Ultrasound-guided targeted biopsies of CT-based radiomic tumour habitats: technical development and initial experience in metastatic ovarian cancer.

PURPOSE: To develop a precision tissue sampling technique that uses computed tomography (CT)-based radiomic tumour habitats for ultrasound (US)-guided targeted biopsies that can be integrated in the clinical workflow of patients with high-grade serous ovarian cancer (HGSOC). METHODS: Six patients with suspected HGSOC scheduled for US-guided biopsy before starting neoadjuvant chemotherapy were included in this prospective study from September 2019 to February 2020. The tumour segmentation was performed manually on the pre-biopsy contrast-enhanced CT scan. Spatial radiomic maps were used to identify tumour areas with similar or distinct radiomic patterns, and tumour habitats were identified using the Gaussian mixture modelling. CT images with superimposed habitat maps were co-registered with US images by means of a landmark-based rigid registration method for US-guided targeted biopsies. The dice similarity coefficient (DSC) was used to assess the tumour-specific CT/US fusion accuracy. RESULTS: We successfully co-registered CT-based radiomic tumour habitats with US images in all patients. The median time between CT scan and biopsy was 21 days (range 7-30 days). The median DSC for tumour-specific CT/US fusion accuracy was 0.53 (range 0.79 to 0.37). The CT/US fusion accuracy was high for the larger pelvic tumours (DSC: 0.76-0.79) while it was lower for the smaller omental metastases (DSC: 0.37-0.53). CONCLUSION: We developed a precision tissue sampling technique that uses radiomic habitats to guide in vivo biopsies using CT/US fusion and that can be seamlessly integrated in the clinical routine for patients with HGSOC. KEY POINTS: • We developed a prevision tissue sampling technique that co-registers CT-based radiomics-based tumour habitats with US images. • The CT/US fusion accuracy was high for the larger pelvic tumours (DSC: 0.76-0.79) while it was lower for the smaller omental metastases (DSC: 0.37-0.53).

Photoacoustic-ultrasonic dual-mode microscopy with local speed-of-sound estimation.

Synthetic aperture imaging and virtual point detection have been exploited to extend the depth of view of photoacoustic microscopy. The approach is commonly based on a constant assumed sound speed, which reduces image quality. We propose a new, to the best of our knowledge, self-adaptive technique to estimate the speed of sound when integrated with this hybrid strategy. It is accomplished through linear regression between the square of time of flight detected at individual virtual detectors and the square of their horizontal distances on the focal plane. The imaging results show our proposed method can significantly improve the lateral resolution, imaging intensity, and spatial precision for inhomogeneous tissue.

Improvements to ultrasonic beamformer design and implementation derived from the task-based analytical framework.

The task-based framework, previously developed for beamformer comparison [Nguyen, Prager, and Insana, J. Acoust. Soc. Am. 140, 1048-1059 (2016)], is extended to design a new beamformer with potential applications in breast cancer diagnosis. The beamformer is based on a better approximation of the Bayesian strategy. It is a combination of the Wiener-filtered beamformer and an iterative process that adapts the generated image to specific features of the object. Through numerical studies, the new method is shown to outperform other beamformers drawn from the framework, but at an increase in computational cost. It requires a preprocessing step where the scattering field is segmented into regions with distinct statistical properties. Segmentation errors become a major limitation to the beamformer performance. All the beamformers under investigation are tested using data obtained from an instrumented ultrasound machine. They are implemented using a new time delay calculation, recently developed in the pixel-based beamforming studies presented here, which helps to overcome the challenge posed by the shift-variant nature of the imaging system. The efficacy of each beamformer is evaluated based on the quality of generated images in the context of the task-based framework. The in vitro results confirm the conclusions drawn from the simulations.

A task-based analytical framework for ultrasonic beamformer comparison.

