Ultralow Power Cold-Fuse Memory Based on Metal-Oxide-CNT Structure.

One-time programmable (OTP) memory is an essential component in chips, which has extremely high security to protect the stored critical information from being altered. However, traditional OTP memory based on the thermal breakdown of the dielectric has a large programming current, which leads to high power consumption. Here, we report a gate tunneling-induced "cold" breakdown phenomenon in carbon nanotube (CNT) field-effect transistors, and based on this we construct a "cold" fuse (C-fuse) memory where applying a mild gate voltage can break down the CNT channel without damaging the gate dielectric.

Cross-sectional imaging of speed-of-sound distribution using photoacoustic reversal beacons.

Photoacoustic tomography (PAT) enables non-invasive cross-sectional imaging of biological tissues, but it fails to map the spatial variation of speed-of-sound (SOS) within tissues. While SOS is intimately linked to density and elastic modulus of tissues, the imaging of SOS distribution serves as a complementary imaging modality to PAT. Moreover, an accurate SOS map can be leveraged to correct for PAT image degradation arising from acoustic heterogeneities.

Organ-level instance segmentation enables continuous time-space-spectrum analysis of pre-clinical abdominal photoacoustic tomography images.

Photoacoustic tomography (PAT), as a novel biomedical imaging technique, is able to capture temporal, spatial and spectral tomographic information from organisms. Organ-level multi-parametric analysis of continuous PAT images are of interest since it enables the quantification of organ specific morphological and functional parameters in small animals. Accurate organ delineation is imperative for organ-level image analysis, yet the low contrast and blurred organ boundaries in PAT images pose challenge for their precise segmentation.

Spiral scanning and self-supervised image reconstruction enable ultra-sparse sampling multispectral photoacoustic tomography.

Multispectral photoacoustic tomography (PAT) is an imaging modality that utilizes the photoacoustic effect to achieve non-invasive and high-contrast imaging of internal tissues but also molecular functional information derived from multi-spectral measurements. However, the hardware cost and computational demand of a multispectral PAT system consisting of up to thousands of detectors are huge. To address this challenge, we propose an ultra-sparse spiral sampling strategy for multispectral PAT, which we named U3S-PAT.

Unsupervised adversarial neural network for enhancing vasculature in photoacoustic tomography images using optical coherence tomography angiography.

Photoacoustic tomography (PAT) is a powerful imaging modality for visualizing tissue physiology and exogenous contrast agents. However, PAT faces challenges in visualizing deep-seated vascular structures due to light scattering, absorption, and reduced signal intensity with depth. Optical coherence tomography angiography (OCTA) offers high-contrast visualization of vasculature networks, yet its imaging depth is limited to a millimeter scale.

Image reconstruction of multispectral sparse sampling photoacoustic tomography based on deep algorithm unrolling.

Photoacoustic tomography (PAT), as a novel medical imaging technology, provides structural, functional, and metabolism information of biological tissue . Sparse Sampling PAT, or SS-PAT, generates images with a smaller number of detectors, yet its image reconstruction is inherently ill-posed. Model-based methods are the state-of-the-art method for SS-PAT image reconstruction, but they require design of complex handcrafted prior.

A model-based MR parameter mapping network robust to substantial variations in acquisition settings.

Deep learning methods show great potential for the efficient and precise estimation of quantitative parameter maps from multiple magnetic resonance (MR) images. Current deep learning-based MR parameter mapping (MPM) methods are mostly trained and tested using data with specific acquisition settings. However, scan protocols usually vary with centers, scanners, and studies in practice.

Deep learning acceleration of iterative model-based light fluence correction for photoacoustic tomography.

Photoacoustic tomography (PAT) is a promising imaging technique that can visualize the distribution of chromophores within biological tissue. However, the accuracy of PAT imaging is compromised by light fluence (LF), which hinders the quantification of light absorbers. Currently, model-based iterative methods are used for LF correction, but they require extensive computational resources due to repeated LF estimation based on differential light transport models.

