Consecutive Computed Tomography Scans Research Articles

Parameterized Gompertz-Guided Morphological AutoEncoder for Predicting Pulmonary Nodule Growth.

The growth rate of pulmonary nodules is a critical clue to the cancerous diagnosis. It is essential to monitor their dynamic progressions during pulmonary nodule management. To facilitate the prosperity of research on nodule growth prediction, we organized and published a temporal dataset called NLSTt with consecutive computed tomography (CT) scans. Based on the self-built dataset, we develop a visual learner to predict the growth for the following CT scan qualitatively and further propose a model to predict the growth rate of pulmonary nodules quantitatively, so that better diagnosis can be achieved with the help of our predicted results. To this end, in this work, we propose a parameterized Gempertz-guided morphological autoencoder (GM-AE) to generate any future-time-span high-quality visual appearances of pulmonary nodules from the baseline CT scan. Specifically, we parameterize a popular mathematical model for tumor growth kinetics, Gompertz, to predict future masses and volumes of pulmonary nodules. Then, we exploit the expected growth rate on the mass and volume to guide decoders generating future shape and texture of pulmonary nodules. We introduce two branches in an autoencoder to encourage shape-aware and textural-aware representation learning and integrate the generated shape into the textural-aware branch to simulate the future morphology of pulmonary nodules. We conduct extensive experiments on the self-built NLSTt dataset to demonstrate the superiority of our GM-AE to its competitive counterparts. Experiment results also reveal the learnable Gompertz function enjoys promising descriptive power in accounting for inter-subject variability of the growth rate for pulmonary nodules. Besides, we evaluate our GM-AE model on an in-house dataset to validate its generalizability and practicality. We make its code publicly available along with the published NLSTt dataset.

Automated intracranial hemorrhage detection in traumatic brain injury using 3D CNN.

Intracranial hemorrhage (ICH) is a prevalent and potentially fatal consequence of traumatic brain injury (TBI). Timely identification of ICH is crucial to ensure timely intervention and to optimize better patient outcomes. However, the current methods for diagnosing ICH from head computed tomography (CT) scans require skilled personnel (Radiologists and/or Neurosurgeons) who may be unavailable in all centers, especially in rural areas. The aim of this study is to develop a neurotrauma screening tool for identifying ICH from head CT scans of TBI patients. We prospectively collected head CT scans from the Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi. Approximately 738 consecutive head CT scans from patients enrolled in the department were collected for this study spanning a duration of 9 months, that is, January 2020 to September 2020. The metadata collected along with the head CT scans consisted of demographic and clinical details and the radiologist's report which was used as the gold standard. A deep learning-based 3D convolutional neural network (CNN) model was trained on the dataset. The pre-processing, hyperparameters, and augmentation were common for training the 3D CNN model whereas the training modules were set differently. The model was trained along with the save best model option and was monitored by validation metrics. The Institute Ethics Committee permission was taken before starting the study. We developed a 3D CNN model for automatically detecting the ICH from head CT scans. The screening tool was tested in 20 cases and trained on 200 head CT scans, with 99 normal head CT and 101 CT scans with some type of ICH. The final model performed with 90% sensitivity, 70% specificity, and 80% accuracy. Our study reveals that the automated screening tool exhibits a commendable level of accuracy and sensitivity in detecting ICH from the head CT scans. The results indicate that the 3D CNN approach has a potential for further exploring the TBI-related pathologies.

Qualitative and quantitative evaluation of computed tomography changes in adults with cystic fibrosis treated with elexacaftor-tezacaftor-ivacaftor: a retrospective observational study.

