Photoacoustic (PA) imaging technology integrates the deep penetration depth of ultrasound imaging with the high resolution of optical imaging, demonstrating significant potential in biomedical applications. Many preclinical studies and clinical applications urgently require a portable, high-quality, low-cost, and fast imaging system. Thus, translating advanced image reconstruction algorithms into hardware implementations is highly desired. However, existing iterative PA image reconstructions, although exhibit higher accuracy than the delay-and-sum algorithm, suffer from high computational cost. In this paper, we introduce a novel model-based hardware architecture for palm-size PA tomography (palm-PAT), aiming at enhancing both the speed and performance of image reconstruction at a much lower system cost. To achieve this, we propose an innovative data reuse method that significantly reduces the consumption of hardware storage resources, achieving a reduction of approximately 75% in hardware storage requirements. We conducted experiments utilizing the FPGA implementation of the algorithm, using phantom, human finger data in vivo and ex vivo breast tumor data to verify the feasibility of the proposed method. The results demonstrate that our proposed architecture can substantially reduce system cost while maintaining high imaging performance. The novel hardware architecture design of the model-based algorithm achieves a speedup of up to approximately 270 times compared to the CPU, while the corresponding energy efficiency ratio is improved by more than 2700 times.

Medical image segmentation often requires large datasets with pixel-wise annotations. To alleviate this, we introduce BD-Net for semi-supervised and weakly supervised semantic segmentation (SWSS) using of sparse annotations (such as scribbles) alongside a few pixel-level annotations. BD-Net leverages a Boundary-Enhanced Module that improves boundary localization by extracting structural cues from annotated images through edge detection and multi-scale feature aggregation. Additionally, Density-Guided Contrastive Regularization enhances feature space compactness by using a contrastive loss informed by pixel density. Evaluation on the ACDC dataset shows BD-Net’s superior segmentation quality and generalizability compared to existing methods. The code will be available at https://github.com/Lemonzhoumeng/BD-Net.

Positron emission tomography (PET) is an important functional imaging technology in early disease diagnosis. Generally, the gamma ray emitted by standard-dose tracer inevitably increases the exposure risk to patients. To reduce dosage, a lower dose tracer is often used and injected into patients. However, this often leads to low-quality PET images. In this article, we propose a learning-based method to reconstruct total-body standard-dose PET (SPET) images from low-dose PET (LPET) images and corresponding total-body computed tomography (CT) images. Different from previous works focusing only on a certain part of human body, our framework can hierarchically reconstruct total-body SPET images, considering varying shapes and intensity distributions of different body parts. Specifically, we first use one global total-body network to coarsely reconstruct total-body SPET images. Then, four local networks are designed to finely reconstruct head-neck, thorax, abdomen-pelvic, and leg parts of human body. Moreover, to enhance each local network learning for the respective local body part, we design an organ-aware network with a residual organ-aware dynamic convolution (RO-DC) module by dynamically adapting organ masks as additional inputs. Extensive experiments on 65 samples collected from uEXPLORER PET/CT system demonstrate that our hierarchical framework can consistently improve the performance of all body parts, especially for total-body PET images with PSNR of 30.6 dB, outperforming the state-of-the-art methods in SPET image reconstruction.

Motor imagery is one of the most popular brain-computer interface (BCI) paradigms with good potential to help disabled people. However, EEG decoding is still challenging because of its low signal-to-noise ratio, with which valuable features are difficult to perceive. In this paper, we propose a hybrid model that combines a convolutional neural network (CNN) with the Transformer for decoding motor imagery EEG signals. The CNN is used to extract local features, while the Transformer is utilized to perceive global dependencies. Moreover, we exploit spatial-spectral-temporal features to improve classification performance. To validate the effectiveness and superiority of the proposed method, we conduct experiments on the BCICIV dataset 2a and compare it with other efficient approaches. The results show that our algorithm outperforms the state-of-the-art methods, indicating that the CNN-Transformer model is a competing strategy.

Photoacoustic sensing and imaging have emerged in the biomedical field and shown great potentials. Miniaturization and multi-functionality are in need for practical sensing scenarios. As a minimal and economical solution, micromachined ultrasound transducers can be implemented into the photoacoustic sensing system. In this work, we present a photoacoustic dual-mode microsensor based on PMUT technique. PMUT has its fundamental resonance and other high-order resonances. It displays high sensitivity for signal detection at the fundamental resonant frequency, and wide bandwidth at its high orders. Taking advantage of PMUT’s frequency-domain characteristic, we propose a dual-mode photoacoustic sensing method from a single received signal. As the preliminary results are shown in the manuscript, such a dual-mode microsensor has the potential to provide biological indicators and healthcare monitoring.

