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Objective The aim of the study was to evaluate the ionizing radiation exposure in patients with Coronavirus disease 2019 (COVID-19). Materials and Methods This was a retrospective study in which all patients presented with suggestive symptoms of COVID-19 were included. The study was carried out in a university-affiliated private hospital in Istanbul, Turkey. Biological radiation dose exposure (cumulative effective dose: CED) was evaluated in millisievert (mSv) units. Results A total of 1410 patients were included in the study. Of all study subjects, 804 patients (57%) underwent only one chest computed tomography (CT) procedure. Six hundred and six patients (43%) had two or more chest CT procedures. Median CED was 6.02 (min–max:1.67–16.27) mSv. The number of patients who were exposed to ≤ 5 mSv were 149 (24.6%), whereas 457 patients (75.4%) were exposed to >5 mSv. Conclusion The radiation exposure in COVID-19 patients seems unjustifiably high. Awareness should be increased as to the proper use of chest CT in COVID-19 as per to the society recommendations.

Purpose: Digital tomosynthesis (DTS) is currently undergoing validation for potential clinical implications. The aim of this study was to investigate the potential for DTS as a low-dose alternative to computed tomography (CT) in imaging of pulmonary pathology in patients with cystic fibrosis (CF). Methods: DTS and CT were performed as part of the routine triannual follow-up in 31 CF patients. Extent of disease was quantified according to modality-specific scoring systems. Statistical analysis included Spearman’s rank correlation coefficient (r) and Krippendorff’s alpha (α). Major findings: The median effective dose was 0.14 for DTS and 2.68 for CT. Intermodality correlation was very strong for total score and the subscores regarding bronchiectasis and bronchial wall-thickening (r = 0.82–0.91, P

Postmortem computed tomography (PMCT) is integrated into the evaluation of decedents in several American medical examiner offices and medicolegal death investigative centers in many other countries. We retrospectively investigated the value of PMCT in a series of firearm homicide cases from a statewide centralized medical examiner’s office that occurred during 2016. Autopsies were performed or supervised by board‐certified forensic pathologists who reviewed the PMCT scans prior to autopsy. PMCT scans were re‐evaluated by a forensic radiologist blinded to the autopsy findings and scored by body region (head–neck, thoracoabdominal, and extremities). Injury discrepancies were scored using a modified Goldman classification and analyzed with McNemar’s test. We included 60 males and 20 females (median age 31 years, range 3–73). Based on PMCT, 56 (79.1%) cases had injuries relevant to the cause of death in a single body region (24 head–neck region, 32 thoracoabdominal region). Out of these 56 cases, 9 had a missed major diagnosis by PMCT outside that region, including 6 extremity injuries visible during standard external examination. Yet all had evident lethal firearm injury. We showed that PMCT identifies major firearm injuries in homicide victims and excludes injuries related to the cause of death in other regions when a single body region is injured. Although PMCT has a known limited sensitivity for soft tissue and vascular pathology, it can be combined with external examination to potentially reduce or focus dissections in some of these cases depending on the circumstances and medicolegal needs.

The use of iodine-131 S values based on reference computational phantoms with fixed thyroid model may lead to significant dosimetric errors in patients who may have different thyroid location from the reference phantoms. In the present study, we investigated individual thyroid location variation by examining the computed tomography image sets of 40 adult male and female patients. Subsequently, the thyroid location of the adult male and female mesh-type reference phantoms of the International Commission on Radiological Protection (ICRP) was adjusted to match each the highest, mean and the lowest locations of the thyroid observed in this dataset. The thyroid-adjusted phantoms were implemented into the Geant4 Monte Carlo code to calculate thyroid location-dependent iodine-131 S values (rT ← thyroid) for a total of 30 target regions. The maximum variation among the observed thyroid locations was 39 mm and 36 mm for male and female patients, respectively. The mean thyroid locations of both male and female patients showed a good agreement with the ICRP reference phantoms. The thyroid location-dependent Iodine-131 S values were significantly different from the reference phantoms for most target regions by up to a factor of 3. The use of thyroid location-dependent S values in dose reconstructions should help quantify the dosimetric uncertainty in epidemiologic investigations of patients receiving iodine-131 therapy for hyperthyroidism and thyroid cancer.