Introduction: The availability of highly effective triple cystic fibrosis transmembrane conductance regulator (CFTR) modulator combination therapy with elexacaftor-tezacaftor-ivacaftor (ETI) has improved pulmonary outcomes and quality of life of people with cystic fibrosis (pwCF). The aim of this study was to assess computed tomography (CT) changes under ETI visually with the Brody score and quantitatively with dedicated software, and to correlate CT measures with parameters of clinical response. Methods: Twenty two adult pwCF with two consecutive CT scans before and after ETI treatment initiation were retrospectively included. CT was assessed visually employing the Brody score and quantitatively by YACTA, a well-evaluated scientific software computing airway dimensions and lung parenchyma with wall percentage (WP), wall thickness (WT), lumen area (LA), bronchiectasis index (BI), lung volume and mean lung density (MLD) as parameters. Changes in CT metrics were evaluated and the visual and quantitative parameters were correlated with each other and with clinical changes in sweat chloride concentration, spirometry [percent predicted of forced expiratory volume in one second (ppFEV1)] and body mass index (BMI). Results: The mean (SD) Brody score improved with ETI [55 (12) vs. 38 (15); p < 0.001], incl. sub-scores for mucus plugging, peribronchial thickening, and parenchymal changes (all p < 0.001), but not for bronchiectasis (p = 0.281). Quantitatve WP (p < 0.001) and WT (p = 0.004) were reduced, conversely LA increased (p = 0.003), and BI improved (p = 0.012). Lung volume increased (p < 0.001), and MLD decreased (p < 0.001) through a reduction of ground glass opacity areas (p < 0.001). Changes of the Brody score correlated with those of quantitative parameters, exemplarily WT with the sub-score for mucus plugging (r = 0.730, p < 0.001) and peribronchial thickening (r = 0.552, p = 0.008). Changes of CT parameters correlated with those of clinical response parameters, in particular ppFEV1 with the Brody score (r = -0.606, p = 0.003) and with WT (r = -0.538, p = 0.010). Discussion: Morphological treatment response to ETI can be assessed using the Brody score as well as quantitative CT parameters. Changes in CT correlated with clinical improvements. The quantitative analysis with YACTA proved to be an objective, reproducible and simple method for monitoring lung disease, particularly with regard to future interventional clinical trials.

Deep learning for automated measurement of CSA related acromion morphological parameters on anteroposterior radiographs

BackgroundThe Critical Shoulder Angle Related Acromion Morphological Parameter (CSA- RAMP) is a valuable tool in the analyzing the etiology of the rotator cuff tears (RCTs). However, its clinical application has been limited by the time-consuming and prone to inter- and intra-user variability of the measurement process. ObjectivesTo develop and validate a deep learning algorithm for fully automated assessment of shoulder anteroposterior radiographs associated with RCTs and calculation of CSA-RAMP. MethodsRetrospective analysis was conducted on radiographs obtained from computed tomography (CT) scans and X-rays performed between 2018 and 2020 at our institution. The development of the system involved the utilization of digitally reconstructed radiographs (DRRs) generated from each CT scan. The system's performance was evaluated by comparing it with manual and semiautomated measurements on two separate test datasets: dataset I (DRRs) and dataset II (X-rays). Standard metrics, including mean average precision (AP), were utilized to assess the segmentation performance. Additionally, the consistency among fully automated, semiautomated, and manual measurements was comprehensively evaluated using the Pearson correlation coefficient and Bland-Altman analysis. ResultsA total of 1080 DRRs generated from 120 consecutive CT scans and 159 X-ray films were included in the study. The algorithm demonstrated excellent segmentation performance, with a mean AP of 57.67 and an AP50 of 94.31. Strong inter-group correlations were observed for all CSA-RAMP measurements in both test datasets I (automated versus manual, automated versus semiautomated, and semiautomated versus manual; r = [0.990–––0.997], P < 0.001) and dataset II (r = [0.984–––0.995], P < 0.001). Bland-Altman analysis revealed low bias for all CSA-RAMP measurements in both test datasets I and II, except for CD (with a maximum bias of 2.49%). ConclusionsWe have successfully developed a fully automated algorithm capable of rapidly and accurately measuring CSA-RAMP on shoulder anteroposterior radiographs. A consistent automated CSA- RAMP measurement system may accelerate powerful and precise studies of disease biology in future large cohorts of RCTs patients.

Development and External Validation of a Deep Learning Algorithm to Identify and Localize Subarachnoid Hemorrhage on CT Scans.

