Imaging Parameters Research Articles

COVID-19 associated pulmonary embolism: clinical, biochemical and CT imaging findings.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection represented a disruptive pathology that emerged in late 2019 with profound implications ranging from individual health to health systems and world economy. Our study aimed to evaluate clinical, biochemical and computerized tomography (CT) parameters values in determining the severity of pulmonary embolism (PE) associated with COVID-19. We performed an observational cohort study evaluating demographic, clinical, biochemical, coagulation markers, as well as CT imaging parameters. In our study on 186 patients with COVID-19, we found that 31 patients (16,66%) had pulmonary embolism. Significant correlations for the patients with PE were detected in C-reactive protein, lactate dehydrogenase, serum ferritin, IL-6, serum myoglobin, NT-proBNP, D-dimers, serum proteins, transaminases as well as white cell blood counts. Patients with pulmonary embolism had a more severe lung involvement, with thrombi distribution mainly involving the lower lobes. Early identification of PE is an important step for timely and efficient treatment in the intensive care management of COVID-19 patients. Our study showed that high plasmatic values of lactate dehydrogenase, ferritin, IL-6, white blood cells and D-dimers and low proteins serum levels are strongly linked with COVID-19-associated pulmonary embolism.

A paradigm shift in oncology imaging: a prospective cross-sectional study to assess low-dose deep learning image reconstruction versus standard-dose iterative reconstruction for comprehensive lesion detection in dual-energy computed tomography.

Low-kiloelectron volt (keV) virtual monochromatic images (VMIs) from low-dose (LD) dual-energy computed tomography (DECT) can enhance lesion contrast but suffer from high image noise. Recently, a deep learning image reconstruction (DLIR) algorithm has been developed and shown significant potential in suppressing image noise and improving image quality. To date, the capacity of LD low-keV thoracic-abdominal-pelvic DECT with DLIR to detect various types of tumor lesions have not been assessed. Hence, this study aimed to evaluate the image quality and lesion detection capabilities of LD VMIs using DLIR with thoracic-abdominal-pelvic DECT versus standard-dose (SD) iterative reconstruction (IR) in oncology patients. This prospective intraindividual study included 56 oncology patients who received a SD (13.86 mGy) and a consecutive LD (7.15 mGy) thoracic-abdominal-pelvic DECT from April 2022 to July 2023 at The First Affiliated hospital of Zhengzhou University. SD VMIs were reconstructed using IR at 50 keV (SD-IR50 keV), while LD VMIs were processed using DLIR at 50 keV (LD-DL50 keV) and 40 keV (LD-DL40 keV), respectively. Quantitative image parameters [computed tomography (CT) values, image noise, and contrast-to-noise ratios (CNRs)], qualitative metrics (image noise, vessel conspicuity, image contrast, artificial sensation, and overall image quality), and lesion CNRs and conspicuity were compared. The lesion detection rates in the SD-IR50 keV, LD-DL50 keV, and LD-DL40 keV VMIs were assessed according to lesion location (lung, liver, and lymph), type, and size. Repeated measures analysis of variance and the Friedman test were applied for comparing quantitative and qualitative measures, respectively. The Cochran Q test was used for comparing lesion detection rates. Compared to SD-IR50 keV VMIs, LD-DL50 keV VMIs showed similar CT values and image noise (P>0.05), similar (P>0.05) or higher(P<0.05) CNRs, similar (P>0.05) or superior (P<0.05) perceptual image quality, and similar (P>0.05) or higher (P<0.001) lesion CNR and conspicuity. LD-DL40 keV VMIs exhibited higher CT values (by 40.4-47.1%) and CNRs (by 21.8-39.8%) (P<0.001), equivalent image noise, similar (P>0.05) or superior (P<0.05) perceptual image quality except for artificial sensation, and similar (P>0.05) or higher (P<0.001) lesion CNRs (by 16.5-46.3%) and conspicuity. The VMIs of LD-DL50 keV and LD-DL40 keV were consistent with those of SD-IR50 keV in terms of lesion detection capability in pulmonary nodules [SD-IR50 keV vs. LD-DL50 keV vs. LD-DL40 keV: 88/88 (100.0%) vs. 88/88 (100.0%) vs. 88/88 (100.0%); P>0.99], for lymph nodes [125/126 (99.2%) vs. 123/126 (97.6%) vs. 124/126 (98.4%); P>0.05], and high-contrast liver lesions [12/12 (100.0%) vs. 12/12 (100.0%) vs. 12/12 (100.0%); P>0.05], but not for small liver lesions (≤0.5 cm) [63/65 (96.9%) vs. 43/65 (66.2%) vs. 51/65 (78.5%); P<0.05] or low-contrast liver lesions [198/200 (99.0%) vs. 174/200 (87.0%) vs. 183/200 (91.5%); P<0.05]. VMIs at 40 keV with DLIR enables a 50% decrease in the radiation dose while largely maintaining diagnostic capabilities for multidetection of pulmonary nodules, lymph nodes, and liver lesions in oncology patients.