Objective. Dual energy computed tomography (DECT) has been shown to provide additional image information compared to conventional CT and has been used in clinical routine for several years. The objective of this work is to present a DECT implementation for a Small Animal Radiation Research Platform (SARRP) and to verify it with a quantitative analysis of a material phantom and a qualitative analysis with an ex-vivo mouse measurement. Approach. For dual energy imaging, two different spectra are required, but commercial small animal irradiators are usually not optimized for DECT. We present a method that enables dual energy imaging on a SARRP with sequential scanning and an Empirical Dual Energy Calibration (EDEC). EDEC does not require the exact knowledge of spectra and attenuation coefficients; instead, it is based on a calibration. Due to the SARRP geometry and reconstruction algorithm, the calibration is done using an artificial CT image based on measured values. The calibration yields coefficients to convert the measured images into material decomposed images. Main results. To analyze the method quantitatively, the electron density and the effective atomic number of a material phantom were calculated and compared with theoretical values. The electron density showed a maximum deviation from the theoretical values of less than 5% and the atomic number of slightly more than 6%. For use in mice, DECT is particularly useful in distinguishing iodine contrast agent from bone. A material decomposition of an ex-vivo mouse with iodine contrast agent was material decomposed to show that bone and iodine can be distinguished and iodine-corrected images can be calculated. Significance. DECT is capable of calculating electron density images and effective atomic number images, which are appropriate parameters for quantitative analysis. Furthermore, virtual monochromatic images can be obtained for a better differentiation of materials, especially bone and iodine contrast agent.

We propose aNETT (augmented NETwork Tikhonov) regularization as a novel data-driven reconstruction framework for solving inverse problems. An encoder-decoder type network defines a regularizer consisting of a penalty term that enforces regularity in the encoder domain, augmented by a penalty that penalizes the distance to the signal manifold. We present a rigorous convergence analysis including stability estimates and convergence rates. For that purpose, we prove the coercivity of the regularizer used without requiring explicit coercivity assumptions for the networks involved. We propose a possible realization together with a network architecture and a modular training strategy. Applications to sparse-view and low-dose CT show that aNETT achieves results comparable to state-of-the-art deep-learning-based reconstruction methods. Unlike learned iterative methods, aNETT does not require repeated application of the forward and adjoint models during training, which enables the use of aNETT for inverse problems with numerically expensive forward models. Furthermore, we show that aNETT trained on coarsely sampled data can leverage an increased sampling rate without the need for retraining.

Quantifying parenchymal tissue changes in the lungs is imperative in furthering the study of radiation induced lung damage (RILD). Registering lung images from different time-points is a key step of this process. Traditional intensity-based registration approaches fail this task due to the considerable anatomical changes that occur between timepoints. This work proposes a novel method to successfully register longitudinal pre- and post-radiotherapy (RT) lung computed tomography (CT) scans that exhibit large changes due to RILD, by extracting consistent anatomical features from CT (lung boundaries, main airways, vessels) and using these features to optimise the registrations. Pre-RT and 12 month post-RT CT pairs from fifteen lung cancer patients were used for this study, all with varying degrees of RILD, ranging from mild parenchymal change to extensive consolidation and collapse. For each CT, signed distance transforms from segmentations of the lungs and main airways were generated, and the Frangi vesselness map was calculated. These were concatenated into multi-channel images and diffeomorphic multichannel registration was performed for each image pair using NiftyReg. Traditional intensity-based registrations were also performed for comparison purposes. For the evaluation, the pre- and post-registration landmark distance was calculated for all patients, using an average of 44 manually identified landmark pairs per patient. The mean (standard deviation) distance for all datasets decreased from 15.95 (8.09) mm pre-registration to 4.56 (5.70) mm post-registration, compared to 7.90 (8.97) mm for the intensity-based registrations. Qualitative improvements in image alignment were observed for all patient datasets. For four representative subjects, registrations were performed for three additional follow-up timepoints up to 48 months post-RT and similar accuracy was achieved. We have demonstrated that our novel multichannel registration method can successfully align longitudinal scans from RILD patients in the presence of large anatomical changes such as consolidation and atelectasis, outperforming the traditional registration approach both quantitatively and through thorough visual inspection.

Computed tomography (CT) provides useful information in patients with known or suspected COVID-19 infection. However, there are substantial variations and challenges in scanner technologies and scan practices that have negative effect on the image quality and can increase radiation dose associated with CT. Objective: In this article, we present major issues and challenges with use of CT at five Brazilian CT facilities for imaging patients with known or suspected COVID-19 infection and offer specific mitigating strategies. Methods: Observational, retrospective and prospective study of five CT facilities from different states and regions of Brazil, with approval of research and ethics committees. Results: The most important issues include frequent use of CT, lack of up-to-date and efficient scanner technologies, over-scanning and patient off-centring. Mitigating strategies can include updating scanner technology and improving scan practices.

