Brain CT Research Articles

AI-Based Automated Quantification of Arterial Stenosis in Head and Neck CT Angiography: AComparison with Manual Measurements from Digital Subtraction Angiography and CT Angiography.

To evaluate the performance of an artificial intelligence (AI) algorithm for automated quantification of arterial stenosis in head and neck CT angiography (CTA). Patients who received head and neck CTA and DSA between January 2019 and December 2021 in two centers were included. The quantitative performance of CerebralDoc per-lesion was evaluated through intraclass correlation coefficients (ICCs) and Bland-Altman analysis, comparing automated stenosis measurements and manual measurements across 0-100%, < 50%, ≥ 50% and ≥ 70% thresholds. Sensitivity analysis included linear and logistic regression, and subgroups analysis was performed to identify influencing factors. 287 patients with 1765 lesions were analyzed. ICCs between CerebralDoc and DSA for ≥ 50% and ≥ 70% stenosis were excellent (0.955, 0.922, respectively), for 0-100% stenosis was good (0.735), and for < 50% stenosis was poor (0.056). For ≥ 50% and ≥ 70% stenosis of CerebralDoc and CTA manual measurements versus DSA, ICCs were close (0.955 vs 0.994; 0.922 vs 0.986), and differences were small (0.258% vs -0.362%; 0.369% vs -0.199%). The sensitivity analysis revealed that specific locations (V1, V2, V3, V4) and slender vessels have large or remarkable differences ranging from 15.551% to 44.238%. CerebralDoc exhibited excellent performance in automatically quantifying arterial stenosis of ≥ 50% and ≥ 70% in head and neck CTA. However, further research was needed to improve its performance for < 50% stenosis and to address differences in specific locations and slender vessels.

Prediction of Brain Cancer Occurrence and Risk Assessment of Brain Hemorrhage Using Hybrid Deep Learning Technique

The prediction of brain cancer occurrence and risk assessment of brain hemorrhage using a hybrid deep learning (DL) technique is a critical area of research in medical imaging analysis. One prominent challenge in this field is the accurate identification and classification of brain tumors and hemorrhages, which can significantly impact patient prognosis and treatment planning. The objectives of the study address the prediction of brain cancer occurrence and the assessment of risk levels associated with both brain cancers due to brain hemorrhage. A diverse dataset of brain MRI and CT scan images. Utilize Unsymmetrical Trimmed Median Filter with Optics Clustering for noise removal while preserving edges and details. The Chan-Vese segmentation process for refined segmentation. Brain cancer detection using Multi-Head Self-Attention Dilated Convolution Neural Network (MH-SA-DCNN) with Efficient Net Model. Brain cancer detection using MH-SA-DCNN with Efficient Net Model. This trains the algorithm to predict cancerous regions in brain images. Further, implement a Graph-Based Deep Neural Network Model (G-DNN) to capture spatial relationships and risk factors from brain images. Cox regression model to estimate cancer risk over time and fine-tune and optimize the model’s parameters and features using the Osprey optimization algorithm (OPA).

Investigation of organs dosimetry precision using ATOM phantom and optically stimulated luminescence detectors in Computed Tomography.

The primary objective of this study was to compare organ doses measured using optically stimulated luminescent dosimeters (OSLDs) with those estimated by the CT-EXPO software for common CT protocols. An anthropomorphic ATOM phantom was employed to measure organ doses across head, chest, and abdominal CT scans performed on a Hitachi Supria 16-slice CT scanner. These OSLD measurements were then compared to the estimates provided by the widely used CT-EXPO software. Organ doses were assessed using OSLDs placed in an adult anthropomorphic phantom, with calibration performed through a comprehensive process involving multiple tube potentials and sensitivity corrections. Results from three CT acquisitions per protocol were compared to estimates provided by CT-EXPO software. Findings reveal significant discrepancies between measured and estimated organ doses, with p-values consistently below 0.05 across all organs. For head CT, measured eye lens doses averaged 33.51 mGy, 6.0% lower than the estimated 35.65 mGy. In chest CT, the thyroid dose was 9.82 mGy, 13.5% higher than the estimated 8.65 mGy. For abdominal CT, the liver dose measured 12.11 mGy, 9.6% higher than the estimated 11.05 mGy. Measured doses for the rest of organs were generally lower than those predicted by CT-EXPO, showing some limitations in current estimation models and the importance of precise dosimetry. This study highlights the potential of OSLD measurements as a complementary method for organ dose assessment in CT imaging, emphasizing the need for more accurate organ dose measurement to optimize patient care.