A task-based approach is employed to develop an analytical framework for ultrasound beamformer design and evaluation. In this approach, a Bayesian ideal-observer provides an idealized starting point and a way to measure information loss in practical beamformer designs. Different approximations of this ideal strategy are shown to lead to popular beamformers in the literature, including the matched filter, minimum variance (MV), and Wiener filter (WF) beamformers. Analysis of the approximations indicates that the WF beamformer should outperform the MV approach, especially in low echo signal-to-noise conditions. The beamformers are applied to five typical tasks from the BIRADS lexicon. Their performance is evaluated based on ability to discriminate idealized malignant and benign features. The numerical results show the advantages of the WF over the MV technique in general; although performance varies predictably in some contrast-limited tasks because of the model modifications required for the MV algorithm to avoid ill-conditioning.

Pixel-based approach to delay multiply and sum beamforming in combination with Wiener filter for improving ultrasound image quality.

Unified pixel-based (PB) beamforming has been implemented for ultrasound imaging, offering significant enhancements in lateral resolution compared to the conventional dynamic focusing. However, it still suffers from clutter and off-axis artifacts, limiting the contrast resolution. This paper proposes an efficient method to improve image quality by integrating filtered delay multiply and sum (F-DMAS) into the framework. This hybrid strategy incorporates the spatial coherence of the received data into the beamforming process to improve contrast resolution and clutter rejection in the generated image. We also integrate a Wiener filter to suppress the spatiotemporal spreading using signals echoed from a single scatterer at the transmit focus as a kernel for the deconvolution. The Wiener filter is applied to the received waveforms before performing the hybrid strategy. The Wiener filter is shown to reduce interference due to the interaction between the excitation pulse and the transfer functions of the transducer elements, thus benefiting the axial resolution of the generated images. We validate the proposed method and compare it with other beamforming strategies through a series of experiments, including simulation, phantom, and in vivo studies. The results show that our approach can substantially improve both spatial resolution and contrast over the unified PB algorithm, while still maintaining the good features of this beamformer. The simplicity and good performance of our method show its potential for use in clinical applications.

Survival After Resection for Epidermoid Cyst-originated Squamous Cell Carcinoma of the Liver: Case Report and Review of the Literature.

Epidermoid cyst is a rare type of congenital hepatic cyst. Reports on squamous cell carcinoma (SCC) of the liver are very limited and mostly have poor outcomes. We present a 45-year-old woman who came to our hospital due to obstructive jaundice caused by an epidermoid cyst-originated SCC. She underwent radical resection and lived for two years without signs of recurrence. The presentation, treatment, pathological results, as well as literature review, are provided below.

Improved ultrasound image quality with pixel-based beamforming using a Wiener-filter and a SNR-dependent coherence factor.

Pixel-based beamforming generates focused data by assuming that the waveforms received on a linear transducer array are composed of spherical pulses. It does not take into account the spatiotemporal spread in the data from the length of the excitation pulse or from the transfer functions of the transducer elements. As a result, these beamformers primarily have impacts on lateral, rather than axial, resolution. This paper proposes an efficient method to improve the axial resolution for pixel-based beamforming. We extend our field pattern analysis and show that the received waveforms should be passed through a Wiener filter before being used in the coherent pixel-based beamformer. This filter is designed based on signals echoed from a single scatterer at the transmit focus. The beamformer output is then combined with a coherence factor, that is adaptive to the signal-to-noise ratio, to improve the image contrast and suppress artifacts that have arisen during the filtering process. We validate the proposed method and compare it with other beamforming strategies using a series of experiments, including simulation, phantom and in vivo studies. It is shown to offer significant improvements in axial resolution and contrast over coherent pixel-based beamforming, as well as other spatial filters derived from synthetic aperture imaging. The method also demonstrates robustness to modeling errors in the experimental data. Overall, the imaging results show that the proposed approach has the potential to be of value in clinical applications.

A Spatial Coherence Approach to Minimum Variance Beamforming for Plane-Wave Compounding.