Reducing radiation-induced side effects is one of the most important challenges in paediatric cancer treatment. Recently, there has been growing interest in using spatial normalisation to enable voxel-based analysis of radiation-induced toxicities in a variety of patient groups. The need to consider three-dimensional distribution of doses, rather than dose-volume histograms, is desirable but not yet explored in paediatric populations. In this paper, we investigate the feasibility of atlas construction and spatial normalisation in paediatric radiotherapy. We used planning computed tomography (CT) scans from twenty paediatric patients historically treated with craniospinal irradiation to generate a template CT that is suitable for spatial normalisation. This childhood cancer population representative template was constructed using groupwise image registration. An independent set of 53 subjects from a variety of childhood malignancies was then used to assess the quality of the propagation of new subjects to this common reference space using deformable image registration (i.e. spatial normalisation). The method was evaluated in terms of overall image similarity metrics, contour similarity and preservation of dose-volume properties. After spatial normalisation, we report a dice similarity coefficient of 0.95 ± 0.05, 0.85 ± 0.04, 0.96 ± 0.01, 0.91 ± 0.03, 0.83 ± 0.06 and 0.65 ± 0.16 for brain and spinal canal, ocular globes, lungs, liver, kidneys and bladder. We then demonstrated the potential advantages of an atlas-based approach to study the risk of second malignant neoplasms after radiotherapy. Our findings indicate satisfactory mapping between a heterogeneous group of patients and the template CT. The poorest performance was for organs in the abdominal and pelvic region, likely due to respiratory and physiological motion and to the highly deformable nature of abdominal organs. More specialised algorithms should be explored in the future to improve mapping in these regions. This study is the first step toward voxel-based analysis in radiation-induced toxicities following paediatric radiotherapy.

Purpose: Coronavirus disease 2019 (COVID-19) is a new infection that has spread worldwide and with no automatic model to reliably detect its presence from images. We aim to investigate the potential of deep transfer learning to predict COVID-19 infection using chest computed tomography (CT) and x-ray images. Approach: Regions of interest (ROI) corresponding to ground-glass opacities (GGO), consolidations, and pleural effusions were labeled in 100 axial lung CT images from 60 COVID-19-infected subjects. These segmented regions were then employed as an additional input to six deep convolutional neural network (CNN) architectures (AlexNet, DenseNet, GoogleNet, NASNet-Mobile, ResNet18, and DarkNet), pretrained on natural images, to differentiate between COVID-19 and normal CT images. We also explored the model’s ability to classify x-ray images as COVID-19, non-COVID-19 pneumonia, or normal. Performance on test images was measured with global accuracy and area under the receiver operating characteristic curve (AUC). Results: When using raw CT images as input to the tested models, the highest accuracy of 82% and AUC of 88.16% is achieved. Incorporating the three ROIs as an additional model inputs further boosts performance to an accuracy of 82.30% and an AUC of 90.10% (DarkNet). For x-ray images, we obtained an outstanding AUC of 97% for classifying COVID-19 versus normal versus other. Combing chest CT and x-ray images, DarkNet architecture achieves the highest accuracy of 99.09% and AUC of 99.89% in classifying COVID-19 from non-COVID-19. Our results confirm the ability of deep CNNs with transfer learning to predict COVID-19 in both chest CT and x-ray images. Conclusions: The proposed method could help radiologists increase the accuracy of their diagnosis and increase efficiency in COVID-19 management.

Purpose: We investigated the performance of a neural network (NN) material decomposition method under varying pileup conditions. Approach: Experiments were performed at tube current settings that provided count rates incident on the detector through air equal to 9%, 14%, 27%, 40%, and 54% of the maximum detector count rate. An NN was trained for each count-rate level using transmission measurements through known thicknesses of basis materials (PMMA and aluminum). The NN trained for each count-rate level was applied to x-ray transmission measurements through test materials and to CT data of a rod phantom. Material decomposition error was evaluated as the distance in basis material space between the estimated thicknesses and ground truth. Results: There was no clear trend between count-rate level and material decomposition error for all test materials except neoprene. As an example result, Teflon error was 0.33 cm at the 9% count-rate level and 0.12 cm at the 54% count-rate level for the x-ray transmission experiments. Decomposition error increased with count-rate level for the neoprene test case, with 0.65-cm error at 9% count-rate level and 1.14-cm error at the 54% count-rate level. In the CT study, material decomposition error decreased with increasing incident count rate. For example, the material decomposition error for Teflon was 0.089, 0.066, 0.054 at count-rate levels of 14%, 27%, and 40%, respectively. Conclusions: Results demonstrate over a range of incident count-rate levels that an NN trained at a specific count-rate level can learn the relationship between photon-counting spectral measurements and basis material thicknesses.