True cost estimation of common imaging procedures for cost-effectiveness analysis - insights from a Singapore hospital emergency department

ObjectivesThere is a lack of clear and consistent cost reporting for cost-effectiveness analysis in radiology. Estimates are often obtained using costing derived from hospital charge records. This study aims to evaluate the accuracy of hospital charge records compared to a Singapore hospital's true diagnostic imaging costs. MethodsA seven-step process involving a bottom-up micro-costing approach was devised and followed to calculate the cost of imaging using actual data from a clinical setting. We retrieved electronic data from a random sample of 96 emergency department patients who had CT brain, CT and X-ray cervical spine, and X-ray lumbar spine performed to calculate the parameters required for cost estimation. We adjusted imaging duration and number of performing personnel to account for variations. ResultsOur approach determined the average cost for the following imaging procedures: CT brain (€154.00), CT and X-ray cervical spine (€177.14 and €68.22), and X-ray lumbar spine (€79.85). We found that the true cost of both conventional radiography procedures was marginally higher than the subsidized patient charge, and all costs were slightly lower than the private patient charge except for X-ray lumbar spine (€73.49 vs.€79.85). We identified larger differences in cost for both CT procedures and smaller differences in cost for conventional radiography procedures, depending on the patient's private or subsidized status. For private status, the differences were: CT brain (Min: €194.20; Max: €264.40), CT cervical spine (Min: €219.54; Max: €399.05), X-ray cervical spine (Min: €5.27; Max: €61.94), and X-ray lumbar spine (Min: €6.36; Max: €108.04), while for subsidized status, the differences were: CT brain (Min: €7.56; Max: €62.64), CT cervical spine (Min: €47.02; Max: €132.49), X-ray cervical spine (Min: €15.88; Max: €103.44), and X-ray lumbar spine (Min: €13.66; Max: €149.44). Considering examination duration and the number of personnel engaged in a procedure, there were significant variations in the minimum, average, and maximum imaging costs. ConclusionThere is a modest gap between hospital charges and actual costs, and we must therefore exercise caution and recognize the limitations of utilizing hospital charge records as absolute metrics for cost-effectiveness analysis. Our detailed approach can potentially enable more accurate imaging cost determination for future studies.

An automated blood test for glial fibrillary acidic protein (GFAP) and ubiquitin carboxy-terminal hydrolase L1 (UCH-L1) to predict the absence of intracranial lesions on head CT in adult patients with mild traumatic brain injury: BRAINI, a multicentre observational study in Europe

Following mild traumatic brain injury (mTBI), elevated concentrations of brain-specific blood proteins glial fibrillary acidic protein (GFAP) and ubiquitin C-terminal hydrolase-L1 (UCH-L1) may be indicative of intracranial lesions normally detected by head CT scans. We sought to validate the performance of this combination of biomarkers at predetermined cutoff values with an automated immunoassay to predict which patients did not have intracranial lesions. This prospective, observational study was conducted in France and Spain at 16 emergency departments. Adult patients with mTBI were eligible if they had a head CT scan and gave a 10-ml blood sample within 12h of injury. GFAP and UCH-L1 serum concentrations were measured and analysed, in less than an hour time, according to predefined cutoff values of 22pg/ml and 327pg/ml, respectively. Serum concentrations of S100B protein were concomitantly determined in a subset of patients. The primary outcome measures were the sensitivity and negative predictive value (NPV) of the combined GFAP-UCH-L1 test to rule out intracranial lesions on head CT scans. gov (NCT04032509). Between August 2019 and June 2021, 1508 patients were recruited, and 1438 were included in the main analysis. Median age was 69 years (IQR 44-83). Most patients (74%) presented 3h after trauma. 179 (12.4%) patients were positive for intracranial lesions by CT. The sensitivity of the combined test was 98.3% (95% CI 95.0-99.7) and the specificity 24.9 (95% CI 22.6-27.4), with a NPV of 99.1% (95% CI 97.1-99.8). Three patients with a positive CT scan had negative biomarker test results. S100B had a sensitivity of 83.0% (95% CI 76.2-88.2) and a NPV of 94.2% (95% CI 91.6-96.0). Patients with higher biomarker values more frequently had poorer recovery at 3 months after injury. Testing for GFAP and UCH-L1, using validated cutoffs obtained with a new, fast automated immunoassay platform, accurately predicted the absence of intracranial lesions on head CT following mTBI. This study is co-funded by the European Institute of Innovation and Technology (EIT) Health, a body of the European Union (Grant nº19474). Biomarkers tests were funded by bioMérieux.

