Automatic segmentation of hemorrhagic transformation on follow-up non-contrast CT after acute ischemic stroke.

Background: Hemorrhagic transformation (HT) following reperfusion therapies is a serious complication for patients with acute ischemic stroke. Segmentation and quantification of hemorrhage provides critical insights into patients' condition and aids in prognosis. This study aims to automatically segment hemorrhagic regions on follow-up non-contrast head CT (NCCT) for stroke patients treated with endovascular thrombectomy (EVT). Methods: Patient data were collected from 10 stroke centers across two countries. We propose a semi-automated approach with adaptive thresholding methods, eliminating the need for extensive training data and reducing computational demands. We used Dice Similarity Coefficient (DSC) and Lin's Concordance Correlation Coefficient (Lin's CCC) to evaluate the performance of the algorithm. Results: A total of 51 patients were included, with 28 Type 2 hemorrhagic infarction (HI2) cases and 23 parenchymal hematoma (PH) cases. The algorithm achieved a mean DSC of 0.66 ± 0.17. Notably, performance was superior for PH cases (mean DSC of 0.73 ± 0.14) compared to HI2 cases (mean DSC of 0.61 ± 0.18). Lin's CCC was 0.88 (95% CI 0.79-0.93), indicating a strong agreement between the algorithm's results and the ground truth. In addition, the algorithm demonstrated excellent processing time, with an average of 2.7 s for each patient case. Conclusion: To our knowledge, this is the first study to perform automated segmentation of post-treatment hemorrhage for acute stroke patients and evaluate the performance based on the radiological severity of HT. This rapid and effective tool has the potential to assist with predicting prognosis in stroke patients with HT after EVT.

Progression of enlarged perivascular spaces contributes to occurrence of silent lacunar infarction in the elderly.

INTRODUCTION: This study aims to assess the effect of enlarged perivascular spaces (EPVS) in patients using the methods of scale score and 3D volume quantification and to determine whether EPVS progression is related to the occurrence of silent lacunar infarction (SLI). METHOD: Three hundred sixty-seven elderly patients with EPVS were screened by MRI on the day of admission and 2 years later; 295 patients were included in the final study, among which 136 patients had EPVS with SLI (EL); and 159 patients had EPVS without SLI (EOL). Both scale score and 3D volume quantification method were used to evaluate EPVS. The 295 patients were divided into three groups based on EPVS progression state: Group 1 (no progression), Group 2 (0-50% EPVS progression), and Group 3 (≥ 50% EPVS progression). Multiple logistic regression analysis was used to analyze the risk of occurrence of SLI. RESULTS: The EPVS scores and ΔEPVS scores were not significantly different between the EL and EOL groups (p > 0.05). EPVS volumes and their progression were significantly higher in EL compared with EOL (p < 0.001). The incidence of SLI was increased in Groups 2 and 3 compared with those in Group 1, and the trend test showed statistically significant (p = 0.032). Multiple logistic regression analysis showed that the risk of occurrence of SLI was significantly increased in Group 2 (OR 2.24; p = 0.024) and Group 3 (OR 3.31; p = 0.037) versus that in Group 1. CONCLUSION: 3D volume quantification allows for a more sensitive assessment of EPVS changes, and the progression of EPVS volume may contribute to the occurrence of SLI.

Myocardial native T1 mapping and extracellular volume quantification in asymptomatic female carriers of Duchenne muscular dystrophy gene mutations.

BACKGROUND: Female carriers of dystrophin gene mutations (DMD-FC) were previously considered non-manifesting, but in recent decades, cardiomyopathy associated with muscular dystrophy and myocardial fibrosis has been described. Our study aimed to assess prospectively myocardial fibrosis in asymptomatic DMD-FC compared to a sex-matched control group (CG) with similar age distribution using native T1 mapping and extracellular volume (ECV) quantification by cardiovascular magnetic resonance (CMR) imaging. MATERIALS AND METHODS: 38 DMD-FC with verified genetic mutation and 22 healthy volunteers were included. Using CMR, native T1 relaxation time and ECV quantification were determined in each group. Late gadolinium enhancement (LGE) was assessed in all cases. RESULTS: There were 38 DMD-FC (mean age 39.1 ± 8.8 years) and 22 healthy volunteers (mean age 39.9 ± 12.6 years) imagined by CMR. The mean global native T1 relaxation time was similar for DMD-FC and CG (1005.1 ± 26.3 ms vs. 1003.5 ± 25.0 ms; p-value = 0.81). Likewise, the mean global ECV value was also similar between the groups (27.92 ± 2.02% vs. 27.10 ± 2.89%; p-value = 0.20). The segmental analysis of mean ECV values according to the American Heart Association classification did not show any differences between DMD-FC and CG. There was a non-significant trend towards higher mean ECV values of DMD-FC in the inferior and inferolateral segments of the myocardium (p-value = 0.075 and 0.070 respectively). CONCLUSION: There were no statistically significant differences in the mean global and segmental native T1 relaxation times and the mean global or segmental ECV values. There was a trend towards higher segmental mean ECV values of DMD-FC in the inferior and inferolateral walls of the myocardium.