Purpose: Given the recent COVID-19 pandemic and its stress on global medical resources, presented here is the development of a machine intelligent method for thoracic computed tomography (CT) to inform management of patients on steroid treatment. Approach: Transfer learning has demonstrated strong performance when applied to medical imaging, particularly when only limited data are available. A cascaded transfer learning approach extracted quantitative features from thoracic CT sections using a fine-tuned VGG19 network. The extracted slice features were axially pooled to provide a CT-scan-level representation of thoracic characteristics and a support vector machine was trained to distinguish between patients who required steroid administration and those who did not, with performance evaluated through receiver operating characteristic (ROC) curve analysis. Least-squares fitting was used to assess temporal trends using the transfer learning approach, providing a preliminary method for monitoring disease progression. Results: In the task of identifying patients who should receive steroid treatments, this approach yielded an area under the ROC curve of 0.85±0.10 and demonstrated significant separation between patients who received steroids and those who did not. Furthermore, temporal trend analysis of the prediction score matched expected progression during hospitalization for both groups, with separation at early timepoints prior to convergence near the end of the duration of hospitalization. Conclusions: The proposed cascade deep learning method has strong clinical potential for informing clinical decision-making and monitoring patient treatment.

Strain engineering is used to obtain desirable materials properties in a range of modern technologies. Direct nanoscale measurement of the three-dimensional strain tensor field within these materials has however been limited by a lack of suitable experimental techniques and data analysis tools. Scanning electron diffraction has emerged as a powerful tool for obtaining two-dimensional maps of strain components perpendicular to the incident electron beam direction. Extension of this method to recover the full three-dimensional strain tensor field has been restricted though by the absence of a formal framework for tensor tomography using such data. Here, we show that it is possible to reconstruct the full non-symmetric strain tensor field as the solution to an ill-posed tensor tomography inverse problem. We then demonstrate the properties of this tomography problem both analytically and computationally, highlighting why incorporating precession to perform scanning precession electron diffraction may be important. We establish a general framework for non-symmetric tensor tomography and demonstrate computationally its applicability for achieving strain tomography with scanning precession electron diffraction data.

This study compares dual-energy computed tomography (DECT) images of a phantom including different material inserts and with additional lateral titanium or stainless steel inserts, simulating bilateral hip prostheses. Dual-source (DS) and fast kV-switching (FKS) DECT with/without metal artefact reduction (MAR) were compared with regards to virtually monoenergetic CT number accuracy and the depiction of different materials. Streak artefacts were observed between the metal inserts that were more severe with steel compared to titanium inserts. The artefact severity and CT number accuracy depended on the photon energy (keV) for both DECT techniques. While MAR generally increased the CT number accuracy and material depiction within the streak artefacts, it sometimes decreased the accuracy outside the streak artefacts for both DS and FKS. FKS depicted the metal inserts more accurately than DS with regards to both CT numbers and external diameter.

Objective measurement of the nasal valve region is valuable for the assessment of functional rhinoplasty surgical outcomes. Anatomical optical coherence tomography (aOCT) is an imaging modality that may be used to obtain real-time, quantitative, and volumetric scans of the nasal airway. We aim to evaluate if volumetric aOCT imaging is useful for the examination of the nasal valve region before and after functional rhinoplasty procedures. aOCT scans of the nasal valves were performed on four cadaveric heads before and after spreader graft and butterfly graft procedures. The resulting aOCT images were compared against video endoscopy images, and the segmented volumes of the nasal airway obtained from aOCT scans were compared with computed tomography (CT) derived volumes acquired under the same conditions. The aOCT-derived volumes match the CT volumes closely, with a mean Dice similarity coefficient of 0.88 and a mean Hausdorff distance of 2.3 mm. Furthermore, the aOCT images were found to represent the shape of the nasal cavity accurately. Due to its ability to perform real-time, quantitative, and accurate evaluation of the nasal airway, aOCT imaging is a promising modality for the objective assessment of the nasal valves before and after functional rhinoplasty procedures.