Arrival-Time Parametric Imaging in Contrast-Enhanced Ultrasound for Diagnosing Fibrosis in Primary Biliary Cholangitis: Erratum.

Arrival-Time Parametric Imaging in Contrast-Enhanced Ultrasound for Diagnosing Fibrosis in Primary Biliary Cholangitis: Erratum.

Quantitative Total-Body Imaging of Blood Flow with High Temporal Resolution Early Dynamic 18F-Fluorodeoxyglucose PET Kinetic Modeling.

The first two minutes of dynamic PET scans were reconstructed at high temporal resolution (60×1 s, 30×2 s) to resolve the rapid passage of the radiotracer through blood vessels. In contrast to existing methods that use blood-to-tissue transport rate ( ) as a surrogate of blood flow, our method directly estimates blood flow using a distributed kinetic model (adiabatic approximation to the tissue homogeneity model; AATH). To validate our 18F-FDG measurements of blood flow against a flow radiotracer, we analyzed total-body dynamic PET images of six human participants scanned with both 18F-FDG and 11C-butanol. An additional thirty-four total-body dynamic 18F-FDG PET scans of healthy participants were analyzed for comparison against literature blood flow ranges. Regional blood flow was estimated across the body and total-body parametric imaging of blood flow was conducted for visual assessment. AATH and standard compartment model fitting was compared by the Akaike Information Criterion at different temporal resolutions. 18F-FDG blood flow was in quantitative agreement with flow measured from 11C-butanol across same-subject regional measurements (Pearson R=0.955, p<0.001; linear regression y=0.973x-0.012), which was visually corroborated by total-body blood flow parametric imaging. Our method resolved a wide range of blood flow values across the body in broad agreement with literature ranges (e.g., healthy cohort average: 0.51±0.12 ml/min/cm3 in the cerebral cortex and 2.03±0.64 ml/min/cm3 in the lungs, respectively). High temporal resolution (1 to 2 s) was critical to enabling AATH modeling over standard compartment modeling. Total-body blood flow imaging was feasible using early-dynamic 18F-FDG PET with high-temporal resolution kinetic modeling. Combined with standard 18F-FDG PET methods, this method may enable efficient single-tracer flow-metabolism imaging, with numerous research and clinical applications in oncology, cardiovascular disease, pain medicine, and neuroscience.

Imaging evaluation focused on microstructural tissue changes using tensor-valued diffusion encoding in breast cancers after neoadjuvant chemotherapy: is it a promising way forward?

Single diffusion encoding is a widely used, noninvasive technique for probing the tissue microstructure in breast tumors. However, it does not provide detailed information about the microenvironmental complexity. This study investigated the clinical utility of tensor-valued diffusion encoding for evaluating microstructural changes in breast cancer after neoadjuvant chemotherapy (NAC). We retrospectively included patients underwent chemotherapy for histologically proven invasive breast cancer between July 2020 and June 2023 and monitored the tumor response with breast magnetic resonance imaging (MRI), including tensor-valued diffusion encoding. We reviewed pre- and post-NAC MRIs regarding chemotherapy in 23 breast cancers. Q-space trajectory imaging (QTI) parameters were estimated at each time-point, and were compared with histopathological parameters. The mean total mean kurtosis (MKT), anisotropic mean kurtosis (MKA), and microscopic fractional anisotropy (µFA) were significantly decreased on post-NAC MRI compared with pre-NAC MRI, with the large effect size (ES) in MKA and µFA (0.81±0.41 vs. 0.99±0.33, ES: 0.48, P=0.03; 0.48±0.30 vs. 0.73±0.27, ES: 0.88, P<0.001; 0.58±0.14 vs. 0.68±0.11, ES: 0.79, P=0.003; respectively). Regarding prognostic factors, tumors with high Ki-67 expression showed significantly lower pre-NAC mean diffusivity (MD) and higher pre-NAC µFA compared to tumors with low Ki-67 expression (0.98±0.09 vs. 1.25±0.20, P=0.002; and 0.72±0.07 vs. 0.57±0.10, P=0.005; respectively). And negative progesterone receptor (PR) group revealed significantly lower MKT, MKA, and isotropic mean kurtosis than positive PR group on the post-NAC MRI (0.60±0.31 vs. 1.03±0.40, P=0.008; 0.36±0.21 vs. 0.61±0.33, P=0.04; and 0.23±0.17 vs. 0.42±0.25, P=0.046; respectively). QTI parameters reflected the microstructural changes in breast cancer treated with NAC and can be used as noninvasive imaging biomarkers correlated with prognostic factors.

