Hepatic perfusion imaging using factor analysis of contrast enhanced ultrasound

Hierarchical Temporal Attention Network for Thyroid Nodule Recognition Using Dynamic CEUS Imaging

Contrast-enhanced ultrasound (CEUS) has emerged as a popular imaging modality in thyroid nodule diagnosis due to its ability to visualize vascular distribution in real time. Recently, a number of learning-based methods are dedicated to mine pathological-related enhancement dynamics and make prediction at one step, ignoring a native diagnostic dependency. In clinics, the differentiation of benign or malignant nodules always precedes the recognition of pathological types. In this paper, we propose a novel hierarchical temporal attention network (HiTAN) for thyroid nodule diagnosis using dynamic CEUS imaging, which unifies dynamic enhancement feature learning and hierarchical nodules classification into a deep framework. Specifically, this method decomposes the diagnosis of nodules into an ordered two-stage classification task, where diagnostic dependency is modeled by Gated Recurrent Units (GRUs). Besides, we design a local-to-global temporal aggregation (LGTA) operator to perform a comprehensive temporal fusion along the hierarchical prediction path. Particularly, local temporal information is defined as typical enhancement patterns identified with the guidance of perfusion representation learned from the differentiation level. Then, we leverage an attention mechanism to embed global enhancement dynamics into each identified salient pattern. In this study, we evaluate the proposed HiTAN method on the collected CEUS dataset of thyroid nodules. Extensive experimental results validate the efficacy of dynamic patterns learning, fusion and hierarchical diagnosis mechanism.

Automatic Respiratory Gating Hepatic DCEUS-based Dual-phase Multi-parametric Functional Perfusion Imaging using a Derivative Principal Component Analysis

Purpose: Angiogenesis in liver cancers can be characterized by hepatic functional perfusion imaging (FPI) on the basis of dynamic contrast-enhanced ultrasound (DCEUS). However, accuracy is limited by breathing motion which results in out-of-plane image artifacts. Current hepatic FPI studies do not correct for these artifacts and lack the evaluation of correction accuracy. Thus, a hepatic DCEUS-based dual-phase multi-parametric FPI (DM-FPI) scheme using a derivative principal component analysis (PCA) respiratory gating is proposed to overcome these limitations.

Materials and Methods: By considering severe 3D out-of-plane respiratory motions, the proposed scheme's accuracy was verified with in vitro DCEUS experiments in a flow model mimicking a hepatic vein. The feasibility was further demonstrated by considering in vivo DCEUS measurements in normal rabbit livers, and hepatic cavernous hemangioma and hepatocellular carcinoma in patients. After respiratory kinetics was extracted through PCA of DCEUS sequences under free-breathing condition, dual-phase respiratory gating microbubble kinetics was identified by using a derivative PCA zero-crossing dual-phase detection, respectively. Six dual-phase hemodynamic parameters were estimated from the dual-phase microbubble kinetics and DM-FPI was then reconstructed via color-coding to quantify 2.5D angiogenic hemodynamic distribution for live tumors.

Results: Compared with no respiratory gating, the mean square error of respiratory gating DM-FPI decreased by 1893.9 ± 965.4 (p < 0.05), and mean noise coefficients decreased by 17.5 ± 7.1 (p < 0.05), whereas correlation coefficients improved by 0.4 ± 0.2 (p < 0.01). DM-FPI observably removed severe respiratory motion artifacts on PFI and markedly enhanced the accuracy and robustness both in vitro and in vivo.

Conclusions: DM-FPI precisely characterized and distinguished the heterogeneous angiogenic hemodynamics about perfusion volume, blood flow and flow rate within two anatomical sections in the normal liver, and in benign and malignant hepatic tumors. DCEUS-based DM-FPI scheme might be a useful tool to help clinicians diagnose and provide suitable therapies for liver tumors.

