Multi-frequency acoustic hologram generation with a physics-enhanced deep neural network.

Here, a physics-enhanced multi-frequency acoustic hologram deep neural network (PhysNet_MFAH) method is proposed for designing multi-frequency acoustic holograms, which is built by incorporating multiple physical models that represent the physical processes of acoustic waves propagation for a set of design frequencies into a deep neural network. It is demonstrated that one needs only to feed a set of frequency-specific target patterns into the network, the proposed PhysNet_MFAH method can automatically, accurately, and rapidly generate a high-quality multi-frequency acoustic hologram for holographic rendering of different target acoustic fields in the same or distinct regions of the target plane when driven at different frequencies. Remarkably, it is also demonstrated that the proposed PhysNet_MFAH method can achieve a higher quality of the reconstructed acoustic intensity fields than the existing optimization methods IASA and DS for designing multi-frequency acoustic holograms at a relatively fast-computational speed. Furthermore, the performance dependencies of the proposed PhysNet_MFAH method on different design parameters are established, which provide insight into the performance of the reconstructed acoustic intensity fields when subject to different design conditions of the proposed PhysNet_MFAH method. We believe that the proposed PhysNet_MFAH method can facilitate many potential applications of acoustic holograms, ranging from dynamic particle manipulation to volumetric display.

Glioma survival prediction from whole-brain MRI without tumor segmentation using deep attention network: a multicenter study.

OBJECTIVES: To develop and validate a deep learning model for predicting overall survival from whole-brain MRI without tumor segmentation in patients with diffuse gliomas. METHODS: In this multicenter retrospective study, two deep learning models were built for survival prediction from MRI, including a DeepRisk model built from whole-brain MRI, and an original ResNet model built from expert-segmented tumor images. Both models were developed using a training dataset (n = 935) and an internal tuning dataset (n = 156) and tested on two external test datasets (n = 194 and 150) and a TCIA dataset (n = 121). C-index, integrated Brier score (IBS), prediction error curves, and calibration curves were used to assess the model performance. RESULTS: In total, 1556 patients were enrolled (age, 49.0 ± 13.1 years; 830 male). The DeepRisk score was an independent predictor and can stratify patients in each test dataset into three risk subgroups. The IBS and C-index for DeepRisk were 0.14 and 0.83 in external test dataset 1, 0.15 and 0.80 in external dataset 2, and 0.16 and 0.77 in TCIA dataset, respectively, which were comparable with those for original ResNet. The AUCs at 6, 12, 24, 26, and 48 months for DeepRisk ranged between 0.77 and 0.94. Combining DeepRisk score with clinicomolecular factors resulted in a nomogram with a better calibration and classification accuracy (net reclassification improvement 0.69, p < 0.001) than the clinical nomogram. CONCLUSIONS: DeepRisk that obviated the need of tumor segmentation can predict glioma survival from whole-brain MRI and offers incremental prognostic value. KEY POINTS: • DeepRisk can predict overall survival directly from whole-brain MRI without tumor segmentation. • DeepRisk achieves comparable accuracy in survival prediction with deep learning model built using expert-segmented tumor images. • DeepRisk has independent and incremental prognostic value over existing clinical parameters and IDH mutation status.

Shading correction for volumetric CT using deep convolutional neural network and adaptive filter.

BACKGROUND: Shading artifact may lead to CT number inaccuracy, image contrast loss and spatial non-uniformity (SNU), which is considered as one of the fundamental limitations for volumetric CT (VCT) application. To correct the shading artifact, a novel approach is proposed using deep learning and an adaptive filter (AF). METHODS: Firstly, we apply the deep convolutional neural network (DCNN) to train a human tissue segmentation model. The trained model is implemented to segment the tissue. According to the general knowledge that CT number of the same human tissue is approximately the same, a template image without shading artifact can be generated using segmentation and then each tissue is filled with the corresponding CT number of a specific tissue. By subtracting the template image from the uncorrected image, the residual image with image detail and shading artifact are generated. The shading artifact is mainly low-frequency signals while the image details are mainly high-frequency signals. Therefore, we proposed an adaptive filter to separate the shading artifact and image details accurately. Finally, the estimated shading artifacts are deleted from the raw image to generate the corrected image. RESULTS: On the Catphan©504 study, the error of CT number in the corrected image's region of interest (ROI) is reduced from 109 to 11 HU, and the image contrast is increased by a factor of 1.46 on average. On the patient pelvis study, the error of CT number in selected ROI is reduced from 198 to 10 HU. The SNU calculated from the ROIs decreases from 24% to 9% after correction. CONCLUSIONS: The proposed shading correction method using DCNN and AF may find a useful application in future clinical practice.

