Characterization of primary and secondary malignant liver lesions from B-mode ultrasound

Hepatic Involvement in Acquired Immunodeficiency Syndrome-Associated Kaposi's Sarcoma: A Descriptive Analysis on CT, MRI, and Ultrasound

Purpose

To retrospectively analyse the different imaging manifestations of acquired immunodeficiency syndrome-associated hepatic Kaposi's sarcoma (AIDS-HKS) on CT, MRI, and Ultrasound.

Patients and Methods

Eight patients were enrolled in the study. Laboratory tests of liver function were performed. The CT, MRI, and Ultrasound manifestations were reviewed by two radiologists and two sonographers, respectively. The distribution and imaging signs of AIDS-HKS were evaluated.

Results

AIDS-HKS patients commonly presented multiple lesions, mainly distributed around the portal vein on CT, MRI, and Ultrasound. AIDS-HKS presented as ring enhancement in the arterial phase on contrast-enhanced CT and MRI scanning, and nodules gradually strengthen in the portal venous phase and the delayed phase. AIDS-HKS presented as intrahepatic bile duct dilatation and bile duct wall thickening around the lesion. Five patients (62.5%, 5/8) were followed up. After chemotherapy, the lesions were completely relieved (60.0%), or decreased (40.0%).

Conclusion

AIDS-HKS presented as multiple nodular lesions with different imaging features. The combination of different imaging methods was helpful for the imaging diagnosis of AIDS-HKS.

Artificial intelligence for the classification of focal liver lesions in ultrasound - a systematic review

Focal liver lesions are detected in about 15% of abdominal ultrasound examinations. The diagnosis of frequent benign lesions can be determined reliably based on the characteristic B-mode appearance of cysts, hemangiomas, or typical focal fatty changes. In the case of focal liver lesions which remain unclear on B-mode ultrasound, contrast-enhanced ultrasound (CEUS) increases diagnostic accuracy for the distinction between benign and malignant liver lesions. Artificial intelligence describes applications that try to emulate human intelligence, at least in subfields such as the classification of images. Since ultrasound is considered to be a particularly examiner-dependent technique, the application of artificial intelligence could be an interesting approach for an objective and accurate diagnosis. In this systematic review we analyzed how artificial intelligence can be used to classify the benign or malignant nature and entity of focal liver lesions on the basis of B-mode or CEUS data. In a structured search on Scopus, Web of Science, PubMed, and IEEE, we found 52 studies that met the inclusion criteria. Studies showed good diagnostic performance for both the classification as benign or malignant and the differentiation of individual tumor entities. The results could be improved by inclusion of clinical parameters and were comparable to those of experienced investigators in terms of diagnostic accuracy. However, due to the limited spectrum of lesions included in the studies and a lack of independent validation cohorts, the transfer of the results into clinical practice is limited.

Radiological Diagnosis of Chronic Liver Disease and Hepatocellular Carcinoma: A Review

Medical image analysis plays a pivotal role in the evaluation of diseases, including screening, surveillance, diagnosis, and prognosis. Liver is one of the major organs responsible for key functions of metabolism, protein and hormone synthesis, detoxification, and waste excretion. Patients with advanced liver disease and Hepatocellular Carcinoma (HCC) are often asymptomatic in the early stages; however delays in diagnosis and treatment can lead to increased rates of decompensated liver diseases, late-stage HCC, morbidity and mortality. Ultrasound (US) is commonly used imaging modality for diagnosis of chronic liver diseases that includes fibrosis, cirrhosis and portal hypertension. In this paper, we first provide an overview of various diagnostic methods for stages of liver diseases and discuss the role of Computer-Aided Diagnosis (CAD) systems in diagnosing liver diseases. Second, we review the utility of machine learning and deep learning approaches as diagnostic tools. Finally, we present the limitations of existing studies and outline future directions to further improve diagnostic accuracy, as well as reduce cost and subjectivity, while also improving workflow for the clinicians.

