Quantitative Computed Tomography Research Articles

Prediction of vertebral body mechanical parameters using opportunistic CT data

Abstract Introduction: The purpose of the study was to test a method describing the mechanical properties of bone using clinically available CT data. Material and Methods: The samples, 50 L3 vertebrae taken from males 22 to 81 years old, were examined with dual-energy X-ray absorptiometry and quantitative CT. An analysis of CT images and their image histograms was performed. The greyscale means – XC1, XC2, their standard deviations – SD1, SD2, and the areas under the curves – X1, X2 characterizing the organic matrix and bone material, respectively, were calculated by fitting two Gaussian functions. The compression tests were performed to determine the elastic modulus (E), ultimate stress (σmax), ultimate strain, and the ratio of work to fracture and the volume of the vertebra. Results: It was found that E and σmax were most precisely described by the parameter related to the trabecular bone density (XC2) obtained from the histogram analysis. Using the linear model, the coefficient of determination (R2) equals to 0.706 and 0.846 for E and σmax, respectively. For volumetric (vBMD) and areal bone mineral density (aBMD), R2 is 0.641 and 0.208 for E, while for σmax equals 0.784 and 0.356. After correction of vBMD using the histogram parameters R2 for E and σmax rise to 0.692 and 0.835, respectively. Conclusions: The superiority of the new method of E and σmax estimation based on clinically available CT data was confirmed. The proposed method does not require calibration and predicts the mechanical parameters of the vertebrae more precisely than vBMD or aBMD separately.

Quantitative Chest CT Analysis: Three Different Approaches to Quantify the Burden of Viral Interstitial Pneumonia Using COVID-19 as a Paradigm.

Objectives: To investigate the relationship between COVID-19 pneumonia outcomes and three chest CT analysis approaches. Methods: Patients with COVID-19 pneumonia who underwent chest CT were included and divided into survivors/non-survivors and intubated/not-intubated. Chest CTs were analyzed through a (1) Total Severity Score visually quantified by an emergency (TSS1) and a thoracic radiologist (TSS2); (2) density mask technique quantifying normal parenchyma (DM_Norm 1) and ground glass opacities (DM_GGO1) repeated after the manual delineation of consolidations (DM_Norm2, DM_GGO2, DM_Consolidation); (3) texture analysis quantifying normal parenchyma (TA_Norm) and interstitial lung disease (TA_ILD). Association with outcomes was assessed through Chi-square and the Mann-Whitney test. The TSS inter-reader variability was assessed through intraclass correlation coefficient (ICC) and Bland-Altman analysis. The relationship between quantitative variables and outcomes was investigated through multivariate logistic regression analysis. Variables correlation was investigated using Spearman analysis. Results: Overall, 192 patients (mean age, 66.8 ± 15.4 years) were included. TSS was significantly higher in intubated patients but only TSS1 in survivors. TSS presented an ICC of 0.83 (0.76; 0.88) and a bias (LOA) of 1.55 (-4.69, 7.78). DM_Consolidation showed the greatest median difference between survivors/not survivors (p = 0.002). The strongest independent predictor for mortality was DM_Consolidation (AUC 0.688), while the strongest independent predictor for the intensity of care was TSS2 (0.7498). DM_Norm 2 was the singular feature independently associated with both the outcomes. DM_GGO1 strongly correlated with TA_ILD (ρ = 0.977). Conclusions: The DM technique and TA achieved consistent measurements and a better correlation with patient outcomes.

Combining computed tomography features of left atrial epicardial and pericoronary adipose tissue with the triglyceride-glucose index to predict the recurrence of atrial fibrillation after radiofrequency catheter ablation: a machine learning study.

Radiofrequency catheter ablation (RFCA) represents an important treatment option for atrial fibrillation (AF); however, the recurrence rate following surgery is relatively high. This study aimed to predict the recurrence of AF after RFCA using interpretable machine learning models that combined the triglyceride-glucose (TyG) index and the quantification of left atrial epicardial and pericoronary adipose tissue. This retrospective study included 325 patients with AF who underwent their first successful RFCA, among whom 79 had confirmed recurrence. The preoperative clinical data of patients were collected, the TyG index was calculated, and computed tomography (CT) image features were quantitatively measured. Multivariate Cox regression analysis was used to identify the independent risk factors for RFCA recurrence, and adjustments being made for various confounding factors. Post-hoc subgroup analysis was conducted to evaluate the predictive value of the TyG index for recurrence in different patient subgroups. Prediction models based on six machine learning algorithms were constructed. The optimal model's features were evaluated using Shapley additive explanations (SHAP). After adjustment were made for various confounding factors such as comorbidities of AF, Cox regression showed that the volume of left atrial epicardial adipose tissue (LA-EAT), LA-EAT attenuation, left circumflex coronary artery fat attenuation index (LCX-FAI), and the TyG index were independent risk factors for recurrence after RFCA (P<0.001). The support vector machine (SVM) model based on these combined indicators had the best predictive performance, with an area under the curve of 0.793 [95% confidence interval (CI): 0.782-0.805] in the validation set, while its accuracy and positive predictive value were 0.804 and 0.710, respectively. The predictive efficiency of the TyG index appeared to be independent of type 2 diabetes mellitus (T2DM) status (Pinteraction=0.660). The SVM model that integrated the TyG index and quantitative CT imaging variables demonstrated good predictive ability for post-RFCA recurrence in patients with AF. Furthermore, the TyG index appeared capable of predicting recurrence independently of T2DM status.