Deep learning-based multi-stage postoperative type-b aortic dissection segmentation using global-local fusion learning.

Objective.Type-b aortic dissection (AD) is a life-threatening cardiovascular disease and the primary treatment is thoracic endovascular aortic repair (TEVAR). Due to the lack of a rapid and accurate segmentation technique, the patient-specific postoperative AD model is unavailable in clinical practice, resulting in impracticable 3D morphological and hemodynamic analyses during TEVAR assessment. This work aims to construct a deep learning-based segmentation framework for postoperative type-b AD.Approach.The segmentation is performed in a two-stage manner. A multi-class segmentation of the contrast-enhanced aorta, thrombus (TH), and branch vessels (BV) is achieved in the first stage based on the cropped image patches. True lumen (TL) and false lumen (FL) are extracted from a straightened image containing the entire aorta in the second stage. A global-local fusion learning mechanism is designed to improve the segmentation of TH and BR by compensating for the missing contextual features of the cropped images in the first stage.Results.The experiments are conducted on a multi-center dataset comprising 133 patients with 306 follow-up images. Our framework achieves the state-of-the-art dice similarity coefficient (DSC) of 0.962, 0.921, 0.811, and 0.884 for TL, FL, TH, and BV, respectively. The global-local fusion learning mechanism increases the DSC of TH and BV by 2.3% (p< 0.05) and 1.4% (p< 0.05), respectively, based on the baseline. Segmenting TH in stage 1 can achieve significantly better DSC for FL (0.921 ± 0.055 versus 0.857 ± 0.220,p< 0.01) and TH (0.811 ± 0.137 versus 0.797 ± 0.146,p< 0.05) than in stage 2. Our framework supports more accurate vascular volume quantifications compared with previous segmentation model, especially for the patients with enlarged TH+FL after TEVAR, and shows good generalizability to different hospital settings.Significance.Our framework can quickly provide accurate patient-specific AD models, supporting the clinical practice of 3D morphological and hemodynamic analyses for quantitative and more comprehensive patient-specific TEVAR assessments.

Multi-layered adaptive neoangiogenesis Intra-Operative quantification (MANIOQ).

Quantification of vascularization volume can provide valuable information for diagnosis and prognosis in vascular pathologies. It can be adapted to inform the surgical management of gliomas, aggressive brain tumors characterized by exuberant sprouting of new blood vessels (neoangiogenesis). Filtered ultrafast Doppler data can provide two main parameters: vascularization index (VI) and fractional moving blood volume (FMBV) that clinically reflect tumor micro vascularization. Current protocols lack robust, automatic, and repeatable filtering methods. We present a filtrating method called Multi-layered Adaptive Neoangiogenesis Intra-Operative Quantification (MANIOQ). First, an adaptive clutter filtering is implemented, based on singular value decomposition (SVD) and hierarchical clustering. Second a method for noise equalization is applied, based on the subtraction of a weighted noise profile. Finally, an in vivo analysis of the periphery of the B-mode hyper signal area allows to measure the vascular infiltration extent of the brain tumors. Ninety ultrasound acquisitions were processed from 23 patients. Compared to reference methods in the literature, MANIOQ provides a more robust tissue filtering, and noise equalization allows for the first time to keep axial and lateral gain compensation (TGC and LGC). MANIOQ opens the way to an intra-operative clinical analysis of gliomas micro vascularization.

Correlation between frailty and cardiac structure and function in echocardiography in elderly patients with normal ejection fraction.