РОЛЬ АЛГОРИТМОВ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА В ДИАГНОСТИКЕ

Hepatosellülyar karsinoma (HSK) ən çox yayılan bədxassəli törəmələr arasında beşinci yeri tutur və dünyada xərçənglə əlaqəli ölümün üçüncü ən çox yayılmış səbəbidir. Süni intellekt (Sİ) sürətlə artan maraq sahəsidir. Müəlliflər HSK-ın diaqnostikasında və qiymətləndirilməsində Sİ-nin tətbiqi barədə məlumat verən məqalələri araşdırmışlar. Bu məqsədlə 27 məqalə təhlil edilmişdir. Təhlil edilmiş məqalələrdən KT görüntülərinin tədqiqinə dair 19 məqalədə (41,30%), USQ görüntülərinin öyrənilməsini əks etdirən 20 (43,47%) və MRT görüntülərindən bəhs edən 7 məqalədə (15,21%) müxtəlif Sİ alqoritmləri qəbul edilmişdir. Heç bir məqalədə PET və rentgen texnologiyasında süni intellektin istifadəsi müzakirə edilməyib. Sistematik yanaşma göstərmişdir ki, HSK-nin diaqnostikası və qiymətləndirilməsi üzrə əvvəlki işlərdə USQ, KT və MRT istifadə edilərək ənənəvi şərhin maşın öyrənməsi ilə müqayisəliliyi qiymətləndirilmişdir. Təhlillərimizdə görüntüləmə üsullarının istifadəsi HSK diaqnostikası üçün tibbi görüntüləmənin faydalılığını və təkamülünü əks etdirir. Bundan əlavə, nəticələrimiz lazımsız təkrarlanmanı və resursların israfını minimuma endirmək üçün birgə məlumat bazasında məlumat mübadiləsinə qaçılmaz ehtiyac olduğunu vurğulayır.

Гепатоцеллюлярная карцинома является пятым по распространенности злокачественным новообразованием и третьей по частоте причиной смерти от рака во всём мире. Искусственный интеллект — это быстрорастущая область интересов. Авторами были рассмотрены статьи, в которых сообщается о применении алгоритмов ИИ в диагностике и оценке ГЦК. Для этого проанализированы 27 статей. В проанализированных статьях в 19 статьях, посвящённых КТ-изображениям (41,30%), в 20 статьях, посвящённых изображениям УЗИ (43,47%), и в 7 статьях, посвящённым МРТ-изображениям (15,21%), использовали разные алгоритмы ИИ. Ни в одной статье не обсуждалось использование искусственного интеллекта в ПЭТ и рентгеновские технологии. Системный подход показал, что предыдущая работа по диагностике и оценке ГЦК оценивала сопоставимость традиционной интерпретации с машинным обучением с использованием УЗИ, КТ и МРТ. Использование методов визуализации в проведенном анализе отражает полезность и эволюцию медицинской визуализации для диагностики ГЦК. Кроме того, результаты поиска литературы подчёркивают острую необходимость совместного использования данных в совместных базах данных, чтобы свести к минимуму ненужное дублирование и растрату ресурсов.

Hepatocellular carcinoma (HCC) is the fifth most common malignancy and the third leading cause of cancer death worldwide. Artificial intelligence (AI) is a rapidly growing area of interest. We have reviewed articles reporting the application of AI algorithms in the diagnosis and evaluation of HCC. To do this, we analyzed 27 articles. In the analyzed articles, 19 articles on CT images (41.30%), 20 articles on ultrasound images (43.47%), and 7 articles on MRI images (15.21%) used different AI algorithms. None of the articles discussed the use of artificial intelligence in PET and X-ray technologies. Our systematic approach showed that previous work on the diagnosis and evaluation of HCC assessed the comparability of traditional interpretation with machine learning using ultrasound, CT, and MRI. The use of imaging modalities in our analysis reflects the usefulness and evolution of medical imaging for diagnosing HCC. In addition, our results highlight the critical need to share data across collaborative databases to minimize unnecessary duplication and waste of resources.