Estimating lumbar bone mineral density from conventional MRI and radiographs with deep learning in spine patients.

This study aimed to develop machine learning methods to estimate bone mineral density and detect osteopenia/osteoporosis from conventional lumbar MRI (T1-weighted and T2-weighted images) and planar radiography in combination with clinical data and imaging parameters of the acquisition protocol. A database of 429 patients subjected to lumbar MRI, radiographs and dual-energy x-ray absorptiometry within 6 months was created from an institutional database. Several machine learning models were trained and tested (373 patients for training, 86 for testing) with the following objectives: (1) direct estimation of the vertebral bone mineral density; (2) classification of T-score lower than - 1 or (3) lower than - 2.5. The models took as inputs either the images or radiomics features derived from them, alone or in combination with metadata (age, sex, body size, vertebral level, parameters of the imaging protocol). The best-performing models achieved mean absolute errors of 0.15-0.16g/cm2 for the direct estimation of bone mineral density, and areas under the receiver operating characteristic curve of 0.82 (MRIs) - 0.80 (radiographs) for the classification of T-scores lower than - 1, and 0.80 (MRIs) - 0.65 (radiographs) for T-scores lower than - 2.5. The models showed good discriminative performances in detecting cases of low bone mineral density, and more limited capabilities for the direct estimation of its value. Being based on routine imaging and readily available data, such models are promising tools to retrospectively analyse existing datasets as well as for the opportunistic investigation of bone disorders.

Development and Validation of a Predictive Score for Three-Year Mortality in Acute Ischemic Stroke Patients.

Background and Objectives: Acute ischemic stroke (AIS) is a leading cause of death and disability with poor long-term outcomes. Creating a predictive score for long-term mortality in AIS might be important for optimizing treatment strategies. The aim of this study is to develop and validate a predictive score for three-year mortality in patients with AIS using several demographic, clinical, laboratory and imaging parameters. Materials and Methods: This study included 244 AIS patients admitted to a tertiary center and followed up for three years. The patients' data included demographics, clinical features, laboratory tests (including resistin and leptin levels) and imaging parameters. The patients were randomly divided into a predictive group (n = 164) and a validation group (n = 80). Results: Advanced age, a high NIHSS score, low levels of hemoglobin, elevated resistin levels and the presence of carotid plaques were independently associated with three-year mortality. The predictive model incorporated these variables, and it was validated in a separate cohort. Leptin levels did not significantly predict mortality. Conclusions: This study developed and validated a promising predictive score for three-year mortality in patients with AIS. Advanced age, high NIHSS scores, low hemoglobin levels, elevated resistin levels and the presence of carotid plaques were the independent predictors of long-term mortality.

Local corticosteroid injection versus surgery for carpal tunnel syndrome.

