Cerebrospinal fluid volume as an early radiological factor for clinical course prediction after aneurysmal subarachnoid hemorrhage. A pilot study.

BACKGROUND: The pathological mechanisms following aneurysmal subarachnoid hemorrhage (SAH) are poorly understood. Limited clinical evidence exists on the association between cerebrospinal fluid (CSF) volume and the risk of delayed cerebral ischemia (DCI) or cerebral vasospasm (CV). In this study, we raised the hypothesis that the amount of CSF or its ratio to hemorrhage blood volume, as determined from non-contrast Computed Tomography (NCCT) images taken on admission, could be a significant predictor for CV and DCI. METHODS: The pilot study included a retrospective analysis of NCCT scans of 49 SAH patients taken shortly after an aneurysm rupture (33 males, 16 females, mean age 56.4 ± 15 years). The SynthStrip and Slicer3D software tools were used to extract radiological factors - CSF, brain, and hemorrhage volumes from the NCCT images. The "pure" CSF volume (VCSF) was estimated in the range of [-15, 15] Hounsfield units (HU). RESULTS: VCSF was negatively associated with the risk of CV occurrence (p = 0.0049) and DCI (p = 0.0069), but was not associated with patients' outcomes. The hemorrhage volume (VSAH) was positively associated with an unfavorable outcome (p = 0.0032) but was not associated with CV/DCI. The ratio VSAH/VCSF was positively associated with, both, DCI (p = 0.031) and unfavorable outcome (p = 0.002). The CSF volume normalized by the brain volume showed the highest characteristics for DCI prediction (AUC = 0.791, sensitivity = 0.80, specificity = 0.812) and CV prediction (AUC = 0.769, sensitivity = 0.812, specificity = 0.70). CONCLUSION: It was demonstrated that "pure" CSF volume retrieved from the initial NCCT images of SAH patients (including CV, Non-CV, DCI, Non-DCI groups) is a more significant predictor of DCI and CV compared to other routinely used radiological biomarkers. VCSF could be used to predict clinical course as well as to personalize the management of SAH patients. Larger multicenter clinical trials should be performed to test the added value of the proposed methodology.

Comparison of Low-Dose Non-contrast CT in Detecting Anatomical and Surgically Important Variants of Paranasal Sinuses to Standard Dose Non-contrast CT: Experience from a Tertiary Care Hospital in Sub-Himalayan Region of Northern India.

Computed tomography (CT) is the gold standard for diagnosing sinusitis and anatomical variations and a guide for paranasal sinus (PNS) surgeries. High doses of radiation lead to increased risk of head and neck malignancies, radiation-induced cataracts, hypothyroidism, and hyperthyroidism. The purpose of this study was to assess the effectiveness of low-dose CT as compared to standard-dose CT in the identification of anatomical variants of paranasal sinus and rhinosinusitis. This was a prospective cross-sectional study consisting of 72 patients who were divided equally into cases (underwent low-dose CT for PNS) and controls (underwent CT for PNS using standard dose protocols). Prevalence of anatomical variants and sinusitis were compared. Image quality was assessed using volume CT dose index (CTDIvol), dose length product (DLP), scan length, and noise. Subjective assessment was done by two radiologists, and scores were given. The comparison and analysis of the quantitative and qualitative variables were done. Anatomical variants were comparable among cases and controls, with post-sellar sphenoid being most common and paradoxical middle turbinate being least common surgically important variant. The difference in mean SD of CTDIvol (mGy), DLP (mGy-cm), effective dose (mSv), globe, and air noise between low and standard doses was statistically significant. A moderate agreement (with kappa 0.50) in cases and substantial agreement (with kappa 0.69) in controls was observed between both observers. Low-dose CT PNS and standard-dose CT PNS are comparable in delineating the paranasal sinus anatomy, with a 3.53× reduction of effective radiation dose to patients.

Role of Noncontrast Computed Tomography Parameters in Predicting the Outcome of Extracorporeal Shock Wave Lithotripsy for Upper Urinary Stones Cases: A Meta-analysis.

