelity using our proposed correction strategy, as well as greatly enhanced and more reliable activation estimates in GLM analyses.

Clinical boundary conditions for propagation-based X-ray phase contrast imaging: from bio-sample models targeting to clinical applications.

Typical propagation-based X-ray phase contrast imaging (PB-PCI) experiments using polyenergetic sources are tested in very ideal conditions: low-energy spectrum (mainly characteristic X-rays), small thickness and homogeneous materials considered weakly absorbing objects, large object-to-detector distance, long exposure times and non-clinical detector. Explore PB-PCI features using boundary conditions imposed by a low power polychromatic X-ray source (X-ray spectrum without characteristic X-rays), thick and heterogenous materials and a small area imaging detector with high low-detection radiation threshold, elements commonly found in a clinical scenario. A PB-PCI setup implemented using a microfocus X-ray source and a dental imaging detector was characterized in terms of different spectra and geometric parameters on the acquired images. Test phantoms containing fibers and homogeneous materials with close attenuation characteristics and animal bone and mixed soft tissues (bio-sample models) were analyzed. Contrast to Noise Ratio (CNR), system spatial resolution and Kerma values were obtained for all images. Phase contrast images showed CNR up to 15% higher than conventional contact images. Moreover, it is better seen when large magnifications (>3) and object-to-detector distances (>13 cm) were used. The influence of the spectrum was not appreciable due to the low efficiency of the detector (thin scintillator screen) at high energies. Despite the clinical boundary condition used in this work, regarding the X-ray spectrum, thick samples, and detection system, it was possible to acquire phase contrast images of biological samples.

Soft Tissue Retraction Maneuver in Cone Beam Computed Tomography Prior to Crown-Lengthening Procedure-A Technical Note.

Background: An accurate determination of the biological width and the relationship of the cemento-enamel junction with the border of the alveolar bone is crucial during a clinical crown-lengthening (CCL) procedure. The aim of this study was to present a technical note about the retraction techniques in cone beam computed tomography (CBCT) prior to CCL, highlighting the significant enhancement in procedural accuracy and predictability that these techniques offer. Methods: Clinical and radiological examinations should be performed before a CCL procedure. It is necessary to determine the length of the tooth crowns, the periodontal pockets' depth, and the phenotype of the gingiva. The ideal CBCT examination should be performed with soft tissue retraction. This can be achieved using retractors or cotton rolls. Results: Retraction of the lips, cheeks, and tongue allows one to assess the marginal gingiva, the cemento-enamel junction, and the alveolar bone. A detailed plan of the CCL procedure, which involves retraction, ensures both the aesthetic appeal and the achievement of a newly defined gingival zenith, enhancing the overall visual harmony. Conclusions: Compared with conventional radiographic imaging, the soft tissue retraction maneuver in CBCT prior to CCL surgery offers an effective approach to the evaluation and diagnosis of soft and hard tissue. This is because of the detailed planning of the aesthetic CCL procedure. Such an approach leads to superior aesthetic outcomes in dentistry, contributing to the advancement of aesthetic dentistry through a harmonious blend of art and science.

Cardiac evaluation of patients with juvenile dermatomyositis.

The present study aims to evaluate possible cardiac involvement in juvenile dermatomyositis (JDM) patients by conventional methods and cardiac magnetic resonance imaging (MRI) along with a systematic review of the literature on cardiac features in JDM. The study group consisted of JDM patients who underwent cardiac MRI. We conducted a systematic review of the published literature involving JDM patients with cardiac involvement. In the present study, although baseline cardiologic evaluations including electrocardiography and echocardiography were within normal limits, we showed late gadolinium enhancement on cardiac MRI in 3 of 11 JDM patients. In the literature review, we identified 25 articles related to cardiac involvement in JDM. However, none of them, except one case report, included cardiac MRI of JDM patients. Cardiac abnormalities have been reported among the less frequent findings in patients with JDM. Cardiovascular complications during the long-term disease course are a leading cause of morbidity and mortality in these patients. Early detection of cardiac involvement by cardiac MRI in patients with JDM and aggressive treatment of them may improve the clinical course of these patients. The myocardium in patients with JDM may be involved by inflammation. Myocardial involvement may be evaluated by using contrast-enhanced cardiac MRI. This is the first study evaluating cardiac involvement by cardiac MRI in JDM patients. MRI may show early cardiac involvement in patients whose baseline cardiologic evaluations are within normal limits. Early detection of cardiac involvement by cardiac MRI may improve the long-term prognosis of patients with JDM.

