Rapid Identification Method for CH4/CO/CH4-CO Gas Mixtures Based on Electronic Nose.

The inherent cross-sensitivity of semiconductor gas sensors makes them extremely challenging to accurately detect mixed gases. In order to solve this problem, this paper designed an electronic nose (E-nose) with seven gas sensors and proposed a rapid method for identifying CH4, CO, and their mixtures. Most reported methods for E-nose were based on analyzing the entire response process and employing complex algorithms, such as neural network, which result in long time-consuming processes for gas detection and identification. To overcome these shortcomings, this paper firstly proposes a way to shorten the gas detection time by analyzing only the start stage of the E-nose response instead of the entire response process. Subsequently, two polynomial fitting methods for extracting gas features are designed according to the characteristics of the E-nose response curves. Finally, in order to shorten the time consumption of calculation and reduce the complexity of the identification model, linear discriminant analysis (LDA) is introduced to reduce the dimensionality of the extracted feature datasets, and an XGBoost-based gas identification model is trained using the LDA optimized feature datasets. The experimental results show that the proposed method can shorten the gas detection time, obtain sufficient gas features, and achieve nearly 100% identification accuracy for CH4, CO, and their mixed gases.

Classification of Thyroid Nodules by Using Deep Learning Radiomics Based on Ultrasound Dynamic Video.

OBJECTIVES: We aimed to design a radiomics model for differential diagnosis of thyroid carcinoma based on dynamic ultrasound video, and compare its diagnostic performance with that of radiomics model based on static ultrasound images. METHODS: Between January 2019 and May 2021, 890 patients with 1015 thyroid nodules (775 for training, 240 for validation) were prospectively enrolled. In total 890 patients underwent thyroidectomy within 1 month, and ultrasound dynamic video and static images were both acquired. Two deep learning (DL) models, namely DL-video and DL-image models, were proposed to diagnose thyroid nodules by analyzing ultrasound video and static images respectively. The performance of models was assessed by areas under the receiver operating characteristic curve (AUC). The DL model on ultrasound cines was re-visualized to help radiologists understand its potential working mechanism. RESULTS: The AUC of DL-video were 0.947 (95% CI: 0.931-0.963) and 0.923 (95% CI: 0.892-0.955) in training and validation cohorts, respectively. For DL-image model, the AUC were 0.928 (95% CI: 0.910-0.945) and 0.864 (95% CI: 0.819-0.910), respectively. The diagnosis performance of the DL-video was superior to that of DL-image, and there was significant difference between the AUC of DL-video and DL-image model in validation cohort (P = .028). The visualization demonstrated certain important ultrasound features that could be recognized by human eyes. CONCLUSIONS: The proposed DL radiomics model based on dynamic ultrasound video can accurately and individually classified thyroid nodules. The constructed DL-video model combining ultrasound video holds good potential for benefiting the management of patients with thyroid nodules.

Automated quantification of COVID-19 severity and progression using chest CT images.

OBJECTIVE: To develop and test computer software to detect, quantify, and monitor progression of pneumonia associated with COVID-19 using chest CT scans. METHODS: One hundred twenty chest CT scans from subjects with lung infiltrates were used for training deep learning algorithms to segment lung regions and vessels. Seventy-two serial scans from 24 COVID-19 subjects were used to develop and test algorithms to detect and quantify the presence and progression of infiltrates associated with COVID-19. The algorithm included (1) automated lung boundary and vessel segmentation, (2) registration of the lung boundary between serial scans, (3) computerized identification of the pneumonitis regions, and (4) assessment of disease progression. Agreement between radiologist manually delineated regions and computer-detected regions was assessed using the Dice coefficient. Serial scans were registered and used to generate a heatmap visualizing the change between scans. Two radiologists, using a five-point Likert scale, subjectively rated heatmap accuracy in representing progression. RESULTS: There was strong agreement between computer detection and the manual delineation of pneumonic regions with a Dice coefficient of 81% (CI 76-86%). In detecting large pneumonia regions (> 200 mm3), the algorithm had a sensitivity of 95% (CI 94-97%) and specificity of 84% (CI 81-86%). Radiologists rated 95% (CI 72 to 99) of heatmaps at least "acceptable" for representing disease progression. CONCLUSION: The preliminary results suggested the feasibility of using computer software to detect and quantify pneumonic regions associated with COVID-19 and to generate heatmaps that can be used to visualize and assess progression. KEY POINTS: • Both computer vision and deep learning technology were used to develop computer software to quantify the presence and progression of pneumonia associated with COVID-19 depicted on CT images. • The computer software was tested using both quantitative experiments and subjective assessment. • The computer software has the potential to assist in the detection of the pneumonic regions, monitor disease progression, and assess treatment efficacy related to COVID-19.