RATIONALE: Extracorporeal shock wave lithotripsy (ESWL) is widely considered the primary approach for managing urinary tract stones. This study aimed to assess the predictive factors associated with non-contrast computed tomography (NCCT)-based parameters of upper urinary stones in relation to the outcomes of ESWL. MATERIALS AND METHODS: A systematic search was conducted in PubMed, ScienceDirect, Web of Science, and Cochrane Library to identify all relevant studies published up to June 3, 2023. Several NCCT-based parameters to predict ESWL outcomes, comprised of mean stone density (MSD), skin-to-stone distance (SSD), and stone size, were extracted and analyzed using Review Manager software. RESULTS: Out of 979 publications screened, a total of 39 publications, involving 7869 patients, were enrolled in the analysis. The pooled estimate demonstrated significant differences between MSD, and stone size between successful and failure of stone fragmentation groups, in which lower values of these parameters are associated with successful ESWL outcomes. CONCLUSION: The results from the current study suggested that lower NCCT parameters, notably MSD, SSD, and stone size, are significantly associated with successful ESWL outcome. However, additional large-scale prospective studies are required to utilize these parameters effectively, and the optimal cutoff value should be determined.

Development and validation of a lung graph-based machine learning model to predict acute pulmonary thromboembolism on chest noncontrast computed tomography.

Background: Computed tomography pulmonary angiography (CTPA) is a first-line noninvasive method to diagnose acute pulmonary thromboembolism (APE); however, whether chest noncontrast CT (NC-CT) could aid in the diagnosis of APE remains unknown. The aim of this study was to build and evaluate a holistic lung graph-based machine learning (HLG-ML) using NC-CT for the diagnosis of APE and to compare its performance with that of radiologists and the YEARS algorithm. Methods: This study enrolled 178 cases (77 males; age 63.9±16.7 years) who underwent NC-CT and CTPA in the same day from January 2019 to December 2020. Of these patients, 133 (75% of cases; 58 males; age 65.4±15.6 years) were placed into a training group and 45 (25% of cases; 19 males; age 59.6±19.2 years) into a testing group. The other 43 cases (18 males; age 62.8±20.0 years) were used to externally validate the model between January 2021 and March 2022. A HLG was developed with a pulmonary radiomics descriptor derived from NC-CT images. The approach extracted local radiomics features and encoded these local features into a radiomics descriptor as a characterization of global radiomics feature distribution. Subsequently, 8 ML models were trained and compared based on the radiomics descriptor. In the validation group, area under the curves (AUCs) of the HLG-ML model in the diagnosis of APE were compared with those of the 3 radiologists and the YEARS algorithm. Results: Among the 8 ML models, gradient boosting decision tree demonstrated the best classification performance (AUC =0.772) on the training set. In the testing set, the AUC of gradient boosting decision trees was 0.857 [95% confidence intervals (CIs): 0.699-0.951]. In the validation set, the performance of gradient boosting decision tree (AUC =0.810; 95% CI: 0.669-0.952; Youden index =0.621) outperformed 3 radiologists (AUC =0.508, 95% CI: 0.335-0.681, Youden index =0.016; AUC =0.504, 95% CI: 0.354-0.654, Youden index =0.008; AUC =0.527, 95% CI: 0.363-0.691, Youden index =0.050) and the YEARS algorithm (AUC =0.618; 95% CI: 0.469-0.767; Youden index =0.237). Conclusions: Compared to all 3 radiologists and the YEARS algorithm, the proposed HLG-based gradient boosting decision tree model achieved a superior performance in the diagnosis of APE on the NC-CT and may thus serve as a valuable tool for physicians in the diagnosis of APE.

Thrombectomy with and without computed tomography perfusion imaging for large-vessel occlusion stroke in the extended time window: a meta-analysis of randomized clinical trials.

Objective: In recent years, several studies have used computed tomography perfusion (CTP) to assess whether mechanical thrombectomy can be performed in patients with large-vessel occlusion (LVO) stroke in an extended time window. However, it has the disadvantage of being time-consuming and expensive. This study aimed to compare the impact of the CTP group with the non-CTP group [non-contrast CT (NCCT) ± CT angiography (CTA)] on the prognosis of this patient population. Methods: A search of PubMed, EMBASE, and the Cochrane Library databases was conducted to collect randomized controlled trials (RCTs) comparing the two strategies. Outcome indicators and factors influencing prognosis were summarized by standardized mean differences, ratios, and relative risks with 95% confidence intervals using a random-effects model. Results: A total of two RCTs were included in the combined analysis. There were no significant differences in the main outcome indicators (modified Rankin Scale score at 90 days, successful postoperative reperfusion rate) or the incidence of adverse events (90-day mortality and symptomatic intracranial hemorrhage) between the NCCT ± CTA and CTP groups. The time from the last puncture appeared to be significantly shorter in the NCCT ± CTA group than in the CTP group (SMD: -0.14; 95% CI: -0.24, -0.04). Among them, age (OR: 0.96; 95% CI: 0.94, 0.98), ASPECTS (OR: 1.18; 95% CI: 1.12, 1.24), NIHSS score (OR: 0.90; 95% CI: 0.89, 0.91), and diabetes (OR: 0.69; 95% CI: 0.54, 0.88) were associated with a 90-day independent functional outcome. Conclusion: These findings suggest that the choice of NCCT ± CTA (without CTP) for the assessment of mechanical thrombectomy within 6-24 h after LVO in the anterior circulation is not significantly different from CTP; instead, the choice of NCCT ± CTA significantly reduces the time from onset to arterial puncture.