OBJECTIVE: This study aims to accurately evaluate the cardiac structure and function of the frail population in elderly patients with normal ejection fraction (EF) using the 3D volume quantification and speckle tracking of echocardiography, to explore the correlation between frailty and cardiac structure and function. METHODS: A total of 350 elderly aged 65 and above in-patients, excluding those with congenital heart disease, cardiomyopathy, and severe valvular heart disease, were included in the study. Patients were divided into non-frail, pre-frail, and frail group. Echocardiography techniques including speckle tracking and 3D volume quantification, were used to analyze the cardiac structure and function of the study subjects. Comparative analysis was statistically significant if P < 0.05. RESULTS: The cardiac structure of the frail group was different compared with non-frail patients, the frail group demonstrated increased left ventricular myocardial mass index (LVMI), but decreased stroke volume. Cardiac function was also impaired in the frail group: reservoir strain and conduit strain of left atrium, strain of right ventricular (RV) free wall, strain of RV septum, 3D EF of RV, and global longitudinal strain of LV were significantly decreased. Frailty was significantly and independently associated with LV hypertrophy (OR 1.889; 95% CI 1.240,2.880; P = 0.003), LV diastolic dysfunction (OR 1.496; 95% CI 1.016,2.203; P = 0.041), left ventricular global longitudinal strain (LVGLS) reduction (OR 1.697; 95% CI 1.192, 2.416; P = 0.003), and reduced RV systolic function (OR 2.200; 95% CI 1.017, 4.759; P = 0.045). CONCLUSION: Frailty is closely associated with several heart structural and functional alterations, which not only manifested as LV hypertrophy and reduced LV systolic function, but also decreased LV diastolic function, RV systolic function, and left atrial systolic function. Frailty is an independent risk factor for LV hypertrophy, LV diastolic dysfunction, LVGLS reduction, and reduced RV systolic function. TRIAL REGISTRATION NUMBER: ChiCTR2000033419. Date of registration: May 31, 2020.

Choosing transcatheter aortic valve replacement in porcelain aorta: outcomes versus surgical replacement.

OBJECTIVES: Porcelain aorta complicates aortic valve replacement and is an indication for transcatheter approaches. No study has compared surgical and transcatheter valve replacement in the setting of porcelain aorta. We characterize porcelain aorta patients undergoing aortic valve replacement and the association of aortic calcification and outcomes. METHODS: Patients undergoing aortic valve replacement with porcelain aorta were identified. Aortic calcium volume was determined using 3D computed tomography thresholding techniques. Propensity scoring was performed to assess the effect of surgical versus transcatheter approaches. Risk factors for composite major hospital complications (death, stroke and dialysis) were identified using random forest machine learning. RESULTS: From January 2006 to January 2015, 164 patients with porcelain aorta underwent aortic valve replacement [105 (64%) surgical replacement, 59 (36%) transcatheter replacement]. Propensity scoring matched 29 pairs (49% of transcatheter patients). Before matching, 5-year survival was 41% [(43% surgical, 35% transcatheter, P(log-rank) = 0.9]. After matching, mortality for surgical versus transcatheter replacement was 3.4% (n = 1) vs 10% (n = 3), stroke 14% (n = 4) vs 3.4% (n = 1) and dialysis 6.9% (n = 2) versus 11% (n = 3). Matched 5-year survival was 40% after surgical replacement and 29% after transcatheter replacement [P(log-rank) = 0.4]. Total aortic calcium volume was greater in transcatheter than surgical patients [18 (8.0) vs 17 (7.7) ml] and was associated with more major hospital complications after either approach. CONCLUSIONS: Surgical and transcatheter approaches are complementary options for aortic stenosis with porcelain aorta. Surgical valve replacement remains an effective treatment for patients requiring concomitant procedures. Quantifying aortic calcium volume is a helpful risk predictor in all patients with porcelain aorta.

Real-Time Monitoring of Biofilm Formation Using a Noninvasive Impedance-Based Method.