Noninvasive Ultrasonic Neuromodulation in Freely Moving Mice.

Neuromodulation is a fundamental method for obtaining basic information about neuronal circuits for use in treatments for neurological and psychiatric disorders. Ultrasound stimulation has become a promising approach for noninvasively inducing neuromodulation in animals and humans. However, the previous investigations were subject to substantial limitations, due to most of them involving anesthetized and fixed small-animal models. Studies of awake and freely moving animals are needed, but the currently used ultrasound devices are too bulky to be applied to a freely moving animal. This study is the first time to design and fabricate a miniature and lightweight head-mounted ultrasound stimulator for inducing neuromodulation in freely moving mice. The main components of the stimulator include a miniature piezoelectric ceramic, a concave epoxy acoustic lens, and housing and connection components. The device was able to induce action potentials recorded in situ and evoke head-turning behaviors by stimulating the primary somatosensory cortex barrel field of the mouse. These findings indicate that the proposed method can be used to induce noninvasive neuromodulation in freely moving mice. This novel method could potentially lead to the application of ultrasonic neuromodulation in more-extensive neuroscience investigations.

Imaging-Guided Dual-Target Neuromodulation of the Mouse Brain Using Array Ultrasound.

Neuromodulation is an important method for investigating neural circuits and treating neurological and psychiatric disorders. Multiple-target neuromodulation is considered an advanced technology for the flexible optimization of modulation effects. However, traditional methods such as electrical and magnetic stimulations are not convenient for multiple-target applications due to their disadvantages of invasiveness or poor spatial resolution. Ultrasonic neuromodulation is a new noninvasive method that has gained wide attention in the field of neuroscience, and it is potentially able to support multiple-target stimulation by allocating multiple focal points in the brain using an array transducer. However, there are no reports in the literature of the efficacy of this technical concept, and an imaging tool for localizing the stimulation area for evaluating the neural effects in vivo has been lacking. In this study, we designed and fabricated a new system specifically for imaging-guided dual-target neuromodulation. The design of the array transducer and overall system is described in detail. The stimulation points were selectable on a B-mode image. In vivo experiments were carried out in mice, in which forelimbs shaking responses and electromyography outcomes were induced by changing the stimulation targets. The system could be a valuable tool for imaging-guided multiple-target stimulation in various neuroscience applications.

Iterative image-domain ring artifact removal in cone-beam CT.

Ring artifacts in cone beam computed tomography (CBCT) images are caused by pixel gain variations using flat-panel detectors, and may lead to structured non-uniformities and deterioration of image quality. The purpose of this study is to propose a method of general ring artifact removal in CBCT images. This method is based on the polar coordinate system, where the ring artifacts manifest as stripe artifacts. Using relative total variation, the CBCT images are first smoothed to generate template images with fewer image details and ring artifacts. By subtracting the template images from the CBCT images, residual images with image details and ring artifacts are generated. As the ring artifact manifests as a stripe artifact in a polar coordinate system, the artifact image can be extracted by mean value from the residual image; the image details are generated by subtracting the artifact image from the residual image. Finally, the image details are compensated to the template image to generate the corrected images. The proposed framework is iterated until the differences in the extracted ring artifacts are minimized. We use a 3D Shepp-Logan phantom, Catphan©504 phantom, uniform acrylic cylinder, and images from a head patient to evaluate the proposed method. In the experiments using simulated data, the spatial uniformity is increased by 1.68 times and the structural similarity index is increased from 87.12% to 95.50% using the proposed method. In the experiment using clinical data, our method shows high efficiency in ring artifact removal while preserving the image structure and detail. The iterative approach we propose for ring artifact removal in cone-beam CT is practical and attractive for CBCT guided radiation therapy.

Improved Anatomical Specificity of Non-invasive Neuro-stimulation by High Frequency (5 MHz) Ultrasound.