We present a method of imaging angiographic structures in human extremities, including hands, arms, legs, and feet, using a newly developed photoacoustic computed tomography (PACT) system. The system features deep penetration (1.8 cm in muscular tissues) with high spatial and temporal resolutions. A volumetric image is acquired within 5 to 15 s while each cross sectional image is acquired within 100 μs. Therefore, we see no blurring from motion in the imaging plane. Longitudinal and latitudinal cross-sectional images of a healthy volunteer clearly show the vascular network of each appendage and highlight the system’s ability to image major and minor vasculatures, without the use of an external contrast or ionizing radiation. We also track heartbeat-induced arterial movement at a two-dimensional frame rate of 10 Hz. This work substantiates the idea that PACT could be used as a noninvasive method for imaging human vasculatures.

In 2013, a nationwide investigation was conducted in Switzerland to establish the population's exposure from medical X rays. A hybrid approach was used combining the Raddose database accessible on-line by the participating practices and the Swiss medical tariffication system for hospitals. This study revealed that the average annual number of examinations is 1.2 per inhabitant, and the associated annual effective dose is 1.4 mSv. It also showed that computed tomography is the most irradiating modality and that it delivers 70 % of the total dose. The annual effective dose per inhabitant registered a 17 % increase in 5 y and is comparable with what was recently reported in neighbouring countries.

The aim of the study was to investigate the potential role of chest tomosynthesis (CTS) at a tertiary referral centre by exploring to what extent CTS could substitute chest radiography (CXR) and computed tomography (CT). The study comprised 1433 CXR, 523 CT and 216 CTS examinations performed 5 years after the introduction of CTS. For each examination, it was decided if CTS would have been appropriate instead of CXR (CXR cases), if CTS could have replaced the performed CT (CT cases) or if CT would have been performed had CTS not been available (CTS cases). It was judged that (a) CTS had been appropriate in 15 % of the CXR examinations, (b) CTS could have replaced additionally 7 % of the CT examinations and (c) CT would have been carried out in 63 % of the performed CTS examinations, had CTS not been available. In conclusion, the potential role for CTS to substitute other modalities during office hours at a tertiary referral centre may be in the order of 20 and 25 % of performed CXR and chest CT, respectively.

Purpose: Visual search using volumetric images is becoming the standard in medical imaging. However, we do not fully understand how eye movement strategies mediate diagnostic performance. A recent study on computed tomography (CT) images showed that the search strategies of radiologists could be classified based on saccade amplitudes and cross-quadrant eye movements [eye movement index (EMI)] into two categories: drillers and scanners. Approach: We investigate how the number of times a radiologist scrolls in a given direction during analysis of the images (number of courses) could add a supplementary variable to use to characterize search strategies. We used a set of 15 normal liver CT images in which we inserted 1 to 5 hypodense metastases of two different signal contrast amplitudes. Twenty radiologists were asked to search for the metastases while their eye-gaze was recorded by an eye-tracker device (EyeLink1000, SR Research Ltd., Mississauga, Ontario, Canada). Results: We found that categorizing radiologists based on the number of courses (rather than EMI) could better predict differences in decision times, percentage of image covered, and search error rates. Radiologists with a larger number of courses covered more volume in more time, found more metastases, and made fewer search errors than those with a lower number of courses. Our results suggest that the traditional definition of drillers and scanners could be expanded to include scrolling behavior. Drillers could be defined as scrolling back and forth through the image stack, each time exploring a different area on each image (low EMI and high number of courses). Scanners could be defined as scrolling progressively through the stack of images and focusing on different areas within each image slice (high EMI and low number of courses). Conclusions: Together, our results further enhance the understanding of how radiologists investigate three-dimensional volumes and may improve how to teach effective reading strategies to radiology residents.

The objective of our study was to determine how authors of published observer–performance experiments dealt with memory bias in study design. We searched American Journal of Roentgenology online and Radiology using “observer study” and “observer performance.” We included articles from 1970 or later that reported an observer performance experiment using human observers. We recorded the methods used by the authors to order presentation of the conditions being tested and images within sets for viewing. We recorded use and length of any time gap between viewings. We included 110 experiments. Forty-five used methods not subject to memory bias. Of 68 remaining experiments, 30 (44.1%) ordered the viewing of tested conditions to decrease memory bias. Fifteen (22.1%) ordered the tested conditions in ways that may create memory bias. Eleven (16.2%) intermixed the tested conditions. Forty-three (63.2%) used random or pseudorandom ordering of images within sets. Forty-six (67.6%) used a time gap (median 14 days) between viewings. Six (8.8%) did not use a time gap. Thirty-six (52.9%) did not indicate what methods they used in at least one studied parameter. Therefore, we conclude that 22.1% of the experiments could improve their methods of ordering tested conditions. Completeness of reporting could be improved by including more details regarding methods of ameliorating memory bias.