Robust Unmixing of Dynamic Sequences Using Regions of Interest

In dynamic planar imaging, extraction of signals specific to structures is complicated by structures superposition. Due to overlapping, signals extraction with classic regions of interest (ROIs) methods suffers from inaccuracy, as extracted signals are a mixture of targeted signals. Partial volume effect raises the same issue in dynamic tomography. Source separation methods, such as factor analysis of dynamic sequences, have been developed to unmix such data. However, the underlying problem is underdetermined and the model used is not relevant in the whole image. This non-uniqueness issue was overcome by introducing prior knowledge, such as sparsity or smoothness, in the separation model. In practice, these methods are barely used because of the lack of reliability of their results. Previously developed methods aimed to be fully automatic, but efficiency can be improved with additional prior knowledge. Some methods using ROIs knowledge in a straightforward way have been proposed. In this paper, we propose an unmixing method, based on an objective function minimization and integrating these ROIs in a different and robust manner. The objective function promotes consistent solutions regarding ROIs while relaxing the model outside ROIs. In order to reduce user-dependent effects, ROIs are used as soft constraints in a robust way through the use of a distance matrix. Consistency, effectiveness, and robustness to the ROIs selection are demonstrated on a toy example, a highly realistic simulated renography data set and a clinical data set. Performance is compared with the competitive methods.

Contrast-Enhanced Ultrasound Reveals Exercise-Induced Perfusion Deficits in Claudicants

Background

Contrast-Enhanced Ultrasonography (CEUS) is an imaging modality allowing perfusion quantification in targeted regions of interest of the lower extremity that has not been possible with color-flow imaging or with measurement of ankle brachial indices. We developed a protocol to quantify lower extremity muscle perfusion impairment in PAD patients in response to exercise.

Methods and findings

Thirteen patients with Rutherford Class I-III Peripheral Arterial Disease (PAD) and no prior revascularization procedures were recruited from the Baltimore Veterans Affairs Medical Center and compared with eight control patients without PAD. CEUS interrogation of the index limb gastrocnemius muscle was performed using an intravenous bolus of lipid-stabilized microsphere contrast before and after a standardized treadmill protocol. Peak perfusion (PEAK) and time to peak perfusion (TTP) were measured before and after exercise. Between and within group differences were assessed. Control subjects demonstrated a more rapid TTP (p<0.01) and an increase in peak perfusion (PEAK, p=0.02) after exercise, when compared to their baseline measures. Patients with PAD demonstrated TTP and PEAK measures equivalent to controls at baseline (p=0.39, p=0.71, respectively). However, they exhibited no significant exercise-induced changes in perfusion (TTP p=0.49 and PEAK 0.67, respectively compared to baseline). After exercise, normal subjects had significantly shorter TTP (p=0.04) and greater PEAK (p=0.02) than PAD patients.

Conclusion

Consistent with their lack of ischemic symptoms at rest, class I to III claudicant PAD patients showed similar perfusion measures (TTP and PEAK) at rest. PAD patients, however, were unable to increase perfusion in response to exercise, whereas controls increased perfusion significantly. This corresponds with claudication and limited walking capacity observed in PAD. CEUS with bolus injection offers a convenient, objective, quantitative and visual physiologic assessment of perfusion limitation in specific muscle groups of PAD patients. This has the potential for substantial clinical and research utility.

Entropy of Ultrasound-Contrast-Agent Velocity Fields for Angiogenesis Imaging in Prostate Cancer

Prostate cancer care can benefit from accurate and cost-efficient imaging modalities that are able to reveal prognostic indicators for cancer. Angiogenesis is known to play a central role in the growth of tumors towards a metastatic or a lethal phenotype. With the aim of localizing angiogenic activity in a non-invasive manner, Dynamic Contrast Enhanced Ultrasound (DCE-US) has been widely used. Usually, the passage of ultrasound contrast agents thought the organ of interest is analyzed for the assessment of tissue perfusion. However, the heterogeneous nature of blood flow in angiogenic vasculature hampers the diagnostic effectiveness of perfusion parameters. In this regard, quantification of the heterogeneity of flow may provide a relevant additional feature for localizing angiogenesis. Statistics based on flow magnitude as well as its orientation can be exploited for this purpose. In this paper, we estimate the microbubble velocity fields from a standard bolus injection and provide a first statistical characterization by performing a spatial entropy analysis. By testing the method on 24 patients with biopsy-proven prostate cancer, we show that the proposed method can be applied effectively to clinically acquired DCE-US data. The method permits estimation of the in-plane flow vector fields and their local intricacy, and yields promising results (receiver-operating-characteristic curve area of 0.85) for the detection of prostate cancer.