Carpal tunnel syndrome (CTS) is a very common clinical syndrome manifested by signs and symptoms of irritation of the median nerve at the carpal tunnel in the wrist. Direct and indirect costs of CTS are substantial, with estimated costs of two billion US dollars for CTS surgery in the USA alone. Local corticosteroid injection has been used as a non-surgical treatment for CTS for many years, but its effectiveness is still debated. To evaluate the benefits and harms of corticosteroids injected in or around the carpal tunnel for the treatment of carpal tunnel syndrome (CTS) compared to surgery. We used standard, extensive Cochrane search methods. We searched the Cochrane Neuromuscular Specialised Register, CENTRAL, MEDLINE, Embase, CINAHL, ClinicalTrials.gov, and WHO ICTRP. The latest search was 26 May 2022. We included randomised controlled trials (RCTs) or quasi-randomised trials of adults with CTS that included at least one comparison group of local corticosteroid injection (LCI) into the wrist and one group of any surgical intervention. We used standard Cochrane methods. Our primary outcome was 1. improvement in symptoms at up to three months of follow-up. Our secondary outcomes were 2. functional improvement, 3. improvement in symptoms at greater than three months of follow-up, 4. improvement in neurophysiological parameters, 5. improvement in imaging parameters, 6. improvement in quality of life and 7. We used GRADE to assess the certainty of evidence for each outcome. We included seven studies involving 569 'hands' (although two studies had unusable data for quantitative analyses). All studies used a one-time LCI as a comparator, using several different types and doses of corticosteroids. In every study, for both surgery and LCI groups, all our primary and secondary outcomes showed improvement from pre- to post-treatment. However, evidence from the combined analysis was too uncertain for us to draw reliable conclusions for the comparison of surgical treatment versus LCI with respect to our primary outcome of symptom relief at up to three months' follow-up (standardised mean difference (SMD) 0.63, 95% confidence interval (CI) -0.61 to 1.88; I2 = 95%; 5 trials, 305 participants; very low-certainty evidence). Findings with respect to secondary outcome measures of symptom relief at greater than three months' follow-up (SMD 0.94, 95% CI -0.31 to 2.19; I2 = 93%; 4 trials, 235 participants), functional improvement at up to three months' follow-up (SMD -0.11, 95% CI -0.94 to 0.72; I2 = 84%; 3 trials, 215 participants) and functional improvement at greater than three months' follow-up (SMD 0.19, 95% CI -1.22 to 1.59; I2 = 93%; 3 trials, 185 participants) were also uncertain (very low-certainty evidence) and showed no clear advantage for surgery or LCI. Surgery may improve neurophysiology (median nerve distal motor latency) more than LCI (mean difference (MD) 0.87 ms, 95% CI 0.32 to 1.42; I2 = 72%; 3 trials, 162 participants; low-certainty evidence). Evidence for quality of life and adverse events was also uncertain; quality of life (EuroQol-5D-3L) may be slightly improved after LCI than after surgery (the difference may not be clinically important) (MD 0.07, 95% CI 0.02 to 0.12; 1 trial, 38 participants; very low-certainty evidence) and there may be fewer adverse events with LCI than with surgery (risk ratio (RR) 0.34, 95% CI 0.04 to 3.26; 3 trials, 112 participants; very low-certainty evidence). The evidence comparing LCI to surgery for CTS, either in the short term or up to 12 months' follow-up, is too uncertain for any reliable conclusions to be drawn.

Prognostic value of pretherapeutic 68Ga-PSMA-11-PET based imaging parameters in mCRPC patients treated with PSMA radioligands.

This study aims to evaluate the prognostic significance of various previously reported PSMA-PET parameters in patients undergoing 177Lu-PSMA radioligand therapy (RLT). While individual studies have investigated the prognostic value of one or few of these factors, comprehensive analyses are rare. Data of 82 patients undergoing 177Lu-PSMA-radiologand-therapy (RLT) were analyzed. Total tumor volume (tumor volume), average SUVmean of all tumor lesions (SUVmean) and the quotient of sum of SUVmean of all tumor lesions to SUVmean of the parotid glands (tumor-parotid-ratio; TPR) and of the kidneys (tumor-kidney-ratio; TKR) were included in analysis. This study showed that a tumor volume of <290.6 ml is associated with a better survival in patients undergoing PSMA-RLT (median PFS: 4.2, median OS: 13.2 months) compared to patients with higher tumor volume (median PFS: 3.4,median OS: 6.2 months; p-value = 0.01 for PFS and <0.001 for OS). The average SUVmean correlated inversely with survival. Patients with a SUVmean > 10.7 had a median PFS of 4.2 and OS of 11.4 months while patients with SUVmean <10.7 had a median PFS of 1.6 and OS of 5 months (p-value <0.001 for both). The assessment of TPR showed no significant difference regarding OS and PFS. TKR showed a better PFS in patients with ratio > 0.33 (p-value 0.009) but no significant difference regarding OS. The present study confirms that pretherapeutic PSMA-PET before RLT with 177Lu-PSMA has a prognostic value.

Unsupervised low-light image enhancement by data augmentation and contrastive learning

ABSTRACT Today, with the increasing demand for visual perception and high-level computational vision tasks, the field of low-light enhancement is rapidly developing. However, models trained on existing datasets often fail or suffer significant performance degradation in real-world low-light scenarios. This performance degradation is frequently due to the limitations of current databases, which typically contain small quantities of paired images of a single type. This article proposes an unsupervised model with a unique data augmentation technique that transforms a regular image database into a paired image database. By adjusting image parameters during training to change exposure, a regular image database can be converted into a paired one. The model restores low-exposure images by extracting lighting features through comparative learning. Evaluations of the LOL and DIV2K datasets demonstrate the proposed model's effectiveness, achieving notable results in low-light image enhancement. This method removes dataset restrictions, broadening the model's range of applications.