A new approach to implement minimum variance distortionless response (MVDR) beamforming is introduced for coherent plane-wave compounding (CPWC). MVDR requires the covariance matrix of the incoming signal to be estimated and a spatial smoothing approximation is usually adopted to prevent this calculation from being underconstrained. In the new approach, we analyze MVDR as a spatial filter that decorrelates signals received at individual channels before summation. Based on the analysis, we develop two MVDR beamformers without using any spatial smoothing. First, MVDR weights are applied to the received signals after accumulating the data over transmits at different angles, while the second involves weighting the data collected in individual transmits and compounding over the transducer elements. In both cases, the covariance matrix is estimated using a set of slightly different combinations of the echo data. We show the sufficient statistic for this estimation that can be described by approximating the correlation among the backscattered ultrasound signals to their spatial coherence. Using the van Cittert-Zernike theorem, their statistical similarity is assessed by relating the spatial coherence to the profile of the source intensity. Both spatial-coherence-based MVDR beamformers are evaluated on data sets acquired from simulation, phantom, and in vivo studies. Imaging results show that they offer improvements over simple coherent compounding in terms of spatial and contrast resolutions. They also outperform other existing MVDR-based methods in the literature that are applied to CPWC.

Minimum Variance Approaches to Ultrasound Pixel-Based Beamforming.

We analyze the principles underlying minimum variance distortionless response (MVDR) beamforming in order to integrate it into a pixel-based algorithm. There is a challenge posed by the low echo signal-to-noise ratio (eSNR) when calculating beamformer contributions at pixels far away from the beam centreline. Together with the well-known scarcity of samples for covariance matrix estimation, this reduces the beamformer performance and degrades the image quality. To address this challenge, we implement the MVDR algorithm in two different ways. First, we develop the conventional minimum variance pixel-based (MVPB) beamformer that performs the MVDR after the pixel-based superposition step. This involves a combination of methods in the literature, extended over multiple transmits to increase the eSNR. Then we propose the coherent MVPB beamformer, where the MVDR is applied to data within individual transmits. Based on pressure field analysis, we develop new algorithms to improve the data alignment and matrix estimation, and hence overcome the low-eSNR issue. The methods are demonstrated on data acquired with an ultrasound open platform. The results show the coherent MVPB beamformer substantially outperforms the conventional MVPB in a series of experiments, including phantom and in vivo studies. Compared to the unified pixel-based beamformer, the newest delay-and-sum algorithm in [1], the coherent MVPB performs well on regions that conform to the diffuse scattering assumptions on which the minimum variance principles are based. It produces less good results for parts of the image that are dominated by specular reflections.

Ultrasound Pixel-Based Beamforming With Phase Alignments of Focused Beams.

We previously developed unified pixel-based (PB) beamforming to generate high-resolution sonograms, based on field pattern analysis. In this framework, we found that the transmit waveshape away from the focus could be characterized by two spherical pulses. These correspond to the maximal and minimal distances from the imaging point to the active aperture. The beamformer uses this model to select the highest energy signals from backscattered data. A spatiotemporal interpolation formula is used to provide a smooth transition in regions near the focal depth where there is no dominant reflected pulse. In this paper, we show that the unified PB approach is less robust at lower center frequencies. The interpolated data is suboptimal for a longer transmit waveshape. As a result, the spatial resolution at the focal depth is lower than that in other regions. By further exploring the field pattern, we propose a beamformer that is more robust to variations in beamwidth. The new method, named coherent PB beamforming, aligns and compounds the pulse data directly in the transition regions. In simulation and phantom studies, the coherent PB approach is shown to outperform the unified PB approach in spatial resolution. It helps regain optimal resolution at the focal depth while still maintaining good image quality in other regions. We also demonstrate the new method on in vivo data where its improvements over the unified PB method are demonstrated on scanned objects with a more complicated structure.

High-Resolution Ultrasound Imaging With Unified Pixel-Based Beamforming.