A computer-aided tool for automatic volume estimation of hematoma using non-contrast brain CT scans.

The computation of hematoma volume is the key parameter for treatment planning of Intracerebral hemorrhage (ICH). Non-contrast computed tomography (NCCT) imaging is routinely used for the diagnosis of ICH. Hence, the development of computer-aided tools for three-dimensional (3D) computed tomography (CT) image analysis is essential to estimate the gross volume of hematoma. We propose a methodology for automatic estimation of the hematoma volume from 3D CT volumes. Our approach integrates two different methods, multiple abstract splitting (MAS) and seeded region growing (SRG) to develop a unified hematoma detection pipeline from pre-processed CT volumes. The proposed methodology was tested on 80 cases. The volume was estimated from the delineated hematoma region, validated against the ground-truth volumes, and compared with those obtained from the conventional ABC/2 approach. We also compared our results with the U-Net model (supervised technique) to show the applicability of the proposed method. The volume calculated from manually segmented hematoma was considered the ground truth. TheR2correlation coefficient between the volume obtained from the proposed algorithm and the ground truth is 0.86, which is equivalent to theR2value resulting from the comparison between the volume calculated by ABC/2 and the ground truth. The experimental results of the proposed unsupervised approach are comparable to the deep neural architecture (U-Net models). The average computation time was 132.76 ± 14 seconds. The proposed methodology provides a fast and automatic estimation of hematoma volume, which is similar to the baseline user-guided ABC/2 approach. Implementation of our method does not demand a high-end computational setup. Thus, recommended in clinical practice for computer-assistive volume estimation of hematoma from 3D CT volumes and can be implemented in a simple computer system.

Unified ICH quantification and prognosis prediction in NCCT images using a multi-task interpretable network.

With the recent development of deep learning, the regression, classification, and segmentation tasks of Computer-Aided Diagnosis (CAD) using Non-Contrast head Computed Tomography (NCCT) for spontaneous IntraCerebral Hematoma (ICH) have become popular in the field of emergency medicine. However, a few challenges such as time-consuming of ICH volume manual evaluation, excessive cost demanding patient-level predictions, and the requirement for high performance in both accuracy and interpretability remain. This paper proposes a multi-task framework consisting of upstream and downstream components to overcome these challenges. In the upstream, a weight-shared module is trained as a robust feature extractor that captures global features by performing multi-tasks (regression and classification). In the downstream, two heads are used for two different tasks (regression and classification). The final experimental results show that the multi-task framework has better performance than single-task framework. And it also reflects its good interpretability in the heatmap generated by Gradient-weighted Class Activation Mapping (Grad-CAM), which is a widely used model interpretation method, and will be presented in subsequent sections.

The "SALPARE study" of spontaneous intracerebral haemorrhage-part 2-early CT predictors of outcome in ICH: keeping it simple.

BACKGROUND: The aim of this study was to investigate the prognostic role of hematoma characteristics and hematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (ICH). METHODS: This multicenter prospective cohort study enrolled consecutive adult patients with non-traumatic ICH admitted to three Italian academic hospitals (Salerno, Padova, Reggio Emilia) over a 2-year period. Early noncontrast CT (NCCT) features of the hematoma, including markers of HE, and 3-month outcome were recorded. Multivariable logistic regression analysis was performed to identify predictors of poor outcome. RESULTS: A total of 682 patients were included in the study [mean age: 73 ± 14 years; 316 (46.3%) females]. Pontine and massive hemorrhage, intraventricular bleeding, baseline hematoma volume > 15 mL, blend sign, swirl sign, margin irregularity ≥ 4, density heterogeneity ≥ 3, hypodensity ≥ 1, island sign, satellite sign, and black hole sign were associated with a higher risk of mortality and disability. However, at multivariate analysis only initial hematoma volume (OR 29.71) proved to be an independent predictor of poor functional outcome at 3 months. CONCLUSION: Simple hematoma volume measured on baseline CT best identifies patients with a worse outcome, while early NCCT markers of HE do not seem to add any clinically significant information.