MOGAD presenting as fulminant intracranial hypertension

ObjectivesMyelin oligodendrocyte glycoprotein antibody disease (MOGAD) has an expanding phenotype. We describe two cases of MOGAD with associated severe intracranial hypertension.Case 1: A 21-year-old male presented with diffuse cortical encephalitis and intracranial hypertension with both serum and CSF MOG antibody positivity. Initial brain CT scan was normal but subsequent demyelination was evident on MRI. Case 2: A 44-year-old female presented with a progressive brainstem encephalitis and intracranial hypertension and normal MRI, with later development of subcortical demyelination which was confirmed on brain biopsy. CSF-restricted MOG antibody was detected following the biopsy results. ResultsBoth patients presented with clinical features of severe intracranial hypertension requiring surgical management followed by immunosuppressive therapy (methylprednisone and plasma exchange; and intravenous immunoglobulin and plasma exchange) leading to clinical improvement. DiscussionMOGAD should be in the differential diagnosis of acute severe intracranial hypertension even in the absence of demyelination on initial neuroimaging. Clinicians should be alert of this syndrome that requires combined management of intracranial pressure in addition to early and intensive immunotherapy.

Rapidly progressive brain atrophy in ventilated patients: a retrospective descriptive study

The relationship between mechanical ventilation-induced brain volume changes and ICU-acquired weakness (ICU-AW) is not clear. We assessed brain volume change in ventilated patients and identified associations with changes in extremity muscle strength. Patients admitted to the ICU due to the need for ventilation, and who underwent at least two head CT scans during hospitalization, were included. We employed an automated segmentation method to measure brain volume, recording changes in volume from baseline. Cases with brain volume reduction > 0% were assigned to the “brain atrophy group” and those with ≤ 0% reduction to the “preserved brain volume group.” Medical Research Council (MRC) scores as an indicator of ICU-AW at discharge were compared between groups. There were 84 eligible patients, 71 in the brain atrophy group and 13 in the preserved brain volume group. Analysis of the brain atrophy group showed a significant brain volume reduction of − 3.3% over a median of 30 days. The median MRC scores were significantly lower in the brain atrophy group than in the preserved brain volume group (36 vs. 48, difference [95% CI]: − 12 [− 19.5– − 7.1]). Many ICU patients on mechanical ventilation showed rapidly progressive brain atrophy, and most of these patients developed ICU-AW.

Imaging for suspected paediatric ventriculoperitoneal shunt dysfunction: are shunt series X-rays always necessary?

Based on an initial hypothesis that the abdominal radiograph is rarely useful in the radiographic shunt series (SS), this study sought to determine which components of the SS are useful in identifying and diagnosing shunt dysfunction. This enquiry broadened to evaluate which modalities were most clinically useful in patients who ultimately underwent shunt revision for their suspected dysfunction. Arm1: Abdominal radiographs (AXR) performed over a 2-year period were reviewed retrospectively to identify shunt abnormalities. Arm 2: Retrospective analysis of acute imaging and shunt series was performed on patients who had undergone shunt revision at the same hospital over a 10-year period. Arm 1: Seventy-five AXR performed were part of a formal SS. Eight reported a shunt abnormality. Two were within the abdomen; both findings were artefactual. Arm 2: One hundred seventy-nine patients are included. Ninety-six had abnormal imaging 72h prior to revision; most were on CT (n = 67). Thirty-one demonstrated shunt leak, fracture or migration. Ninety-three patients had an SS the week before their revision; only 15% (n = 14) were abnormal. This is a study evaluating the utility of radiographs in paediatric shunt malfunction. SS has low clinical yield; based on our findings, as well as background research, we recommend low-dose CT head (US or MRI) should be used as first line investigation, with tailored use of plain films to assist neurosurgical planning.