This paper describes the development and evaluation of a new beamforming strategy based on pixel-based focusing for ultrasound linear array systems. We first implement conventional pixel-based beamforming in which the transmitted wave is assumed as spherical and diverging from the centre of the transmit subaperture. This assumed wave-shape is only valid within a limited angle on each side of the beam and this restricts the number of different subaperture positions from which data can be combined to improve image quality. By analyzing the field patterns, we propose a new unified pixel-based beamforming algorithm that better adapts to the non-spherical wave-shape of the transmit beam. This approach enables us to select the best-possible signal from each transducer waveform for data superposition. In simulations and a phantom study, we show that the unified pixel-based beamformer offers significant improvements in image quality compared to other delay-and-sum methods but at a higher computational cost. The new algorithm also demonstrates robust performance in a limited in vivo study. Overall, the results show that it is potentially of value in clinical applications.

Objective assessment of sonographic: quality II acquisition information spectrum.

This paper describes a task-based, information-theoretic approach to the assessment of image quality in diagnostic sonography. We expand the Kullback-Leibler divergence metric J, which quantifies the diagnostic information contained within recorded radio-frequency echo signals, into a spatial-frequency integral comprised of two spectral components: one describes patient features for low-contrast diagnostic tasks and the other describes instrumentation properties. The latter quantity is the acquisition information spectrum (AIS), which measures the density of object information that an imaging system is able to transfer to the echo data at each spatial frequency. AIS is derived based on unique properties of acoustic scattering in tissues that generate object contrast. Predictions made by the J integral expression were validated through Monte Carlo studies using echo-signal data from simulated lesions. Our analysis predicts the diagnostic performance of any sonographic system at specific diagnostic tasks based on engineering properties of the instrument that constitute image quality.

Objective assessment of sonographic quality I: task information.

In this paper, we explore relationships between the performance of the ideal observer and information-based measures of class separability in the context of sonographic breast-lesion diagnosis. This investigation was motivated by a finding that, since the test statistic of the ideal observer in sonography is a quadratic function of the echo data, it is not generally normally distributed. We found for some types of boundary discrimination tasks often required for sonographic lesion diagnosis, the deviation of the test statistic from a normal distribution can be significant. Hence the usual relationships between performance and information metrics become uncertain. Using Monte Carlo studies involving five common sonographic lesion-discrimination tasks, we found in each case that the detectability index d(A)(2) from receiver operating characteristic analysis was well approximated by the Kullback-Leibler divergence J, a measure of clinical task information available from the recorded radio-frequency echo data. However, the lesion signal-to-noise ratio, SNR(I)(2), calculated from moments of the ideal observer test statistic, consistently underestimates d(A)(2) for high-contrast boundary discrimination tasks. Thus, in a companion paper, we established a relationship between image-quality properties of the imaging system and J in order to predict ideal performance. These relationships provide a rigorous basis for sonographic instrument evaluation and design.

Acquisition information spectrum for evaluating sonographic quality.

We are developing a first-principles task-based approach to the optimal design and evaluation of ultrasonic imaging systems. Examining five clinical features related to breast lesion diagnosis, we quantified information flow at several stages in the image formation process. We found that the diagnostic performance of a given system configuration will vary with the patient feature, sometimes significantly. Our analysis expresses diagnostic performance of an imaging system for a specific clinical task as a function of patient properties that are separable from instrument properties. Hence it is possible to show how image quality metrics, like spatial and contrast resolution, combine with patient features to determine feature discriminability. In this paper, we describe an information theoretic approach to diagnostic performance evaluation that has given us a new quantity, the acquisition information spectrum (AIS). Like NEQ in radiography, AIS in sonography provides a foundation for medical ultrasonic imaging system design.

Effects of frequency and bandwidth on diagnostic information transfer in ultrasonic B-mode imaging.