Role of Non-contrast Computed Tomography of Nose and Paranasal Sinus in Preoperative Evaluation of Patients with Symptomatic Deviated Nasal Septum.

Symptomatic deviated nasal septum is a commonly encountered clinical condition by otorhinolaryngologist. Non-contrast Computed tomography (NCCT) of nose and Paranasal sinuses is a good tool in the preoperative evaluation of symptomatic deviated nasal septum so as to find hidden sinonasal pathologies in addition to deviated nasal septum. The aim of this article is to find the role and scope of preoperative Computed tomography in the management of symptomatic deviated nasal septum. This cross-sectional observational study was conducted on 120 patients with symptomatic deviated nasal septum, out of which 27 had concomitant hypertrophy of inferior turbinate. All were planned for septoplasty with or without turbinoplasty. Non contrast computed tomography (NCCT) scan was done in all cases. Different findings on NCCT were noted. After NCCT was done, 33 (27.5%) out of 120 patients underwent additional surgical procedures. In conclusion, NCCT of Nose and Paranasal sinus surgery should be given due consideration in the evaluation of symptomatic deviated nasal septum so as to avoid second surgery.

Diagnostic Accuracy and Reliability of Noncontrast Computed Tomography Markers for Acute Hematoma Expansion among Radiologists.

BACKGROUND: Noncontrast Computed Tomography (NCCT) features are promising markers for acute hematoma expansion (HE) in patients with intracerebral hemorrhage (ICH). It remains unclear whether accurate identification of these markers is also reliable in raters with different levels of experience. METHODS: Patients with acute spontaneous ICH admitted at four tertiary centers in Germany and Italy were retrospectively included from January 2017 to June 2020. In total, nine NCCT markers were rated by one radiology resident, one radiology fellow, and one neuroradiology fellow with different levels experience in ICH imaging. Interrater reliabilities of the resident and radiology fellow were evaluated by calculated Cohen's kappa (κ) statistics in reference to the neuroradiology fellow who was referred as the gold standard. Gold-standard ratings were evaluated by calculated interrater κ statistics. Global interrater reliabilities were evaluated by calculated Fleiss kappa statistics across all three readers. A comparison of receiver operating characteristics (ROCs) was used to evaluate differences in the diagnostic accuracy for predicting acute hematoma expansion (HE) among the raters. RESULTS: Substantial-to-almost-perfect interrater concordance was found for the resident with interrater Cohen's kappa from 0.70 (95% CI 0.65-0.81) to 0.96 (95% CI 0.94-0.98). The interrater Cohen's kappa for the radiology fellow was moderate to almost perfect and ranged from 0.58 (95% CI 0.52-0.65) to 94 (95% CI 92-0.97). The intrarater gold-standard Cohen's kappa was almost perfect and ranged from 0.79 (95% CI 0.78-0.90) to 0.98 (95% CI 0.78-0.90). The global interrater Fleiss kappa ranged from 0.62 (95%CI 0.57-0.66) to 0.93 (95%CI 0.89-0.97). The diagnostic accuracy for the prediction of acute hematoma expansion (HE) was different for the island sign and fluid sign, with p-values < 0.05. CONCLUSION: The NCCT markers had a substantial-to-almost-perfect interrater agreement among raters with different levels of experience. Differences in the diagnostic accuracy for the prediction of acute HE were found in two out of nine NCCT markers. The study highlights the promising utility of NCCT markers for acute HE prediction.

Quantitative hematoma heterogeneity associated with hematoma growth in patients with early intracerebral hemorrhage.