Low frequency ultrasound (<1 MHz) has been demonstrated to be a promising approach for non-invasive neuro-stimulation. However, the focal width is limited to be half centimeter scale. Minimizing the stimulation region with higher frequency ultrasound will provide a great opportunity to expand its application. This study first time examines the feasibility of using high frequency (5 MHz) ultrasound to achieve neuro-stimulation in brain, and verifies the anatomical specificity of neuro-stimulation in vivo. 1 MHz and 5 MHz ultrasound stimulation were evaluated in the same group of mice. Electromyography (EMG) collected from tail muscles together with the motion response videos were analyzed for evaluating the stimulation effects. Our results indicate that 5 MHz ultrasound can successfully achieve neuro-stimulation. The equivalent diameter (ED) of the stimulation region with 5 MHz ultrasound (0.29 ± 0.08 mm) is significantly smaller than that with 1 MHz (0.83 ± 0.11 mm). The response latency of 5 MHz ultrasound (45 ± 31 ms) is also shorter than that of 1 MHz ultrasound (208 ± 111 ms). Consequently, high frequency (5 MHz) ultrasound can successfully activate the brain circuits in mice. It provides a smaller stimulation region, which offers improved anatomical specificity for neuro-stimulation in a non-invasive manner.

Determination of acquisition frequency for intrafractional motion of pancreas in CyberKnife radiotherapy.

PURPOSE: To report the characteristics of pancreas motion as tracked using implanted fiducials during radiotherapy treatments with CyberKnife. METHODS AND MATERIALS: Twenty-nine patients with pancreas cancer treated using CyberKnife system were retrospectively selected for this study. During the treatment, the deviation is examined every 3-4 nodes (~45 s interval) and compensated by the robot. The pancreas displacement calculated from X-ray images acquired within the time interval between two consecutive couch motions constitute a data set. RESULTS: A total of 498 data sets and 4302 time stamps of X-ray images were analyzed in this study. The average duration for each data set is 634 s. The location of the pancreas becomes more dispersed as the time elapses. The acquisition frequency depends on the prespecified movement distance threshold of pancreas. If the threshold between two consecutive images is 1 mm, the acquisition frequency should be less than 30 s, while if the threshold is 2 mm, the acquisition frequency can be around 1 min. CONCLUSIONS: The pancreas target moves significantly and unpredictably during treatment. Effective means of compensating the intrafractional movement is critical to ensure adequate dose coverage of the tumor target.

Fuzhengpaidu granule regulates immune activation molecules CD38 and human leukocyte antigen-D related on CD4+ and CD8+ T cells in patients with acquired immunodeficiency syndrome/human immunodeficiency virus.

OBJECTIVE: To evaluate the effect of Fuzhengpaidu granule (FZPDG) on immune activation molecules CD38 and human leukocyte antigen-D related (HLA-DR) on CD4+ and CD8+ cells in HIV/AIDS patients, and to explore the underlying mechanism of this therapy. METHODS: Plasma changes in CD3+, CD4+, CD8+, CD3 + CD4 + CD38 +, CD3 + CD4 + HLA-DR+, CD3 + CD8+CD38+, and CD3+CD8+HLA-DR+ levels in HIV/ AIDS patients treated with FZPDG for six months were examined by flow cytometry and compared with levels in healthy controls. RESULTS: The clinical trial included 34 outpatients with HIV/AIDS. Before treatment, plasma levels of CD38+ and HLA-DR+ on CD4/CD8 cells were higher than those in 28 health controls (P < 0.05). There were no significant changes in serum levels of CD3+, CD4+, and CD8+ T cells between pretreatment baseline versus after treatment, which were 82.85% +/- 5.41%, 14.57% +/- 10.31% and 54.55% +/- 11.43% before treatment and 79.15% +/- 8.21%, 19.96% +/- 9.58% and 56.36% +/- 11.67% after treatment, respectively (P > 0.05). Plasma levels of CD3+ CD4+CD38+ and CD3+CD4+HLA-DR+ were 2.3% +/-2.2% and 7.8% +/- 5.5% before treatment and 1.2% +/-0.8% and 2.6% +/- 1.0% after treatment, respectively. Plasma levels of CD3+CD8+CD38+ and CD3+CD8+ HLA-DR+ were 41.4% +/- 13.4% and 17.8% +/- 11.3% before treatment, which changed to 27.1% +/- 10.2% and 3.8% +/- 2.4% after treatment, respectively (P < 0.05). CONCLUSION: HIV/AIDS patients exhibited an immune activation profile following FZPDG treatment. A potential mechanism of action for FZPDG appears to lie in its ability to up-regulate CD38 and HLA-DR levels on CD4+ T cells, and down-regulate them on CD8+ cells, thereby modulating immune activation of CD4+and CD8+T cells.