Computer-aided quantitative MSCT measurements may be useful for congenital lung malformations surgical approach selection.

PURPOSE: To examine the association between the MSCT quantitative measurements of congenital lung malformations (CLM) and the selection of surgical approaches (lobectomy vs. lung-sparing surgery). METHODS: This retrospective study evaluated CLM surgical cases at our institution from 2016 to 2018. MSCT quantitative measurements were generated by a semi-automated approach: the volume of the lesion (Vlesion), the volume of the lesion-involved lobe (Vlobe), the volume of the lesion-involved lung (Vlung) and the volume of the total lung (Vtotal lung). The proportions of Vlesion to Vlobe (Plesion/lobe), Vlesion to Vlung (Plesion/lung), and Vlesion to V total lung (Plesion/total lung) were calculated. We used Logistics regression to examine whether quantitative measurements were associated with the selection of surgical approaches. RESULTS: 151 patients were included (median age at surgery 6 months). 82 patients underwent lung-sparing surgery, and 69 patients underwent lobectomy. Vlesion (OR 1.51, 95% CI 1.09-2.07), Plesion/lobe (OR 1.78, 95% CI 1.16-2.72), Plesion/lung (OR 1.63, 95% CI 1.13-2.35), and Plesion/total lung (OR 1.58, 95% CI 1.12-2.22) were positively associated with the selection of lobectomy. CONCLUSION: The application of quantified MSCT analysis may provide insight into the quantitative characteristics of CLM, which could be potentially useful for surgical approach selection.

Any unique image biomarkers associated with COVID-19?

OBJECTIVE: To define the uniqueness of chest CT infiltrative features associated with COVID-19 image characteristics as potential diagnostic biomarkers. METHODS: We retrospectively collected chest CT exams including n = 498 on 151 unique patients RT-PCR positive for COVID-19 and n = 497 unique patients with community-acquired pneumonia (CAP). Both COVID-19 and CAP image sets were partitioned into three groups for training, validation, and testing respectively. In an attempt to discriminate COVID-19 from CAP, we developed several classifiers based on three-dimensional (3D) convolutional neural networks (CNNs). We also asked two experienced radiologists to visually interpret the testing set and discriminate COVID-19 from CAP. The classification performance of the computer algorithms and the radiologists was assessed using the receiver operating characteristic (ROC) analysis, and the nonparametric approaches with multiplicity adjustments when necessary. RESULTS: One of the considered models showed non-trivial, but moderate diagnostic ability overall (AUC of 0.70 with 99% CI 0.56-0.85). This model allowed for the identification of 8-50% of CAP patients with only 2% of COVID-19 patients. CONCLUSIONS: Professional or automated interpretation of CT exams has a moderately low ability to distinguish between COVID-19 and CAP cases. However, the automated image analysis is promising for targeted decision-making due to being able to accurately identify a sizable subsect of non-COVID-19 cases. KEY POINTS: • Both human experts and artificial intelligent models were used to classify the CT scans. • ROC analysis and the nonparametric approaches were used to analyze the performance of the radiologists and computer algorithms. • Unique image features or patterns may not exist for reliably distinguishing all COVID-19 from CAP; however, there may be imaging markers that can identify a sizable subset of non-COVID-19 cases.

Assessment of Acute Pulmonary Embolism by Computer-Aided Technique: A Reliability Study.

