Computer-Aided Diagnosis and Quantification of Cirrhotic Livers Based on Morphological Analysis and Machine Learning

It is widely known that morphological changes of the liver and the spleen occur during the clinical course of chronic liver diseases. In this paper, we proposed a morphological analysis method based on statistical shape models (SSMs) of the liver and spleen for computer-aided diagnosis and quantification of the chronic liver. We constructed not only the liver SSM but also the spleen SSM and a joint SSM of the liver and the spleen for a morphologic analysis of the cirrhotic liver in CT images. The effective modes are selected based on both its accumulation contribution rate and its correlation with doctor’s opinions (stage labels). We then learn a mapping function between the selected mode and the stage of chronic liver. The mapping function was used for diagnosis and staging of chronic liver diseases

Semiautomated 3D liver segmentation using computed tomography and magnetic resonance imaging

Le foie est un organe vital ayant une capacité de régénération exceptionnelle et un rôle crucial dans le fonctionnement de l’organisme. L’évaluation du volume du foie est un outil important pouvant être utilisé comme marqueur biologique de sévérité de maladies hépatiques. La volumétrie du foie est indiquée avant les hépatectomies majeures, l’embolisation de la veine porte et la transplantation. La méthode la plus répandue sur la base d'examens de tomodensitométrie (TDM) et d'imagerie par résonance magnétique (IRM) consiste à délimiter le contour du foie sur plusieurs coupes consécutives, un processus appelé la «segmentation». Nous présentons la conception et la stratégie de validation pour une méthode de segmentation semi-automatisée développée à notre institution. Notre méthode représente une approche basée sur un modèle utilisant l’interpolation variationnelle de forme ainsi que l’optimisation de maillages de Laplace. La méthode a été conçue afin d’être compatible avec la TDM ainsi que l' IRM. Nous avons évalué la répétabilité, la fiabilité ainsi que l’efficacité de notre méthode semi-automatisée de segmentation avec deux études transversales conçues rétrospectivement. Les résultats de nos études de validation suggèrent que la méthode de segmentation confère une fiabilité et répétabilité comparables à la segmentation manuelle. De plus, cette méthode diminue de façon significative le temps d’interaction, la rendant ainsi adaptée à la pratique clinique courante. D’autres études pourraient incorporer la volumétrie afin de déterminer des marqueurs biologiques de maladie hépatique basés sur le volume tels que la présence de stéatose, de fer, ou encore la mesure de fibrose par unité de volume.The liver is a vital abdominal organ known for its remarkable regenerative capacity and fundamental role in organism viability. Assessment of liver volume is an important tool which physicians use as a biomarker of disease severity. Liver volumetry is clinically indicated prior to major hepatectomy, portal vein embolization and transplantation. The most popular method to determine liver volume from computed tomography (CT) and magnetic resonance imaging (MRI) examinations involves contouring the liver on consecutive imaging slices, a process called “segmentation”. Segmentation can be performed either manually or in an automated fashion. We present the design concept and validation strategy for an innovative semiautomated liver segmentation method developed at our institution. Our method represents a model-based approach using variational shape interpolation and Laplacian mesh optimization techniques. It is independent of training data, requires limited user interactions and is robust to a variety of pathological cases. Further, it was designed for compatibility with both CT and MRI examinations. We evaluated the repeatability, agreement and efficiency of our semiautomated method in two retrospective cross-sectional studies. The results of our validation studies suggest that semiautomated liver segmentation can provide strong agreement and repeatability when compared to manual segmentation. Further, segmentation automation significantly shortens interaction time, thus making it suitable for daily clinical practice. Future studies may incorporate liver volumetry to determine volume-averaged biomarkers of liver disease, such as such as fat, iron or fibrosis measurements per unit volume. Segmental volumetry could also be assessed based on subsegmentation of vascular anatomy