Biofilms are complex three-dimensional microbial communities that adhere to a variety of surfaces and interact with their surroundings. Because of the dynamic nature of biofilm formation, establishing a uniform technique for quantifying and monitoring biofilm volume, shape, and features in real-time is challenging. Herein, we describe a noninvasive electrochemical impedance approach for real-time monitoring of dental plaque-derived multispecies biofilm growth on a range of substrates. A working equation relating electrochemical impedance to live biofilm volume has been developed that is applicable to all three surfaces examined, including glass, dental filling resin, and Ca2+-releasing resin composites. Impedance changes of 2.5, 35, 50, and 65% correlated to biofilm volumes of 0.10 ± 0.01, 16.9 ± 2.2, 29.7 ± 2.3, and 38.6 ± 2.8 µm3/µm2, respectively. We discovered that glass, dental filling resin, and Ca2+-releasing dental composites required approximately 3.5, 4.5, and 6 days, respectively, to achieve a 50% change in impedance. The local pH change at the biofilm-substrate interfaces also monitored with potentiometry pH microsensor, and pH change varied according to biofilm volume. This impedance-based technique can be a useful analytical method for monitoring the growth of biofilms on a variety of substrates in real-time. Therefore, this technique may be beneficial for examining antibacterial properties of novel biomaterials.

Lesion Volume Quantification Using Two Convolutional Neural Networks in MRIs of Multiple Sclerosis Patients.

BACKGROUND: Multiple sclerosis (MS) is a neurologic disease of the central nervous system which affects almost three million people worldwide. MS is characterized by a demyelination process that leads to brain lesions, allowing these affected areas to be visualized with magnetic resonance imaging (MRI). Deep learning techniques, especially computational algorithms based on convolutional neural networks (CNNs), have become a frequently used algorithm that performs feature self-learning and enables segmentation of structures in the image useful for quantitative analysis of MRIs, including quantitative analysis of MS. To obtain quantitative information about lesion volume, it is important to perform proper image preprocessing and accurate segmentation. Therefore, we propose a method for volumetric quantification of lesions on MRIs of MS patients using automatic segmentation of the brain and lesions by two CNNs. METHODS: We used CNNs at two different moments: the first to perform brain extraction, and the second for lesion segmentation. This study includes four independent MRI datasets: one for training the brain segmentation models, two for training the lesion segmentation model, and one for testing. RESULTS: The proposed brain detection architecture using binary cross-entropy as the loss function achieved a 0.9786 Dice coefficient, 0.9969 accuracy, 0.9851 precision, 0.9851 sensitivity, and 0.9985 specificity. In the second proposed framework for brain lesion segmentation, we obtained a 0.8893 Dice coefficient, 0.9996 accuracy, 0.9376 precision, 0.8609 sensitivity, and 0.9999 specificity. After quantifying the lesion volume of all patients from the test group using our proposed method, we obtained a mean value of 17,582 mm3. CONCLUSIONS: We concluded that the proposed algorithm achieved accurate lesion detection and segmentation with reproducibility corresponding to state-of-the-art software tools and manual segmentation. We believe that this quantification method can add value to treatment monitoring and routine clinical evaluation of MS patients.

An Efficient Method for Aneurysm Volume Quantification Applicable in Any Shape and Modalities.

OBJECTIVE: Aneurysm volume quantification (AVQ) using the equation of ellipsoid volume is widely used although it is inaccurate. Furthermore, AVQ with 3-dimensional (3D) rendered data has limitations in general use. A novel universal method for AVQ is introduced for any diagnostic modality and application to any shape of aneurysms. METHODS: Relevant AVQ studies published from January 1997 to June 2019 were identified to determine common methods of AVQ. The basic idea is to eliminate the normal artery volume from 3D model with the aneurysm. After Digital Imaging and Communications in Medicine (DICOM) data is converted and exported to stereolithography (STL) file format, the 3D STL model is modified to remove the aneurysm and the volume difference between the 3D model with/without the aneurysm is defined as the aneurysm volume. Fifty randomly selected aneurysms from DICOM database were used to validate the different AVQ methods. RESULTS: We reviewed and categorized AVQ methods in 121 studies. Approximately 60% used the ellipsoid method, while 24% used the 3D model. For 50 randomly selected aneurysms, volumes were measured using 3D Slicer, RadiAnt, and ellipsoid method. Using 3D Slicer as the reference, the ratios of mean difference to mean volume obtained by RadiAnt and ellipsoid method were -1.21±7.46% and 4.04±30.54%, respectively. The deviations between RadiAnt and 3D Slicer were small despite of aneurysm shapes, but those of ellipsoid method and 3D Slicer were large. CONCLUSION: In spite of inaccuracy, ellipsoid method is still mostly used. We propose a novel universal method for AVQ that is valid, low cost, and easy to use.

COVID-19 pneumonia: computer-aided quantification of healthy lung parenchyma, emphysema, ground glass and consolidation on chest computed tomography (CT).