Septum pellucidum pilocytic astrocytoma: case report and literature review

IntroductionSeptum pellucidum pilocytic astrocytoma is rare. There are only 3 reported cases in literature. Our patient is the fourth case.Case presentationA 17-year-old male presented with headache. CT brain and MRI brain showed septum pellucidum lesion with obstructive hydrocephalus which looked like colloid cyst. Intraoperatively, the tumor was attached to the lower part of septum pellucidum and it didn’t look like a colloid cyst. Histopathology was pilocytic astrocytoma. Postoperatively, patient recovered well with no neurological deficits or adverse neurological sequalae.ConclusionSeptum pellucidum pilocytic astrocytoma should be considered as one of the differential diagnosis of septum pellucidum tumors. We observed that the interhemispheric transcallosal transventricular approach might be associated with lesser comorbidities and neurological deficits in tumors in this location.

Applying Conformal Prediction to a Deep Learning Model for Intracranial Hemorrhage Detection to Improve Trustworthiness.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To apply conformal prediction to a deep learning (DL) model for intracranial hemorrhage (ICH) detection and evaluate model performance in detection as well as model accuracy in identifying challenging cases. Materials and Methods This was a retrospective (November 2017 through December 2017) study of 491 noncontrast head CT volumes from the CQ500 dataset in which three senior radiologists annotated sections containing ICH. The dataset was split into definite and challenging (uncertain) subsets, where challenging images were defined as those in which there was disagreement among readers. A DL model was trained on 146 patients (mean age = 45.7, 70 females, 76 males) from the definite data (training dataset) to perform ICH localization and classification into five classes. To develop an uncertainty-aware DL model, 1,546 sections of the definite data (calibration dataset) was used for Mondrian conformal prediction (MCP). The uncertainty-aware DL model was tested on 8,401 definite and challenging sections to assess its ability to identify challenging sections. The difference in predictive performance (P value) and ability to identify challenging sections (accuracy) were reported. Results After the MCP procedure, the model achieved an F1 score of 0.920 for ICH classification on the test dataset. Additionally, it correctly identified 6,837 of the 6,856 total challenging sections as challenging (99.7% accuracy). It did not incorrectly label any definite sections as challenging. Conclusion The uncertainty-aware MCP-augmented DL model achieved high performance in ICH detection and high accuracy in identifying challenging sections, suggesting its usefulness in automated ICH detection and potential to increase trustworthiness of DL models in radiology. ©RSNA, 2024.

DDANet: A deep dilated attention network for intracerebral haemorrhage segmentation.

Intracranial haemorrhage (ICH) is an urgent and potentially fatal medical condition caused by brain blood vessel rupture, leading to blood accumulation in the brain tissue. Due to the pressure and damage it causes to brain tissue, ICH results in severe neurological impairment or even death. Recently, deep neural networks have been widely applied to enhance the speed and precision of ICH detection yet they are still challenged by small or subtle hemorrhages. The authors introduce DDANet, a novel haematoma segmentation model for brain CT images. Specifically, a dilated convolution pooling block is introduced in the intermediate layers of the encoder to enhance feature extraction capabilities of middle layers. Additionally, the authors incorporate a self-attention mechanism to capture global semantic information of high-level features to guide the extraction and processing of low-level features, thereby enhancing the model's understanding of the overall structure while maintaining details. DDANetalso integrates residual networks, channel attention, and spatial attention mechanisms for joint optimisation, effectively mitigating the severe class imbalance problem and aiding the training process. Experiments show that DDANet outperforms current methods, achieving the Dice coefficient, Jaccard index, sensitivity, accuracy, and a specificity of 0.712, 0.601, 0.73, 0.997, and 0.998, respectively. The code is available at https://github.com/hpguo1982/DDANet.

Dual-task vision transformer for rapid and accurate intracerebral hemorrhage CT image classification

Intracerebral hemorrhage (ICH) is a severe and sudden medical condition caused by the rupture of blood vessels in the brain, leading to permanent damage to brain tissue and often resulting in functional disabilities or death in patients. Diagnosis and analysis of ICH typically rely on brain CT imaging. Given the urgency of ICH conditions, early treatment is crucial, necessitating rapid analysis of CT images to formulate tailored treatment plans. However, the complexity of ICH CT images and the frequent scarcity of specialist radiologists pose significant challenges. Therefore, we collect a dataset from the real world for ICH and normal classification and three types of ICH image classification based on the hemorrhage location, i.e., Deep, Subcortical, and Lobar. In addition, we propose a neural network structure, dual-task vision transformer (DTViT), for the automated classification and diagnosis of ICH images. The DTViT deploys the encoder from the Vision Transformer (ViT), employing attention mechanisms for feature extraction from CT images. The proposed DTViT framework also incorporates two multilayer perception (MLP)-based decoders to simultaneously identify the presence of ICH and classify the three types of hemorrhage locations. Experimental results demonstrate that DTViT performs well on the real-world test dataset.