[Ultrasound diagnostics of the liver : Principles and important pathologies]

Diffuse changes in the liver parenchyma, focal lesions and blood flow in hepatic vessels can be assessed using ultrasound. Screening by ultrasound can be used to detect hepatocellular carcinomas as possible malignant sequelae of liver cirrhosis. As metastases are far more frequent than primary malignant liver tumors, secondary malignant neoplasms should be taken into consideration as a differential diagnosis in the presence of focal liver lesions. This particularly concerns patients with a known metastatic disease. Benign focal liver lesions are often incidentally discovered in women of childbearing age. Cysts, hemangiomas and focal nodular hyperplasia mostly show typical morphological features in ultrasound and do not require further follow-up; however, with hepatic adenomas, regular follow-up is recommended due to the risk of bleeding and/or malignant transformation.

Quantifying the Impact of Imaging Through Body Walls on Shear Wave Elasticity Measurements

In the context of ultrasonic hepatic shear wave elasticity imaging (SWEI), measurement success has been determined to increase when using elevated acoustic output pressures. As SWEI sequences consist of two distinct operations (pushing and tracking), acquisition failures could be attributed to (i) insufficient acoustic radiation force generation resulting in inadequate shear wave amplitude and/or (ii) distorted ultrasonic tissue motion tracking. In the study described here, an opposing window experimental setup that isolated body wall effects separately between the push and track SWEI operations was implemented. A commonly employed commercial track configuration was used, harmonic multiple-track-location SWEI. The effects of imaging through body walls on the pushing and tracking operations of SWEI as a function of mechanical index (MI), spanning 5 different push beam MIs and 10 track beam MIs, were independently assessed using porcine body walls. Shear wave speed yield was found to increase with both increasing push and track MI. Although not consistent across all samples, measurements in a subset of body walls were found to be signal limited during tracking and to increase yield by up to 35% when increasing electronic signal-to-noise ratio by increasing harmonic track transmit pressure.

Ensemble Learning of Multiple Models Using Deep Learning for Multiclass Classification of Ultrasound Images of Hepatic Masses

Ultrasound (US) is often used to diagnose liver masses. Ensemble learning has recently been commonly used for image classification, but its detailed methods are not fully optimized. The purpose of this study is to investigate the usefulness and comparison of some ensemble learning and ensemble pruning techniques using multiple convolutional neural network (CNN) trained models for image classification of liver masses in US images. Dataset of the US images were classified into four categories: benign liver tumor (BLT) 6320 images, liver cyst (LCY) 2320 images, metastatic liver cancer (MLC) 9720 images, primary liver cancer (PLC) 7840 images. In this study, 250 test images were randomly selected for each class, for a total of 1000 images, and the remaining images were used as the training. 16 different CNNs were used for training and testing ultrasound images. The ensemble learning used soft voting (SV), weighted average voting (WAV), weighted hard voting (WHV) and stacking (ST). All four types of ensemble learning (SV, ST, WAV, and WHV) showed higher values of accuracy than the single CNN. All four types also showed significantly higher deep learning (DL) performance than ResNeXt101 alone. For image classification of liver masses using US images, ensemble learning improved the performance of DL over a single CNN.

Extent of use of artificial intelligence & machine learning protocols in cancer diagnosis: A scoping review

Background & objectives

Artificial intelligence (AI) and machine learning (ML) have shown promising results in cancer diagnosis in validation tests involving retrospective patient databases. This study was aimed to explore the extent of actual use of AI/ML protocols for diagnosing cancer in prospective settings.

Methods

PubMed was searched for studies reporting usage of AI/ML protocols for cancer diagnosis in prospective (clinical trial/real world) setting with the AI/ML diagnosis aiding clinical decision-making, from inception till May 17, 2021. Data pertaining to the cancer, patients and the AI/ML protocol were extracted. Comparison of AI/ML protocol diagnosis with human diagnosis was recorded. Through a post hoc analysis, data from studies describing validation of various AI/ML protocols were extracted.