OBJECTIVE: To calculate by means of a computer-aided tool the volumes of healthy residual lung parenchyma, of emphysema, of ground glass opacity (GGO) and of consolidation on chest computed tomography (CT) in patients with suspected viral pneumonia by COVID-19. MATERIALS AND METHODS: This study included 116 patients that for suspected COVID-19 infection were subjected to the reverse transcription real-time fluorescence polymerase chain reaction (RT-PCR) test. A computer-aided tool was used to calculate on chest CT images healthy residual lung parenchyma, emphysema, GGO and consolidation volumes for both right and left lung. Expert radiologists, in consensus, assessed the CT images using a structured report and attributed a radiological severity score at the disease pulmonary involvement using a scale of five levels. Nonparametric test was performed to assess differences statistically significant among groups. RESULTS: GGO was the most represented feature in suspected CT by COVID-19 infection; it is present in 102/109 (93.6%) patients with a volume percentage value of 19.50% and a median value of 0.64 L, while the emphysema and consolidation volumes were low (0.01 L and 0.03 L, respectively). Among quantified volume, only GGO volume had a difference statistically significant between the group of patients with suspected versus non-suspected CT for COVID-19 (p < < 0.01). There were differences statistically significant among the groups based on radiological severity score in terms of healthy residual parenchyma volume, of GGO volume and of consolidations volume (p < < 0.001). CONCLUSION: We demonstrated that, using a computer-aided tool, the COVID-19 pneumonia was mirrored with a percentage median value of GGO of 19.50% and that only GGO volume had a difference significant between the patients with suspected or non-suspected CT for COVID-19 infection.

Quantified coronary total plaque volume from computed tomography angiography provides superior 10-year risk stratification.

AIMS: Automated coronary total plaque volume (TPV) quantification derived from coronary computed tomographic angiography (CTA) datasets provide exact and reliable assessment of calcified and non-calcified coronary atherosclerosis burden. The aim of this analysis was to investigate the long-term predictive value of TPV. METHODS AND RESULTS: TPV was quantified in 1577 patients undergoing coronary CTA and cardiovascular events were collected during 10.5 years (interquartile range 6.0-11.4) of follow-up. The study endpoint comprised cardiac death and acute coronary syndrome and occurred in 59 (3.7%) patients. Coronary TPV provided additive prognostic value over clinical risk assessed with the Morise Score and coronary artery disease severity (rise in C-index from 0.744 to 0.769, P = 0.03). A category-based reclassification approach combining the Morise Score and TPV revealed superior risk stratification (categorical net reclassification improvement: 0.48 with 95% CI 0.13-0.68, P < 0.001) and resulted in reclassification of 800 (51%) patients compared with the Morise Score alone. The 10-year risk for the study endpoint was 0.6% (95% CI 0-1.3) for patients classified as low risk (n = 807), 4.8% (95% CI 2.4-7.2) for patients at intermediate risk (n = 400), and 10.3% (95% CI 6.6-13.9) for patients at high risk (n = 370) using the combined reclassification approach. CONCLUSION: Quantification of TPV from coronary CTA permits an improved 10-year cardiovascular risk stratification.

Contrast-Enhanced MRI Combined With the Glycerol Test Reveals the Heterogeneous Dynamics of Endolymphatic Hydrops in Patients With Menière's Disease.

BACKGROUND: The sensitivity of endolymphatic hydrops (EH) to the glycerol test varies in patients with Menière's disease (MD). PURPOSE: To explore the features of EH and its glycerol-induced dynamics in MD. STUDY TYPE: Case-control study. POPULATION: Twenty patients with MD (24 affected ears) were included. FIELD STRENGTH/SEQUENCE: 3.0T 3D-FLAIR (fluid-attenuated inversion recovery) MRI and late gadolinium enhancement. ASSESSMENT: Intratympanic gadolinium-enhanced MRI was performed in the MD-affected ears before and after the glycerol test. The borders of the endolymphatic and total lymphatic space were contoured on the axial MRI slices to evaluate the volume of hydrops in both the cochlear and vestibular regions. STATISTICAL TESTS: Paired and unpaired t-tests, the Mann-Whitney U-test, linear discriminant analysis, Pearson's correlation, and linear regression. RESULTS: After glycerol ingestion, vestibular EH decreased in all patients, whereas cochlear EH significantly decreased only in patients with positive glycerol test results (all P < 0.01). At baseline, cochlear EH in the positive result group was greater than in the negative result group (P = 0.007). Unexpectedly, in the positive result group a drastic glycerol-induced dehydrating effect was observed in patients whose pretest cochlear EH ratio was >16% (P = 0.011). Moreover, the dehydrating role of glycerol was positively correlated with the baseline cochlear hydrops level (r = 0.7691, P < 0.001). DATA CONCLUSION: MRI provides evidence that glycerol administration improves the hearing threshold via dehydrating the EH. In the cochlear region, the baseline level of cochlear EH is a closely related factor for the validity of the glycerol test, whereas EH is consistently dehydrated in the vestibular component. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 3 J. Magn. Reson. Imaging 2020;52:1066-1073.