Skeletal effects of sleeve gastrectomy, by sex and menopausal status and compared to Roux-en-Y gastric bypass surgery.

Roux-en-Y gastric bypass (RYGB) has deleterious effects on bone mass, microarchitecture, and strength. The skeletal effects of sleeve gastrectomy (SG), now the most commonly performed bariatric surgical procedure, are incompletely understood. We examined changes in bone turnover, areal and volumetric bone mineral density (aBMD, vBMD), and appendicular bone microarchitecture and estimated strength after SG. We compared the results to those previously reported after RYGB, hypothesizing lesser effects after SG than RYGB. Prospective observational cohort study of 54 adults with obesity undergoing SG at an academic center. Skeletal characterization with biochemical markers of bone turnover, dual-energy X-ray absorptiometry (DXA), quantitative computed tomography (QCT), and high-resolution peripheral QCT (HR-pQCT) was performed preoperatively and 6- and 12-months postoperatively. Over 12 months, mean percentage weight loss was 28.8%. Bone turnover marker levels increased, and total hip aBMD decreased -8.0% (95% CI -9.1%, -6.7%, p<0.01). Spinal aBMD and vBMD declines were larger in postmenopausal women than men. Tibial and radial trabecular and cortical microstructure worsened, as did tibial estimated strength, particularly in postmenopausal women. When compared to data from a RYGB cohort with identical design and measurements, some SG biochemical, vBMD, and radial microstructural changes were smaller, while other changes were not. Bone mass, microstructure, and strength decrease after SG. Some skeletal parameters change less after SG than after RYGB, while for others, we find no evidence for smaller effects after SG. Postmenopausal women may be at highest risk of skeletal consequences after SG.

Does the Intensity of Therapy Correspond to the Severity of Acute Respiratory Distress Syndrome (ARDS)?

Objectives: The intensity of respiratory treatment in acute respiratory distress syndrome (ARDS) is traditionally adjusted based on oxygenation severity, as defined by the mild, moderate, and severe Berlin classifications. However, ventilator-induced lung injury (VILI) is primarily determined by ventilator settings, namely tidal volume, respiratory rate, and positive end-expiratory pressure (PEEP). All these variables, along with respiratory elastance, are included in the concept of mechanical power. The aim of this study is to investigate whether applied mechanical power is proportional to oxygenation severity. Methods: We analyzed 291 ARDS patients (71 mild, 155 moderate, and 65 severe). We defined low, middle, and high mechanical power by dividing the entire population into tertiles with a similar number of patients. In each oxygenation class, we measured computed tomography (CT) anatomy, gas exchange, respiratory mechanics, mechanical power, and mortality rate. Results: ARDS severity was proportional to lung anatomy impairment, as defined by quantitative CT scans (i.e., lung volume and well-aerated tissue decreased across the ARDS classes, while respiratory elastance increased, as did mortality). Mechanical power, however, was similarly distributed across the severity classes, as the decrease in tidal volume in severe ARDS was offset by an increase in respiratory rate. Within each ARDS class, mortality increased from low to high mechanical power (roughly 1% for each J/min increase). Conclusions: Both lung severity and mechanical power independently impact mortality rates. It is tempting to speculate that ARDS severity primarily reflects the natural course of the disease, while mechanical power primarily reflects the risk of VILI.

Quantification of Interstitial Lung Diseases, From the AJR Special Series on Quantitative Imaging.