Results

Only 18/960 initial hits (1.88%) utilized AI/ML protocols for diagnostic decision-making. Most protocols used artificial neural network and deep learning. AI/ML protocols were utilized for cancer screening, pre-operative diagnosis and staging and intra-operative diagnosis of surgical specimens. The reference standard for 17/18 studies was histology. AI/ML protocols were used to diagnose cancers of the colorectum, skin, uterine cervix, oral cavity, ovaries, prostate, lungs and brain. AI/ML protocols were found to improve human diagnosis, and had either similar or better performance than the human diagnosis, especially made by the less experienced clinician. Validation of AI/ML protocols was described by 223 studies of which only four studies were from India. Also there was a huge variation in the number of items used for validation.

Interpretation & conclusions

The findings of this review suggest that a meaningful translation from the validation of AI/ML protocols to their actual usage in cancer diagnosis is lacking. Development of regulatory framework specific for AI/ML usage in healthcare is essential.

Artificial Intelligence in the Diagnosis of Hepatocellular Carcinoma: A Systematic Review

Hepatocellular carcinoma ranks fifth amongst the most common malignancies and is the third most common cause of cancer-related death globally. Artificial Intelligence is a rapidly growing field of interest. Following the PRISMA reporting guidelines, we conducted a systematic review to retrieve articles reporting the application of AI in HCC detection and characterization. A total of 27 articles were included and analyzed with our composite score for the evaluation of the quality of the publications. The contingency table reported a statistically significant constant improvement over the years of the total quality score (p = 0.004). Different AI methods have been adopted in the included articles correlated with 19 articles studying CT (41.30%), 20 studying US (43.47%), and 7 studying MRI (15.21%). No article has discussed the use of artificial intelligence in PET and X-ray technology. Our systematic approach has shown that previous works in HCC detection and characterization have assessed the comparability of conventional interpretation with machine learning using US, CT, and MRI. The distribution of the imaging techniques in our analysis reflects the usefulness and evolution of medical imaging for the diagnosis of HCC. Moreover, our results highlight an imminent need for data sharing in collaborative data repositories to minimize unnecessary repetition and wastage of resources.

Ultrasound-based artificial intelligence in gastroenterology and hepatology

Artificial intelligence (AI), especially deep learning, is gaining extensive attention for its excellent performance in medical image analysis. It can automatically make a quantitative assessment of complex medical images and help doctors to make more accurate diagnoses. In recent years, AI based on ultrasound has been shown to be very helpful in diffuse liver diseases and focal liver lesions, such as analyzing the severity of nonalcoholic fatty liver and the stage of liver fibrosis, identifying benign and malignant liver lesions, predicting the microvascular invasion of hepatocellular carcinoma, curative transarterial chemoembolization effect, and prognoses after thermal ablation. Moreover, AI based on endoscopic ultrasonography has been applied in some gastrointestinal diseases, such as distinguishing gastric mesenchymal tumors, detection of pancreatic cancer and intraductal papillary mucinous neoplasms, and predicting the preoperative tumor deposits in rectal cancer. This review focused on the basic technical knowledge about AI and the clinical application of AI in ultrasound of liver and gastroenterology diseases. Lastly, we discuss the challenges and future perspectives of AI.

Radiomics and Radiogenomics in Evaluation of Colorectal Cancer Liver Metastasis

Colorectal cancer is one common digestive malignancy, and the most common approach of blood metastasis of colorectal cancer is through the portal vein system to the liver. Early detection and treatment of liver metastasis is the key to improving the prognosis of the patients. Radiomics and radiogenomics use non-invasive methods to evaluate the biological properties of tumors by deeply mining the texture features of images and quantifying the heterogeneity of metastatic tumors. Radiomics and radiogenomics have been applied widely in the detection, treatment, and prognostic evaluation of colorectal cancer liver metastases. Based on the imaging features of the liver, this paper reviews the current application of radiomics and radiogenomics in the diagnosis, treatment, monitor of disease progression, and prognosis of patients with colorectal cancer liver metastases.