In medical imaging, a limited number of trained deep learning algorithms have been externally validated and released publicly. We hypothesized that a deep learning algorithm can be trained to identify and localize subarachnoid hemorrhage (SAH) on head computed tomography (CT) scans and that the trained model performs satisfactorily when tested using external and real-world data. We used noncontrast head CT images of patients admitted to Helsinki University Hospital between 2012 and 2017. We manually segmented (i.e., delineated) SAH on 90 head CT scans and used the segmented CT scans together with 22 negative (no SAH) control CT scans in training an open-source convolutional neural network (U-Net) to identify and localize SAH. We then tested the performance of the trained algorithm by using external data sets (137 SAH and 1,242 control cases) collected in 2 foreign countries and also by creating a data set of consecutive emergency head CT scans (8 SAH and 511 control cases) performed during on-call hours in 5 different domestic hospitals in September 2021. We assessed the algorithm's capability to identify SAH by calculating patient- and slice-level performance metrics, such as sensitivity and specificity. In the external validation set of 1,379 cases, the algorithm identified 136 of 137 SAH cases correctly (sensitivity 99.3% and specificity 63.2%). Of the 49,064 axial head CT slices, the algorithm identified and localized SAH in 1845 of 2,110 slices with SAH (sensitivity 87.4% and specificity 95.3%). Of 519 consecutive emergency head CT scans imaged in September 2021, the algorithm identified all 8 SAH cases correctly (sensitivity 100.0% and specificity 75.3%). The slice-level (27,167 axial slices in total) sensitivity and specificity were 87.3% and 98.8%, respectively, as the algorithm identified and localized SAH in 58 of 77 slices with SAH. The performance of the algorithm can be tested on through a web service. We show that the shared algorithm identifies SAH cases with a high sensitivity and that the slice-level specificity is high. In addition to openly sharing a high-performing deep learning algorithm, our work presents infrequently used approaches in designing, training, testing, and reporting deep learning algorithms developed for medical imaging diagnostics. This study provides Class III evidence that a deep learning algorithm correctly identifies the presence of subarachnoid hemorrhage on CT scan.

Automatic 3-Dimensional Cephalometric Landmarking via Deep Learning

The increasing use of 3-dimensional (3D) imaging by orthodontists and maxillofacial surgeons to assess complex dentofacial deformities and plan orthognathic surgeries implies a critical need for 3D cephalometric analysis. Although promising methods were suggested to localize 3D landmarks automatically, concerns about robustness and generalizability restrain their clinical use. Consequently, highly trained operators remain needed to perform manual landmarking. In this retrospective diagnostic study, we aimed to train and evaluate a deep learning (DL) pipeline based on SpatialConfiguration-Net for automatic localization of 3D cephalometric landmarks on computed tomography (CT) scans. A retrospective sample of consecutive presurgical CT scans was randomly distributed between a training/validation set (n = 160) and a test set (n = 38). The reference data consisted of 33 landmarks, manually localized once by 1 operator(n = 178) or twice by 3 operators (n = 20, test set only). After inference on the test set, 1 CT scan showed “very low” confidence level predictions; we excluded it from the overall analysis but still assessed and discussed the corresponding results. The model performance was evaluated by comparing the predictions with the reference data; the outcome set included localization accuracy, cephalometric measurements, and comparison to manual landmarking reproducibility. On the hold-out test set, the mean localization error was 1.0 ± 1.3 mm, while success detection rates for 2.0, 2.5, and 3.0 mm were 90.4%, 93.6%, and 95.4%, respectively. Mean errors were −0.3 ± 1.3° and −0.1 ± 0.7 mm for angular and linear measurements, respectively. When compared to manual reproducibility, the measurements were within the Bland–Altman 95% limits of agreement for 91.9% and 71.8% of skeletal and dentoalveolar variables, respectively. To conclude, while our DL method still requires improvement, it provided highly accurate 3D landmark localization on a challenging test set, with a reliability for skeletal evaluation on par with what clinicians obtain.

Low-dose abdominopelvic computed tomography in patients with lymphoma: An image quality and radiation dose reduction study.

This study aimed to evaluate image quality, the detection rate of enlarged lymph nodes, and radiation dose exposure of ultralow-dose and low-dose abdominopelvic computed tomography (CT) in patients with lymphoma. Patients with lymphoma who underwent abdominopelvic CT using dual-source scanner were retrospectively recruited from a single center. CT images were obtained at 90 kVp dual-source mode reformatted in three data sets using the advanced modelled iterative reconstruction algorithm: 100% (standard-dose CT), 66.7% (low-dose CT), and 33.3% (ultralow-dose CT). Two radiologists analyzed subjective image quality and detection of abdominal enlarged lymph nodes on ultralow-dose, low-dose, and standard-dose CT blindly and independently. The results were compared with reference standards. Three readers (two radiologists and one hematologist) reviewed overall image quality and spleen size. In total, 128 consecutive CT scans (77 complete response, 44 partial response, 6 progressive disease, and 1 initial evaluation) from 86 patients (64 B-cell lymphoma, 14 T/NK-cell lymphoma, and 8 Hodgkin’s lymphoma cases) were assessed. The enlarged lymph node-based detection rates for two readers were 97.0% (96/99) and 94.0% (93/99) on standard-dose CT, 97.0% (96/99) and 94.0% (93/99) on low-dose CT, and 94.0% (93/99) and 89.9% (89/99) on ultralow-dose CT. Overall image quality was 3.8 ± 0.5, 3.9 ± 0.5, and 4.1 ± 0.5 on ultralow-dose CT; 4.7 ± 0.4, 4.6 ± 0.5, and 4.8 ± 0.3 on low-dose CT; and 4.8 ± 0.4, 4.7 ± 0.4, and 4.9 ± 0.2 on standard-dose CT, according to two radiologists and one hematologist, respectively. Intraclass correlation coefficients of spleen size were 0.90 (95% confidence interval [CI], 0.87–0.93), 0.91 (95% CI, 0.88–0.93), and 0.91 (95% CI, 0.88–0.93) on ultralow-dose, low-dose, and standard-dose CT, respectively. Mean effective radiation doses of standard-dose, low-dose, and ultralow-dose CT were 5.7 ±1.8 mSv, 3.8 ± 1.2 mSv, and 1.9 ± 0.6 mSv, respectively. Our findings suggest that ultralow-dose and low-dose CT, even with radiation doses reduced by 66.7% and 33.3%, respectively, maintained adequate image quality. These imaging modalities may be employed for follow-up lymphoma evaluation in consideration of the long surveillance periods.