High-resolution CT (HRCT) plays an important role in diagnosing and monitoring interstitial lung diseases (ILDs). Despite advances, predicting disease progression and treatment response remains challenging. HRCT enables noninvasive visualization and classification of patterns of lung injury and assessment of disease extent. Visual estimation of CT extent of fibrotic lung disease is an independent predictor of mortality and progression, but is subjective, with only modest interobserver agreement for radiologic interpretation of ILD. Machine learning-based textural analysis of fibrosis extent on baseline and serial HRCT scans shows robust correlations with physiologic measures and strong association with risk of disease progression or mortality across various fibrosing ILDs. In idiopathic pulmonary fibrosis, quantitative CT (QCT) assessment is associated with physiologic impairment and risk of progression and death, and increasing severity of fibrosis on longitudinal evaluation is associated with increased risk of progression and death. Similar results have been noted for fibrotic hypersensitivity pneumonitis and connective tissue disease. This review focuses on QCT techniques for ILDs. We describe the clinical need for quantification of lung disease and illustrate the role of conventional visual evaluation and of QCT approaches in defining disease severity, prognosis, and longitudinal progression, both in established disease and in preclinical interstitial abnormality.

Quantitative multi-energy CT in oncology: State of the art and future directions

Multi-energy computed tomography (CT) involves acquisition of two or more CT measurements with distinct energy spectra. Using the differential attenuation of tissues and materials at different X-ray energies, multi-energy CT allows distinction of tissues and materials. Multi-energy technology encompasses different types of CT systems, such as dual-energy CT and photon-counting CT, that can use information from the energy and type of material present in acquired images to create multiple datasets. These scanners have overcome many of the limitations of conventional CT, making it possible to improve the diagnostic performance of CT and expand its use to new applications through better tissue characterization and multiple quantitative parameters. Quantitative imaging biomarkers based on multi-energy CT have enormous potential in oncologic imaging, from the diagnosis and characterization of tumor phenotypes to the evaluation of the response to treatment. Nevertheless, implementing these techniques in clinical practice remains challenging. This article reviews the basic principles underlying multi-energy CT and the most recent technical developments in these systems together with their advantages and limitations to establish the value of quantitative imaging derived from multi-energy CT in the field of oncology.

The application of QCT in the prognostic assessment of mCRC undergoing first-line treatment based on bevacizumab.

Bevacizumab induces muscle atrophy by changing the gene expression level of muscle tissue. Quantitative computed tomography (QCT) enables precise measurement of various body compositions, including muscle area. A total of 102 patients with metastatic colorectal cancer (mCRC) undergoing first-line chemotherapy based on bevacizumab were enrolled at thirst Affiliated Hospital of the University of Science and Technology of China. Their body compositions were measured respectively 1 month before and 1 month after the treatment. Treatment-related decline in skeletal muscle index and visceral fat infiltration significantly affect patient prognosis. A predictive model constructed by integrating changes in body composition with patient clinical characteristics effectively predicts the 9-month progression-free survival (PFS) of patients with mCRC.

Effect of adipose tissue deposition on insulin resistance in middle-aged and elderly women: Based on QCT and MRI mDIXON-Quant.

To explore the relationship between adipose tissue deposition and triglyceride-glucose (TyG) index, an indicator clinically used to assess insulin resistance (IR), in middle-aged and elderly women using quantitative computed tomography (QCT) and MRI mDIXON-Quant sequence. All participants underwent quantitative computed tomography (QCT) and MRI mDIXON-Quant examination and calculated the TyG index based on the fasting blood glucose and triacylglycerol. Bounded by the median TyG index, all participants were divided into low TyG group and high TyG group. Visceral fat mass (VFM) and subcutaneous fat mass (SFM) were measured on QCT images. Hepatic proton density fat fraction (H-PDFF), pancreatic proton density fat fraction (P-PDFF), and lumbar bone marrow fat fraction (L-BMFF) were measured on MRI mDIXON-Quant images. Adjusting for age and body mass index (BMI), TyG was moderately positively correlated with H-PDFF, and r/P was 0.416/<0.001, TyG index was weakly positively correlated with VFM and P-PDFF, and r/P were 0.385/<0.001 and 0.221/0.030. There was a difference of VFM, H-PDFF, and P-PDFF between low TyG group and high TyG group (P < 0.05). Adjusting for age and BMI, VFM, and H-PDFF were the risk factors of high TyG, and H-PDFF was the independent risk factor of high TyG. VFM and H-PDFF were the risk factors of IR, and H-PDFF was the independent risk factor. Early identification and active treatment of adipose tissue deposition, especially hepatic fat deposition, may reserve and delay the progression of IR and even metabolic syndrome.