BACKGROUND Acute pulmonary embolism is one of the most common cardiovascular diseases. Computer-aided technique is widely used in chest imaging, especially for assessing pulmonary embolism. The reliability and quantitative analyses of computer-aided technique are necessary. This study aimed to evaluate the reliability of geometry-based computer-aided detection and quantification for emboli morphology and severity of acute pulmonary embolism. MATERIAL AND METHODS Thirty patients suspected of acute pulmonary embolism were analyzed by both manual and computer-aided interpretation of vascular obstruction index and computer-aided measurements of emboli quantitative parameters. The reliability of Qanadli and Mastora scores was analyzed using computer-aided and manual interpretation. RESULTS The time costs of manual and computer-aided interpretation were statistically different (374.90±150.16 versus 121.07±51.76, P<0.001). The difference between the computer-aided and manual interpretation of Qanadli score was 1.83±2.19, and 96.7% (29 out of 30) of the measurements were within 95% confidence interval (intraclass correlation coefficient, ICC=0.998). The difference between the computer-aided and manual interpretation of Mastora score was 1.46±1.62, and 96.7% (29 out of 30) of the measurements were within 95% confidence interval (ICC=0.997). The emboli quantitative parameters were moderately correlated with the Qanadli and Mastora scores (all P<0.001). CONCLUSIONS Computer-aided technique could reduce the time costs, improve the and reliability of vascular obstruction index and provided additional quantitative parameters for disease assessment.

Evaluation of dynamic lung changes during coronavirus disease 2019 (COVID-19) by quantitative computed tomography.

OBJECTIVES: This study aims to trace the dynamic lung changes of coronavirus disease 2019 (COVID-19) using computed tomography (CT) images by a quantitative method. METHODS: In this retrospective study, 28 confirmed COVID-19 cases with 145 CT scans are collected. The lesions are detected automatically and the parameters including lesion volume (LeV/mL), lesion percentage to lung volume (LeV%), mean lesion density (MLeD/HU), low attenuation area lower than - 400HU (LAA-400%), and lesion weight (LM/mL*HU) are computed for quantification. The dynamic changes of lungs are traced from the day of initial symptoms to the day of discharge. The lesion distribution among the five lobes and the dynamic changes in each lobe are also analyzed. RESULTS: LeV%, MLeD, and LM reach peaks on days 9, 6 and 8, followed by a decrease trend in the next two weeks. LAA-400% (mostly the ground glass opacity) declines to the lowest on days 4-5, and then increases. The lesion is mostly seen in the bilateral lower lobes, followed by the left upper lobe, right upper lobe and right middle lobe (p < 0.05). The right middle lobe is the earliest one (on days 6-7), while the right lower lobe is the latest one (on days 9-10) that reaches to peak among the five lobes. CONCLUSIONS: Severity of COVID-19 increases from the day of initial symptoms, reaches to the peak around on day 8, and then decreases. Lesion is more commonly seen in the bilateral lower lobes.

Small airway dysfunction may be an indicator of early asthma: findings from high-resolution CT.

BACKGROUND: Increasing attention is now focused on the role of the small airways in asthma. Patients with small airway asthma typically have a preserved forced expiratory volume in 1 second, with disproportionate small airway dysfunction. OBJECTIVE: The aim of this study was to determine whether there are structural changes in the large airways in patients with small airway asthma. METHODS: Nine patients with small airway asthma and 20 healthy controls underwent high-resolution computed tomography (HRCT) scan. The apicoposterior segmental bronchus in the left upper lobe (LB1+2) and the posterior basal segmental bronchus in the left lower lobe (LB10) were identified on HRCT images. The luminal area (LA), wall area (WA), and wall area percentage (WA%) of each bronchus were measured from the 2nd (lobar) to the 6th generation and compared between the patients with small airway asthma and the healthy controls. RESULTS: The WA% of the 6th generation in the patients with small airway asthma was higher than that in the healthy controls; the difference was statistically significant (LB1 + 2, P = .040; LB10, P = .033). CONCLUSION: Structural changes in the large airways of patients with small airway asthma may represent an early stage of asthma.