Semantic Segmentation of Extraocular Muscles on Computed Tomography Images Using Convolutional Neural Networks.

Computed tomography (CT) imaging of the orbit with measurement of extraocular muscle size can be useful for diagnosing and monitoring conditions that affect extraocular muscles. However, the manual measurement of extraocular muscle size can be time-consuming and tedious. The purpose of this study is to evaluate the effectiveness of deep learning algorithms in segmenting extraocular muscles and measuring muscle sizes from CT images. Consecutive CT scans of orbits from 210 patients between 1 January 2010 and 31 December 2019 were used. Extraocular muscles were manually annotated in the studies, which were then used to train the deep learning algorithms. The proposed U-net algorithm can segment extraocular muscles on coronal slices of 32 test samples with an average dice score of 0.92. The thickness and area measurements from predicted segmentations had a mean absolute error (MAE) of 0.35 mm and 3.87 mm2, respectively, with a corresponding mean absolute percentage error (MAPE) of 7 and 9%, respectively. On qualitative analysis of 32 test samples, 30 predicted segmentations from the U-net algorithm were accepted while 2 were rejected. Based on the results from quantitative and qualitative evaluation, this study demonstrates that CNN-based deep learning algorithms are effective at segmenting extraocular muscles and measuring muscles sizes.

Effect of the Segmentation Threshold on Computed Tomography–Based Reconstruction of Skull Bones with Reference Optical Three-Dimensional Scanning

A variety of applications related to neurosurgical procedures, education, and training require accurate reconstruction of the involved structures from the medical images such as computed tomography (CT). This study evaluates the quality of CT-based reconstruction of dry skull bones for advanced neurosurgical applications. The accuracy and precision of these models were examined with reference optical scanning. Three consecutive CT and optical scans of different skull bones were acquired and used to develop three-dimensional models. The accuracy of three-dimensional models was examined by manual inspection of the defined anatomical landmarks of the skull. Reproducibility was examined by deviation analysis of the models developed from repeated CT and optical scans. Precision was excellent in both the techniques with less than 0.1mm deviation error. On the interscan evaluation of the CT versus optical scan model, deviations of more than 0.1mm were observed in 16 out of 21 instances. CT reconstruction using standard segmentation algorithms results in missing bone portions while using the default bone segmentation threshold. The segmentation threshold was varied to construct missing bone regions, and its effect on the iso-surface generation was evaluated. The threshold variation led to increased mean deviations of surfaces up to 0.6mm. The study reveals that bone structure, complexity, and segmentation threshold lead to CT reconstruction variability. The trade-off between the desirable model and accepted mean deviation should be considered as per traits of the desired application.

Prevalence of Paget's disease of bone: review of consecutive abdominopelvic CT scans and literature.