Abstract 4135636: Age- and Sex-Related Coronary Atherosclerosis by Artificial Intelligence Quantitative Coronary CT Angiography from the INVICTUS Registry

Background: Coronary plaque analysis using artificial intelligence quantitative coronary CT (AI-QCT) is a promising tool to identify coronary atherosclerosis. However, there is insufficient data on coronary atherosclerosis volume and morphology using AI-QCT in patients who are at high risk of atherosclerosis. The purpose of this study is to clarify the differences in the volume and phenotypes of coronary atherosclerosis by age and gender using AI-QCT. Methods: The INVICTUS Registry is a multi-center registry enrolling patients who clinically underwent coronary CT angiography and invasive coronary angiography with invasive imaging modalities in Japan. Patients with a history of percutaneous coronary intervention or coronary artery bypass graft were excluded in this study. Using AI-QCT, the volume, and phenotypes (calcified, non-calcified, low-density non-calcified plaque [LD-NCP]) of coronary atherosclerosis were analyzed and assessed by age (&lt;55, 55-64, 65-74, and 75 and older) and gender. Results: A total of 982 patients were enrolled in the study. The mean age was 70.3 ± 10.8 years, 71.9% were male. Approximately two-thirds of patients had hypertension (73.3%) and dyslipidemia (71.5%), and 36.7% had diabetes. Mean total plaque volume (PV), calcified PV, non-calcified PV, and low-density PV were 745.5±529.8 mm 3 , 248.3±302.0 mm 3 , 482.2±340.6 mm 3 , and 15.0±29.4 mm 3 , respectively. The results of plaque volume and phenotypes by age are shown in the figure. Compared to those under 55 years of age, total PV increased gradually and significantly by about 10% for every 10 years of age (p=0.022). Though no significant difference was observed in non-calcified PV between the age groups (p=0.379), calcified PV progressively and significantly increases approximately 70% for every 10 years of age (p&lt;0.001). In contrast, the low-density plaque volume showed a trend toward a significant decrease with increasing age (p=0.073). These trends did not differ by gender. Conclusion: In a large cohort of patients at high risk for atherosclerosis analyzed by AI-QCT, total coronary PV increased with age, primarily calcified PV. While no change in non-calcified PV between the age groups, LD-NCPV was likely to decrease with increasing age. These findings may contribute to the guidance for the prevention of coronary artery diseases based on coronary atherosclerosis phenotype by age and gender.

Abstract 4144554: Revascularization of Patients with Low-Density Non-Calcified Plaque was Associated with Lower Occurrence of Acute Coronary Syndrome

INTRODUCTION: Coronary CT angiography (CCTA) is a powerful noninvasive tool for identifying high-risk plaque, such as low-density non-calcified plaque (LD-NCP). Though, the optimal treatment of patients with LD-NCP remains unclear. This study explored the association of revascularization in the setting of LD-NCP with the occurrence of acute coronary syndrome (ACS). Methods: This was a post-hoc analysis of the ICONIC study. A subset of 234 patients that underwent CCTA with subsequent ACS were matched to 234 control patients who also underwent CCTA but did not have ACS during follow-up. Patients were also followed for occurrence of revascularization, either coronary artery bypass graft or percutaneous coronary intervention. Atherosclerosis imaging-enabled quantitative CT (AI-QCT) was used to measure diameter stenosis, and LD-NCP, non-calcified plaque, and calcified plaque volumes from each CCTA. LD-NCP was defined as plaque with -190 to 30 Hounsfield Units. Patients were stratified based on the presence of LD-NCP. Subgroup analysis was conducted to compare the occurrence of ACS with the rate of revascularization. Kaplan-Meier survival curves and extended Cox regression analysis were used to evaluate the effect size of revascularization and LD-NCP on occurrence of ACS. Results: AI-QCT was completed in 448/468 subjects (follow-up time [MEAN±SD] 2.44±2.48 years). The median of LD-NCP was 1.2 mm 3 for patients with &gt;0 mm 3 LD-NCP. There were 85 patients with LD-NCP &gt;1.2 mm 3 and 363 patients with LD-NCP ≤1.2 mm 3 . In patients with LD-NCP &gt;1.2 mm 3 , the rate of revascularization in patients with and without ACS was 3/52 (5.8%) versus 14/33 (42.4%) (p&lt;0.001). In patients with LD-NCP ≤1.2 mm 3 , the rate of revascularization in patients with and without ACS was 36/170 (21.2%) versus 39/193 (20.2%) (p=0.897). In comparison to patients without revascularization and LD-NCP ≤1.2 mm 3 , patients with LD-NCP &gt;1.2 mm 3 and revascularization were less likely to have ACS during follow-up (adjusted HR: 0.20 [0.07, 0.61]; p=0.005). Additionally, patients with LD-NCP &gt;1.2 mm 3 who did not undergo revascularization were more likely to have ACS (adjusted HR: 1.47 [1.03, 2.12]; p=0.036). Hazard ratios were adjusted for diameter stenosis, and non-calcified and calcified plaque volume. Time-dependent coefficients were included for diameter stenosis. Conclusion: Revascularization of patients with LD-NCP &gt;1.2 mm 3 identified on CCTA with AI-QCT was associated with less risk for ACS.