Larger Tumor Size and Elevated Serum Chromogranin A Levels Predict Metastatic Disease on DOTATATE Imaging in Patients with Gastroenteropancreatic Neuroendocrine Tumors.

DOTATATE PET/CT (DOTATATE) is superior to conventional imaging in detecting metastasis for gastroenteropancreatic neuroendocrine tumors (GEP-NETs). However, limited availability, high-cost, and additive radiation exposure necessitate guidelines for its use. This study seeks to investigate the relationship between clinical characteristics and metastasis on DOTATATE. This was a retrospective analysis of 815 patients who underwent DOTATATE at UCLA from 2014 to 2022. After applying inclusion and exclusion criteria, the study cohort consisted of 163 patients with pathologically diagnosed GEP-NETs, who either underwent primary tumor resection within 1-year prior, or had not undergone resection at the time of DOTATATE imaging. The presence of metastasis was determined using DOTATATE. Fisher's exact test, chi-squared test, and Mann-Whitney test were conducted to compare intergroup difference. Multivariate analysis was performed to identify clinical characteristics associated with metastasis on DOTATATE. Of patients with GEP-NETs, 40.5% (n = 66) were diagnosed with metastases by using DOTATATE. Those with metastatic disease were more likely to exhibit a larger primary tumor size (median 3.4 vs. 1.2, cm, P<0.001), elevated serum chromogranin A level (CgA, median 208 vs. 97, mg/ml, P = 0.005), and higher tumor grade (P < 0.001). Primary tumor size ≥2 cm and serum CgA level ≥150 ng/mL for metastatic disease had a sensitivity and specificity of 64% and 89%, and 72% and 59%, respectively. Multivariate analysis demonstrated that primary tumor size (≥2/<2, cm, odds ratio [OR] 47.90, P < 0.001), tumor functionality (functional/nonfunctional, adjusted OR 10.17 P = 0.008), serum CgA level (≥150/<150, ng/ml, OR 6.25, P = 0.005), and tumor grade G2 (G2/G1, OR 9.6, P < 0.001) were independently associated with metastases on DOTATATE. Among patients with GEP-NETs, primary tumor size ≥2 cm, serum CgA level ≥150 ng/mL, and tumor grade G2 are associated with an increased risk of metastases on DOTATATE, and these predictors may be helpful to identify patients where DOTATATE is indicated for complete staging.

Dual-source dual-energy CT and deep learning for equivocal lymph nodes on CT images for thyroid cancer.

This study investigated the diagnostic performance of dual-energy computed tomography (CT) and deep learning for the preoperative classification of equivocal lymph nodes (LNs) on CT images in thyroid cancer patients. In this prospective study, from October 2020 to March 2021, 375 patients with thyroid disease underwent thin-section dual-energy thyroid CT at a small field of view (FOV) and thyroid surgery. The data of 183 patients with 281 LNs were analyzed. The targeted LNs were negative or equivocal on small FOV CT images. Six deep-learning models were used to classify the LNs on conventional CT images. The performance of all models was compared with pathology reports. Of the 281 LNs, 65.5% had a short diameter of less than 4 mm. Multiple quantitative dual-energy CT parameters significantly differed between benign and malignant LNs. Multivariable logistic regression analyses showed that the best combination of parameters had an area under the curve (AUC) of 0.857, with excellent consistency and discrimination, and its diagnostic accuracy and sensitivity were 74.4% and 84.2%, respectively (p < 0.001). The visual geometry group 16 (VGG16) based model achieved the best accuracy (86%) and sensitivity (88%) in differentiating between benign and malignant LNs, with an AUC of 0.89. The VGG16 model based on small FOV CT images showed better diagnostic accuracy and sensitivity than the spectral parameter model. Our study presents a noninvasive and convenient imaging biomarker to predict malignant LNs without suspicious CT features in thyroid cancer patients. Our study presents a deep-learning-based model to predict malignant lymph nodes in thyroid cancer without suspicious features on conventional CT images, which shows better diagnostic accuracy and sensitivity than the regression model based on spectral parameters. Many cervical lymph nodes (LNs) do not express suspicious features on conventional computed tomography (CT). Dual-energy CT parameters can distinguish between benign and malignant LNs. Visual geometry group 16 model shows superior diagnostic accuracy and sensitivity for malignant LNs.