Correlation analysis of 18F-FDG PET/CT for the staging and treatment effect assessment of breast cancer.

OBJECTIVE: Since cancer treatment and outcome differ among the patients diagnosed with breast cancer at different stages, this study aims to elucidate the factors associated with PET/CT staging, treatment effect, and maximal standardized uptake value (SUVmax) in breast cancer patients. METHODS: Twenty-eight patients who underwent two PET/CT examinations with the complete pathological and immunohistochemical data were retrospectively reviewed. Pearson and Spearman correlation analyses were performed to investigate the relationships of patient PET/CT staging (first round PET/CT), treatment effect (comparison between the results of two rounds of PET/CT), and tumor SUVmax, with respect to patient age, tumor location, tumor long diameter, short diameter, time of first round PET/CT examination, histological type and grade, axillary lymph node metastasis rate, interval between the two PET/CT examinations, treatment method, and the expression of the estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), P53, and Ki67. RESULTS: PET/CT staging (first round PET/CT) relates to HER2 and Ki67 expression with the correlation coefficients of 0.432 and 0.552, and P-values of 0.022 and 0.002, respectively. The treatment effect (comparison between the results of two rounds of PET/CT) associates with tumor site and P53 expression in which the correlation coefficients are - 0.412 and 0.845, and P-values are 0.029 and 0.000, respectively. The measured SUVmax correlates well with the tumor long diameter, short diameter, and treatment effect in which the correlation coefficients are 0.943, 0.886, and 0.878, and P-values are 0.005, 0.019, and 0.02, respectively. CONCLUSIONS: Study results indicate that PET/CT staging and treatment effect associate well with SUVmax and immunohistochemical results of tumor, while SUVmax of tumor associates well with tumor size.

Morphological analysis of blood vessels near lung tumors using 3-D quantitative CT.

BACKGROUND: Improved visualization of lung cancer-associated vessels is vital. OBJECTIVE: To evaluate the efficacy of 3-D quantitative CT in lung cancer-associated pulmonary vessel assessment. METHODS: Vascular CT changes were assessed visually and using FACT-Digital lung TM software (n = 162 patients, 178 controls). The total number of pulmonary vessels (TNV) and mean lumen area of pulmonary vessels (MAV) vertical to cross-sections of fifth/sixth-generation bronchioles were measured. RESULTS: Visual investigation revealed fewer ipsilateral pulmonary vascular abnormalities in lung cancer (151/162) than did quantitative CT (162/162), and required more time (3.2±1.5 vs. 2.5±1.3 min) (P < 0.05). CT measurements revealed that the TNV vertical to the fifth-generation bronchial cross-section of the ipsilateral, contralateral, and control groups was 14.58±4.75, 9.58±3.74, and 10.22±4.07 and the MAV in these groups was 99.70±26.20, 58.76±29.29, and 57.76±18.32, respectively. The TNV vertical to the sixth-generation bronchial cross-section of the ipsilateral, contralateral, and control groups was 16.64±5.14, 11.59±4.06, and 11.75±4.16 and the MAV was 110.22±31.47, 67.62±30.41, and 60.24±16.18, respectively. The TNV and MAV in ipsilateral lung cancer tissues exceeded those in the contralateral side and control group tissues (P < 0.001). CONCLUSIONS: Automated 3-D quantitative CT could successfully characterize pulmonary vessels and their lung cancer-associated changes.

Computer-aided identification of interstitial lung disease based on computed tomography.