Prediction of Bone Mineral Density based on Computer Tomography Images Using Deep Learning Model

Introduction: The problem of population aging is intensifying worldwide. Osteoporosis has become an important cause affecting the health status of older populations. However, the diagnosis of osteoporosis and people’s understanding of it are seriously insufficient. We aim to develop a deep learning model to automatically measure bone mineral density (BMD) and improve the diagnostic rate of osteoporosis. Methods: The images of 801 subjects with 2,080 vertebral bodies who underwent chest or abdominal paired computer tomography (CT) and quantitative computer tomography (QCT) scanning was retrieved from June 2020 to January 2022. The BMD of T11-L4 vertebral bodies was measured by QCT. Developing a multistage deep learning-based model to simulate the segmentation of the vertebral body and predict BMD. The subjects were randomly divided into training dataset, validation dataset and test dataset. Analyze the fitting effect between the BMD measured by the model and the standard BMD by QCT. Accuracy, precision, recall and f1-score were used to analyze the diagnostic performance according to categorization criterion measured by QCT. Results: 410 males (51.2%) and 391 females (48.8%) were included in this study. Among them, there were 154 (19.2%) males and 118 (14.7%) females aged 23–44; 182 (22.7%) males and 205 (25.6%) females aged 45–64; 74 (9.2%) males and 68 (8.5%) females aged 65–84. The number of vertebral bodies in the training dataset, the validation dataset, and the test dataset was 1433, 243, 404, respectively. In each dataset, the BMD of males and females decreases with age. There was a significant correlation between the BMD measured by the model and QCT, with the coefficient of determination (R2) 0.95–0.97. The diagnostic accuracy based on the model in the three datasets was 0.88, 0.91, and 0.91, respectively. Conclusion: The proposed multistage deep learning-based model can achieve automatic measurement of vertebral BMD and performed well in the prediction of osteoporosis.

Utilizing artificial intelligence to determine bone mineral density using spectral CT

BackgroundDual-energy computed tomography (DECT) has demonstrated the feasibility of using HAP-water to respond to BMD changes without requiring dedicated software or calibration. Artificial intelligence (AI) has been utilized for diagnosising osteoporosis in routine CT scans but has rarely been used in DECT. This study investigated the diagnostic performance of an AI system for osteoporosis screening using DECT images with reference quantitative CT (QCT). MethodsThis prospective study included 120 patients who underwent DECT and QCT scans from August to December 2023. Two convolutional neural networks, 3D RetinaNet and U-Net, were employed for automated vertebral body segmentation. The accuracy of the bone mineral density (BMD) measurement was assessed with relative measurement error (RME%). Linear regression and Bland–Altman analyses were performed to compare the BMD values between the AI and manual systems with those of the QCT. The diagnostic performance of the AI and manual systems for osteoporosis and low BMD was evaluated using receiver operating characteristic curve analysis. ResultsThe overall mean RME% for the AI and manual systems were − 15.93 ± 12.05 % and − 25.47 ± 14.83 %, respectively. BMD measurements using the AI system achieved greater agreement with the QCT results than those using the manual system (R2 = 0.973, 0.948, p < 0.001; mean errors, 23.27, 35.71 mg/cm3; 95 % LoA, −9.72 to 56.26, −11.45 to 82.87 mg/cm3). The areas under the curve for the AI and manual systems were 0.979 and 0.933 for detecting osteoporosis and 0.980 and 0.991 for low BMD. ConclusionThis AI system could achieve relatively high accuracy for automated BMD measurement on DECT scans, providing great potential for the follow-up of BMD in osteoporosis screening.

Endobronchial valve treatment improves chest-CT diaphragm configuration in COPD

This study investigated the impact of bronchoscopic lung volume reduction treatment using endobronchial valves (EBV) on diaphragm configuration. We successfully analyzed the diaphragm index using a newly developed quantitative computed tomography (QCT) tool before and after EBV treatment in forty patients with severe emphysema. We evaluated whether changes in the diaphragm index were associated with improvements in forced expiratory volume in 1 s (FEV1), residual volume (RV), Saint Georges Respiratory Questionnaire (SGRQ), and 6-min walking distance (6MWD) using Spearman's rho. The EBV treatment influenced the diaphragm configuration only on the treated side, resulting in an increased diaphragm curvature. There were significant associations of relative diaphragm index changes on the treated lung side or the entire diaphragm with improvements in FEV1 and 6MWD. These findings provide valuable insights into how EBV treatment affects the diaphragm in COPD patients.