Ultrasound shear wave imaging for diagnosis of nonalcoholic fatty liver disease

Pour le diagnostic et la stratification de la fibrose hépatique, la rigidité du foie est un biomarqueur quantitatif estimé par des méthodes d'élastographie. L'élastographie par ondes de cisaillement (« shear wave », SW) utilise des ultrasons médicaux non invasifs pour évaluer les propriétés mécaniques du foie sur la base des propriétés de propagation des ondes de cisaillement. La vitesse des ondes de cisaillement (« shear wave speed », SWS) et l'atténuation des ondes de cisaillement (« shear wave attenuation », SWA) peuvent fournir une estimation de la viscoélasticité des tissus. Les tissus biologiques sont intrinsèquement viscoélastiques et un modèle mathématique complexe est généralement nécessaire pour calculer la viscoélasticité en imagerie SW. Le calcul précis de l'atténuation est essentiel, en particulier pour une estimation précise du module de perte et de la viscosité. Des études récentes ont tenté d'augmenter la précision de l'estimation du SWA, mais elles présentent encore certaines limites. Comme premier objectif de cette thèse, une méthode de décalage de fréquence revisitée a été développée pour améliorer les estimations fournies par la méthode originale de décalage en fréquence [Bernard et al 2017]. Dans la nouvelle méthode, l'hypothèse d'un paramètre de forme décrivant les caractéristiques spectrales des ondes de cisaillement, et assumé initialement constant pour tous les emplacements latéraux, a été abandonnée permettant un meilleur ajustement de la fonction gamma du spectre d'amplitude. En second lieu, un algorithme de consensus d'échantillons aléatoires adaptatifs (« adaptive random sample consensus », A-RANSAC) a été mis en œuvre pour estimer la pente du paramètre de taux variable de la distribution gamma afin d’améliorer la précision de la méthode. Pour valider ces changements algorithmiques, la méthode proposée a été comparée à trois méthodes récentes permettant d’estimer également l’atténuation des ondes de cisaillements (méthodes de décalage en fréquence, de décalage en fréquence en deux points et une méthode ayant comme acronyme anglophone AMUSE) à l'aide de données de simulations ou fantômes numériques. Également, des fantômes de gels homogènes in vitro et des données in vivo acquises sur le foie de canards ont été traités. Comme deuxième objectif, cette thèse porte également sur le diagnostic précoce de la stéatose hépatique non alcoolique (NAFLD) qui est nécessaire pour prévenir sa progression et réduire la mortalité globale. À cet effet, la méthode de décalage en fréquence revisitée a été testée sur des foies humains in vivo. La performance diagnostique de la nouvelle méthode a été étudiée sur des foies humains sains et atteints de la maladie du foie gras non alcoolique. Pour minimiser les sources de variabilité, une méthode d'analyse automatisée faisant la moyenne des mesures prises sous plusieurs angles a été mise au point. Les résultats de cette méthode ont été comparés à la fraction de graisse à densité de protons obtenue de l'imagerie par résonance magnétique (« magnetic resonance imaging proton density fat fraction », MRI-PDFF) et à la biopsie du foie. En outre, l’imagerie SWA a été utilisée pour classer la stéatose et des seuils de décision ont été établis pour la dichotomisation des différents grades de stéatose. Finalement, le dernier objectif de la thèse consiste en une étude de reproductibilité de six paramètres basés sur la technologie SW (vitesse, atténuation, dispersion, module de Young, viscosité et module de cisaillement). Cette étude a été réalisée chez des volontaires sains et des patients atteints de NAFLD à partir de données acquises lors de deux visites distinctes. En conclusion, une méthode robuste de calcul du SWA du foie a été développée et validée pour fournir une méthode de diagnostic de la NAFLD.For diagnosis and staging of liver fibrosis, liver stiffness is a quantitative biomarker estimated by elastography methods. Ultrasound shear wave (SW) elastography utilizes noninvasive medical ultrasound to assess the mechanical properties of the liver based on the monitoring of the SW propagation. SW speed (SWS) and SW attenuation (SWA) can provide an estimation of tissue viscoelasticity. Biological tissues are inherently viscoelastic in nature and a complex mathematical model is usually required to compute viscoelasticity in SW imaging. Accurate computation of attenuation is critical, especially for accurate loss modulus and viscosity estimation. Recent studies have made attempts to increase the precision of SWA estimation, but they still face some limitations. As a first objective of this thesis, a revisited frequency-shift method was developed to improve the estimates provided by the original implementation of the frequency-shift method [Bernard et al 2017]. In the new method, the assumption of a constant shape parameter of the gamma function describing the SW magnitude spectrum has been dropped for all lateral locations, allowing a better gamma fitting. Secondly, an adaptive random sample consensus algorithm (A-RANSAC) was implemented to estimate the slope of the varying rate parameter of the gamma distribution to improve the accuracy of the method. For the validation of these algorithmic changes, the proposed method was compared with three recent methods proposed to estimate SWA (frequency-shift, two-point frequency-shift and AMUSE methods) using simulation data or numerical phantoms. In addition, in vitro homogenous gel phantoms and in vivo animal (duck) liver data were processed. As a second objective, this thesis also aimed at improving the early diagnosis of nonalcoholic fatty liver disease (NAFLD), which is necessary to prevent its progression and decrease the overall mortality. For this purpose, the revisited frequency-shift method was tested on in vivo human livers. The new method's diagnosis performance was investigated with healthy and NAFLD human livers. To minimize sources of variability, an automated analysis method averaging measurements from several angles has been developed. The results of this method were compared to the magnetic resonance imaging proton density fat fraction (MRI-PDFF) and to liver biopsy. SWA imaging was used for grading steatosis and cut-off decision thresholds were established for dichotomization of different steatosis grades. As a third objective, this thesis is proposing a reproducibility study of six SW-based parameters (speed, attenuation, dispersion, Young’s modulus, viscosity and shear modulus). The assessment was performed in healthy volunteers and NAFLD patients using data acquired at two separate visits. In conclusion, a robust method for computing the liver’s SWA was developed and validated to provide a diagnostic method for NAFLD