Background We report our experience with mediastinoscopy at Auckland city hospital, a tertiary referral centre. We wished to examine correlations between clinical diagnosis and that made by histological sampling of enlarged mediastinal nodes particularly in patients with isolated mediastinal adenopathy. Methods We retrospectively reviewed clinical records of all patients who underwent mediastinoscopy in a five year period, mediastinoscopy was performed in the presence of enlarged lymph nodes (short axis > 1cm) found at CT. Mediastinoscopy was indicated for diagnostic staging of mediastinal adenopathy related to a parenchymal lung mass, diagnosis of isolated mediastinal adenopathy and diagnosis of mediastinal adenopathy with other CT findings. Data relating to indication, pre-test diagnosis, node stations sampled, histology, and operative complications were collected. Results Mediastinoscopy was performed in 137 consecutive patients. Seventy five patients had a lung mass, 47 had isolated mediastinal adenopathy and 15 had other CT findings. One operative complication occurred. In those patients with isolated adenopathy the following diagnoses were reached; sarcoidosis 23, TB 15, lymphoma 4, carcinoma 4, no diagnosis 1. Final diagnosis was significantly associated with patient's ethnicity. There was high sensitivity and specificity on comparison of clinical and histological diagnosis for both TB and sarcoidosis cases. Conclusions Mediastinoscopy proved to be safe and effective in nodal assessment of the mediastinum. In carefully selected cases procedural morbidity and mortality may be avoided by application of features related to patient's ethnicity and radiological findings.

Diagnostic reference levels (DRLs) for medical x-ray procedures are being implemented currently in the Netherlands. By order of the Dutch Healthcare Inspectorate, a survey has been conducted among 20 Dutch hospitals to investigate the level of implementation of the Dutch DRLs in current radiological practice. It turns out that hospitals are either well underway in implementing the DRLs or have already done so. However, the DRLs have usually not yet been incorporated in the QAsystem of the department nor in the treatment protocols. It was shown that the amount of radiation used, as far as it was indicated by the hospitals, usually remains below the DRLs. A procedure for comparing dose levels to the DRLs has been prescribed but is not Always followed in practice. This is especially difficult in the case of children, as most general hospitals receive few children. Health Phys. 108(4):462–464; 2015

Diagnostic reference levels (DRLs) for medical x-ray procedures are being implemented currently in the Netherlands. By order of the Dutch Healthcare Inspectorate, a survey has been conducted among 20 Dutch hospitals to investigate the level of implementation of the Dutch DRLs in current radiological practice. It turns out that hospitals are either well underway in implementing the DRLs or have already done so. However, the DRLs have usually not yet been incorporated in the QAsystem of the department nor in the treatment protocols. It was shown that the amount of radiation used, as far as it was indicated by the hospitals, usually remains below the DRLs. A procedure for comparing dose levels to the DRLs has been prescribed but is not Always followed in practice. This is especially difficult in the case of children, as most general hospitals receive few children. Health Phys. 108(4):462–464; 2015

Background: To assess the predictive value of prognosis of different computed tomography (CT) features and National Institutes of Health Stroke Scale score (NIHSS) in acute extensive middle cerebral artery (MCA) infarct. Methods: Fifty five patients with acute extensive MCA infarct had the CT performed within 24 hours of the onset of symptoms. A total of 11 CT features were analysed. The age distribution, presence of risk factors, presence of individual CT feature, the total CT score, and the NIHSS were correlated with the 30 day mortality. Results: Single explanatory variable analysis showed NIHSS, presence of midline shift, midline shift of more than 1 cm, extent of infarct, presence of hydrocephalus, effacement of subarachnoid space/cella media, attenuation of corticomedullary differentiation, and total CT score were associated with the 30 day mortality. Both extent of infarct >67% and attenuation of corticomedullary differentiation gave a sensitivity and specificity of 93% and 95%, respectively, for the prediction of survival. Logistic regression analysis showed that the extent of infarct and NIHSS were the only independent predictors. Conclusions: CT features and admission NIHSS are important parameters for prediction of survival in extensive MCA infarct.