Deep Learning for the Study of Urinary Stone Composition from Computed Tomography Images.

Urinary stones composed of uric acid can be treated with medicine. Computed tomography (CT) can diagnose urinary stone disease, but it is difficult to predict the type of uric stones. This study aims to develop a method to distinguish pure uric acid (UA) stones from non-uric acid (non-UA) stones by describing quantitative CT parameters of single-energy slices of urinary stones related to chemical stone types. Clinical data, CT images, and stone composition analysis results of patients with urinary stones clinically diagnosed at The Department of Urology, Affiliated Hospital of Qingdao University between 1 January 2018 and 31 December 2020 were collected and retrospectively analyzed. The above data were preprocessed and fed into a convolutional neural network to perform deep learning (DL) of the model, and the dataset was validated at a ratio of 4:1. The area under the curve (AUC) value of the receiver operating characteristic (ROC) curve and the confusion matrix were utilized to evaluate the predictive effect of the model. A retrospective analysis of 918 non-enhanced thin-slice single-energy CT images of known chemical stone types (124 with UA stones and 794 with non-UA stones) was conducted using a DL model. Compared with the results of ex vivo analysis by infrared spectroscopy, the prediction model obtained an AUC of 0.83 for the dichotomous classification of UA stones and non-UA stones. The accuracy of the model was 97.01%, with an F1 score of 89.04%, sensitivity of 84.62%, and specificity of 82.28%. This DL model constructed based on convolutional neural network analysis of thin-slice single-energy CT images is highly accurate in predicting the composition of pure UA and non-UA stones, providing a simple and rapid diagnosis method.

Quantifying lower extremity blood flow using low-dose CT perfusion: validation in a swine model

Abstract Background Quantitative assessment of blood flow in peripheral extremities in conjunction with simultaneous CT angiography measurements can improve risk assessment and provide a critical decision-making tool for patients across a wide spectrum of vascular disease severity. Purpose This study assessed the reproducibility and accuracy of lower extremity blood flow measurements with a low-dose first-pass analysis CT perfusion technique. Materials and Methods This prospective study utilized 16 Yorkshire Swine to obtain lower extremity blood flow CT measurements at baseline and under induced femoral stenosis using a vascular occluder. Thirty-three pairs of CT measurements evaluated reproducibility, and 43 CT measurements assessed accuracy against ultrasound flow probe references. Contrast agent and saline chaser were both injected peripherally at a rate of 5 mL/s. Bolus tracking was used, and a pre-contrast and post-contrast helical scan were acquired at the base and approximately the peak of the femoral enhancement (CT angiogram), respectively. The acquired data were then used as analytical inputs into a first-pass analysis model to derive perfusion in mL/min/g. The reproducibility and accuracy of lower extremity perfusion measurements were assessed via Mixed model regression and Bland-Altman analysis. Results Calculated CT perfusion measurements derived from first-pass analysis technique (PCT), and the reference standard ultrasound perfusion measurements (Pref) were related by PCT = 1.06 Pref + 0.00 (r2 = 0.90, Root-Mean-Square Error [RMSE] = 0.01 mL/min/g). The first (P1) and second (P2) CT perfusion measurements were related by P2 = 0.98 P1 + 0.02 (r = 0.97, RMSE = 0.11 mL/min/g). The average effective dose of perfusion measurement using first-pass analysis technique was calculated to be only 2.13 mSv. Conclusion The low-dose quantitative CT perfusion technique can accurately measure lower extremity perfusion (mL/min/g) using only 2 helical scans. The CT angiogram and perfusion measurements can be used as a comprehensive technique for morphological and physiological assessment of limb ischemia.

Long-Term Follow-Up of Interstitial Lung Abnormalities in Low-Dose Chest CT in Health Screening: Exploring the Predictors of Clinically Significant Interstitial Lung Diseases Using Artificial Intelligence-Based Quantitative CT Analysis.