SOCR‐YOLO: Small Objects Detection Algorithm in Medical Images

ABSTRACTIn the field of medical image analysis, object detection plays a crucial role by providing interpretable diagnostic information to healthcare professionals. Although current object detection models have achieved remarkable success in conventional images, their performance in detecting abnormalities in medical images has not been as satisfactory. This is primarily due to the complexity of anatomical structures in medical images, and the fact that some lesions may have subtle features, particularly in the case of early‐stage, small‐scale abnormalities. To address this challenge, we introduce SOCR‐YOLO, a novel lesion detection model with online convolutional reparameterization based on channel shuffling. First, it employs the SOCR (Shuffled Channel with Online Convolutional Re‐parameterization) module to establish a connection between feature concatenation and computational efficiency, aiming to extract more comprehensive information while reducing time consumption. Second, it incorporates the Bi‐FPN structure to achieve multiscale feature fusion. Lastly, the loss function has been optimized to improve the model training process. We evaluated two datasets, chest x‐ray (Vindr‐CXR) and brain tumor (Br35H), provided by the Kaggle competition. Experimental results show that the proposed method has outperformed several state‐of‐the‐art models, including YOLOv8, YOLO‐NAS, and RT‐DETR, in both speed and accuracy. Notably, in the context of chest x‐ray anomaly detection, SOCR‐YOLO exhibits a 1.8% enhancement in accuracy over YOLOv8 while simultaneously reducing floating‐point operations by 26.3%. Additionally, a similar 1.8% improvement in accuracy is observed in the detection of brain tumors. The results indicate the superior ability of our model to detect multiscale variations and small lesions.

Source localization comparison and combination of OPM-MEG and fMRI to detect sensorimotor cortex responses

Background and objectives:The exploration of various neuroimaging techniques have become focal points within the field of neuroscience research. Magnetoencephalography based on optically pumped magnetometers (OPM-MEG) has shown significant potential to be the next generation of functional neuroimaging with the advantages of high signal intensity and flexible sensor arrangement. In this study, we constructed a 31-channel OPM-MEG system and performed a preliminary comparison of the temporal and spatial relationship between magnetic responses measured by OPM-MEG and blood-oxygen-level-dependent signals detected by functional magnetic resonance imaging (fMRI) during a grasping task. Methods:For OPM-MEG, the β-band (15–30 Hz) oscillatory activities can be reliably detected across multiple subjects and multiple session runs. To effectively localize the inhibitory oscillatory activities, a source power-spectrum ratio-based imaging method was proposed. This approach was compared with conventional source imaging methods, such as minimum norm-type and beamformer methods, and was applied in OPM-MEG source analysis. Subsequently, the spatial and temporal responses at the source-level between OPM-MEG and fMRI were analyzed. Results:The effectiveness of the proposed method was confirmed through simulations compared to benchmark methods. Our demonstration revealed an average spatial separation of 10.57 ± 4.41 mm between the localization results of OPM-MEG and fMRI across four subjects. Furthermore, the fMRI-constrained OPM-MEG localization results indicated a more focused imaging extent. Conclusions:Taken together, the performance exhibited by OPM-MEG positions it as a potential instrument for functional surgery assessment.

The role of neuromorphic and biomimetic sensors

PurposeThe purpose of this paper is to provide details of biomimetic and neuromorphic sensor research and developments and discuss their applications in robotics.Design/methodology/approachFollowing a short introduction, this first provides examples of recent biomimetic gripping and sensing skin research and developments. It then considers neuromorphic vision sensing technology and its potential robotic applications. Finally, brief conclusions are drawn.FindingsBiomimetics aims to exploit mechanisms, structures and signal processing techniques which occur in the natural world. Biomimetic sensors and control techniques can impart robots with a range of enhanced capabilities such as learning, gripping and multidimensional tactile sensing. Neuromorphic vision sensors offer several key operation benefits over conventional frame-based imaging techniques. Robotic applications are still largely at the research stage but uses are anticipated in enhanced safety systems in autonomous vehicles and in robotic gripping.Originality/valueThis illustrates how tactile and imaging sensors based on biological principles can contribute to imparting robots with enhanced capabilities.