Background: Early hematoma growth is associated with poor functional outcomes in patients with intracerebral hemorrhage (ICH). We aimed to explore whether quantitative hematoma heterogeneity in non-contrast computed tomography (NCCT) can predict early hematoma growth. Methods: We used data from the Risk Stratification and Minimally Invasive Surgery in Acute Intracerebral Hemorrhage (Risa-MIS-ICH) trial. Our study included patients with ICH with a time to baseline NCCT <12 h and a follow-up CT duration <72 h. To get a Hounsfield unit histogram and the coefficient of variation (CV) of Hounsfield units (HUs), the hematoma was segmented by software using the auto-segmentation function. Quantitative hematoma heterogeneity is represented by the CV of hematoma HUs. Multivariate logistic regression was utilized to determine hematoma growth parameters. The discriminant score predictive value was assessed using the area under the ROC curve (AUC). The best cutoff was determined using ROC curves. Hematoma growth was defined as a follow-up CT hematoma volume increase of >6 mL or a hematoma volume increase of 33% compared with the baseline NCCT. Results: A total of 158 patients were enrolled in the study, of which 31 (19.6%) had hematoma growth. The multivariate logistic regression analysis revealed that time to initial baseline CT (P = 0.040, odds ratio [OR]: 0.824, 95 % confidence interval [CI]: 0.686-0.991), "heterogeneous" in the density category (P = 0.027, odds ratio [OR]: 5.950, 95 % confidence interval [CI]: 1.228-28.828), and CV of hematoma HUs (P = 0.018, OR: 1.301, 95 % CI: 1.047-1.617) were independent predictors of hematoma growth. By evaluating the receiver operating characteristic curve, the CV of hematoma HUs (AUC = 0.750) has a superior predictive value for hematoma growth than for heterogeneous density (AUC = 0.638). The CV of hematoma HUs had an 18% cutoff, with a specificity of 81.9 % and a sensitivity of 58.1 %. Conclusion: The CV of hematoma HUs can serve as a quantitative hematoma heterogeneity index that predicts hematoma growth in patients with early ICH independently.

The prominent hypointense vessel sign on susceptibility-weighted imaging (SWI) as a potential imaging biomarker for poor clinical outcome in acute ischemic stroke (AIS).

Susceptibility-weighted imaging (SWI) is a relatively new magnetic resonance imaging (MRI) technique used in the workup and diagnosis of brain pathologies. In the context of acute ischemic stroke (AIS), it is increasingly becoming useful in the diagnosis, treatment, and further management of these patients. An elderly man with metabolic syndrome presented to us with an acute onset of right sided body weakness and aphasia. Urgent imaging via MRI noted a left middle cerebral artery (MCA) occlusion. Diffusion-weighted imaging (DWI)/fluid attenuated inversion recovery (FLAIR) mismatch was noted with an acute infarct involving the left MCA territory; hence, treatment with intravenous (IV) thrombolysis was administered. On SWI, the prominent hypointense vessel sign was noted. Recanalization of the occluded left MCA was seen on diagnostic cerebral angiography post IV thrombolysis, however, the patient was noted to have early neurological deterioration (END) and poor early stage clinical outcome, despite repeat MRI showing recanalization of the left MCA occlusion and reversal of the prominent hypointense vessel sign on SWI. Presence of the prominent hypointense vessel sign on SWI in AIS patients is associated with poor clinical outcome, unsuccessful recanalization rates, END, poor early stage clinical outcome, and infarct core progression. Some studies have shown an association between this imaging sign and poor collateral circulation status. Therefore, this imaging sign could potentially prove to be a useful imaging biomarker. However, more studies are needed to validate this theory.

Corrigendum: New Prediction Models of Functional Outcome in Acute Intracerebral Hemorrhage: The dICH Score and uICH Score.

[This corrects the article DOI: 10.3389/fneur.2021.655800.].

Pulmonary Artery Enlargement: An Independent Risk Factor for Mortality in Patients Hospitalized With COVID-19.