This study examined longitudinal changes in interstitial lung abnormalities (ILAs) and predictors of clinically significant interstitial lung diseases (ILDs) in a screening population with ILAs. We retrieved 36891 low-dose chest CT records from screenings between January 2003 and May 2021. After identifying 101 patients with ILAs, the clinical findings, spirometry results, and initial and follow-up CT findings, including visual and artificial intelligence-based quantitative analyses, were compared between patients diagnosed with ILD (n = 23, 23%) and those who were not (n = 78, 77%). Logistic regression analysis was used to identify significant parameters for the clinical diagnosis of ILD. Twenty-three patients (n = 23, 23%) were subsequently diagnosed with clinically significant ILDs at follow-up (mean, 8.7 years). Subpleural fibrotic ILAs on initial CT and signs of progression on follow-up CT were common in the ILD group (both p < 0.05). Logistic regression analysis revealed that emerging respiratory symptoms (odds ratio [OR], 5.56; 95% confidence interval [CI], 1.28-24.21; p = 0.022) and progression of ILAs at follow-up chest CT (OR, 4.07; 95% CI, 1.00-16.54; p = 0.050) were significant parameters for clinical diagnosis of ILD. Clinically significant ILD was subsequently diagnosed in approximately one-quarter of the screened population with ILAs. Emerging respiratory symptoms and progression of ILAs at follow-up chest CT can be predictors of clinically significant ILDs.

Gel dosimetry: An overview of dosimetry systems and read out methods

Gel dosimetry has emerged over the past three decades in response to a growing need in high-precision radiotherapy to assess, in three dimensions, the absorbed radiation dose, as would be administered in cancer patients.Radiation-induced reaction mechanisms are dependent on the class of gel dosimeter, with four classes emerging as primary dosimeters for use in radiation therapy dose verification: (i) Fricke gel dosimeters contain a Fricke solution consisting of ammonium iron (II) sulfate in an acidic solution of sulfuric acid. In Fricke systems an oxidation of ferrous ions results in a change in the nuclear magnetic resonance (NMR) relaxation rate, which enables reading out Fricke gel dosimeters by use of MRI. The radiation-induced oxidation in Fricke gel dosimeters can also be visualized by adding a redox indicator. (ii) Polymer gel dosimeters exploit the radiation induced polymerization reaction of vinyl monomers and are predominantly read out by quantitative MRI or X-ray CT. (iii) Radiochromic dosimeters do not demonstrate a significant radiation-induced change in NMR properties but can be scanned by use of optical scanners (optical CT). In contrast to Fricke gel dosimeters, radiochromic gel dosimeters do not rely on the oxidation of a metal ion but exhibit a color change upon radiation. (iv) Radiofluorogenic dosimeters become fluorescent when exposed to ionizing radiation and can be read out with a planar scanning light beam.Likewise, the imaging modality used to extract quantitative dose information depends on the class of dosimeter being used, and three primary imaging modalities have emerged in this context: quantitative MRI, x-ray CT, and optical CT imaging. The accuracy and precision of the dose information extracted from gel dosimetry systems depends on both the dosimetric properties of the gel dosimeters and the readout technique, and the optimal readout method depends on the gel dosimeter response.Despite remaining an active field of research and illustrations of the application of gel dosimetry for the validation of clinical dose distributions, the utilization of gel dosimetry as a routine clinical dosimeter has been rather limited. However, with the introduction of new radiotherapy techniques that focus on organ motion compensation, new fractionation schemes, and extreme dose rates, the need for 3D radiation dosimetry is apparent. Even with the need for 3D dosimetry being apparent, gel dosimetry faces continued challenges in areas regarding the extraction of reproducible, accurate, and precise dose information.This review paper focuses on an introduction to gel dosimeter classes; a detailed examination of the three readout techniques with emphasis on the achievable accuracy, precision, and optimization of readout parameters; an outlook on future applications in emerging new radiotherapy techniques. We note that the introduction of theragnostic hybrid MRI-Linacs that combine an MRI-scanner and a clinical linear accelerator create new opportunities for inline polymer gel dosimetry. Likewise, the use of cone beam CT on linear accelerators opens up the possibility to read out gel dosimeters on the linac. Multiple optical CT designs have shown that optical CT gel dosimetry is eminently capable of providing high quality dosimetric information from clinically relevant treatment regimes. As a result, gel dosimetry provides exciting opportunities for 3D radiation dosimetry that were not available even a few years ago. The unique feature set of a properly executed gel dosimetry workflow allows for the extraction of dosimetric information, in 3D, that is not possible with any other dosimetry system and hence gel dosimetry provides exciting opportunities for clinical and research work in the area of radiation dose measurement.