OBJECTIVE: Identification of interstitial lung disease (ILD) may be difficult in certain cases using pulmonary function tests (PFTs) or subjective radiological analysis. We evaluated the efficacy of quantitative computed tomography (CT) with 3-dimensional (3D) reconstruction for distinguishing ILD patients from healthy controls. MATERIALS AND METHODS: We retrospectively collected chest CT images of 102 ILD patients and 102 healthy matched controls, and measured the following parameters: lung parenchymal volume, emphysema indices low attenuation area LAA910 volume, LAA950 volume, LAA910%, and LAA950%, and mean lung density (MLD) for whole lung, left lung, right lung, and each lobe, respectively. The Mann-Whitney U test was used to compare quantitative CT parameters between groups. Receiver operating characteristic (ROC) curves, Bayesian stepwise discriminant analysis, and deep neural network analysis were used to test the discriminative performance of quantitative CT parameters. Binary logistic regression was performed to identify ILD markers. RESULTS: Total lung volume was lower in ILD patients than controls, while emphysema and MLD values were higher (P < 0.001) except LAA910 volume in right middle lobe. LAA910 volume, LAA950 volume, LAA910%, LAA950%, and MLD accurately distinguished ILD patients from healthy controls (AUC >0.5, P < 0.05), and high MLD was a significant marker for ILD (OR = 1.047, P < 0.05). CONCLUSIONS: This quantitative CT analysis can effectively identify ILD patients, providing an alternative to subjective image analysis and PFTs.

Application of automated bronchial 3D-CT measurement in pulmonary contusion complicated with acute respiratory distress syndrome.

BACKGROUNDQuantitative measurement of bronchial morphological changes in pulmonary contusion with acute respiratory distress syndrome (ARDS) has important clinical implications.OBJECTIVETo investigate the morphological changes in bronchus before and after treatment in patients with pulmonary contusion combined with ARDS using an automated bronchial three-dimensional computed tomography (3D-CT) measurement method.METHODSThe study involves a dataset of CT images of 62 patients diagnosed with pulmonary contusion combined with ARDS. The volume of pulmonary contusion lesions was calculated as a percentage of the total lung volume using the automated 3D-CT method. The bronchial luminal cross-sectional area, wall cross-sectional area, the maximum and average wall thickness, the maximum and average luminal densities, intraluminal and extraluminal diameters, and circumferences of generations 2-4 bronchi before and after treatment were measured. Furthermore, the corresponding differences were analyzed statistically.RESULTSThe luminal cross-sectional area, wall cross-sectional area, intraluminal and extraluminal diameters, and circumferences of generations 2-4 bronchi were all significantly lower before treatment than after treatment (P < 0.05). However, the maximum and average wall thicknesses were both significantly higher before treatment than after treatment (P < 0.05). No significant difference was found in the maximum and average luminal densities before and after treatment (P > 0.05). The percentage of the pulmonary contusion lesion volume to the total lung volume correlated positively with the thoracic trauma severity score (r = 0.74, P < 0.01).CONCLUSIONSQuantitative bronchial CT image analysis enables to detect and assess bronchial morphological changes in patients diagnosed with pulmonary contusion combined with ARDS.

Imaging Features of Chronic Bronchitis with Preserved Ratio and Impaired Spirometry (PRISm).

PURPOSE: The purpose of the study was to investigate the quantitative chest tomographic features of chronic bronchitis with preserved ratio and impaired spirometry (PRISm), including airway wall area, emphysema index, and lung capacity. METHODS: An observational, cross-sectional study of 343 patients at the Ninth Hospital of Xi'an Affiliated Hospital of Xi'an Jiaotong University between October 2014 and September 2017. The patients were divided into three groups: 77 cases of chronic bronchitis with normal lung function (forced expiratory volume in one second/forced vital capacity) (FEV1/FVC > 70%, FEV1%pred > 80%), 80 cases of chronic bronchitis with PRISm (FEV1/FVC > 70%, FEV1%pred < 80%), and 186 cases of the early chronic obstructive pulmonary disease (COPD) (FEV1/FVC < 70%, FEV1%pred > 50%, that is, Global Initiative for Chronic Obstructive Lung Disease (GOLD) grade 1 + 2). We compared and analyzed the differences in imaging between the chronic bronchitis with PRISm and the other two groups. RESULTS: Compared with the early COPD group, the PRISm group revealed significant differences in airway wall area, emphysema index, and lung capacity (P < 0.05). Compared with the chronic bronchitis with normal lung function group, the PRISm group showed increased WA%LUL5, decreased lung capacity, and higher mean lung density. CONCLUSION: In terms of airway wall area and emphysema index, patients with chronic bronchitis with PRISm were essentially no different than those with chronic bronchitis without abnormal spirometry, whereas for symptoms, they are more like GOLD 1 and 2 patients. Our findings show that it is not yet clear whether it constitutes an intermediate stage of chronic bronchitis with normal lung function that progression to early COPD.

