In the last 30 years, the contrast-to-noise ratio (CNR) has been used to estimate the contrast and lesion detectability in ultrasound images. Recent studies have shown that the CNR cannot be used with modern beamformers, as dynamic range alterations can produce arbitrarily high CNR values with no real effect on the probability of lesion detection. We generalize the definition of CNR based on the overlap area between two probability density functions. This generalized CNR (gCNR) is robust against dynamic range alterations; it can be applied to all kind of images, units, or scales; it provides a quantitative measure for contrast; and it has a simple statistical interpretation, i.e., the success rate that can be expected from an ideal observer at the task of separating pixels. We test gCNR on several state-of-the-art imaging algorithms and, in addition, on a trivial compression of the dynamic range. We observe that CNR varies greatly between the state-of-the-art methods, with improvements larger than 100%. We observe that trivial compression leads to a CNR improvement of over 200%. The proposed index, however, yields the same value for compressed and uncompressed images. The tested methods showed mismatched performance in terms of lesion detectability, with variations in gCNR ranging from -0.08 to +0.29. This new metric fixes a methodological flaw in the way we study contrast and allows us to assess the relevance of new imaging algorithms.

Conventional ultrasound images are formed by delay-and-sum beamforming of the backscattered echoes received by individual elements of the transducer aperture. Although the delay-and-sum beamformer is well suited for ultrasound image formation, it is corrupted by speckle noise and challenged by acoustic clutter and phase aberration. We propose an alternative method of imaging utilizing the short-lag spatial coherence (SLSC) of the backscattered echoes. Compared with matched B-mode images, SLSC images demonstrate superior SNR and contrast-to-noise ratio in simulated and experimental speckle-generating phantom targets, but are shown to be challenged by limited point target conspicuity. Matched B-mode and SLSC images of a human thyroid are presented. The challenges and opportunities of real-time implementation of SLSC imaging are discussed.

Interventional applications of photoacoustic imaging typically require visualization of point-like targets, such as the small, circular, cross-sectional tips of needles, catheters, or brachytherapy seeds. When these point-like targets are imaged in the presence of highly echogenic structures, the resulting photoacoustic wave creates a reflection artifact that may appear as a true signal. We propose to use deep learning techniques to identify these type of noise artifacts for removal in experimental photoacoustic data. To achieve this goal, a convolutional neural network (CNN) was first trained to locate and classify sources and artifacts in pre-beamformed data simulated with k-Wave. Simulations initially contained one source and one artifact with various medium sound speeds and 2D target locations. Based on 3,468 test images, we achieved a 100% success rate in classifying both sources and artifacts. After adding noise to assess potential performance in more realistic imaging environments, we achieved at least 98% success rates for channel signal-to-noise ratios (SNRs) of -9dB or greater, with a severe decrease in performance below -21dB channel SNR. We then explored training with multiple sources and two types of acoustic receivers and achieved similar success with detecting point sources. Networks trained with simulated data were then transferred to experimental waterbath and phantom data with 100% and 96.67% source classification accuracy, respectively (particularly when networks were tested at depths that were included during training). The corresponding mean ± one standard deviation of the point source location error was 0.40 ± 0.22 mm and 0.38 ± 0.25 mm for waterbath and phantom experimental data, respectively, which provides some indication of the resolution limits of our new CNN-based imaging system. We finally show that the CNNbased information can be displayed in a novel artifact-free image format, enabling us to effectively remove reflection artifacts from photoacoustic images, which is not possible with traditional geometry-based beamforming.

Single plane wave transmissions are promising for automated imaging tasks requiring high ultrasound frame rates over an extended field of view. However, a single plane wave insonification typically produces suboptimal image quality. To address this limitation, we are exploring the use of deep neural networks (DNNs) as an alternative to delay-and-sum (DAS) beamforming. The objectives of this work are to obtain information directly from raw channel data and to simultaneously generate both a segmentation map for automated ultrasound tasks and a corresponding ultrasound B-mode image for interpretable supervision of the automation. We focus on visualizing and segmenting anechoic targets surrounded by tissue and ignoring or deemphasizing less important surrounding structures. DNNs trained with Field II simulations were tested with simulated, experimental phantom, and in vivo data sets that were not included during training. With unfocused input channel data (i.e., prior to the application of receive time delays), simulated, experimental phantom, and in vivo test data sets achieved mean ± standard deviation Dice similarity coefficients of 0.92 ± 0.13, 0.92 ± 0.03, and 0.77 ± 0.07, respectively, and generalized contrast-to-noise ratios (gCNRs) of 0.95 ± 0.08, 0.93 ± 0.08, and 0.75 ± 0.14, respectively. With subaperture beamformed channel data and a modification to the input layer of the DNN architecture to accept these data, the fidelity of image reconstruction increased (e.g., mean gCNR of multiple acquisitions of two in vivo breast cysts ranged 0.89-0.96), but DNN display frame rates were reduced from 395 to 287 Hz. Overall, the DNNs successfully translated feature representations learned from simulated data to phantom and in vivo data, which is promising for this novel approach to simultaneous ultrasound image formation and segmentation.

Clutter is a noise artifact in ultrasound images that appears as diffuse echoes overlying signals of interest. It is most easily observed in anechoic or hypoechoic regions, such as in cysts, blood vessels, amniotic fluid, and urine-filled bladders. Clutter often obscures targets of interest and complicates anatomical measurements. An analytical expression that characterizes the extent to which clutter degrades lesion contrast was derived and compared to the measured contrast loss due to clutter in a bladder phantom. Simulation and phantom studies were performed to determine ideal and achievable signal-to-clutter ratios. In vivo clutter magnitudes were quantified in simultaneously-acquired fundamental and harmonic bladder images from five volunteers. Clutter magnitudes ranged from −30 dB to 0 dB, relative to the mean signal of the bladder wall. For this range of clutter magnitudes, the analytical expression predicts a contrast loss of 0–45 dB for lesions with clutter-free contrasts of 6–48 dB. A pixel-wise comparison of simultaneously-acquired fundamental and harmonic bladder images from each volunteer revealed an overall signal reduction in harmonic images, with average reductions ranging from 11–18 dB in the bladder interior and 9–11 dB in the tissue surrounding the bladder. Harmonic imaging did not reduce clutter in all volunteers.

We conducted a canine study to investigate the in vivo feasibility of photoacoustic imaging for intraoperative updates to brachytherapy treatment plans. A fiber coupled to a 1064-nm Nd:YAG laser was inserted into high-dose-rate brachytherapy needles, which diffused light spherically. These needles were inserted through the perineum into the prostate for interstitial light delivery and the resulting acoustic waves were detected with a transrectal ultrasound probe. Postoperative computed tomography images and ex vivo photoacoustic images confirmed seed locations. Limitations with insufficient light delivery were mitigated with short-lag spatial coherence (SLSC) beamforming, providing a 10–20 dB contrast improvement over delay-and-sum (DAS) beamforming for pulse energies ranging from 6.8 to 10.5 mJ with a fiber-seed distance as large as 9.5 mm. For the same distance and the same range of energy densities, signal-to-noise ratios (SNRs) were similar while the contrast-to-noise ratio (CNR) was higher in SLSC compared to DAS images. Challenges included visualization of signals associated with the interstitial fiber tip and acoustic reverberations between seeds separated by ≤2 mm. Results provide insights into the potential for clinical translation to humans.