Inter-person occlusion and depth ambiguity make estimating the 3D poses of monocular multiple persons as camera-centric coordinates a challenging problem. Typical top-down frameworks suffer from high computational redundancy with an additional detection stage. By contrast, the bottom-up methods enjoy low computational costs as they are less affected by the number of humans. However, most existing bottom-up methods treat camera-centric 3D human pose estimation as two unrelated subtasks: 2.5D pose estimation and camera-centric depth estimation. In this paper, we propose a unified model that leverages the mutual benefits of both these subtasks. Within the framework, a robust structured 2.5D pose estimation is designed to recognize inter-person occlusion based on depth relationships. Additionally, we develop an end-to-end geometry-aware depth reasoning method that exploits the mutual benefits of both 2.5D pose and camera-centric root depths. This method first uses 2.5D pose and geometry information to infer camera-centric root depths in a forward pass, and then exploits the root depths to further improve representation learning of 2.5D pose estimation in a backward pass. Further, we designed an adaptive fusion scheme that leverages both visual perception and body geometry to alleviate inherent depth ambiguity issues. Extensive experiments demonstrate the superiority of our proposed model over a wide range of bottom-up methods. Our accuracy is even competitive with top-down counterparts. Notably, our model runs much faster than existing bottom-up and top-down methods.

Photoacoustic tomography (PAT) is an emerging biomedical imaging technology. Many kinds of PAT imaging modalities are applied in various biomedical applications. However, for the conventional PAT system setup, the ringdistribution ultrasonic transducer (UT) array only receives PA signals from fixed region of interest (ROI) that limit the flexibility for imaging objects with different sizes. In this paper, a size-adjustable PAT system with sectorial ultrasonic transducer array (SUTA) is proposed. Four 70.8-degree 32- elements SUTAs with different center frequency are placed around the target to receive 128 channels' PA signals. The four SUTAs can adjust the imaging ROI's radius ranging from 50 mm to 90 mm, which greatly improves the size-adjustable capability of PAT system. The four SUTAs received signals are used to recover the original PA signals based on physical model accordingly. To prove the concept, the feasibility of the size- adjustable PAT imaging is firstly verified by the simulation results using MATLAB k-wave toolbox. Moreover, a 3-D printed vascular phantom is imaged by the proposed PAT system validating its feasibility very well.

Photoacoustic tomography (PAT) is a newcomer in the biomedical imaging community. The image is constructed through photoacoustic waves emitted from the sample after absorbing light energy. The ultrasound signals are converted into electrical signals by ultrasound transducers and sent to a multi-channel data acquisition (DAQ) system for transmission to the personal computer. Traditionally, one ultrasound signal would occupy one channel of the DAQ. However, high-performance DAQ is awfully expensive. Nowadays, to improve PAT's imaging result, imaging systems with multi-detector is usually required, which means multi-channel DAQ is exceedingly desired. Therefore, in this paper, a 4-to-1 circuit module is proposed to reduce the PAT system's cost by fusing four channels of signals into one signal employing frequency- division multiplexing (FDM). Specifically, the original four PA signals are multiplied with high-frequency sine waves with different frequencies and then summed into one channel so that the signals are carried separately in a bandwidth that does not overlap in the frequency domain. After the modulated signal's acquisition, the original signal can be recovered through bandpass filters and an associated demodulation algorithm. We have performed imaging experiments and proved the feasibility of this module in the PAT system.

Photoacoustic imaging has attracted increasing research interest in recent years due to its unique merit of combining light and sound. Enabling deep tissue imaging with high ultrasound spatial resolution and optical absorption contrast, photoacoustic imaging has been applied in various application scenarios including anatomical, functional and molecular imaging. However, the bulky and expensive laser source is one of the key bottlenecks that need to address for further compact system development. Photoacoustic imaging system based on low-cost laser diode (LD) is one of the promising solutions. In this paper, we report a custom-made fingertip laser diode system enabling both pulsed and continuous modulation modes with shortest pulse-width of 40 ns, largest driving current of 13 A, and highest modulation frequency of 3 MHz, which are suitable for both time and frequency domain photoacoustic imaging. To the best of our knowledge, this may be the most compact laser source reported for photoacoustic imaging. Owing to its super-compact size, the proposed LD system could pave the pathway to low-cost photoacoustic sensing and imaging device, even wearable photoacoustic biomedical sensors.

A handheld approach to 3D photoacoustic imaging is essential in clinical applications. To this end, we develop a 3D handheld photoacoustic imager for dynamic (temporally and spatially) volumetric visualization. In this 3D imager, the optically transmitting part and the acoustically receiving part are integrated into a single handheld probe with a compact size about 160 mm ×64 mm ×40 mm. Besides, a dedicated imaging reconstruction algorithm for the heterogeneous medium is developed based on the phase-shift migration method in the frequency domain, which deals well with the stratified condition in the designed system. Dynamic 3D imaging supporting flexible handheld operation is demonstrated with needle biopsy and in vitro temperature measurement for photothermal therapy. The development of such a 3D handheld photoacoustic system paves the way for compact and handheld-operating implementations, and its further clinical exploration is promising.