Development of a deep learning method to identify acute ischaemic stroke lesions on brain CT

BackgroundCT is commonly used to image patients with ischaemic stroke but radiologist interpretation may be delayed. Machine learning techniques can provide rapid automated CT assessment but are usually developed from...

Multi-type stroke lesion segmentation: comparison of single-stage and hierarchical approach.

Stroke, a major cause of death and disability worldwide, can be haemorrhagic or ischaemic depending on the type of bleeding in the brain. Rapid and accurate identification of stroke type and lesion segmentation is critical for timely and effective treatment. However, existing research primarily focuses on segmenting a single stroke type, potentially limiting their clinical applicability. This study addresses this gap by exploring multi-type stroke lesion segmentation using deep learning methods. Specifically, we investigate two distinct approaches: a single-stage approach that directly segments all tissue types in one model and a hierarchical approach that first classifies stroke types and then utilises specialised segmentation models for each subtype. Recognising the importance of accurate stroke classification for the hierarchical approach, we evaluate ResNet, ResNeXt and ViT networks, incorporating focal loss and oversampling techniques to mitigate the impact of class imbalance. We further explore the performance of U-Net, U-Net++ and DeepLabV3 models for segmentation within each approach. We use a comprehensive dataset of 6650 images provided by the Ministry of Health of the Republic of Türkiye. This dataset includes 1130 ischaemic strokes, 1093 haemorrhagic strokes and 4427 non-stroke cases. In our comparative experiments, we achieve an AUC score of 0.996 when classifying stroke and non-stroke slices. For lesion segmentation task, while the performance of different architectures is comparable, the hierarchical training approach outperforms the single-stage approach in terms of intersection over union (IoU). The performance of the U-Net model increased significantly from an IoU of 0.788 to 0.875 when the hierarchical approach is used. This comparative analysis aims to identify the most effective approach and deep learning model for multi-type stroke lesion segmentation in brain CT scans, potentially leading to improved clinical decision-making, treatment efficiency and outcomes.

Abstract Sa1104: Head and Pulmonary Artery Chest CT Detect Most but Not All Causes of Sudden Death and Resuscitation Injury Compared to Head to Pelvis CT Angiography in Survivors of Out of Hospital Circulatory Arrest

Introduction: Head-to-pelvis “sudden death computed tomography” angiography (SDCT) has a high diagnostic yield to identify causes and complications of out-of-hospital cardiac arrest (OHCA). The differences in yields of non-contrast head CT (H-CT) and pulmonary embolism chest CT (CT PE) angiography alone or in combination is not clear. Methods: Our prospective observational cohort enrolled patients that survived OHCA resuscitation and had a SDCT (H-CT, ECG-gated chest CT, and non-ECG gated venous phase abdomen/pelvis CT) within 6 hours of arrival (December 2015 – February 2018). For this sub-study, we assessed the same scans for diagnoses obtainable from routine H-CT and CT PE axial coverage alone, excluding any added (SDCT) findings from ECG-gating and abdomen/pelvis assessment. The primary outcome was the difference in diagnostic yield of time-critical and non-time-critical diagnoses by CT PE with or without H-CT compared to SDCT. Results: SDCT identified 86 time-critical diagnoses in 104 enrolled patients, including 39 pneumonias (45% of diagnoses), 13 myocardial infarctions [MI] (15%), 8 pulmonary embolisms (9%), 8 pneumothoraces (9%), 8 abdominal catastrophes (9%), 3 intracranial hemorrhages (3%), and 1 vascular access hemorrhage (1%) [ Table ]. CT PE alone identified 61 of the 86 time-critical diagnoses (71%), missing 25 diagnoses (29%) compared to SDCT. Missing diagnoses included MI (13/13), abdominal catastrophe (8/8), and active vascular access hemorrhage (1/1). H-CT diagnosed all 3 intracranial hemorrhages. Of the 208 non-time-critical diagnoses identified by SDCT, CT PE identified 183 non-time critical diagnoses (88%), missing 25 diagnoses (12%). H-CT did not identify any non-time-critical diagnoses. Conclusions: CT PE imaging with H-CT identifies a majority—but not all—time-critical and non-time-critical diagnoses in OHCA patients, mainly failing to capture coronary and intra-abdominal pathologies. SDCT, which includes ECG-gated chest CT and abdomen/pelvis CT, improves total diagnostic yield, but its incremental utility requires further study.

