Tissue Characterization Features Research Articles

Identification of Myocardial Scarring Using Contrast-Free Cardiac MRI in Patients With Autoimmune Rheumatic Diseases.

Late gadolinium enhancement (LGE) cardiac MRI is the method of choice in revealing the presence of myocardial scarring, but its availability remains limited in clinical practice. To assess myocardial scarring in patients with autoimmune rheumatic diseases (ARDs) using contrast-free cardiac MRI with a radiomics model. Retrospective. One hundred ninety-two patients (mean age, 41 years ± 15, 62 men) with or without ARDs, grouped into a training set of 153 patients and a testing set of 39 patients. 3.0 T/ cine imaging with a balanced steady-state free precession sequence, T1 mapping with a modified Look-Locker inversion recovery sequence, and LGE imaging with a phase-sensitive inversion recovery gradient echo sequence. LGE assessment was the reference standard for identifying myocardial scarring. Based on motion features extracted from cine images and tissue characterization features extracted from native T1 maps, a fully automated radiomics model with T1, cine MRI, or combined inputs was developed. Logistic regression model was used to detect myocardial scarring using contrast-free cardiac MRI parameters. Receiver operating characteristic curves were analyzed to assess the accuracy, sensitivity, and specificity in detecting myocardial scarring. Sensitivities of the models were further assessed in patients with various myocardial scarring proportions. Z-statistic and dice coefficient were assessed to compare the performance. P-values <0.05 were considered significant. The multivariable regression model exhibited an accuracy of 85.3%, a sensitivity of 93.5%, and a specificity of 50.0%. The radiomics model with T1 and cine MRI input exhibited an accuracy of 75.7%, a sensitivity of 60.9%, and a specificity of 85.5%. Moreover, the radiomics model showed a sensitivity of 90.9% among patients with >25% myocardial scarring. The proposed radiomics model allowed for the identification of myocardial scarring similar to LGE, but on contrast-free cardiac MRI in patients with ARDs. 3 TECHNICAL EFFICACY: Stage 1.

Pre-clinical left ventricular myocardial remodeling in patients with Friedreich's ataxia: A cardiac MRI study.

BackgroundHeart Failure (HF) is the most common cause of death in Friedreich’s ataxia (FRDA), an inherited mitochondrial disease. Myocardial fibrosis and myocardial hypertrophy are well-documented autopsy features among FRDA patients with HF.ObjectivesTo leverage the unique tissue characterization features of cardiac magnetic resonance (CMR) for characterizing myocardial remodeling in patients with genetically confirmed FRDA without HF and preserved left ventricular ejection fraction (LVEF > 55%).MethodsTwenty-seven FRDA’s patients (age 27.6 ± 9.7 years, 15 women) and 10 healthy controls (32.6±7.3 years, 5 women) underwent a CMR for assessment of LV function, myocardial T1, late gadolinium enhancement (LGE), extracellular volume fraction (ECV), and intracellular water-lifetime (τic), a marker of cardiomyocyte size.ResultsAs compared to controls, FRDA patients had a preserved LVEF (LVEF: 70.5±7.4% vs. 63.9±9.0%, P<0.058), larger LV mass index (LVMASSi: 61±21.7 vs. 45±4.2g/m2, P<0.02), and decreased LV end-diastolic volume index (LVEDVi 53.1±12.0 vs. 75.7±16.1ml/m2, P<0.001), compared with controls. Additionally, ECV and cardiomyocyte size (τic,) were larger in FRDA patients (ECV: 0.36 ±0.05 vs. 0.25±0.02, P<0.001; τic: 0.15±0.08 vs. 0.06±0.03 s, P = 0.02). ECV and τic were positively associated with LV mass-to-volume ratio (ECV: r = 0.57, P = 0.003; τic: r = 0.39; P = 0.05). LVMASSi and cardiomyocyte mass-index [(1−ECV)·LVMASSi] declined with age at the CMR exam, independent of the age at initial diagnosis.ConclusionsLV hypertrophy and concentric LV remodeling in FRDA are associated at the tissue level with an expansion of the ECV and an increase in cardiomyocyte size. The adverse tissue remodeling assessed by ECV and τic is associated with more severe cardiomyopathy classification, suggesting a role for these markers in tracking disease progression.