Objective: To assess whether baseline pulmonary artery diameter (PAD), obtained from noncontrast nongated computed tomography (NCCT), can be associated with coronavirus disease 2019 (COVID-19) outcomes. Patients and Methods: This is a retrospective study of patients hospitalized with COVID-19 admitted to Hôtel-Dieu de France university hospital (Beirut, Lebanon) between March 1, 2020 and March 1, 2021. Pulmonary artery diameter was measured at baseline NCCT. Various outcomes were assessed, including hospital length of stay, intensive care unit admission, invasive mechanical ventilation, mortality, and Post-COVID-19 Functional Status scale at discharge and at 2-month follow-up. Results: Four hundred sixty-five patients underwent baseline NCCT, including 315 men (67.7%) with a mean age of 63.7±16 years. Baseline PAD was higher in critically ill patients admitted to the intensive care unit (mean difference, 0.8 mm; 95% CI, 0.4-1.59 mm) and those receiving invasive mechanical ventilation (mean difference, 1.1 mm; 95% CI, 0.11-2.04 mm). Pulmonary artery diameter at baseline correlated significantly with hospital length of stay (r=0.130; P=.005), discharge status (r=0.117; P=.023), and with Post-COVID-19 Functional Status scale at 2-month follow-up (r=0.121; P=.021). Moreover, multivariable logistic regression showed that a PAD of 24.5 mm and above independently predicted in-hospital all-cause mortality remained unaffected in patients with COVID-19 (odds ratio, 2.07; 95% CI, 1.05-4.09). Conclusion: Baseline PAD measurement using NCCT can be a useful prognostic parameter. Its measurement can help to identify early severe cases and adapt the initial management of patients hospitalized with COVID-19.

FeMA: Feature matching auto-encoder for predicting ischaemic stroke evolution and treatment outcome.

Although, predicting ischaemic stroke evolution and treatment outcome provide important information one step towards individual treatment planning, classifying functional outcome and modelling the brain tissue evolution remains a challenge due to data complexity and visually subtle changes in the brain. We propose a novel deep learning approach, Feature Matching Auto-encoder (FeMA) that consists of two stages, predicting ischaemic stroke evolution at one week without voxel-wise annotation and predicting ischaemic stroke treatment outcome at 90 days from a baseline scan. In the first stage, we introduce feature similarity and consistency objective, and in the second stage, we show that adding stroke evolution information increase the performance of functional outcome prediction. Comparative experiments demonstrate that our proposed method is more effective to extract representative follow-up features and achieves the best results for functional outcome of stroke treatment.

Deep-Learning-Based Thrombus Localization and Segmentation in Patients with Posterior Circulation Stroke.

Thrombus volume in posterior circulation stroke (PCS) has been associated with outcome, through recanalization. Manual thrombus segmentation is impractical for large scale analysis of image characteristics. Hence, in this study we develop the first automatic method for thrombus localization and segmentation on CT in patients with PCS. In this multi-center retrospective study, 187 patients with PCS from the MR CLEAN Registry were included. We developed a convolutional neural network (CNN) that segments thrombi and restricts the volume-of-interest (VOI) to the brainstem (Polar-UNet). Furthermore, we reduced false positive localization by removing small-volume objects, referred to as volume-based removal (VBR). Polar-UNet is benchmarked against a CNN that does not restrict the VOI (BL-UNet). Performance metrics included the intra-class correlation coefficient (ICC) between automated and manually segmented thrombus volumes, the thrombus localization precision and recall, and the Dice coefficient. The majority of the thrombi were localized. Without VBR, Polar-UNet achieved a thrombus localization recall of 0.82, versus 0.78 achieved by BL-UNet. This high recall was accompanied by a low precision of 0.14 and 0.09. VBR improved precision to 0.65 and 0.56 for Polar-UNet and BL-UNet, respectively, with a small reduction in recall to 0.75 and 0.69. The Dice coefficient achieved by Polar-UNet was 0.44, versus 0.38 achieved by BL-UNet with VBR. Both methods achieved ICCs of 0.41 (95% CI: 0.27-0.54). Restricting the VOI to the brainstem improved the thrombus localization precision, recall, and segmentation overlap compared to the benchmark. VBR improved thrombus localization precision but lowered recall.

Axillary vascular malformation visualized on mammogram: A case report.

Chest wall lesions can mimic masses on mammograms and can cause diagnostic difficulty in interpretation. Here, we report a case of an axillary and retro-pectoral vascular malformation visualized on mammography in a 67-year-old patient presenting with fullness in the right axilla and right supraclavicular region. Mammography, ultrasonography (US), and computed tomography (CT) angiography of the patient were done to make the final diagnosis.

Quantification of infarct core signal using CT imaging in acute ischemic stroke.