Quantification of Endolymphatic Space Volume after Intravenous Administration of a Single Dose of Gadolinium-based Contrast Agent: 3D-real Inversion Recovery versus HYDROPS-Mi2.

PURPOSE: Recently, the use of 3D real inversion recovery (3D-real IR) imaging has been proposed for the evaluation of endolymphatic hydrops (EH). This method shows similar contrast between the endolymphatic and perilymphatic spaces and surrounding bone compared with the hybrid of reversed image of positive endolymph signal and native image of perilymph signal multiplied with heavily T2-weighted MR cisternography (HYDROPS-Mi2) image. We measured the volume of the endolymphatic space using 3D-real IR and HYDROPS-Mi2 images, and compared the measurements obtained with both techniques. METHODS: HYDROPS-Mi2 and 3D-real IR images were obtained for 30 ears from 15 patients with clinical suspicion of EH; imaging was performed 4 h after intravenous administration of a single dose of gadolinium-based contrast agent. We measured the volume of the endolymphatic space in the cochlea and vestibule by manually drawing the regions of interest. The correlation between endolymphatic volume determined from HYDROPS-Mi2 images and 3D-real IR images was calculated. RESULTS: There was a strong positive linear correlation between the cochlear and vestibular endolymphatic volume determined from HYDROPS-Mi2 and 3D-real IR images. The Spearman's rank correlation coefficient (ρ) between the measurements obtained with both images was 0.805 (P < 0.001) for the cochlea and 0.826 (P < 0.001) for the vestibule. CONCLUSION: The endolymphatic volume measured using 3D-real IR images strongly correlated with that measured using HYDROPS-Mi2 images. Thus, 3D-real IR imaging might be a suitable method for the measurement of endolymphatic volume.

Computer-Aided Diagnosis System of Alzheimer's Disease Based on Multimodal Fusion: Tissue Quantification Based on the Hybrid Fuzzy-Genetic-Possibilistic Model and Discriminative Classification Based on the SVDD Model.

: An improved computer-aided diagnosis (CAD) system is proposed for the early diagnosis of Alzheimer's disease (AD) based on the fusion of anatomical (magnetic resonance imaging (MRI)) and functional (8F-fluorodeoxyglucose positron emission tomography (FDG-PET)) multimodal images, and which helps to address the strong ambiguity or the uncertainty produced in brain images. The merit of this fusion is that it provides anatomical information for the accurate detection of pathological areas characterized in functional imaging by physiological abnormalities. First, quantification of brain tissue volumes is proposed based on a fusion scheme in three successive steps: modeling, fusion and decision. (1) Modeling which consists of three sub-steps: the initialization of the centroids of the tissue clusters by applying the Bias corrected Fuzzy C-Means (FCM) clustering algorithm. Then, the optimization of the initial partition is performed by running genetic algorithms. Finally, the creation of white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) tissue maps by applying the Possibilistic FCM clustering algorithm. (2) Fusion using a possibilistic operator to merge the maps of the MRI and PET images highlighting redundancies and managing ambiguities. (3) Decision offering more representative anatomo-functional fusion images. Second, a support vector data description (SVDD) classifier is used that must reliably distinguish AD from normal aging and automatically detects outliers. The "divide and conquer" strategy is then used, which speeds up the SVDD process and reduces the load and cost of the calculating. The robustness of the tissue quantification process is proven against noise (20% level), partial volume effects and when inhomogeneities of spatial intensity are high. Thus, the superiority of the SVDD classifier over competing conventional systems is also demonstrated with the adoption of the 10-fold cross-validation approach for synthetic datasets (Alzheimer disease neuroimaging (ADNI) and Open Access Series of Imaging Studies (OASIS)) and real images. The percentage of classification in terms of accuracy (%), sensitivity (%), specificity (%) and area under ROC curve was 93.65%, 90.08%, 92.75% and 0.973; 91.46%, 92%, 91.78% and 0.967; 85.09%, 86.41%, 84.92% and 0.946 in the case of the ADNI, OASIS and real images respectively.