Unsupervised Fusion of Misaligned PAT and MRI Images via Mutually Reinforcing Cross-Modality Image Generation and Registration.

Photoacoustic tomography (PAT) and magnetic resonance imaging (MRI) are two advanced imaging techniques widely used in pre-clinical research. PAT has high optical contrast and deep imaging range but poor soft tissue contrast, whereas MRI provides excellent soft tissue information but poor temporal resolution. Despite recent advances in medical image fusion with pre-aligned multimodal data, PAT-MRI image fusion remains challenging due to misaligned images and spatial distortion.

Assessment of rhabdomyolysis-induced acute kidney injury with chemical exchange saturation transfer magnetic resonance imaging.

Background: Rhabdomyolysis (RM)-induced acute kidney injury (AKI) is a common renal disease with low survival rate and inadequate prognosis. In this study, we investigate the feasibility of chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) for assessing the progression of RM-induced AKI in a mouse model.

Methods: AKI was induced in C57BL/6J mice via intramuscular injection of 7.

CylinGCN: Cylindrical structures segmentation in 3D biomedical optical imaging by a contour-based graph convolutional network.

Cylindrical organs, e.g., blood vessels, airways, and intestines, are ubiquitous structures in biomedical optical imaging analysis.

A new 3D phase unwrapping method by region partitioning and local polynomial modeling in abdominal quantitative susceptibility mapping.

Background: Accurate phase unwrapping is a critical prerequisite for successful applications in phase-related MRI, including quantitative susceptibility mapping (QSM) and susceptibility weighted imaging. However, many existing 3D phase unwrapping algorithms face challenges in the presence of severe noise, rapidly changing phase, and open-end cutline.

Methods: In this study, we introduce a novel 3D phase unwrapping approach utilizing region partitioning and a local polynomial model.

Automatic 3D reconstruction of an anatomically correct upper airway from endoscopic long range OCT images.

Endoscopic airway optical coherence tomography (OCT) is a non-invasive and high resolution imaging modality for the diagnosis and analysis of airway-related diseases. During OCT imaging of the upper airway, in order to reliably characterize its 3D structure, there is a need to automatically detect the airway lumen contour, correct rotational distortion and perform 3D airway reconstruction. Based on a long-range endoscopic OCT imaging system equipped with a magnetic tracker, we present a fully automatic framework to reconstruct the 3D upper airway model with correct bending anatomy.

Segmental and transmural motion of the rat myocardium estimated using quantitative ultrasound with new strategies for infarct detection.

The estimation of myocardial motion abnormalities has great potential for the early diagnosis of myocardial infarction (MI). This study aims to quantitatively analyze the segmental and transmural myocardial motion in MI rats by incorporating two novel strategies of algorithm parameter optimization and transmural motion index (TMI) calculation. Twenty-one rats were randomly divided into three groups ( = 7 per group): sham, MI, and ischemia-reperfusion (IR) groups.

Learning spatially variant degradation for unsupervised blind photoacoustic tomography image restoration.

Photoacoustic tomography (PAT) images contain inherent distortions due to the imaging system and heterogeneous tissue properties. Improving image quality requires the removal of these system distortions. While model-based approaches and data-driven techniques have been proposed for PAT image restoration, achieving accurate and robust image recovery remains challenging.

In vivo co-registered hybrid-contrast imaging by successive photoacoustic tomography and magnetic resonance imaging.

Magnetic resonance imaging (MRI) and photoacoustic tomography (PAT) offer two distinct image contrasts. To integrate these two modalities, we present a comprehensive hardware-software solution for the successive acquisition and co-registration of PAT and MRI images in in vivo animal studies. Based on commercial PAT and MRI scanners, our solution includes a 3D-printed dual-modality imaging bed, a 3-D spatial image co-registration algorithm with dual-modality markers, and a robust modality switching protocol for in vivo imaging studies.

New 3D phase-unwrapping method based on voxel clustering and local polynomial modeling: application to quantitative susceptibility mapping.