Coronary protective effect of the J-Valve during transcatheter aortic valve implantation for patients with aortic stenosis and low coronary artery openings

BackgroundIn recent years, transcatheter aortic valve implantation (TAVI) has become a widely used treatment for low-risk elderly patients. As an alternative to TAVI via the femoral artery, transapical TAVI (TA-TAVI) is a better surgical choice for patients with severe vascular diseases. The J-Valve assists doctors in completing valve implantation easily using its positioning locator device, which prevents the self-leaflet from approaching the sinus wall. This function acts as coronary artery protection to avoid coronary occlusion. However, the clinical prognosis of J-Valve implantation for patients with aortic valve stenosis and low coronary openings is unclear.MethodsA retrospective analysis was performed on 30 patients with aortic stenosis (AS) and coronary openings measuring ≤ 10 mm in height. All patients underwent TA-TAVI with J-Valve implantation. Patients were screened using preoperative computed tomography three-dimensional imaging of the aortic root, and the safety and efficacy of the procedure were evaluated. The collected indexes included patients’ general data, cardiac function, preoperative imaging parameters, intraoperative data and postoperative short-term prognosis.ResultsOf the 30 patients in the study successfully underwent TA-TAVI and J-Valve implantation. Two patients required temporary cardiopulmonary bypass assistance during the operation due to heart failure. The implant success rate was 100%, and there were no deaths within 30 days postoperatively. No patients experienced intraoperative or postoperative coronary artery occlusion. Postoperative echocardiography, physiological state and laboratory test results indicated that all patients recovered well. The electrocardiograms remained normal after TA-TAVI, and heart function improved within 30 days.ConclusionTransapical TAVI with J-Valve implantation is a safe and effective treatment option for patients with AS and a low coronary artery opening. Preoperative coronary artery evaluation and the locators of the J-Valve are crucial in preventing coronary artery occlusion. This treatment regimen provides beneficial outcomes and warrants further multi-centre clinical research in the future.

Analysis of near-infrared autofluorescence imaging for detection of inadvertently resected parathyroid glands after endoscopic thyroidectomy

BackgroundPreserving parathyroid function during thyroidectomy is crucial, but remains challenging. Real-time near-infrared autofluorescence (NIRAF) aids surgeons in intraoperative parathyroid gland (PTG) identification. However, its role in detecting PTGs unintentionally removed during surgery is unclear. Study designThis prospective study included adult patients undergoing endoscopic thyroidectomy. Surgeons identified and documented PTGs visually. Excised specimens underwent visual inspection and NIRAF imaging (PDE-Neo II). All fluorescent tissues were dissected and pathologically evaluated (reference standard). One scanned image per lobe was chosen to quantify autofluorescence (AF) intensity. ResultsOverall, 95 patients underwent endoscopic thyroidectomies, with NIRAF imaging applied to 152 excised lobes. Of these, 19 lobes displayed a total of 23 spots with increased intensity. 175 specimens were sent for pathological evaluation, and 7 were confirmed to be parathyroid tissue. NIRAF demonstrated 100.0 % sensitivity and 90.5 % specificity for predicting parathyroid tissue, with 30.4 % positive predictive value, 100.0 % negative predictive value of and 90.9 % accuracy. Quantitatively normalized, the AF signal intensity was significantly higher in NIRAF-positive tissues than negative (4.3 vs 1.2 times, p < 0.0001). Additionally, the AF signal intensity in regions pathologically confirmed of parathyroid tissue was higher than non-parathyroid tissue (9.1 vs 2.1 times, p < 0.0001). ConclusionThis study suggests that NIRAF has high sensitivity and specificity for detecting inadvertently resected PTGs after endoscopic thyroidectomy, contributing to preservation efforts. However, NIRAF-positive tissues still require additional confirmation through multiple methods, emphasizing other examinations to verify that they are indeed parathyroid tissues. Further research is warranted to refine NIRAF imaging parameters.

Optimizing Prostate Imaging Practices in Saudi Arabian Hospitals: A Comprehensive Analysis of PI-RADS Compliance in Multiparametric MRI.

Prostate cancer, a significant contributor to male cancer mortality globally, demands improved diagnostic strategies. In Saudi Arabia, where the incidence is expected to double, this study assessed the compliance of multiparametric MRI (mpMRI) practices with Prostate Imaging-Reporting and Data System version 2 (PI-RADS v2) guidelines across diverse healthcare institutions. A survey was distributed to the radiology departments of all tertiary referral hospitals in Saudi Arabia (n=60) to assess their compliance with the technical specifications outlined in PI-RADS v2. Statistical analysis included chi-square, Fisher exact, ANOVA, and Student t-tests to examine the collected data. The study revealed an overall commendable compliance rate of 95.23%. However, significant variations were observed in technical parameters, particularly between 1.5 Tesla and 3 Tesla scanners and tertiary versus non-tertiary hospitals. Notable adherence in certain sequences contrasted with discrepancies in T2-weighted and diffusion-weighted imaging parameters. These findings underscore the need for nuanced approaches to optimize prostate imaging protocols, considering field strength and institutional differences. The study contributes to the ongoing refinement of standardized mpMRI practices, aiming to enhance diagnostic accuracy and improve clinical outcomes in prostate cancer.