The features of AECOPD with carbon dioxide retention.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) with carbon dioxide retention is associated with a worsening clinical condition and the beginning of pulmonary ventilation decompensation. This study aimed to identify the factors associated with carbon dioxide retention. METHODS: This was a retrospective study of consecutive patients with COPD (meeting the Global Initiative for Chronic Obstructive Lung Disease diagnostic criteria) hospitalized at The Ninth Hospital of Xi'an Affiliated Hospital of Xi'an Jiaotong University between October 2014 and September 2017. The baseline demographic, clinical, laboratory, pulmonary function, and imaging data were compared between the 86 cases with carbon dioxide retention and the 144 cases without carbon dioxide retention. RESULTS: Compared with the non-carbon dioxide retention group, the group with carbon dioxide retention had a higher number of hospitalizations in the previous 12 months (p = 0.013), higher modified Medical Research Council (mMRC) dyspnea scores (p = 0.034), lower arterial oxygen pressure (p = 0.018), worse pulmonary function (forced expiratory volume in one second/forced vital capacity [FEV1/FVC; p < 0.001], FEV1%pred [p < 0.001], Z5%pred [p = 0.004], R5%pred [p = 0.008], R5-R20 [p = 0.009], X5 [p = 0.022], and Ax [p = 0.011]), more severe lung damage (such as increased lung volume [p = 0.011], more emphysema range [p = 0.007], and lower mean lung density [p = 0.043]). FEV1 < 1 L (odds ratio [OR] = 4.011, 95% confidence interval [CI]: 2.216-7.262) and emphysema index (EI) > 20% (OR = 1.926, 95% CI: 1.080-3.432) were independently associated with carbon dioxide retention in COPD. CONCLUSION: Compared with the non-carbon dioxide retention group, the group with carbon dioxide retention had different clinical, pulmonary function, and imaging features. FEV1 < 1 L and EI > 20% were independently associated with carbon dioxide retention in AECOPD. TRIAL REGISTRATION: ChiCTR-OCH-14004904 . Registered 25 June 2014.

Limitations of an automated embolism segmentation method in clinical practice.

PURPOSE: Automated pulmonary embolism (PE) segmentation is frequently used as a preprocessing step in the quantitative analysis of pulmonary embolism. Objective of this study is to analyze the potential limitation in automated PE segmentation using clinical cases. METHODS: A database of 304 computer tomography pulmonary angiography (CTPA) examinations was collected and confirmed to be PE. After processing using an automated scheme, two radiologists classified these cases into four groups of A, B, C and D, which represent 4 different segmentation results namely, (1) entire pulmonary artery identified without motivation artifacts, (2) entire pulmonary artery identified with motivation artifacts, (3) part of the pulmonary artery identified, and (4) none of the pulmonary artery identified. Then, the possible failed reasons in PE segmentation were analyzed and determined based on the image characterization of the diseases and the applied CTPA scanning protocols. RESULTS: In the study, 143 (47.0%., 30 (9.9%., 110 (36.2%. and 21 (6.9%. examinations were classified into groups A, B, C and D, respectively. Group C and D included the cases with failed segmentation. Fifteen failure reasons, including intrapulmonary abnormalities, extra-pulmonary abnormalities, diffuse pulmonary diseases, enlarged heart, absolute occluded vessels, embolism attached to artery wall, delayed scan time, skewed location, low scan dose, obvious artifact of superior vena cava, previous chest surgery, congenital deformities of the chest, incorrect positioning, missed images and other unknown reasons, were determined with corresponding case percentages ranging from 0.3%.o 9.2%. CONCLUSIONS: Automated segmentation failures were caused by specific lung diseases, anatomy varieties, improper scan time, improper scan dose, manual errors or other unknown reasons. Realization of those limitations is crucial for developing robust automated schemes to handle these issues in a single pass when a large number of CTPA examinations need to be analyzed.