A new automated quantification algorithm for the detection and evaluation of focal liver lesions with contrast-enhanced ultrasound

PURPOSE

Detect and classify focal liver lesions (FLLs) from contrast-enhanced ultrasound (CEUS) imaging by means of an automated quantification algorithm.

METHODS

The proposed algorithm employs a sophisticated segmentation method to detect and contour focal lesions from 52 CEUS video sequences (30 benign and 22 malignant). Lesion detection involves wavelet transform zero crossings utilization as an initialization step to the Markov random field model toward the lesion contour extraction. After FLL detection across frames, time intensity curve (TIC) is computed which provides the contrast agents' behavior at all vascular phases with respect to adjacent parenchyma for each patient. From each TIC, eight features were automatically calculated and employed into the support vector machines (SVMs) classification algorithm in the design of the image analysis model.

RESULTS

With regard to FLLs detection accuracy, all lesions detected had an average overlap value of 0.89 ± 0.16 with manual segmentations for all CEUS frame-subsets included in the study. Highest classification accuracy from the SVM model was 90.3%, misdiagnosing three benign and two malignant FLLs with sensitivity and specificity values of 93.1% and 86.9%, respectively.

CONCLUSIONS

The proposed quantification system that employs FLLs detection and classification algorithms may be of value to physicians as a second opinion tool for avoiding unnecessary invasive procedures.

Ultrasound perfusion analysis combining bolus-tracking and burst-replenishment

A new signal model and processing method for quantitative ultrasound perfusion analysis is presented, called bolus-and-burst. The method has the potential to provide absolute values of blood flow, blood volume, and mean transit time. Furthermore, it provides an estimate of the local arterial input function which characterizes the arterial tree, allowing accurate estimation of the bolus arrival time. The method combines two approaches to ultrasound perfusion analysis: bolus-tracking and burst-replenishment. A pharmacokinetic model based on the concept of arterial input functions and tissue residue functions is used to model both the bolus and replenishment parts of the recording. The pharmacokinetic model is fitted to the data using blind deconvolution. A preliminary assessment of the new perfusion-analysis method is presented on clinical recordings.

Contrast-enhanced ultrasound diagnosis of splenic artery steal syndrome after orthotopic liver transplantation

The aim of this study was to investigate the use of contrast-enhanced ultrasound (CEUS) for the detection of splenic artery steal syndrome (SASS) after orthotopic liver transplantation (OLT). Two hundred forty-seven patients underwent OLT. Blood tests and color Doppler flow imaging (CDFI) were performed at various time points after the operation. CEUS and celiac angiography were used for patients suspected of having SASS. If the diagnosis of SASS was confirmed, splenic artery embolization was performed to enhance hepatic artery flow. CEUS and angiography were performed for the assessment of postinterventional clinical outcomes. Three of the 247 patients died postoperatively, and 8 patients were suspected of having SASS because of elevated liver enzyme levels and slim or undetectable hepatic artery blood signals by CDFI at various points after the operation. In these 8 patients, CEUS showed a delayed and weak contrast-enhanced blood signal in the hepatic artery associated with a rapid and intense enhancement of the portal vein blood. No narrowing of the hyperintense signal was observed in the hepatic artery by CEUS. The 8 diagnoses of SASS were proven by celiac angiography, which showed delayed perfusion of the hepatic artery and rapid filling of the splenic artery. Immediately after the interventional procedure, CEUS demonstrated a significantly enlarged hyperintense blood signal in the hepatic artery. In conclusion, approximately 3.27% of SASS cases occur after OLT. SASS can be identified as a sluggish and weak hyperintense blood signal in the hepatic artery without the narrowing and interruption of the hypointense signal in CEUS imaging. CEUS is an effective imaging modality for the detection of SASS after OLT.