Ultrasound Image-Based Radiomics: An Innovative Method to Identify Primary Tumorous Sources of Liver Metastases

OBJECTIVES

To develop radiomic models of B-mode ultrasound (US) signatures for determining the origin of primary tumors in metastatic liver disease.

METHODS

A total of 254 patients with a diagnosis of metastatic liver disease were included in this retrospective study. The patients were divided into 3 groups depending on the origin of the primary tumor: group 1 (digestive tract versus non-digestive tract tumors), group 2 (breast cancer versus non-breast cancer), and group 3 (lung cancer versus other malignancies). The patients in each group were allocated to a training or testing set (a ratio of 8:2). The region of interest of liver metastasis was determined through manual differentiation of the tumors, and radiomic signatures were acquired from B-mode US images. Optimal features were selected to develop 3 radiomic models using multiple-dimensionality reduction and classifier screening. The area under the curve (AUC) of the receiver operating characteristic curve was applied to assess each model's performance.

RESULTS

A total of 5936 features were extracted, and 40, 6, and 14 optimal features were sequentially identified for the development of radiomic models for groups 1, 2, and 3, respectively, with training set AUC values of 0.938, 0.974, and 0.768 and testing set AUC values of 0.767, 0.768, and 0.750. The differences in age, sex, and number of liver metastatic lesions varied greatly between the 4 primary tumors (P < .050).

CONCLUSIONS

B-mode US radiomic models could be effective supplemental means to identify the origin of hepatic metastatic lesions (ie, unknown primary sites).

Quantifying the Effect of Abdominal Body Wall on In Situ Peak Rarefaction Pressure During Diagnostic Ultrasound Imaging

In this study, 3-D non-linear ultrasound simulations and experimental measurements were used to estimate the range of in situ pressures that can occur during transcutaneous abdominal imaging and to identify the sources of error when estimating in situ peak rarefaction pressures (PRPs) using linear derating, as specified by the mechanical index (MI) guideline. Using simulations, it was found that, for a large transmit aperture (F/1.5), MI consistently over-estimated in situ PRP by 20%-48% primarily owing to phase aberration. For a medium transmit aperture (F/3), the MI accurately estimated the in situ PRP to within 8%. For a small transmit aperture (F/5), MI consistently underestimated the in situ PRP by 32%-50%, with peak locations occurring 1-2 cm before the focal depth, often within the body wall itself. The large variability across body wall samples and focal configurations demonstrates the limitations of the simplified linear derating scheme. The results suggest that patient-specific in situ PRP estimation would allow for increases in transmit pressures, particularly for tightly focused beams, to improve diagnostic image quality while ensuring patient safety.

Quantitative Ultrasound Image Analysis Helps in the Differentiation of Hepatocellular Carcinoma (HCC) From Borderline Lesions and Predicting the Histologic Grade of HCC and Microvascular Invasion

OBJECTIVES

Quantitative image analysis is one of the methods to overcome the lack of objectivity of ultrasound (US). The aim of this study was to clarify the correlation between the features from a US image analysis and the histologic grade and microvascular invasion (MVI) of hepatocellular carcinoma (HCC) and differentiation of HCC smaller than 2 cm from borderline lesions.

METHODS

We retrospectively analyzed grayscale US images with histopathologic evidence of HCC or a precancerous lesion using ImageJ version 1.47 software (National Institutes of Health, Bethesda, MD).