Previous studies suggest an overall decrease of Paget's disease of bone (PDB) prevalence. However, a large number of asymptomatic patients make previously reported prevalence likely underrepresented. To evaluate the prevalence of PDB in our patient population. We retrospectively identified 1295 (mean age = 59 years; age range = 18-98 years) consecutive abdominopelvic computed tomography (CT) scans over a 15-day period in 2014. Abdominopelvic CT images were reviewed to assess for the presence of PDB in the lower thoracic spine, lumbar spine, pelvis, or proximal femora. This prevalence was compared with prevalence reported in earlier literature using Fisher's exact test. Of the included patients, 5/1295 (0.39%) patients had imaging findings of PDB on abdominopelvic CT. Those five patients were all aged ≥55 years and had pelvic bone involvement, with one patient having additional involvement of multiple lower thoracic vertebral bodies. In our studied cohort, 812/1295 (62.7%) patients were aged ≥55 years, which corresponds to a prevalence of 0.62% (5/812) of PDB in patients aged ≥55 years. When accounting for fact that bones of the pelvis are involved in 40%-91% of patients with PDB, the prevalence is estimated at 0.43%-0.98% in the total adult population and estimated at 0.68%-1.55% in older adults. The prevalence was greater than two studies before 1960, and less than some studies during 1960-2019. We found that the prevalence of PDB on abdominopelvic CTs was 0.39% of all adult patients with an increase after the age of 55 years.

Cortical step sign in spinal clearance on trauma computed tomography – Indicator of acute thoracolumbar compression fracture

Introduction Differentiating an acute from chronic compression fracture of the thoracolumbar (TL) spine can pose a dilemma for radiologists interpreting spinal imaging following trauma. Mild wedging of the vertebrae can be due to spondylosis or osteoporosis, whilst acute simple compression fractures may not always be associated with loss of body height. In this observational study, we hypothesize that the presence of a vertebral body cortical step is a reliable sign of an acute compression fracture on Computed Tomography (CT) scans. Methods In a retrospective review of thoracolumbar CT scans following trauma, two observers analysed for the presence of a cortical step at the anterior or posterior vertebral body cortex, fracture morphology and associated injuries. A ‘cortical step’ is defined as a break of hyperdense cortex on CT scans, intervening non-sclerosed trabecular bone, and sharp overlap of the underlying cortex. MRI of the spine was used as gold standard. Results 187 consecutive CT scans over 2 years were assessed. Sensitivity, specificity and accuracy of cortical step sign were 100%, 90.2% and 97% in diagnosing an acute thoracolumbar compression fracture, respectively. The interobserver reliability was high (kappa = 0.97). False positive cortical step was seen in Kummel’s disease and large Schmorl’s nodule. Conclusion Our results demonstrate high sensitivity and specificity of ‘cortical step sign’ in diagnosing acute vertebral body compression fractures of TL spine on CT scans in patients with trauma. This sign can be useful to radiologists for safe clearance of the thoracolumbar spine following trauma, helping distinguish acute trauma from chronic causes of vertebral body height loss.

Development of a deep learning-based method to diagnose pulmonary ground-glass nodules by sequential computed tomography imaging.

BackgroundEarly identification of the malignant propensity of pulmonary ground‐glass nodules (GGNs) can relieve the pressure from tracking lesions and personalized treatment adaptation. The purpose of this study was to develop a deep learning‐based method using sequential computed tomography (CT) imaging for diagnosing pulmonary GGNs.MethodsThis diagnostic study retrospectively enrolled 762 patients with GGNs from West China Hospital of Sichuan University between July 2009 and March 2019. All patients underwent surgical resection and at least two consecutive time‐point CT scans. We developed a deep learning‐based method to identify GGNs using sequential CT imaging on a training set consisting of 1524 CT sections from 508 patients and then evaluated 256 patients in the testing set. Afterwards, an observer study was conducted to compare the diagnostic performance between the deep learning model and two trained radiologists in the testing set. We further performed stratified analysis to further relieve the impact of histological types, nodule size, time interval between two CTs, and the component of GGNs. Receiver operating characteristic (ROC) analysis was used to assess the performance of all models.ResultsThe deep learning model that used integrated DL‐features from initial and follow‐up CT images yielded the best diagnostic performance, with an area under the curve of 0.841. The observer study showed that the accuracies for the deep learning model, junior radiologist, and senior radiologist were 77.17%, 66.89%, and 77.03%, respectively. Stratified analyses showed that the deep learning model and radiologists exhibited higher performance in the subgroup of nodule sizes larger than 10 mm. With a longer time interval between two CTs, the deep learning model yielded higher diagnostic accuracy, but no general rules were yielded for radiologists. Different densities of components did not affect the performance of the deep learning model. In contrast, the radiologists were affected by the nodule component.ConclusionsDeep learning can achieve diagnostic performance on par with or better than radiologists in identifying pulmonary GGNs.

Is the Prevalence of Estimated Pelvic Congestion Higher than Examined? A Retrospective Study of Consecutive Abdominopelvic Computed Tomography Analyses.