A retrospective study of SPECT/CT scans using SUV measurement of the normal pelvis with Tc-99m methylene diphosphonate.

OBJECTIVE: To perform quantitative measurement based on the standardized uptake value (SUV) of Tc-99m methylene diphosphonate (MDP) in the normal pelvis using a single-photon emission tomography (SPECT)/computed tomography (CT) scanner. MATERIAL AND METHODS: This retrospective study was performed on 31 patients with cancer undergoing bone SPECT/CT scans with 99mTc-MDP. SUVmax and SUVmean of the normal pelvis were calculated based on the body weight. SUVmax and SUVmean of the bilateral anterior superior iliac spine, posterior superior iliac spine, facies auricularis ossis ilii, ischial tuberosity, and sacrum were also calculated. Furthermore, the correlation of SUVmax and SUVmean of all parts of pelvis with weight, height, and CT was assessed. RESULTS: The data for 31 patients (20 women and 11 men; mean age 58.97±9.12 years; age range 37-87 years) were collected. SUVmax and SUVmean changed from 1.65±0.40 to 3.8±1.0 and from 1.15±0.25 to 2.07±0.58, respectively. The coefficient of variation of SUVmax and SUVmean ranged from 0.22 to 0.31. SUVmax and SUVmean had no statistically significant difference between men and women. SUVmax and SUVmean also showed no significant correlation with weight and height. However, part of SUVmax and SUVmean showed a significant correlation with CT. In addition, SUVmax and SUVmean of the bilateral ischial tuberosity showed a significant correlation with CT values. CONCLUSIONS: Determination of the SUV value of the normal pelvis with 99m Tc-MDP SPECT/CT is feasible and highly reproducible. SUVs of the normal pelvis showed a relatively large variability. As a quantitative imaging biomarker, SUVs might require standardization with adequate reference data for the participant to minimize variability.

Saddle Pulmonary Embolism: Laboratory and Computed Tomographic Pulmonary Angiographic Findings to Predict Short-term Mortality.

BACKGROUND: Saddle pulmonary embolism (SPE) is rare type of acute pulmonary embolism and there is debate about its treatment and prognosis. Our aim is to assess laboratory and computed tomographic pulmonary angiographic (CTPA) findings to predict short-term mortality in patients with SPE. METHODS: This was a five-centre, retrospective study. The clinical information, laboratory and CTPA findings of 88 consecutive patients with SPE were collected. One-month mortality after diagnosis of SPE was the primary end-point. The correlation of laboratory and CTPA findings with one-month mortality was analysed with area under curve (AUC) of receiver operating characteristic (ROC) curves and logistic regression analysis. RESULTS: Eighteen patients with SPE died within one month. Receiver operating characteristic curves revealed that the cutoff values for the right and left atrial diameter ratio, the right ventricular area and left ventricular area ratio (RVa/LVa ratio), Mastora score, septal angle, N-terminal pro-brain natriuretic peptide and cardiac troponin I (cTnI) for detecting early mortality were 2.15, 2.13, 69%, 57°, 3036 pg/mL and 0.18ng/mL, respectively. Using logistic regression analysis of laboratory and CTPA findings with regard to one-month mortality of SPE, RVa/LVa ratio and cTnI were shown to be independently associated with early death. A combination of cTnI and RVa/LVa ratio revealed an increase in the AUC value, but the difference did not reach significance compared with RVa/LVa or cTnI, alone (P>0.05). CONCLUSION: In patients with SPE, both the RVa/LVa ratio on CTPA and cTnI appear valuable for the prediction of short-term mortality.

A quantitative evaluation of pleural effusion on computed tomography scans using B-spline and local clustering level set.