RESULTS

A total of 148 nodules were included (borderline lesion, n = 31; early HCC [eHCC], n = 3; well-differentiated HCC [wHCC], n = 16; moderately differentiated HCC [mHCC], n = 79; and poorly differentiated HCC [pHCC], n = 19). A multivariate analysis selected lower minimum gray values (odds ratio [OR], 0.431; P = .003) and a higher standard deviation (OR, 1.880; P = .019) as predictors of HCC smaller than 2 cm. Median (range) minimum gray values of borderline lesions, eHCC, wHCC, mHCC, and pHCC were 29 (0-103), 7 (0-47), 6 (0-60), 10 (0-53), and 2 (0-38), respectively, and gradually decreased from borderline lesions to pHCC (P < 0.001). The multivariate analysis showed a higher aspect ratio (OR, 2.170; P = .001) and lower minimum gray value (OR, 0.475; P = .043) as predictors of MVI. An anechoic area diagnosed by a subjective evaluation was correlated with the minimum gray value (P < .0001). The proportion of the anechoic area gradually increased from eHCC to pHCC (P = .031).

CONCLUSIONS

In a US image analysis, HCC smaller than 2 cm had features of greater heterogeneity and a lower minimum gray value than borderline lesions. Moderately differentiated HCC was smoother than borderline lesions, and the anechoic area correlated with histologic grading. Microvascular invasion was correlated with a slender shape and a lower minimum gray value.

Artificial Intelligence in Medical Imaging and Its Application in Sonography for the Management of Liver Tumor

Recent advancement in artificial intelligence (AI) facilitate the development of AI-powered medical imaging including ultrasonography (US). However, overlooking or misdiagnosis of malignant lesions may result in serious consequences; the introduction of AI to the imaging modalities may be an ideal solution to prevent human error. For the development of AI for medical imaging, it is necessary to understand the characteristics of modalities on the context of task setting, required data sets, suitable AI algorism, and expected performance with clinical impact. Regarding the AI-aided US diagnosis, several attempts have been made to construct an image database and develop an AI-aided diagnosis system in the field of oncology. Regarding the diagnosis of liver tumors using US images, 4- or 5-class classifications, including the discrimination of hepatocellular carcinoma (HCC), metastatic tumors, hemangiomas, liver cysts, and focal nodular hyperplasia, have been reported using AI. Combination of radiomic approach with AI is also becoming a powerful tool for predicting the outcome in patients with HCC after treatment, indicating the potential of AI for applying personalized medical care. However, US images show high heterogeneity because of differences in conditions during the examination, and a variety of imaging parameters may affect the quality of images; such conditions may hamper the development of US-based AI. In this review, we summarized the development of AI in medical images with challenges to task setting, data curation, and focus on the application of AI for the managements of liver tumor, especially for US diagnosis.

Current status and perspectives for computer-aided ultrasonic diagnosis of liver lesions using deep learning technology

An ultrasound (US) examination is a common noninvasive technique widely applied for diagnosis of a variety of diseases. Based on the rapid development of US equipment, many US images have been accumulated and are now available and ready for the preparation of a database for the development of computer-aided US diagnosis with deep learning technology. On the contrary, because of the unique characteristics of the US image, there could be some issues that need to be resolved for the establishment of computer-aided diagnosis (CAD) system in this field. For example, compared to the other modalities, the quality of a US image is, currently, highly operator dependent; the conditions of examination should also directly affect the quality of US images. So far, these factors have hampered the application of deep learning-based technology in the field of US diagnosis. However, the development of CAD and US technologies will contribute to an increase in diagnostic quality, facilitate the development of remote medicine, and reduce the costs in the national health care through the early diagnosis of diseases. From this point of view, it may have a large enough potential to induce a paradigm shift in the field of US imaging and diagnosis of liver diseases.