Using Telemedicine To Optimize The Delivery Of Care In Patients With Liver Disease

Chronic Liver Disease (CLD) is one of the top causes of morbidity and mortality globally. Among the spectrum of liver diseases, non-alcoholic steatohepatitis (NASH) is the most common cause of cirrhosis and one of the top reasons for a liver transplant. Patients with NASH end up with hepatic decompensation and liver-related complications such as malnutrition, sarcopenia, frailty, and death. Telemedicine has transformed health care by improving access, reducing cost, and increasing the quality of care. However, telemedicine has not been widely used to manage patients with CLD. This DNP project created a telemedicine protocol to manage patients with NASH, implemented the project at a transplant facility, and evaluated the effectiveness of the intervention using preliminary data from visits one and two. Although a larger sample size and longer study duration are needed, the telemedicine protocol appears to improve patient clinical outcomes as evidenced by a significant decrease in weight and BMI, fewer liver-related hospitalizations, no ED or urgent care visits, and improvement on patients’ perception of activity level and worry score. No significant changes noted in the patients’ MELD-Na score, overall health-related quality of life, frailty, and malnutrition status

Quantitative Analysis and Monte Carlo Modeling of Fat-Mediated MRI Relaxation

Hepatic steatosis is the accumulation of fat in the liver, affecting about 25% of the world population. Steatosis can cause lipo-toxicity and eventually lead to fibrosis, cirrhosis and ultimately liver failure if timely interventions are not provided. So, early diagnosis and disease monitoring of steatosis is crucial to reduce morbidity and mortality.Chemical shift based Magnetic Resonance Imaging (MRI) techniques using single and dual R2*(transverse relaxation rate) models have been reported to quantify fat fraction (FF) for assessment of steatosis. However, there is no common consensus between these two models and current data is limited for which model is accurate to quantify FF. Fully characterizing the behavior of the modelsover the entire clinical range of hepatic steatosis is essential to determine the limits of each of the models. However, performing a systematic investigation of the R2*models in patient population is infeasible. This thesis presents a computational approach by building a Monte Carlo based model as an alternative way to examine the R2*-MRI models. A 3D liver volume with impenetrable fat spheres was simulated to mimic hepatic steatosis. The simulation of steatosis was done using realistic data obtained from automatic segmentation and characterization of fat droplets using liver biopsy images. MRI signals were synthesized in the virtual liver volume using Monte Carlo modeling approach. Finally, the R2*behavior was analyzed using both the single and dual R2*models and they were compared against in-vivo calibration to determine their accuracy. Predicted R2*values were within confidence bounds of the published in vivo calibration and single R2*modelshowed higher accuracy than dual R2*model to estimate FF. In conclusion, this research developed a computational framework for creating realistic hepatic steatosis model and synthesizing MRI signal and analyzing R2*behavior in the presence of fat. The developed computational methods will also be generalizable to create other tissue-specific models and study R2*behavior at higher field strengths, for testing new MRI pulse sequences and in presence of other co-existing pathologies such as hepatic iron overload

Perfusion computed tomography of the liver