An automatic respiratory gating method for the improvement of microcirculation evaluation: application to contrast-enhanced ultrasound studies of focal liver lesions

Contrast-enhanced ultrasound (CEUS), with the recent development of both contrast-specific imaging modalities and microbubble-based contrast agents, allows noninvasive quantification of microcirculation in vivo. Nevertheless, functional parameters obtained by modeling contrast uptake kinetics could be impaired by respiratory motion. Accordingly, we developed an automatic respiratory gating method and tested it on 35 CEUS hepatic datasets with focal lesions. Each dataset included fundamental mode and cadence contrast pulse sequencing (CPS) mode sequences acquired simultaneously. The developed method consisted in (1) the estimation of the respiratory kinetics as a linear combination of the first components provided by a principal components analysis constrained by a prior knowledge on the respiratory rate in the frequency domain, (2) the automated generation of two respiratory-gated subsequences from the CPS mode sequence by detecting end-of-inspiration and end-of-expiration phases from the respiratory kinetics. The fundamental mode enabled a more reliable estimation of the respiratory kinetics than the CPS mode. The k-means algorithm was applied on both the original CPS mode sequences and the respiratory-gated subsequences resulting in clustering maps and associated mean kinetics. Our respiratory gating process allowed better superimposition of manually drawn lesion contours on k-means clustering maps as well as substantial improvement of the quality of contrast uptake kinetics. While the quality of maps and kinetics was satisfactory in only 11/35 datasets before gating, it was satisfactory in 34/35 datasets after gating. Moreover, noise amplitude estimated within the delineated lesions was reduced from 62 ± 21 to 40 ± 10 (p < 0.01) after gating. These findings were supported by the low residual horizontal (0.44 ± 0.29 mm) and vertical (0.15 ± 0.16 mm) shifts found during manual motion correction of each respiratory-gated subsequence. The developed technique could be used as a basis for accurate quantification of perfusion parameters for the evaluation and follow-up of patients under antiangiogenic therapies.

Quantitative contrast-enhanced ultrasonographic analysis of perfusion in the kidneys, liver, pancreas, small intestine, and mesenteric lymph nodes in healthy cats

OBJECTIVE

To evaluate perfusion of abdominal organs in healthy cats by use of contrast-enhanced ultrasonography.

ANIMALS

10 young healthy anesthetized cats.

PROCEDURES

Contrast-enhanced ultrasonography of the liver, left kidney, pancreas, small intestine, and mesenteric lymph nodes was performed on anesthetized cats.

RESULTS

Typical perfusion patterns were found for each of the studied organs. Differences in perfusion among organs were associated with specific physiologic features. The liver was enhanced gradually and had a more heterogeneous perfusion pattern because of its dual blood supply and close proximity to the diaphragm, compared with other organs. An obvious and significant difference in perfusion was detected between the renal cortex and medulla. No significant differences in perfusion were detected among the pancreas, small intestine, and mesenteric lymph nodes.

CONCLUSIONS AND CLINICAL RELEVANCE

Results indicated that contrast-enhanced ultrasonography can be used in cats to estimate organ perfusion as in other species. Observed differences in perfusion variables can be mostly explained by physiologic differences in vascularity.

Indicator dilution models for the quantification of microvascular blood flow with bolus administration of ultrasound contrast agents

Indicator dilution methods have a long history in the quantification of both macro- and microvascular blood flow in many clinical applications. Various models have been employed in the past to isolate the primary pass of an indicator after an intravenous bolus injection. The use of indicator dilution techniques allows for the estimation of hemodynamic parameters of a tumor or organ and thus may lead to useful diagnostic and therapy monitoring information. In this paper, we review and discuss the properties of the lognormal function, the gamma variate function, the diffusion with drift models, and the lagged normal function, which have been used to model indicator dilution curves in different fields of medicine. We fit these models to contrast-enhanced ultrasound time-intensity curves from liver metastases and the ovine corpora lutea. We evaluate the models' performance on the image data and compare their predictions for hemodynamic-related parameters such as the area under the curve, the mean transit time, the full-width at half-maximum, the time to the peak intensity, and wash-in time. The models that best fit the experimental data are the lognormal function and the diffusion with drift.