Chronic Pelvic Pain (CPP) is a common complaint in women, and is the key factor in the diagnosis of Pelvic Congestion Syndrome (PCS). Consecutive abdominal and pelvic Computed Tomography (CT) scans in adult female patients not diagnosed with PCS and collected over a period of 3 years were evaluated retrospectively to determine the prevalence of underestimated Pelvic Congestion (PC). 500 consecutive abdominal and pelvic CT scans collected from female patients aged 18-80 years were retrospectively analyzed for the presence of PC. 90 of the CT scans examined showed the presence of PC (18%). These patients were divided into two groups: Group I had 52 scans with unilateral PC, while Group II had 38 scans with the bilateral enlarged Ovarian Vein (OV). Left and right OV diameters were measured as 7.14±2.15 and 5.56±1.87 mm, respectively. Co-occurrence of additional vascular anomalies, such as nutcracker- type compression of the left renal vein, and May Thurner, was significantly higher in Group I than Group II (p<0.001). The diameter of the OV remained wide irrespective of age in Group I, but showed a decrease with increasing age in Group II. The most common complaint was abdominal pain; these patients required an average of six referrals to two different clinics (primarily general surgery and internal medicine) before being diagnosed with PC. The diagnosis of PCS remains to be an important problem for patients because of insufficient perception of physicians. PCS should be considered in female patients with complaints of chronic abdominal and pelvic pain and CT may be a valuable examination tool for diagnosis.

Clinical Features and Temporal Lung Radiographic Changes in 25 Patients Recovering from COVID-19 Pneumonia: A Retrospective Case-Control Study.

BackgroundLittle is known of the changes in lung radiographic characteristics over time in patients recovering from COVID-19. This study analyzed the clinical features and temporal lung radiographic changes in patients with moderate and severe COVID-19 pneumonia who did not require invasive mechanical ventilation during the acute and convalescent periods.Material/MethodsThe data of 25 patients with COVID-19 pneumonia from January 29, 2020, to November 24, 2020, who did not require invasive mechanical ventilation and who were followed up were retrospectively collected. The 25 patients were divided into severe and moderate groups. Clinical characteristics and computed tomography (CT) manifestations were compared. A total of 121 consecutive thin-slice CT scans were collected at 4 weeks, 2 months, and 5 months after admission to evaluate lung abnormalities in the patients. The CT score was used to assess disease severity.ResultsThe severe group had a lower rate of nucleic acid conversion within 10 days of admission and higher D-dimer, creatine kinase, and lactate dehydrogenase values. In the severe group, hospital stay was longer and hospitalization costs were higher. The average CT score of the severe group peaked in the second week, while the moderate group peaked in the first week and then decreased over time. There were no statistically significant differences in the average CT score between the 2 groups at the 5-month follow-up.ConclusionsThe pulmonary lesions of patients recovering from COVID-19 and who do not require invasive mechanical ventilation were gradually absorbed and resolved over time.

Computed Tomography in Adults with Bronchiectasis and Nontuberculous Mycobacterial Pulmonary Disease: Typical Imaging Findings

Among patients with bronchiectasis, nontuberculous mycobacterial pulmonary disease (NTM-PD) ranged between 1 and 6% and it is suspected that its prevalence is underestimated. Our aim was to evaluate differences in computed tomography (CT) features in patients with bronchiectasis, with and without NTM-PD, in order to facilitate earlier diagnosis in the future. In addition, we evaluated longitudinal changes after successful NTM-PD treatment. One hundred and twenty-eight CTs performed in adults with bronchiectasis were scored for the involvement, type, and lobar distribution of bronchiectasis, bronchial dilatation, and bronchial wall thickening according to Reiff. In addition, associated findings, such as mucus plugging, tree-in-bud, consolidations, ground-glass opacities, interlobular thickening, intralobular lines, cavities, and atelectasis, were registered. Patients with NTM-PD (n = 36), as defined by ATS/IDSA diagnostic criteria, were compared to bronchiectasis patients without NTM-PD (n = 92). In twelve patients with an available consecutive CT scan after microbiological cure of NTM-PD imaging findings were also scored according to Kim and compared in the course. In patients with NTM-PD, there was a higher prevalence of bronchiectasis in the middle lobes (p < 0.001), extended bronchiolitis (p = 0.032) and more small and large nodules (p < 0.001). Furthermore, cavities turned out to be larger (p = 0.038), and walls thickened (p = 0.019) and extended (p = 0.016). Patients without NTM more often showed peripheral ground-glass opacities (0.003) and interstitial changes (p = 0.001). CT findings decreased after successful NTM-PD treatment in the follow-up CT; however, without statistical significance for most features (p = 0.056), but bronchiolitis was the only significantly reduced score item (p = 0.043). CT patterns in patients with bronchiectasis and NTM-PD differ from those of patients with bronchiectasis without NTM-PD, although the findings are non-specific radiological features. Follow-up CT findings after microbiological cure differed interindividual regarding the decline in imaging features. Our findings may help practitioners to identify NTM-PD in patients with bronchiectasis. Further research is needed regarding the use of CT as a potential imaging biomarker for the evaluation of treatment response.