Novel 3D heart left ventricle muscle segmentation method for PET-gated protocol and its verification.

OBJECTIVE: The aim of this study was to propose and verify a universal method of left ventricular myocardium segmentation, able to operate on heart gated PET data with different sizes, shapes and uptake distributions. The proposed method can be classified as active model method and is based on the BEAS (B-spline Explicit Active Surface) algorithm published by Barbosa et al. The method was implemented within the Pmod PCARD software package. Method verification by comparison with reference software and phantom data is also presented in the paper. METHODS: The proposed method extends the BEAS model by defining mechanical features of the model: tensile strength and bending resistance. Formulas describing model internal energy increase during its stretching and bending are proposed. The segmentation model was applied to the data of 60 patients, who had undergone cardiac gated PET scanning. QGS by Cedars-Sinai and ECTb by Emory University Medical Centre served as reference software for comparing ventricular volumes. The method was also verified using data of left ventricular phantoms of known volume. RESULTS: The results of the proposed method are well correlated with the results of QGS (slope: 0.841, intercept: 0.944 ml, R2: 0.867) and ECTb (slope: 0.830, intercept: 2.109 ml, R2: 0.845). The volumes calculated by the proposed method were very close to the true cavity volumes of two different phantoms. CONCLUSIONS: The analysis of gated PET data by the proposed method results in volume measurements comparable to established methods. Phantom experiments demonstrate that the volume values correspond to the physical ones.

Impact of diabetes on coronary artery plaque volume by coronary CT angiography and subsequent adverse cardiac events.

BACKGROUND: To investigate the impact of diabetes on coronary artery total plaque volume (TPV) and adverse events in long-term follow-up. METHODS: One-hundred-and-eight diabetic patients were matched to 324 non-diabetic patients, with respect to age, sex, body-mass index, hypertension, smoking habits, LDL and HDL cholesterol, family history for CAD as well as aspirin and statin medication. In all patients, TPV was quantified from coronary CT angiographies (CTA) using dedicated software. All-cause mortality, acute coronary syndrome and late revascularisation (>90 days) served as combined endpoint. RESULTS: Patients were followed for 5.6 years. The endpoint occurred in 18 (16.7%) diabetic and 26 (8.0%) non-diabetic patients (odds ratio 2.3, p = 0.03). Diabetic patients had significantly higher TPV than non-diabetic patients (55.1 mm³ [IQR: 6.2 and 220.4 mm³] vs. 24.9 mm³ [IQR: 0 and 166.7 mm³], p = 0.02). A TPV threshold of 110.5 mm³ provided good separation of diabetic and non-diabetic patients at higher and lower risk for adverse events. Noteworthy, diabetic and non-diabetic patients with a TPV<110.5 mm³ had comparable outcome (hazard ratio: 1.3, p = 0.59), while diabetic patients with TPV>110.5 mm³ had significantly higher incidence of adverse events (hazard ratio 2.3, p = 0.03) compared to non-diabetic patients with TPV>110.5 mm³. There was incremental prognostic value in diabetic and non-diabetic patients over the Framingham Risk Score (Integrated Discrimination Improvement: 0.052 and 0.012, p for both <0.05). CONCLUSION: Diabetes is associated with significantly higher TPV, which is independent of other CAD risk factors. Quantification of TPV improves the identification of diabetic patients at higher risk for future adverse events.

Computed Tomography-Based Ventricular Volumes and Morphometric Parameters for Deciding the Treatment Strategy in Children with a Hypoplastic Left Ventricle: Preliminary Results.