Transmitted pressure pulses in ultrasonic B-mode imaging systems are commonly characterized by their center frequency and bandwidth. Both parameters are associated with tradeoffs in spatial resolution and signal-to-noise in ultrasonic system design, with no general understanding of where they are optimal when applied to specific clinical exams. We use the ideal observer and simple psychophysical studies with human observers to evaluate the efficiency of information transfer in B-mode imaging as a function of the transmitted pulse center frequency and fractional bandwidth. Our approach uses a statistical model of backscatter relevant to breast imaging, and a 2-D model of pulse propagation based on Rayleigh-Sommerfeld diffraction theory. The statistics of the backscattered signal are combined in an ideal observer calculation that quantifies the task-relevant information contained in the radio-frequency (RF) signal after delay-and-sum beamforming. This is followed by a psychophysical evaluation of observer performance on B-mode envelope-detected images in three simple tasks. This experimental design allows us to track the flow of diagnostic information through RF acquisition and subsequent reading of the envelope image. In a low-contrast detection task and a high-contrast boundary discrimination task, optimal efficiency for human observers is observed at the highest center frequencies tested (15 MHz) and at moderate bandwidth (40%). For detection of scattering material in a high-contrast hypoechoic lesion, optimal efficiency was observed at lower center frequencies (5 MHz) and higher bandwidth (80%). The ideal observer analysis shows that this task dependence does not arise in the acquisition stage, where efficiency is maximized at 15 MHz with bandwidths of 60% or greater, but rather in the subsequent processing and reading of the envelope image. In addition, at higher frequencies more information is lost in the processing and reading than in the acquisition of reflected signals.

An ideal observer approach to mechanical limits in b-mode ultrasound imaging.

In medical applications, the amplitude of ultrasonic pulses is often constrained by mechanical considerations summarized by a mechanical index. We apply the ideal observer approach in a simulation environment to evaluating the role of mechanical index limits on task performance in b-mode ultrasonic imaging. We simulate a linear array operating at 15 MHz and 60% fractional bandwidth, and consider three tasks related to breast sonography at a depth of 4 cm. The ideal observer suggests that there are gains in performance--and hence the quantity of diagnostic information--as the limit on mechanical index is raised. However these gains are almost completely erased after computation of a standard b-mode envelope.

An adaptive filter to approximate the Bayesian strategy for sonographic beamforming.

A first-principles task-based approach to the design of medical ultrasonic imaging systems for breast lesion discrimination is described. This study explores a new approximation to the ideal Bayesian observer strategy that allows for object heterogeneity. The new method, called iterative Wiener filtering, is implemented using echo data simulations and a phantom study. We studied five lesion features closely associated with visual discrimination for clinical diagnosis. A series of human observer measurements for the same image data allowed us to quantitatively compare alternative beamforming strategies through measurements of visual discrimination efficiency. Employing the Smith-Wagner model observer, we were able to breakdown efficiency estimates and identify the processing stage at which performance losses occur. The methods were implemented using a commercial scanner and a cyst phantom to explore development of spatial filters for systems with shift-variant impulse response functions. Overall we found that significant improvements were realized over standard B-mode images using a delay-and-sum beamformer but at the cost of higher complexity and computational load.

Optimal beamforming in ultrasound using the ideal observer.

Beamforming of received pulse-echo data generally involves the compression of signals from multiple channels within an aperture. This compression is irreversible, and therefore allows the possibility that information relevant for performing a diagnostic task is irretrievably lost. The purpose of this study was to evaluate information transfer in beamforming using a previously developed ideal observer model to quantify diagnostic information relevant to performing a task. We describe an elaborated statistical model of image formation for fixed-focus transmission and single-channel reception within a moving aperture, and we use this model on a panel of tasks related to breast sonography to evaluate receive-beamforming approaches that optimize the transfer of information. Under the assumption that acquisition noise is well described as an additive wide-band Gaussian white-noise process, we show that signal compression across receive-aperture channels after a 2-D matched-filtering operation results in no loss of diagnostic information. Across tasks, the matched-filter beamformer results in more information than standard delay-and-sum beamforming in the subsequent radio-frequency signal by a factor of two. We also show that for this matched filter, 68% of the information gain can be attributed to the phase of the matched-filter and 21% can be attributed to the amplitude. A 1-D matched filtering along axial lines shows no advantage over delay-andsum, suggesting an important role for incorporating correlations across different aperture windows in beamforming. We also show that a post-compression processing before the computation of an envelope is necessary to pass the diagnostic information in the beamformed radio-frequency signal to the final envelope image.