Background: Perfusion CT (P-CT) is a relatively new imaging technique that permits the visual and quantitative assessment of the micro- and macrocirculation of a target organ and focal lesions. P-CT has shown promising results in the evaluation of hyper-vascular tumors such as hepatocellular carcinoma (HCC). HCC is the sixth most common cancer globally and it has a poor prognosis when discovered at a late tumor stage. Any improvement in HCC detection would be directly beneficial for patient care. This thesis aims to investigate the strengths and limitations of whole liver P-CT and to evaluate if PCT can improve the detection of hyper-vascular liver lesions in patients with chronic liver disease. Methods: Study I: Twenty-four patients, who underwent dynamic P-CT for detection of HCC were retrospectively divided into three groups: (1) without portal-venous hypertension (PVH) (n = 8), (2) with PVH (n = 8), (3) with PVH and thrombosis (n = 8). Time to peak splenic- and peak renal enhancement (PSE resp. PRE), as well as arterial liver perfusion (ALP), portal- venous liver perfusion (PLP) and hepatic perfusion-index (HPI) of the liver and HCC derived from PSE- versus PRE- based modelling were compared between the groups. Study II: Group A (n=15) and Group B (n= 38) underwent P-CT using 50 ml contrast medium (CM). Group B underwent an additional standard multiphasic liver CT using 120ml (70-143 ml). Triple-arterial CT image sets were reconstructed from P-CT by fusing three dedicated arterial time points. Triple-arterial CT and single-arterial CT were compared by two readers (R1, R2), who assessed subjective image quality (IQ) and HCC detection rate. A third reader assessed objective IQ.Study III: Fifty study participants (Group A) were scanned with P-CT, a high CM volume protocol and bolus-tracking technique to depict ten arterial phases. Time attenuation curves were created for hyper-vascular liver lesions, liver parenchyma and hepatic vascular structures. 16 participants of Group A with lesions were further analyzed and radiation dose-neutral quadruple arterial phase image sets were created (Group A1). Group A1 was then compared to a Control Group (Group B) consisting of 16 consecutive patients undergoing standard single arterial phase scans. Lesion depiction and quantitative IQ were compared. Results: Study I: Time to PSE was significantly delayed in PVH groups 2 and 3 (P = 0.02), whereas PRE was similar in groups 1, 2 and 3 (P > 0.05). In group 1, liver and HCC perfusion parameters were similar for PSE- and PRE-based modelling (all P > 0.05), while significant differences were seen for PLP and HPI (liver only) in group 2 and ALP in group 3 (all P < 0.05). Study II: The mean CTDIvol of triple-arterial CT and single-arterial CT was equivalent (P=0.73). Triple-arterial CT showed lower image noise and better contrast-to-noise-ratio (P<0.001, P=0.032, respectively), but no significant difference in lesion-to-liver-contrast-ratio (P=0.31). Subjective IQ was good for both protocols. The detection rate of the 65 HCC lesions was 82%/83% (R1/R2) at triple-arterial CT and 80%/77% (R1/R2) at single-arterial CT (P=0.4). Study III: Both Group A1 and B had 33 hyper-enhancing liver lesions each. The mean CTDIvol of quadruple-arterial CT and single-arterial CT was equivalent (P=0.16). The mean time to reach peak lesion-to-liver contrast (LLC) was 20.1s (±4.2s) with a range of 12.5s to 29.1s. Quadruple arterial CT performed significantly better than the Control Group in regards to LLC (P= .009), CNR (P= .002), Image Noise (P<0.001) and hepatic artery enhancement(P<0.001). Conclusions: Study I: PSE is significantly delayed in patients with portal venous hypertension, which results in a miscalculation of P-CT parameters. Maximum-slope based P-CT could be improved by replacing the spleen with the kidney as the reference organ. The difference between time to PSE and time to PRE might serve as a non-invasive biomarker of portal venous hypertension. Study II: Radiation dose-equivalent triple arterial phase imaging is feasible and showed superior image quality and similar HCC detection rate as standard single arterial phase CT despite a substantially smaller CM volume. Study III: The optimal scan delay at single arterial phase CT for depiction of hyper-vascular liver lesions occurs at 20 s, when using a high iodine dose CM protocol and bolus-tracking. Fused quadruple arterial phase CT significantly increases lesion depiction, quantitative IQ and hepatic artery enhancement as compared to standard single arterial phase CT, without elevating the total radiation dose