Background: To develop an accurate and robust 3-dimensional (3D) phase-unwrapping method that works effectively in the presence of severe noise, disconnected regions, rapid phase changes, and open-ended lines for quantitative susceptibility mapping (QSM).

Methods: We developed a 3D phase-unwrapping method based on voxel clustering and local polynomial modeling named CLOSE3D, which firstly explores the 26-neighborhood to calculate local variation of the phasor and the phase, and then according to the local variation of the phasor, clusters the phase data into easy-to-unwrap blocks and difficult-to-unwrap residual voxels. Next, CLOSE3D sequentially performs intrablock, interblock, and residual-voxel unwrapping by using the region-growing local polynomial modeling method.

Impact of Aggregation Methods for Texture Features on Their Robustness Performance: Application to Nasopharyngeal F-FDG PET/CT.

Purpose: This study aims to investigate the impact of aggregation methods used for the generation of texture features on their robustness of nasopharyngeal carcinoma (NPC) based on F-FDG PET/CT images.

Methods: 128 NPC patients were enrolled and 95 texture features were extracted for each patient including six feature families under different aggregation methods. For GLCM and GLRLM features, six aggregation methods were considered.

MsGoF: Breast lesion classification on ultrasound images by multi-scale gradational-order fusion framework.

Background And Objective: Predicting the malignant potential of breast lesions based on breast ultrasound (BUS) images is a crucial component of computer-aided diagnosis system for breast cancers. However, since breast lesions in BUS images generally have various shapes with relatively low contrast and present complex textures, it still remains challenging to accurately identify the malignant potential of breast lesions.

Methods: In this paper, we propose a multi-scale gradational-order fusion framework to make full advantages of multi-scale representations incorporating with gradational-order characteristics of BUS images for breast lesions classification.

3D-EPI blip-up/down acquisition (BUDA) with CAIPI and joint Hankel structured low-rank reconstruction for rapid distortion-free high-resolution mapping.

Purpose: This work aims to develop a novel distortion-free 3D-EPI acquisition and image reconstruction technique for fast and robust, high-resolution, whole-brain imaging as well as quantitative mapping.

Methods: 3D Blip-up and -down acquisition (3D-BUDA) sequence is designed for both single- and multi-echo 3D gradient recalled echo (GRE)-EPI imaging using multiple shots with blip-up and -down readouts to encode B field map information. Complementary k-space coverage is achieved using controlled aliasing in parallel imaging (CAIPI) sampling across the shots.

An automatic segmentation method with self-attention mechanism on left ventricle in gated PET/CT myocardial perfusion imaging.

Objectives: We aimed to propose an automatic segmentation method for left ventricular (LV) from 16 electrocardiogram (ECG) -gated N-NH PET/CT myocardial perfusion imaging (MPI) to improve the performance of LV function assessment.

Methods: Ninety-six cases with confirmed or suspected obstructive coronary artery disease (CAD) were enrolled in this research. The LV myocardial contours were delineated by physicians as ground truth.

Astrocyte dysfunction drives abnormal resting-state functional connectivity in depression.

Major depressive disorder (MDD) is a devastating mental disorder that affects up to 17% of the population worldwide. Although brain-wide network-level abnormalities in MDD patients via resting-state functional magnetic resonance imaging (rsfMRI) exist, the mechanisms underlying these network changes are unknown, despite their immense potential for depression diagnosis and management. Here, we show that the astrocytic calcium-deficient mice, inositol 1,4,5-trisphosphate-type-2 receptor knockout mice ( mice), display abnormal rsfMRI functional connectivity (rsFC) in depression-related networks, especially decreased rsFC in medial prefrontal cortex (mPFC)-related pathways.

Mutual-Assistance Learning for Standalone Mono-Modality Survival Analysis of Human Cancers.

Current survival analysis of cancers confronts two key issues. While comprehensive perspectives provided by data from multiple modalities often promote the performance of survival models, data with inadequate modalities at the testing phase are more ubiquitous in clinical scenarios, which makes multi-modality approaches not applicable. Additionally, incomplete observations (i.