Detection of cancer-associated cachexia in lung cancer patients using whole-body [18F]FDG-PET/CT imaging: A multi-centre study.

Cancer-associated cachexia (CAC) is a metabolic syndrome contributing to therapy resistance and mortality in lung cancer patients (LCP). CAC is typically defined using clinical non-imaging criteria. Given the metabolic underpinnings of CAC and the ability of [18F]fluoro-2-deoxy-D-glucose (FDG)-positron emission tomography (PET)/computer tomography (CT) to provide quantitative information on glucose turnover, we evaluate the usefulness of whole-body (WB) PET/CT imaging, as part of the standard diagnostic workup of LCP, to provide additional information on the onset or presence of CAC. This multi-centre study included 345 LCP who underwent WB [18F]FDG-PET/CT imaging for initial clinical staging. A weight loss grading system (WLGS) adjusted to body mass index was used to classify LCP into 'No CAC' (WLGS-0/1 at baseline prior treatment and at first follow-up: N=158, 51F/107M), 'Dev CAC' (WLGS-0/1 at baseline and WLGS-3/4 at follow-up: N=90, 34F/56M), and 'CAC' (WLGS-3/4 at baseline: N=97, 31F/66M). For each CAC category, mean standardized uptake values (SUV) normalized to aorta uptake (<SUVaorta>) and CT-defined volumes were extracted for abdominal and visceral organs, muscles, and adipose-tissue using automated image segmentation of baseline [18F]FDG-PET/CT images. Imaging and non-imaging parameters from laboratory tests were compared statistically. A machine-learning (ML) model was then trained to classify LCP as 'No CAC', 'Dev CAC', and 'CAC' based on their imaging parameters. SHapley Additive exPlanations (SHAP) analysis was employed to identify the key factors contributing to CAC development for each patient. The three CAC categories displayed multi-organ differences in <SUVaorta>. In all target organs, <SUVaorta> was higher in the 'CAC' cohort compared with 'No CAC' (P<0.01), except for liver and kidneys, where <SUVaorta> in 'CAC' was reduced by 5%. The 'Dev CAC' cohort displayed a small but significant increase in <SUVaorta> of pancreas (+4%), skeletal-muscle (+7%), subcutaneous adipose-tissue (+11%), and visceral adipose-tissue (+15%). In 'CAC' patients, a strong negative Spearman correlation (ρ=-0.8) was identified between <SUVaorta> and volumes of adipose-tissue. The machine-learning model identified 'CAC' at baseline with 81% of accuracy, highlighting <SUVaorta> of spleen, pancreas, liver, and adipose-tissue as most relevant features. The model performance was suboptimal (54%) when classifying 'Dev CAC' versus 'No CAC'. WB [18F]FDG-PET/CT imaging reveals groupwise differences in the multi-organ metabolism of LCP with and without CAC, thus highlighting systemic metabolic aberrations symptomatic of cachectic patients. Based on a retrospective cohort, our ML model identified patients with CAC with good accuracy. However, its performance in patients developing CAC was suboptimal. A prospective, multi-centre study has been initiated to address the limitations of the present retrospective analysis.

SiMix: A domain generalization method for cross-site brain MRI harmonization via site mixing

Brain magnetic resonance imaging (MRI) is widely used in clinical practice for disease diagnosis. However, MRI scans acquired at different sites can have different appearances due to the difference in the hardware, pulse sequence, and imaging parameter. It is important to reduce or eliminate such cross-site variations with brain MRI harmonization so that downstream image processing and analysis is performed consistently. Previous works on the harmonization problem require the data acquired from the sites of interest for model training. But in real-world scenarios there can be test data from a new site of interest after the model is trained, and training data from the new site is unavailable when the model is trained. In this case, previous methods cannot optimally handle the test data from the new unseen site. To address the problem, in this work we explore domain generalization for brain MRI harmonization and propose Site Mix (SiMix). We assume that images of travelling subjects are acquired at a few existing sites for model training. To allow the training data to better represent the test data from unseen sites, we first propose to mix the training images belonging to different sites stochastically, which substantially increases the diversity of the training data while preserving the authenticity of the mixed training images. Second, at test time, when a test image from an unseen site is given, we propose a multiview strategy that perturbs the test image with preserved authenticity and ensembles the harmonization results of the perturbed images for improved harmonization quality. To validate SiMix, we performed experiments on the publicly available SRPBS dataset and MUSHAC dataset that comprised brain MRI acquired at nine and two different sites, respectively. The results indicate that SiMix improves brain MRI harmonization for unseen sites, and it is also beneficial to the harmonization of existing sites.