Is conventional magnetic resonance imaging superior to radiography in the functional integrity evaluation of anterior cruciate ligament in patients with knee osteoarthritis?

BackgroundThe functional integrity of the anterior cruciate ligament (ACL) influences surgical decision-making in patients with knee osteoarthritis (KOA). This study aimed to compare the diagnostic value of radiography and magnetic resonance imaging (MRI) in determining the functional status of ACL.MethodsWe analyzed 306 knees retrospectively using preoperative hip-to-ankle anteroposterior standing (APS) radiographs, anteroposterior (AP) and lateral knee radiographs, AP valgus stress (VS) force radiographs, and standard orthogonal MRI. Based on the intraoperative visualization, the knees were grouped into ACL functionally-intact and ACL functionally-deficient (ACLD) groups. The diagnostic validity and reliability were calculated based on the radiograph parameters such as hip-knee-ankle angle (HKA), medial proximal tibial angle (MPTA), lateral distal femoral angle (LDFA), posterior tibial slope (PTS), sagittal tibiofemoral subluxation (STFS), coronal tibiofemoral subluxation (CTFS), joint line convergence angle (JLCA), the maximum wear point of the proximal tibia plateau (MWPPT%), and MRI parameters including ACL grades and MWPPT%.ResultsHKA, MPTA, PTS, STFS, JLCA, and CTFS on APS and AP radiographs, and MWPPT% on radiographs and MRI showed a significant diagnostic value (P < 0.05). There were no statistically significant differences in the single parameters from radiographs and MRI. After constructing the logistic regression models, MRI showed higher sensitivity, specificity, and accuracy, reaching 96.8%, 79.9%, and 83.3%, respectively (P < 0.001).ConclusionsIn patients with KOA, the diagnostic value of single radiographic or MRI parameter in assessing the functional integrity of the ACL are equivalent. However, by constructing predictive models, MRI could significantly improve diagnostic validity compared with radiography.

Innovative Rotating SAR Mode for 3D Imaging of Buildings

Three-dimensional SAR imaging of urban buildings is currently a hotspot in the research area of remote sensing. Synthetic Aperture Radar (SAR) offers all-time, all-weather, high-resolution capacity, and is an important tool for the monitoring of building health. Buildings have geometric distortion in conventional 2D SAR images, which brings great difficulties to the interpretation of SAR images. This paper proposes a novel Rotating SAR (RSAR) mode, which acquires 3D information of buildings from two different angles in a single rotation. This new RSAR mode takes the center of a straight track as its rotation center, and obtains images of the same facade of a building from two different angles. By utilizing the differences in geometric distortion of buildings in the image pair, the 3D structure of the building is reconstructed. Compared to the existing tomographic SAR or circular SAR, this method does not require multiple flights in different elevations or observations from varying aspect angles, and greatly simplifies data acquisition. Furthermore, both simulation analysis and actual data experiment have verified the effectiveness of the proposed method.

A Clinical Model predicting the 90-Day Prognosis after Mechanical Thrombectomy in Patients with Acute Ischemic Stroke: A Retrospective Study.