DSIFNet: Implicit feature network for nasal cavity and vestibule segmentation from 3D head CT

This study is dedicated to accurately segment the nasal cavity and its intricate internal anatomy from head CT images, which is critical for understanding nasal physiology, diagnosing diseases, and planning surgeries. Nasal cavity and it’s anatomical structures such as the sinuses, and vestibule exhibit significant scale differences, with complex shapes and variable microstructures. These features require the segmentation method to have strong cross-scale feature extraction capabilities. To effectively address this challenge, we propose an image segmentation network named the Deeply Supervised Implicit Feature Network (DSIFNet). This network uniquely incorporates an Implicit Feature Function Module Guided by Local and Global Positional Information (LGPI-IFF), enabling effective fusion of features across scales and enhancing the network's ability to recognize details and overall structures. Additionally, we introduce a deep supervision mechanism based on implicit feature functions in the network's decoding phase, optimizing the utilization of multi-scale feature information, thus improving segmentation precision and detail representation. Furthermore, we constructed a dataset comprising 7116 CT volumes (including 1,292,508 slices) and implemented PixPro-based self-supervised pretraining to utilize unlabeled data for enhanced feature extraction. Our tests on nasal cavity and vestibule segmentation, conducted on a dataset comprising 128 head CT volumes (including 34,006 slices), demonstrate the robustness and superior performance of proposed method, achieving leading results across multiple segmentation metrics.

Abstract 4119073: Zaza: A New Etiology of an Acquired Brugada-Pattern ECG

Introduction: Tianeptine, an unregulated supplement, adds a new problem to the ever-growing drug epidemic. We describe a case of Tianeptine overdose presenting with acute agitation and an acquired Brugada-pattern ECG. Case Description: A 26-year-old male presented with acute agitation after bystanders witnessed the patient ingest 15 capsules of “Zaza Silver”. Initial ECG showed a type I Brugada pattern with coved ST-elevations in V1 and prolonged QRS and QTc. Previous ECGs on record were negative for evidence of a hereditary Brugada pattern. His urine toxicology was positive for THC. Troponins, echocardiogram and CT head were unremarkable. The patient was treated with Diazepam and a sodium bicarbonate drip with close monitoring of arterial blood pH, maintaining a goal between 7.50-7.55. After resolution of his symptoms and ECG findings, he was discharged home. Discussion: Patients with a Brugada pattern ECG are at increased risk of fatal arrhythmias. Classified as both inherited and acquired, this unregulated supplement presents a new etiology of an acquired Brugada-pattern ECG. Tianeptine is a non-FDA approved tricyclic antidepressant that is marketed as a supplement under the brand name “Zaza” at gas stations and convenience stores. Currently approved for the treatment of depression in Europe, it is solely distributed in the US as an unregulated health supplement. We hypothesize that Tianeptiene’s Tricyclic Antidepressant properties can affect the myocardial voltage-gated sodium channels leading to the Brugada pattern on EKG with a similar mechanism as has been previously described in cases of Tricyclic Antidepressant overdoses. Early identification of EKG findings, coupled with a detailed history can be instrumental in early diagnosis and critical treatment with bicarbonate therapy. This case also demonstrates the dangers of the non-FDA marketing of tianeptine and underscores the urgent need to regulate such supplements.

Abstract 4117646: The Ultimate Test in Hemocompatibility: HeartMate3 Restart After Prolonged Pump Shut Down