Evaluation of Large-Aperture Imaging Through the ex Vivo Human Abdominal Wall

Current clinical abdominal imaging arrays are designed to maximize angular field of view rather than the extent of the coherent aperture. We illustrate, in ex vivo experiments, the use of a large effective aperture to perform high-resolution imaging, even in the presence of abdominal wall-induced acoustic clutter and aberration. Point and lesion phantom targets were imaged through a water path and through three excised cadaver abdominal walls to create different clinically relevant clutter effects with matched imaging targets. A 7.36-cm effective aperture was used to image the targets at a depth of 6.4 cm, and image quality metrics were measured over a range of aperture sizes using synthetic aperture techniques. In all three cases, although degradation compared with the control was observed, lateral resolution improved with increasing aperture size without loss of contrast. Spatial compounding of the large-aperture data drastically improved lesion detectability and produced contrast-to-noise ratio improvements of 83%-106% compared with the large coherent aperture. These studies indicate the need for the development of large arrays for high-resolution abdominal diagnostic imaging.

Automated diagnosis of focal liver lesions using bidirectional empirical mode decomposition features

Liver is the heaviest internal organ of the human body and performs many vital functions. Prolonged cirrhosis and fatty liver disease may lead to the formation of benign or malignant lesions in this organ, and an early and reliable evaluation of these conditions can improve treatment outcomes. Ultrasound imaging is a safe, non-invasive, and cost-effective way of diagnosing liver lesions. However, this technique has limited performance in determining the nature of the lesions. This study initiates a computer-aided diagnosis (CAD) system to aid radiologists in an objective and more reliable interpretation of ultrasound images of liver lesions. In this work, we have employed radon transform and bi-directional empirical mode decomposition (BEMD) to extract features from the focal liver lesions. After which, the extracted features were subjected to particle swarm optimization (PSO) technique for the selection of a set of optimized features for classification. Our automated CAD system can differentiate normal, malignant, and benign liver lesions using machine learning algorithms. It was trained using 78 normal, 26 benign and 36 malignant focal lesions of the liver. The accuracy, sensitivity, and specificity of lesion classification were 92.95%, 90.80%, and 97.44%, respectively. The proposed CAD system is fully automatic as no segmentation of region-of-interest (ROI) is required.

Quantifying Image Quality Improvement Using Elevated Acoustic Output in B-Mode Harmonic Imaging

Tissue harmonic imaging has been widely used in abdominal imaging because of its significant reduction in acoustic noise compared with fundamental imaging. However, tissue harmonic imaging can be limited by both signal-to-noise ratio and penetration depth during clinical imaging, resulting in decreased diagnostic utility. A logical approach would be to increase the source pressure, but the in situ pressures used in diagnostic ultrasound are subject to a de facto upper limit based on the U.S. Food and Drug Administration guideline for the mechanical index (<1.9). A recent American Institute of Ultrasound in Medicine report concluded that an effective mechanical index ≤4.0 could be warranted without concern for increased risk of cavitation in non-fetal tissues without gas bodies, but would only be justified if there were a concurrent improvement in image quality and diagnostic utility. This work evaluates image quality differences between normal and elevated acoustic output hepatic harmonic imaging using a transmit frequency of 1.8 MHz. The results indicate that harmonic imaging using elevated acoustic output leads to modest improvements (3%-7%) in contrast-to-noise ratio of hypo-echoic hepatic vessels and increases in imaging penetration depth on the order of 4 mm per mechanical index increase of 0.1 for a given focal depth. Difficult-to-image patients who suffer from poor ultrasound image quality exhibited larger improvements than easy-to-image study participants.

A Decision Support System for Classification of Normal and Medical Renal Disease Using Ultrasound Images

Early detection of medical renal disease is important as the same may lead to chronic kidney disease which is an irreversible stage. The present work proposes an efficient decision support system for detection of medical renal disease using small feature space consisting of only second order GLCM statistical features computed from raw renal ultrasound images. The GLCM mean feature vector and GLCM range feature vector are computed for inter-pixel distance d varying from 1 to 10. These texture feature vectors are combined in various ways yielding GLCM ratio feature vector, GLCM additive feature vector and GLCM concatenated feature vector. The present work explores the potential of five texture feature vectors computed using GLCM statistics exhaustively for differential diagnosis between normal and MRD images using SVM classifier. The result of the study indicates that GLCM range feature vector computed with d = 1 yields the highest overall classification accuracy of 85.7% with individual classification accuracy values of 93.3% and 77.9% for normal and MRD classes respectively.