Mechanical thrombectomy (MT) is usually recommended for acute ischemic stroke (AIS) due to large vessel occlusion (LVO) within the time window (6 hours after the disease onset). However, poor prognosis in acute great vascular occlusive stroke after MT, which is not an uncommon occurrence, can be attributed to an absence of appropriate postoperative monitoring. Transcranial Doppler (TCD) ultrasound and quantitative electroencephalography (QEEG) offer the advantages of fast, convenient, and bedside examinations compared with conventional imaging techniques. We aimed to analyze the predictive performance of clinical factors, Transcranial Doppler (TCD) ultrasound and quantitative electroencephalography (QEEG) for the prognosis of patients with acute ischemic stroke (AIS) due to large vessel occlusion (LVO) at 90 days after discharge. Patients achieved revascularization through MT performed within 6 hours after the onset of AIS due to LVO were included. We use the data to build four predictive models of prognosis and compared the predictive performance measured by the area under the curve, sensitivity, and specificity. A total of 74 patients were included in the study. Among them, 47 patients had a poor prognosis (63.5%) on discharge, and 45 patients had a poor prognosis (60.8%) at 90 days after discharge. Independent predictors of poor prognosis at 90 days after discharge were identified as follows: age, NIHSS score on admission, PI on the affected/healthy side, and RAP. Among the four models built, AUC was the highest (reaching 0.831) when age was combined with NIHSS score on admission, TCD parameters (VD on the affected side, PI on the affected/healthy side), and QEEG parameter (RAP) for prognostic prediction. However, AUC of the four predictive models did not differ significantly (P>0.05). Age, NIHSS score on admission, TCD parameters, and QEEG parameter were independent predictors of the prognosis at 90 days after discharge in patients receiving MT for AIS due to LVO in the anterior circulation. The model combining the above four parameters may be helpful for prognostic prediction in such patients.

Diagnostic Performance of F-18 FDG PET/CT in the Detection of Recurrent Colorectal Cancer: Correlation with Biochemical Markers and Conventional Imaging Modalities.

Background/Objectives: Although the role of PET/CT imaging is well established in oncology, its diagnostic value in routine monitoring for recurrent colorectal cancer (CRC) is still controversial. The aim was to evaluate the diagnostic value of F-18 FDG PET/CT in detecting recurrent CRC in correlation with CEA, CA 19-9 levels, and conventional imaging modalities (CIM). Methods: Between 2009 and 2023, a retrospective study was performed including 134 CRC patients referred for PET/CT imaging on the suspicion of recurrence, based on elevated CEA and/or CA 19-9 and/or equivocal CIM findings. According to our institution's Tumor Board CRC protocol, after the initial treatment, which was dependent on the TNM stage (neoadjuvant therapy, primary resection, or adjuvant treatment), patients underwent a standard 5-year surveillance including CEA and CA 19-9 measurements, CIM, and colonoscopy, every six months. The statistics, including univariate and multivariate analyses were conducted using the IBM SPSS 20.0 statistical software. p-values < 0.05 were considered statistically significant. Results: Recurrent CRC was confirmed in 54/134 (40.3%) patients with elevated tumor markers. PET/CT showed high diagnostic performance in detecting recurrent CRC with sensitivity, specificity, PPV, NPV, and accuracy of 94.4%, 82.5%, 78.5%, 95.7%, and 87.3%, respectively. The CEA showed a high sensitivity of 98.1% but both low specificity and accuracy of 15% and 48.5%, respectively. The sensitivity, specificity, and accuracy for CA 19-9 and CIM for diagnosis of CRC recurrence were 44.4%, 67.5%, 58.2%, and 51.9%, 98.8%, 79.9%, respectively. The AUC for PET/CT, elevated CEA levels, CIM, and elevated CA 19-9 levels was 0.885 (95% CI: 0.824-0.946; p < 0.001), 0.844 (95% CI: 0.772-0.916; p < 0.001), 0.753 (95% CI: 0.612-0.844; p < 0.001), and 0.547 (95% CI: 0.442-0.652; p = 0.358), respectively. Univariate analysis showed that both PET/CT and CIM positive results were highly associated with CRC recurrence (p < 0.001 and p < 0.001, respectively). At the same time, gender, mucinous tumor type, presence of initial lymph node metastasis (N+), and presence of initial distant metastasis (M+) had no significance (p = 0.211, p = 0.158, p = 0.583, and p = 0.201, respectively). Our multivariate analysis showed that independent predictors for CRC recurrence are positive PET/CT scans (p < 0.001), positive CIM results (p = 0.001), and elevated CA 19-9 levels (p = 0.023). Although CA 19-9 was not detected as a statistically significant predictor in the univariate analysis (p = 0.358), in a multivariate analysis it was recognized as a significant predicting factor in detecting the CRC recurrence (p = 0.023). Conclusions: F-18 FDG PET/CT showed high diagnostic efficacy in CRC recurrence detection, in correlation with CEA levels, CA 19-9 levels, and CIM. This imaging modality should be routinely integrated into the post-operative follow-op in patients with elevated tumor markers.