Ultrasound Elastography

Elastography, the science of creating noninvasive images of mechanical characteristics of tissues, has been rapidly evolving in recent years. The advantage of this technique resides in the ability to rapidly detect and quantify the changes in the stiffness of soft tissues resulting from specific pathological or physiological processes. Ultrasound elastography is nowadays applied especially on the liver and breast, but the technique has been increasingly used for other tissues including the thyroid, lymph nodes, spleen, pancreas, gastrointestinal tract, kidney, prostate, and the musculoskeletal and vascular systems. This book presents some of the applications of strain and shear-wave ultrasound elastography in hepatic, pancreatic, breast, and musculoskeletal conditions

Assessing Doppler-Derived Pressure Gradients and Liver Echogenicity to Predict Liver Disease

Liver disease causes an estimated 36,000 deaths in the United States each year. Currently, to detect liver disease, an invasive biopsy is required. Other, less invasive diagnostic alternatives are needed. The purpose of this study was to assess the efficacy of a modified form of sonographic screening, including portal, hepatic, and splenic venous pressure, hepatic venous waveform analysis, portal vein diameter, and echogenicity of liver parenchyma in predicting liver disease. The study was based on conversion of a velocity measurement to a pressure gradient, allowing a fluid comparison between known catheterization venous pressures and sonographic Doppler-derived pressure gradients. This study was a secondary data analysis of a data set from 546 patients who received abdominal sonograms at a medical facility in the western United States between March 2010 and December 2010. The dependent variable was liver disease and the independent variables were ECHOGRADE, hepatic venous waveform (HVW), splenic vein pressure gradient (SVPG), modified portal vein pressure gradient (MPVPG), and hepatic vein pressure gradient (HVPG). Logistic regression was used to analyze the data. ECHOGRADE, HVW, and MPVPG in males were found to be statistically significant in detecting liver disease, supporting the theoretical framework and thus documenting a novel use of Doppler for the detection of liver disease. The social change significance of these results is to provide clinicians with an alternative, noninvasive method of diagnosing early liver disease before it progresses into chronic liver disease. With earlier detection, severe adverse health outcomes leading to irreversible liver cirrhosis may be avoided