Functional imaging of hepatocellular carcinoma

Imaging plays a key role in the clinical management of hepatocellular carcinoma (HCC), but conventional imaging techniques have limited sensitivity in visualizing small tumors and assessing response to locoregional treatments and sorafenib. Functional imaging techniques allow visualization of organ and tumor physiology. Assessment of functional characteristics of tissue, such as metabolism, proliferation and stiffness, may overcome some of the limitations of structural imaging. In particular, novel molecular imaging agents offer a potential tool for early diagnosis of HCC, and radiomics may aid in response assessment and generate prognostic models. Further prospective research is warranted to evaluate emerging techniques and their cost-effectiveness in the context of HCC in order to improve detection and response assessment.

SVM-Based CAC System for B-Mode Kidney Ultrasound Images

The present study proposes a computer-aided classification (CAC) system for three kidney classes, viz. normal, medical renal disease (MRD) and cyst using B-mode ultrasound images. Thirty-five B-mode kidney ultrasound images consisting of 11 normal images, 8 MRD images and 16 cyst images have been used. Regions of interest (ROIs) have been marked by the radiologist from the parenchyma region of the kidney in case of normal and MRD cases and from regions inside lesions for cyst cases. To evaluate the contribution of texture features extracted from de-speckled images for the classification task, original images have been pre-processed by eight de-speckling methods. Six categories of texture features are extracted. One-against-one multi-class support vector machine (SVM) classifier has been used for the present work. Based on overall classification accuracy (OCA), features from ROIs of original images are concatenated with the features from ROIs of pre-processed images. On the basis of OCA, few feature sets are considered for feature selection. Differential evolution feature selection (DEFS) has been used to select optimal features for the classification task. DEFS process is repeated 30 times to obtain 30 subsets. Run-length matrix features from ROIs of images pre-processed by Lee's sigma concatenated with that of enhanced Lee method have resulted in an average accuracy (in %) and standard deviation of 86.3 ± 1.6. The results obtained in the study indicate that the performance of the proposed CAC system is promising, and it can be used by the radiologists in routine clinical practice for the classification of renal diseases.

Neural network ensemble based CAD system for focal liver lesions from B-mode ultrasound

A neural network ensemble (NNE) based computer-aided diagnostic (CAD) system to assist radiologists in differential diagnosis between focal liver lesions (FLLs), including (1) typical and atypical cases of Cyst, hemangioma (HEM) and metastatic carcinoma (MET) lesions, (2) small and large hepatocellular carcinoma (HCC) lesions, along with (3) normal (NOR) liver tissue is proposed in the present work. Expert radiologists, visualize the textural characteristics of regions inside and outside the lesions to differentiate between different FLLs, accordingly texture features computed from inside lesion regions of interest (IROIs) and texture ratio features computed from IROIs and surrounding lesion regions of interests (SROIs) are taken as input. Principal component analysis (PCA) is used for reducing the dimensionality of the feature space before classifier design. The first step of classification module consists of a five class PCA-NN based primary classifier which yields probability outputs for five liver image classes. The second step of classification module consists of ten binary PCA-NN based secondary classifiers for NOR/Cyst, NOR/HEM, NOR/HCC, NOR/MET, Cyst/HEM, Cyst/HCC, Cyst/MET, HEM/HCC, HEM/MET and HCC/MET classes. The probability outputs of five class PCA-NN based primary classifier is used to determine the first two most probable classes for a test instance, based on which it is directed to the corresponding binary PCA-NN based secondary classifier for crisp classification between two classes. By including the second step of the classification module, classification accuracy increases from 88.7 % to 95 %. The promising results obtained by the proposed system indicate its usefulness to assist radiologists in differential diagnosis of FLLs.