Symtosis: A liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm

Background and ObjectiveFatty Liver Disease (FLD) - a disease caused by deposition of fat in liver cells, is predecessor to terminal diseases such as liver cancer. The machine learning (ML) techniques applied for FLD detection and risk stratification using ultrasound (US) have limitations in computing tissue characterization features, thereby limiting the accuracy. MethodsUnder the class of Symtosis for FLD detection and risk stratification, this study presents a Deep Learning (DL)-based paradigm that computes nearly seven million weights per image when passed through a 22 layered neural network during the cross-validation (training and testing) paradigm. The DL architecture consists of cascaded layers of operations such as: convolution, pooling, rectified linear unit, dropout and a special block called inception model that provides speed and efficiency. All data analysis is performed in optimized tissue region, obtained by removing background information. We benchmark the DL system against the conventional ML protocols: support vector machine (SVM) and extreme learning machine (ELM). ResultsThe liver US data consists of 63 patients (27 normal/36 abnormal). Using the K10 cross-validation protocol (90% training and 10% testing), the detection and risk stratification accuracies are: 82%, 92% and 100% for SVM, ELM and DL systems, respectively. The corresponding area under the curve is: 0.79, 0.92 and 1.0, respectively. We further validate our DL system using two class biometric facial data that yields an accuracy of 99%. ConclusionDL system shows a superior performance for liver detection and risk stratification compared to conventional machine learning systems: SVM and ELM.

Breast tissue characterization based on modeling of ultrasonic echoes using the power-law shot noise model

Ultrasound is a safe, inexpensive and widely available tissue imaging modality, but its use in breast cancer detection is rather limited. Currently there is a significant amount of research devoted to extracting objective features from the ultrasound return signal, to be used in automatic tissue characterization. In this paper we investigate the parameters of the power-law shot noise (PLSN) model that was proposed in [IEEE Trans. UFFC 48 (2001) 953] as potential tissue characterization features. The PLSN model parameters are estimated from a database of 100 clinical ultrasound images of the breast, taken from 25 patients, and receiver operating characteristic (ROC) analysis is subsequently applied to quantify their ability to differentiate between tumorous and non-tumorous tissue and also between malignant and benign tumors. The obtained results indicate a maximum ROC area of 97% for the tumorous versus non-tumorous decision, while a maximum ROC area of 81% is obtained for the benign versus malignant decision. The latter result can be further improved by using the PLSN model parameters along with parameters of the K-distribution model, the combination yielding a maximum ROC area of 88.9%.

On modeling biomedical ultrasound RF echoes using a power-law shot-noise model.

We propose a new model for the RF ultrasound echo, namely the power-law shot-noise process. Based on this model, the in-phase and quadrature components of the echo are shown to exhibit 1/f beta-type spectral behavior, in a sense that is defined in the paper. The envelope also exhibits this type of spectral behavior, but with a different exponent. This result explains the experimental observations by other researchers of the power-law trend of the RF echo spectrum. Although the shot-noise model has been used in the past for modeling the RF echo, this is the first time that a power-law impulse response filter is used and that the resulting 1/f beta-type spectral behavior of the RF echo has been investigated. The model parameters are linked to tissue characteristics, such as scatterer density and attenuation; thus, they have the potential to be used as tissue characterization features. The validity of the proposed model is tested based on a database of 100 clinical ultrasound images of the breast.

An in vivo ultrasonic model of liver parenchyma

Several ultrasonic tissue characterization features are known to discriminate pathological from normal tissue in vivo. Previously, the authors developed an in vivo attenuation- and backscatter estimation method with each frequency dependent coefficient being reduced to a slope and intercept at central frequency. They derived expressions predicting the standard deviation (SD) of these features, assuming a commonly used ultrasonic model of liver parenchyma. In its application to in vivo data, the SD of the intercept features was unexpectedly high. Another feature, signal-to-noise ratio (SNR), showed a significant bias related to the window size. In this paper, the model is extended with the notion of inhomogeneous parenchyma background (IPB). IPB is shown to be present in normal liver parenchyma and is statistically described by a noise term with small amplitude and large correlation cell size. A method is presented to estimate the IPB characteristics. The expressions predicting SD are extended, and an expression is derived predicting the window size bias of the feature SNR. The accuracy and precision estimated from a large in vivo data set shows good agreement with the predictions with the extended model. It is concluded that IPB is a realistic and relevant phenomenon and should be part of the in vivo ultrasonic model of liver parenchyma.

System independence of RF-based tissue characterization features: Results of in-vivo clinical trials in liver

System independence of RF-based tissue characterization features: Results of in-vivo clinical trials in liver

Evaluation of diffuse renal disease using quantitative ultrasonic tissue characterization features

Evaluation of diffuse renal disease using quantitative ultrasonic tissue characterization features