In stroke care, the extent of irreversible brain injury, termed infarct core, plays a key role in determining eligibility for acute treatments, such as intravenous thrombolysis and endovascular reperfusion therapies. Many of the pivotal randomized clinical trials testing those therapies used MRI Diffusion-Weighted Imaging (DWI) or CT Perfusion (CTP) to define infarct core. Unfortunately, these modalities are not available 24/7 outside of large stroke centers. As such, there is a need for accurate infarct core determination using faster and more widely available imaging modalities including Non-Contrast CT (NCCT) and CT Angiography (CTA). Prior studies have suggested that CTA provides improved predictions of infarct core relative to NCCT; however, this assertion has never been numerically quantified by automatic medical image computing pipelines using acquisition protocols not confounded by different scanner manufacturers, or other protocol settings such as exposure times, kilovoltage peak, or imprecision due to contrast bolus delays. In addition, single-phase CTA protocols are at present designed to optimize contrast opacification in the arterial phase. This approach works well to maximize the sensitivity to detect vessel occlusions, however, it may not be the ideal timing to enhance the ischemic infarct core signal (ICS). In this work, we propose an image analysis pipeline on CT-based images of 88 acute ischemic stroke (AIS) patients drawn from a single dynamic acquisition protocol acquired at the acute ischemic phase. We use the first scan at the time of the dynamic acquisition as a proxy for NCCT, and the rest of the scans as a proxy for CTA scans, with bolus imaged at different brain enhancement phases. Thus, we use the terms "NCCT" and "CTA" to refer to them. This pipeline enables us to answer the questions "Does the injection of bolus enhance the infarct core signal?" and "What is the ideal bolus timing to enhance the infarct core signal?" without being influenced by aforementioned factors such as scanner model, acquisition settings, contrast bolus delay, and human reader errors. We use reference MRI DWI images acquired after successful recanalization acting as our gold standard for infarct core. The ICS is quantified by calculating the difference in intensity distribution between the infarct core region and its symmetrical healthy counterpart on the contralateral hemisphere of the brain using a metric derived from information theory, the Kullback-Leibler divergence (KL divergence). We compare the ICS provided by NCCT and CTA and retrieve the optimal timing of CTA bolus to maximize the ICS. In our experiments, we numerically confirm that CTAs provide greater ICS compared to NCCT. Then, we find that, on average, the ideal CTA acquisition time to maximize the ICS is not the current target of standard CTA protocols, i.e., during the peak of arterial enhancement, but a few seconds afterward (median of 3 s; 95% CI [1.5, 3.0]). While there are other studies comparing the prediction potential of ischemic infarct core from NCCT and CTA images, to the best of our knowledge, this analysis is the first to perform a quantitative comparison of the ICS among CT based scans, with and without bolus injection, acquired using the same scanning sequence and a precise characterization of the bolus uptake, hence, reducing potential confounding factors.

Relationship between lung injury extent and phenotype manifested in non-contrast CT and cardiac injury during acute stage of COVID-19.

PURPOSE: This study evaluated the diagnostic values of the extent of lung injury manifested in non-contrast enhanced CT (NCCT) images, the inflammatory and immunological biomarkers C-reactive protein (CRP) and lymphocyte for detecting acute cardiac injury (ACI) in patients with COVID-19. The correlations between the NCCT-derived parameters and arterial blood oxygen level were also investigated. METHODS: NCCT lung images and blood tests were obtained in 143 patients with COVID-19 in approximately two weeks after symptom onset, and arterial blood gas measurement was also acquired in 113 (79%) patients. The diagnostic values of normal, moderately and severely abnormal lung parenchyma volume relative to the whole lungs (RVNP, RVMAP, RVSAP, respectively) measured from NCCT images for detecting the heart injury confirmed with high-sensitivity troponin I assay was determined. RESULTS: RVNP, RVMAP and RVSAP exhibited similar accuracy for detecting ACI in COVID-19 patients. RVNP was significantly lower while both RVMAP and RVSAP were significantly higher in the patients with ACI. All of the NCCT-derived parameters exhibited poor linear and non-linear correlations with PaO2 and SaO2. The patients with ACI had a significantly higher CRP level but a lower lymphocyte level compared to the patients without ACI. Combining one of these two biomarkers with any of the three NCCT-derived parameter further improved the accuracy for predicting ACI in patients with COVID-19. CONCLUSION: The NCCT-delineated normal and abnormal lung parenchmyma tissues were statistically significant predictors of ACI in patients with COVID-19, but both exhibited poor correlations with the arterial blood oxygen level. The incremental diagnostic values of lymphocyte and CRP suggested viral infection and inflammation were closely related to the heart injury during the acute stage of COVID-19.