Quantitative texture analysis using machine learning for predicting interpretable pulmonary perfusion from non-contrast computed tomography in pulmonary embolism patients

BackgroundPulmonary embolism (PE) is life-threatening and requires timely and accurate diagnosis, yet current imaging methods, like computed tomography pulmonary angiography, present limitations, particularly for patients with contraindications to iodinated contrast agents. We aimed to develop a quantitative texture analysis pipeline using machine learning (ML) based on non-contrast thoracic computed tomography (CT) scans to discover intensity and textural features correlated with regional lung perfusion (Q) physiology and pathology and synthesize voxel-wise Q surrogates to assist in PE diagnosis.MethodsWe retrospectively collected 99mTc-labeled macroaggregated albumin Q-SPECT/CT scans from patients suspected of PE, including an internal dataset of 76 patients (64 for training, 12 for testing) and an external testing dataset of 49 patients. Quantitative CT features were extracted from segmented lung subregions and underwent a two-stage feature selection pipeline. The prior-knowledge-driven preselection stage screened for robust and non-redundant perfusion-correlated features, while the data-driven selection stage further filtered features by fitting ML models for classification. The final classification model, trained with the highest-performing PE-associated feature combination, was evaluated in the testing cohorts based on the Area Under the Curve (AUC) for subregion-level predictability. The voxel-wise Q surrogate was then synthesized using the final selected feature maps (FMs) and model score maps (MSMs) to investigate spatial distributions. The Spearman correlation coefficient (SCC) and Dice similarity coefficient (DSC) were used to assess the spatial consistency between FMs or MSMs and Q-SPECT scans.ResultsThe optimal model performance achieved an AUC of 0.863 during internal testing and 0.828 on the external testing cohort. The model identified a combination containing 14 intensity and textural features that were non-redundant, robust, and capable of distinguishing between high- and low-functional lung regions. Spatial consistency assessment in the internal testing cohort showed moderate-to-high agreement between MSMs and reference Q-SPECT scans, with median SCC of 0.66, median DSCs of 0.86 and 0.64 for high- and low-functional regions, respectively.ConclusionsThis study validated the feasibility of using quantitative texture analysis and a data-driven ML pipeline to generate voxel-wise lung perfusion surrogates, providing a radiation-free, widely accessible alternative to functional lung imaging in managing pulmonary vascular diseases.Clinical trial numberNot applicable.

Quantitative myocardial blood flow distribution derived from CCTA and determination of myocardium at risk in the FAST TRACK CABG trial

Abstract Background In the anatomic SYNTAX score derived from invasive coronary angiography, the ischemic impact of each stenotic coronary segment is weighed with a fixed score based on the severity of luminal diameter narrowing and the theoretical amount of blood flow supplied to the myocardium(1). This method neither accounts for individual variation nor reflects the true extent of myocardium ischemia. Purpose We aim to provide a quantitative CT SYNTAX score derived from coronary computed tomography angiography (CCTA) with customized percent blood flow distribution to the myocardium supplied by each stenotic coronary segment to determine the myocardium at risk of ischemia. Methods Patients with 3VD and/or left main coronary artery disease (CAD) were included in the first-in-human FAST TRACK CABG trial and received surgical revascularization guided solely by CCTA and fractional flow reserve derived from CCTA (FFRCT). The recently reported anatomic CT-SYNTAX score was calculated for all cases. The FFRCT value and percent myocardial blood flow distribution(%MBF) were computed for all 16 SYNTAX score segments using the validated method of Keulards et al(2). On the pre-CABG CCTA, the segments located distal to the site where the FFRCT value dropped below 0.80 were considered at risk of ischemia, and the individual weight point per segment was calculated as 6×%MBF for each segment. The SYNTAX score derived from %MBF is compared to the anatomic and functional CT-SYNTAX score. Results Out of 114 patients enrolled, FFRCT and %MBF were analyzable in 106 patients. After excluding vessels with total occlusion, the coronary flow distribution between the right and left coronary systems was 20.5% vs 79.5%, respectively, similar to the 1:5 ratio in the anatomic SYNTAX score. The anatomic and functional CT-SYNTAX scores were 43.6 and 41.1, respectively. The average total %MBF per patient was 72.0(19.1)%, and the average SYNTAX score derived from %MBF was 38.0. There is a strong correlation between functional SYNTAX score and SYNTAX score derived from %MBF (Peason’s R=0.93). Passing-Bablock regression trends showed that the functional SYNTAX score slightly overestimated the SYNTAX score derived from %MBF (Figure 1). The coronary segment weight varied significantly per individual, especially in the presence of total occlusion. Overall, the fixed weight of the anatomic SYNTAX score is a good surrogate for the individualized weighing based on %MBF (Figure 2). Conclusion CCTA-derived %MBF enabled clinicians to quantify the individualized myocardium at risk of ischemia or injury in patients with severe CAD. Retrospectively, the fixed weight score used in the anatomical SYNTAX score appeared to be a robust surrogate for myocardial blood flow distribution.Figure 1Figure 2