Diagnostic accuracy of computed tomography for identifying hospitalizations for patients with COVID-19

BACKGROUND: In Russia, a semi-quantitative CT 04 scoring system is used in the analysis of thoracic computed tomography (CT) scans of COVID-19 patients to grade the severity of lung lesions. Despite the widespread use of this approach, the scoring systems diagnostic accuracy for identification hospitalizations for patients with the disease is currently unknown.&#x0D; AIM: To evaluate the sensitivity, specificity, positive (PPV) and negative (NPV) predictive value of the CT 04 system for the triage of COVID-19 patients.&#x0D; MATERIALS AND METHODS: This retrospective study enrolled 575 patients of Moscow clinics with laboratory-verified COVID-19, aged 57.213.9 years, 55% females. All patients were examined with four consecutive chest CT scans, and the disease severity was assessed using the CT 04 scoring system. Sensitivity and specificity were calculated as conditional probabilities that a patient would experience clinical improvement or deterioration, depending on the preceding CT examination results. For the calculation of the NPV and PPV, we estimated the COVID-19 prevalence in Moscow. The data on total cases of COVID-19 from March 6 to November 28, 2020, were taken from the Rospotrebnadzor website. We used several ARIMA and EST models with different parameters to fit the data and forecast the incidence.&#x0D; RESULTS: The median specificity of the CT 04 scoring system was 69% (95% CI 32%, 100%), and the sensitivity was 92% (95% CI 74%, 100%). The best statistical model describing the epidemiological situation in Moscow was ARIMA (0,2,1). According to our calculations, with the predicted point prevalence of 9.6%, the values of PPV and NPV were 56% and 97%, correspondingly.&#x0D; CONCLUSION: The maximum Youdens index was observed for the period between the first and the second chest CT examinations when the majority of the included patients experienced clinical deterioration. The CT 04 scoring system makes it possible to safely exclude the development of pathological changes in patients with mild and moderate disease (categories CT-0 and CT-1), thereby optimizing the burden on hospitals in an unfavorable epidemic situation.

Comparison of image quality and lesion diagnosis in abdominopelvic unenhanced CT between reduced-dose CT using deep learning post-processing and standard-dose CT using iterative reconstruction: A prospective study

PurposeTo compare image quality and lesion diagnosis between reduced-dose abdominopelvic unenhanced computed tomography (CT) using deep learning (DL) post-processing and standard-dose CT using iterative reconstruction (IR). MethodTotally 251 patients underwent two consecutive abdominopelvic unenhanced CT scans of the same range, including standard and reduced doses, respectively. In group A, standard-dose data were reconstructed by (blend 30 %) IR. In group B, reduced-dose data were reconstructed by filtered back projection reconstruction to obtain group B1 images, and post-processed using the DL algorithm (NeuAI denosing, Neusoft medical, Shenyang, China) with 50 % and 100 % weights to obtain group B2 and B3 images, respectively. Then, CT values of the liver, the second lumbar vertebral centrum, the erector spinae and abdominal subcutaneous fat were measured. CT values, noise levels, signal-to-noise ratios (SNRs), contrast-to-noise ratios (CNRs), radiation doses and subjective scores of image quality were compared. Subjective evaluations of low-density liver lesions were compared by diagnostic results from enhanced CT or Magnetic Resonance Imaging. ResultsGroups B3 and B1 showed the lowest and highest noise levels, respectively (P < 0.001). The SNR and CNR in group B3 were highest (P < 0.001). The radiation dose in group B was reduced by 71.5 % on average compared to group A. Subjective scores in groups A and B2 were highest (P < 0.001). Diagnostic sensitivity and confidence for liver metastases in groups A and B2 were highest (P < 0.001). ConclusionsReduced-dose abdominopelvic unenhanced CT combined with DL post-processing could ensure image quality and satisfy diagnostic needs.