Objective: To determine the utility of computed tomography (CT) ventricular volumes and morphometric parameters for deciding the treatment strategy in children with a hypoplastic left ventricle (LV). Materials and Methods: Ninety-four consecutive children were included in this study and divided into small LV single ventricle repair (SVR) (n = 28), small LV biventricular repair (BVR) (n = 6), disease-matched control (n = 19), and control (n = 41) groups. The CT-based indexed LV volumes, LV-to-right-ventricular (LV/RV) volume ratio, left-to-right atrioventricular valve (AVV) area ratio, left-to-right AVV diameter ratio, and LV/RV long dimension ratio were compared between groups. Proportions of preferred SVR in the small LV SVR group suggested by the parameters were evaluated. Results: Indexed LV end-systolic (ES) and end-diastolic (ED) volumes in the small LV SVR group (6.3 ± 4.0 mL/m2 and 14.4 ± 10.2 mL/m2, respectively) were significantly smaller than those in the disease-matched control group (16.0 ± 4.7 mL/m2 and 37.7 ± 12.0 mL/m2, respectively; p < 0.001) and the control group (16.0 ± 5.5 mL/m2 and 46.3 ± 10.8 mL/m2, respectively; p < 0.001). These volumes were 8.3 ± 2.4 mL/m2 and 21.4 ± 5.3 mL/m2, respectively, in the small LV BVR group. ES and ED indexed LV volumes of < 7 mL/m2 and < 17 mL/m2, LV/RV volume ratios of < 0.22 and < 0.25, AVV area ratios of < 0.33 and < 0.24, and AVV diameter ratios of < 0.52 and < 0.46, respectively, enabled the differentiation of a subset of patients in the small LV SVR group from those in the two control groups. One patient in the small LV biventricular group died after BVR, indicating that this patient might not have been a good candidate based on the suggested cut-off values. Conclusion: CT-based ventricular volumes and morphometric parameters can suggest cut-off values for SVR in children with a hypoplastic LV.

Which Parameter Is More Important for the Prognosis of New-Onset Adult Glioblastoma: Residual Tumor Volume or Extent of Resection?

BACKGROUND: The extent of resection (EOR) and residual tumor volume (RTV) are 2 pivotal predictors influencing the survival of patients with new-onset adult glioblastoma. Which of these 2 factors is more important remains unclear, however. The present aimed to evaluate and compare the accuracy of EOR and RTV, based on contrast-enhancing (CE) T1-weighted magnetic resonance imaging (MRI) and T2-weighted/fluid-attenuated inversion recovery (F) MRI, as prognostic factors in these patients. METHODS: In this retrospective study, data were extracted from the databases of 2 hospitals between January 1, 2013, and December 31, 2015. The subjects were divided into 2 groups, the total resection group and the partial resection group. The analysis comprised EOR and RTV. Statistical analysis was performed after controlling for other relevant factors. RESULTS: We analyzed 292 patients with new-onset glioblastoma who met the inclusion criteria. In the partial resection group, univariate analysis revealed that CE-EOR, CE-RTV, F-EOR, and F-RTV were correlated with progression-free survival (PFS) and overall survival (OS), but multivariate analysis identified no correlation between CE-EOR or F-EOR and PFS or OS. In the total resection group, F-EOR and F-RTV were correlated with PFS and OS in univariate analysis, but F-EOR was not correlated in multivariate analysis. CONCLUSIONS: Regardless of total or partial CE tumor resection, EOR might not be an independent prognostic factor. In contrast, RTV has the potential to offer greater predictive power for the prognosis of new-onset adult glioblastoma. Further investigations of the correlations of RTV and EOR with survival in patients with new-onset glioblastoma are needed.

Rapid fully automatic segmentation of subcortical brain structures by shape-constrained surface adaptation.

This work presents a novel approach for the rapid segmentation of clinically relevant subcortical brain structures in T1-weighted MRI by utilizing a shape-constrained deformable surface model. In contrast to other approaches for segmenting brain structures, its design allows for parallel segmentation of individual brain structures within a flexible and robust hierarchical framework such that accurate adaptation and volume computation can be achieved within a minute of processing time. Furthermore, adaptation is driven by local and not global contrast, potentially relaxing requirements with respect to preprocessing steps such as bias-field correction. Detailed evaluation experiments on more than 1000 subjects, including comparisons to FSL FIRST and FreeSurfer as well as a clinical assessment, demonstrate high accuracy and test-retest consistency of the presented segmentation approach, leading, for example, to an average segmentation error of less than 0.5 mm. The presented approach might be useful in both, research as well as clinical routine, for automated segmentation and volume quantification of subcortical brain structures in order to increase confidence in the diagnosis of neuro-degenerative disorders, such as Alzheimer's disease, Parkinson's disease, Multiple Sclerosis, or clinical applications for other neurologic and psychiatric diseases.