Estimation of the pleural effusion's volume is an important clinical issue. The existing methods cannot assess it accurately when there is large volume of liquid in the pleural cavity and/or the patient has some other disease (e.g. pneumonia). In order to help solve this issue, the objective of this study is to develop and test a novel algorithm using B-spline and local clustering level set method jointly, namely BLL. The BLL algorithm was applied to a dataset involving 27 pleural effusions detected on chest CT examination of 18 adult patients with the presence of free pleural effusion. Study results showed that average volumes of pleural effusion computed using the BLL algorithm and assessed manually by the physicians were 586 ml±339 ml and 604±352 ml, respectively. For the same patient, the volume of the pleural effusion, segmented semi-automatically, was 101.8% ±4.6% of that was segmented manually. Dice similarity was found to be 0.917±0.031. The study demonstrated feasibility of applying the new BLL algorithm to accurately measure the volume of pleural effusion.

A three-dimensional approach for identifying small pulmonary vessels in smokers.

BACKGROUND: This study aims to develop a computerized scheme that utilizes a differential geometric approach to identify pulmonary vessels and then evaluate the performance of the scheme on the CT images of heavy smokers. METHODS: The scheme consists of two primary steps to segment entire lung vascular tree and identify the number of pulmonary vessels in a cross section. The scheme performance including accuracy, consistency, and efficiency was assessed using 102 chest CT scans. Further assessment was performed on the relationship between pulmonary vessels and the extent of emphysema as well as pulmonary artery alteration. RESULTS: The mean number of vessels in the cross section at the 5th generation was 17.84±4.74 and 17.23±4.85 assessed by computerized scheme and radiologists, respectively, which are significantly different (t = 2.12, p = 0.055). The results were consistent with those obtained by using a semi-automatic tool (r = 0.75, p = 0.01). In addition, in the 5th generation, the mean number of vessels was inversely related to the percentage of the low attenuation area (r = -0.704, p = 0.000), the mean lumen area of pulmonary vessel was inversely related to the mean value of main pulmonary artery diameter (r = -0.617, p = 0.000). The computational time of segmenting vessels was 6.50±0.02 seconds, which is much less than the average 8 minutes of the time spent by radiologists using the semi-automatic tool. CONCLUSION: Applying the computerized scheme yields reasonable performance on the segmentation of pulmonary vessels. The alteration of pulmonary vessels may reflect the presence of pulmonary hypertension, as well as the extent of emphysema.

Computed Tomographic Pulmonary Angiographic Findings Can Predict Short-Term Mortality of Saddle Pulmonary Embolism: A Retrospective Multicenter Study.

OBJECTIVE: In patients with saddle pulmonary embolism (PE), the correlation between computed tomographic pulmonary angiographic (CTPA) findings and short-term outcome remains unclear. The purpose is to determine if CTPA findings predict 1-month mortality of patients with saddle PE. METHODS: This is a multicenter, retrospective study of saddle PE. Computed tomographic pulmonary angiographic findings of 115 consecutive patients (male-to-female ratio, 65:50; mean age, 64.3 ± 16.3 years) with saddle PE were evaluated. One-month mortality after diagnosis was the primary end point. RESULTS: Twenty-four patients died within 1 month. Among CTPA findings, quantitative parameters including right/left ventricular area ratios (RVa/LVa), right/left atrial diameter ratios, Cobb angle, and Mastora score were significantly enlarged in survivors. Also, qualitative findings including contrast agent reflux into the azygos vein and pericardial effusion were significantly different between survivors and nonsurvivors. Areas under the curve on receiver operating characteristic curves revealed the cutoff values for predicting early mortality of saddle PE using right/left atrial diameter ratios, RVa/LVa, Mastora score, and Cobb angle, respectively, were 2.15, 2.00, 69%, and 58°. Logistic regression analysis suggested that both RVa/LVa (odds ratio, 5.100; P = 0.0004) and Cobb angle (odds ratio, 1.596; P = 0.0321) were independent predictors of early mortality. The combination of RVa/LVa and Cobb angle increased the area under the curve to 0.882, but the difference did not reach significance compared with RVa/LVa or Cobb angle, alone (P > 0.05). CONCLUSION: In patients with saddle PE, RVa/LVa and Cobb angle seem valuable in predicting short-term mortality.