Time is bone — Quantitative comparison of decalcification solvents in human femur samples using dual-X-ray-absorptiometry and computed tomography

IntroductionBone decalcification is a necessary preprocessing step in histological and anatomical studies. Several solutions for decalcification with different claimed times for full decalcification are commercially available. Current literature lacks direct, quantitative measurement of calcium hydrocyapatite degradation during decalcification to compare different solutions. Therefore, the aim of this study was to test the performance of three different decalcification solutions in human bone by direct measurement of calcium hydroxyapatite using dual-X-ray-absorptiometry (DEXA) and volumetric computed tomography (CT). MethodsFour femur slices were acquired from the proximal femur of a 76-year-old body donor. The slices were submerged in formaldehyde (control), EDTA, Osteosoft (Merck, Darmstadt, Germany) and “Rapid Bone Decalcifier” (RBD) (American MasterTech Scientific, Lodi, USA). Consecutive DEXA and CT scans were performed at 2 h, 4 h, 8 h, 11 h, 20 h, 44 h and 77 h after solutions were added. Besides the calcium hydroxyapatite concentration, the bone volume was measured each time. ResultsFastest decline in volume was seen in the RBD probe. Further, RBD was the only solution, being able to fully decalcify the bone slice after 77 h. Although a steady decline in volume and hydroxyapatite concentration was seen for EDTA and Osteosoft as well, both were not able to decalcify the slices. ConclusionOverall, the purely qualititve acquired literature data on bone decalcifiers was verified by our quantitative data for human, cortical-rich bones. Hydrochloric-acid based solutions seem to be preferable in order to rapidly dissolve the calcium hydroxyapatite.

Morphological Analysis of Metacarpal Shafts With Respect to Retrograde Intramedullary Headless Screw Fixation.

Background: The use of retrograde intramedullary headless compression screw fixation for metacarpal neck and shaft fractures has been described in the literature. The purpose of this study was to perform a computed tomography (CT)-based morphological analysis of metacarpal size to help surgeons anticipate expected hardware needs. Methods: In all, 108 consecutive hand CT scans were evaluated for the medullary diameter in the volar-dorsal and radial-ulnar planes at the narrowest point of the canal, as well as for the distance from the articular surface to this point. Results were then analyzed by finger and by sex. Results: The ring finger had the smallest average medullary canal diameter for both men and women (2.7 and 2.6 mm, respectively); the small finger had the largest average diameter (3.9 mm) for men and the middle finger (3.6 mm) for women. Radial-ulnar was the rate-limiting dimension in the index, middle, and ring fingers, whereas volar-dorsal was the smallest dimension in the small finger, regardless of sex. Medullary diameter tended to be larger in patients aged more than 50 years. More than 50% of fingers have diameters >3.0 mm, and at least 40% of index, middle, and small fingers have diameters >3.5 mm, which are common diameters of commercially available headless compression screws. Conclusions: When preparing to perform open reduction internal fixation of a metacarpal using retrograde intramedullary headless compression screws, the surgeon needs to be prepared with screws of larger diameters to optimize fixation. Screws of larger diameters are needed to achieve endosteal purchase, regardless of sex.

Developmental anomalies of the lateral portion of the cervical neural arch: Multimodal imaging and clinical implications.

This study aimed to describe the imaging spectrum of developmental anomalies of the lateral portion of the cervical neural arch. This was a five-year retrospective review of consecutive computed tomography (CT) scans of the cervical spine for structural anomalies of the cervical vertebral pedicle and facets. CT, radiographs and, when available, magnetic resonance imaging studies were independently reviewed. Anomalies were grouped into the following three categories: the absence of a pedicle, clefts in the vertebral arch or isolated dysmorphism of the facet. Clinical data on demographics and neurological outcomes were documented. Among 9134 consecutive patients undergoing a CT scan of the cervical spine, 18 (0.2%) patients were found to have developmental anomalies of the pedicle and facets. Findings included 7/18 (39%) with congenital absence of a pedicle, 8/18 (44%) with clefts in the vertebral arch and 3/18 (17%) with isolated dysmorphism of the articular facets. No acute neurological deficits or spinal cord injuries were reported. Associated chronic symptoms included neck pain 10/18 (56%), radiculopathy 7/18 (39%) and myelopathy 1/18 (6%). Developmental anomalies of the pedicle and facet may mimic traumatic spinal pathologies. Recognising a diverse spectrum of imaging findings is vital to prevent misdiagnosis and unnecessary intervention.