Introduction: The HeartMate 3 (HM3) LVAD, was shown to have a higher survival free of hemocompatibility related adverse events (HRAE) compared to its predecessors (HM2, HVAD). Superior HM3 outcomes are attributed to wide blood flow pathways coupled with frictionless movement and intrinsic pulsatility, reducing shear stress and blood stasis. It is unknown if improved hemocompatibility can withstand pump restart after prolonged shutdown. We herein report a case of HM3 pump stoppage without subsequent HRAE. Case Presentation: A 41-year-old male underwent HVAD implant in 2019 for advanced non-ischemic cardiomyopathy. This was exchanged to HM3 for recurrent neurological events despite therapeutic anticoagulation. Ten months after exchange, he awakened one morning to find his LVAD had been off for an unknown period but had not heard any device alarms (85dB if on battery power, 165dB if on wall power). Since he felt well he changed to battery power resulting in immediate pump restart. Log file analysis showed pump stoppage 2 am – 10 am, without preceding low flow alarms or power elevations. Management and Outcomes: In the ER INR was 1.5 (2.8 one week prior) and systemic heparin was started. Evaluation included: 1) CT brain without acute infarcts 2) echocardiogram without intracardiac thrombus 3) CT Angiography with patency of the inflow cannula and outflow graft 4) stable serial LDH measurements. The controller was exchanged, and analysis noted normal function. 1-year later the patient is maintained on warfarin and aspirin 325mg without further HRAE. Given the patient’s neurologic history and pump stoppage event, we did not invoke ARIES trial guidance and thus continued aspirin. Conclusion: Pump stoppage occurs when there is complete battery depletion, disconnection of both power leads, or the percutaneous lead from system controller. Our case is unique in that the duration of pump shutdown was 8 hours and INR subtherapeutic, without HRAE in the background of neurologic events on HVAD support. It has been previously reported that complete outflow graft thrombosis can occur shortly after LVAD decommissioning. The HM3 User Manual recommends restarting the pump immediately if off for a few minutes and using clinical judgment for longer durations due to increased risk for thromboembolic events. Our case adds to the paucity of existing data on improved hemocompatibility of the HM3 during rare circumstances of the ultimate test in hemocompatibility: complete pump shut down.

Investigations of Acute Ischemic Stroke Detection Techniques from CT Images Using Machine Learning Approaches

Stroke is a kind of cerebrovascular accident (CVA) that primarily affects individuals over the age of 50, while it can afflict people of all ages. Stroke patients suffer from a chronic condition that leaves them physically unable till their death. To identify the onset of this illness, numerous studies have been carried out. Common signs of a stroke include stiffness, a shift in posture, and tremors in the arms and legs. Functional and emotional mediation are linked to a significant area of the brain. The symptoms of many brain disorders are similar in the early stages because of disruptions to dopamine and other regulating mechanisms. This is a crucial step in how stroke lesions develop. The CAD system is the most efficient method for identifying a stroke. By speeding up the analysis of the required aberration, the CAD system improves the process of illness diagnosis. The goal of the proposed study is to create a compact system that can lessen processing errors, false positive rates, and complexity—three major problems with the current system. The proposed study uses a machine learning model to interpret and classify a CT brain image that shows an Acute Ischaemic Stroke (AIS) lesion. To distinguish between normal and AIS stroke lesions, the pre-processed image is segmented and classed using a Random Forest classifier. Performance criteria like Peak signal ratio, average gradient, accuracy, specificity, sensitivity, and dice index are used to evaluate the experimental output of the proposed work. The suggested model has a straightforward processing structure and the highest efficiency.

Transcranial Ultrasound Guided Assessment of Brain Midline Shift in Neuro Intensive Care Unit

Background: Neuro Intensive Care Unit (Neuro-ICU) handles neurological emergencies that involve intracranial (IC) hemorrhages. It can be challenging to manage elevated intracranial pressure (ICP) and midline shift (MLS), as they requires constant monitoring and frequent imaging. Trans-cranial Color Coded Sonography (TCCS), a bedside monitoring tool, may be helpful in monitoring MLS under such conditions. Objectives: To correlate MLS of the third ventricle in the brain measured by TCCS with CT midline shift. Method: A prospective observational study was conducted on patients with IC bleed who were mechanically ventilated and had a head CT done to rule out MLS. Within an hour of doing a CT brain, TCCS-MLS was identified. Data was collected and analyzed. Results: A total of fifty patients studied, the MLS (mean ±SD) was 5.3mm ± 3.1 mm using TCCS and 6.7±4.4 mm using CT brain. The calculated midline shift between TCCS and CT brain demonstrated a Pearson's correlation of 0.78 (P&lt;0.001). With TCCS, the area under the ROC curve to identify a significant MLS was 0.91 (95%CI=0.8-1). TCCS-MLS of 3.9mm as a cut-off, predicted the occurrence of &gt;5mm MLS on CT scan with a sensitivity of 95% and specificity of 90%. Conclusions: This study suggests that TCCS could serve as an alternative bedside monitoring tool in patients whose transportation is risky, in predicting the transcranial MLS &gt;5mm with reasonable accuracy.