Multiparametric MRI based assessment of kidney injury in a mouse model of ischemia reperfusion injury

Kidney diseases pose a global healthcare burden, with millions requiring renal replacement therapy. Ischemia/reperfusion injury (IRI) is a common pathology of acute kidney injury, causing hypoxia and subsequent inflammation-induced kidney damage. Accurate detection of acute kidney injury due to IRI is crucial for timely intervention. We used longitudinal, multi-parametric magnetic resonance imaging (MRI) employing arterial spin labelling (ASL), diffusion weighted imaging (DWI), and dynamic contrast enhanced (DCE)-MRI to assess IRI induced changes in both the injured and healthy contralateral kidney, in a unilateral IRI mouse model (n = 9). Multi-parametric MRI demonstrated significant differences in kidney volume (p = 0.001), blood flow (p = 0.002), filtration coefficient (p = 0.038), glomerular filtration rate (p = 0.005) and apparent diffusion coefficient (p = 0.048) between the injured kidney and contralateral kidney on day 1 post-IRI surgery. Identification of the injured kidney using principal component analysis including most of the imaging parameters demonstrated an area under the curve (AUC) of 0.97. These results point to the utility of multi-parametric MRI in early detection of IRI-induced kidney damage suggesting that the combination of various MRI parameters may be suitable for monitoring the extent of injury in this model.

Application of 18F-fluorodeoxyglucose positron emission tomography/computed tomography imaging in recurrent anastomotic tumors after surgery in digestive tract tumors.

This study was to investigate the application value of whole-body dynamic 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) imaging in recurrent anastomotic tumors of digestive tract after gastric and esophageal cancer surgery. Postoperative patients with gastric and esophageal cancer have a high risk of tumor recurrence, and traditional imaging methods have certain limitations in early detection of recurrent tumors. Whole-body dynamic 18F-FDG PET/CT imaging, due to its high sensitivity and specificity, can provide comprehensive information on tumor metabolic activity, which is expected to improve the early diagnosis rate of postoperative recurrent tumors, and provide an important reference for clinical treatment decision-making. To investigate the clinical value of whole-body dynamic 18F-FDG PET/CT imaging in differentiating anastomotic recurrence and inflammation after the operation of upper digestive tract tumors. A retrospective analysis was performed on 53 patients with upper digestive tract tumors after operation and systemic dynamic 18F-FDG PET/CT imaging indicating abnormal FDG uptake by anastomosis, including 29 cases of gastric cancer and 24 cases of esophageal cancer. According to the follow-up results of gastroscopy and other imaging examinations before and after PET/CT examination, the patients were divided into an anastomotic recurrence group and anastomotic inflammation group. Patlak multi-parameter analysis software was used to obtain the metabolic rate (MRFDG), volume of distribution maximum (DVmax) of anastomotic lesions, and MRmean and DVmean of normal liver tissue. The lesion/background ratio (LBR) was calculated by dividing the MRFDG and DVmax of the anastomotic lesion by the MRmean and DVmean of the normal liver tissue, respectively, to obtain LBR-MRFDG and LBR-DVmax. An independent sample t test was used for statistical analysis, and a receiver operating characteristic curve was used to analyze the differential diagnostic efficacy of each parameter for anastomotic recurrence and inflammation. The dynamic 18F-FDG PET/CT imaging parameters MRFDG, DVmax, LBR-MRFDG, and LBR-DVmax of postoperative anastomotic lesions in gastric cancer and esophageal cancer showed statistically significant differences between the recurrence group and the inflammatory group (P < 0.05). The parameter LBR-MRFDG showed good diagnostic efficacy in differentiating anastomotic inflammation from recurrent lesions. In the gastric cancer group, the area under the curve (AUC) value was 0.935 (0.778, 0.993) when the threshold was 1.83, and in the esophageal cancer group, the AUC value was 1. When 86 is the threshold, the AUC value is 0.927 (0.743, 0.993). Whole-body dynamic 18F-FDG PET/CT imaging can accurately differentiate the diagnosis of postoperative anastomotic recurrence and inflammation of gastric cancer and esophageal cancer and has the potential to be an effective monitoring method for patients with upper digestive tract tumors after surgical treatment.

Inversion of Cotton Soil and Plant Analytical Development Based on Unmanned Aerial Vehicle Multispectral Imagery and Mixed Pixel Decomposition

In order to improve the accuracy of multispectral image inversion of soil and plant analytical development (SPAD) of the cotton canopy, image segmentation methods were utilized to remove the background interference, such as soil and shadow in UAV multispectral images. UAV multispectral images of cotton bud stage canopies at three different heights (30 m, 50 m, and 80 m) were acquired. Four methods, namely vegetation index thresholding (VIT), supervised classification by support vector machine (SVM), spectral mixture analysis (SMA), and multiple endmember spectral mixture analysis (MESMA), were used to segment cotton, soil, and shadows in the multispectral images of cotton. The segmented UAV multispectral images were used to extract the spectral information of the cotton canopy, and eight vegetation indices were calculated to construct the dataset. Partial least squares regression (PLSR), Random forest (FR), and support vector regression (SVR) algorithms were used to construct the inversion model of cotton SPAD. This study analyzed the effects of different image segmentation methods on the extraction accuracy of spectral information and the accuracy of SPAD modeling in the cotton canopy. The results showed that (1) The accuracy of spectral information extraction can be improved by removing background interference such as soil and shadows using four image segmentation methods. The correlation between the vegetation indices calculated from MESMA segmented images and the SPAD of the cotton canopy was improved the most; (2) At three different flight altitudes, the vegetation indices calculated by the MESMA segmentation method were used as the input variable, and the SVR model had the best accuracy in the inversion of cotton SPAD, with R2 of 0.810, 0.778, and 0.697, respectively; (3) At a flight altitude of 80 m, the R2 of the SVR models constructed using vegetation indices calculated from images segmented by VIT, SVM, SMA, and MESMA methods were improved by 2.2%, 5.8%, 13.7%, and 17.9%, respectively, compared to the original images. Therefore, the MESMA mixed pixel decomposition method can effectively remove soil and shadows in multispectral images, especially to provide a reference for improving the inversion accuracy of crop physiological parameters in low-resolution images with more mixed pixels.

Thermal Reading of Texts Buried in Historical Bookbindings.

In the manufacture of ancient books, it was quite common to insert written scraps belonging to earlier library material into bookbindings. For scholars like codicologists and paleographers, it is extremely important to have the possibility of reading the text lying on such scraps without dismantling the book. In this regard, in this paper, we report on the detection of these texts by means of infrared (IR) pulsed thermography (PT), which, in recent years, has been specifically proven to be an effective tool for the investigation of Cultural Heritage. In particular, we present a quantitative analysis based, for the first time, on PT images obtained from books of historical relevance preserved at the Biblioteca Angelica in Rome. The analysis has been carried out by means of a theoretical model for the PT signal, which makes use of two image parameters, namely, the distortion and the contrast, related to the IR readability of the buried texts. As shown in this paper, the good agreement between the experimental data obtained in the historical books and the theoretical analysis proved that the capability of the adopted PT method could be fruitfully applied, in real case studies, to the detection of buried texts and to the quantitative characterization of the parameters affecting their thermal readability.

Can volumetric magnetic resonance imaging evaluations be helpful in the follow-up of cognitive functions in cognitively normal Parkinson's disease patients?

In this study, besides the evaluation of gray and white matter changes in cognitively normal Parkinson's disease (PD-CN) patients with volumetric magnetic resonance imaging (MRI) parameters, it was tried to show that some neuropsychological tests may be impaired in PD-CN patients. Twenty-six PD-CN patients and 26 healthy elderly (HC) participants were included in the current study. Global cognitive status was assessed using the mini-mental state examination (MMSE), and the Montreal cognitive assessment scale (MoCA). Attention and executive functions were evaluated using the Wechsler memory scale-revised (WMS-R) digit span test and trail making test (TMT) part A and part B, the Stroop test, semantic and phonemic fluency tests, and clock drawing test. Magnetic resonance imaging (MRI) was acquired according to the Alzheimer's disease neuroimaging initiative (ADNI) protocol. There were no significant differences among groups regarding age, sex, handedness, and years of education. In the comparison of the PD-CN group and the HC group, there was a statistical decrease in the total animal scores, lexical fluency, TMT part A and TMT part B scores in the PD-CN group. Subcortical gray matter volumes (GMV) were significantly lower in PD-CN patients. The PD-CN group had a significantly reduced total volume of right putamen and left angular gyrus compared to that in the HC group. We observed that putamen and angular gyrus volumes were lower in PD-CN patients. On the other hand, TMT part B may be a useful pretest in detecting the conversion of mild cognitive impairment in PD. Significant MRI volumetric measurements and neuropsychological test batteries can be helpful in the clinical follow-up in PD-CN patients.