Photoacoustic-ultrasound (PA/US) fusion imaging is an emerging dual-modality technique that integrates the high optical contrast of photoacoustic imaging (PAI, also referred to as optoacoustic imaging or photoacoustic tomography) with the anatomical resolution of US. This review summarizes the principles, technical advances, and clinical applications of PA/US in breast cancer diagnosis and therapy. Recent studies demonstrate that PA/US markedly improves diagnostic specificity while maintaining high sensitivity, particularly in differentiating benign from malignant lesions, predicting molecular subtypes, and monitoring therapeutic response. Radiomics and artificial intelligence further enhance the interpretive power of PA/US, enabling functional phenotyping, Ki-67 expression prediction, and axillary lymph node (ALN) metastasis risk assessment. Moreover, multicenter clinical trials, such as the PIONEER study, have validated the clinical feasibility of PA/US, reducing unnecessary biopsies and refining BI-RADS categorization. Despite challenges in system standardization, quantitative accuracy, and large-scale validation, PA/US holds promise as the “fourth major breast imaging modality,” complementing mammography, US, and MRI. With continued progress in AI integration, standardized protocols, and policy recognition, PA/US is expected to achieve routine clinical implementation in the next 5-10 years, supporting individualized breast cancer diagnosis and precision oncology.

Recent advancements in medical imaging have greatly enhanced our understanding of tissue structure and disease mechanisms. Habitat imaging, which segments imaging data into distinct spatial subregions or "habitats, " offers valuable insights into the heterogeneous nature of tumors, challenging traditional treatment strategies and supporting precision medicine. Super-resolution ultrasound (SRUS) has emerged as a promising tool for habitat imaging by exceeding the diffraction limits of conventional ultrasound, thus enabling visualization of microcirculation at the micron scale. Unlike MRI, CT, and PET, SRUS offers superior resolution in depicting microvascular structures, providing complementary information that enhances our understanding of tissue perfusion and microcirculatory heterogeneity. SRUS-based habitat imaging can delineate vascular habitats with high precision, supporting dynamic analysis and offering potential benefits in oncology, such as assessing tumor aggressiveness and monitoring therapeutic responses. As SRUS technology continues to mature, it is poised to become an integral part of personalized medicine, with future studies focusing on standardizing protocols and validating biomarkers to integrate SRUS into routine clinical practice.

Ultrasound imaging holds a significant position in medical diagnostics due to its non-invasive and real-time capabilities. However, traditional ultrasound is constrained by the diffraction limit, making it challenging to capture fine blood vessels. Ultrasound localization microscopy (ULM) overcomes this limitation by achieving super-resolution imaging through tracking the trajectories of microbubbles (MBs) within microvasculature. This review summarizes the applications of deep learning (DL) techniques in ULM post-processing algorithms, including key steps such as beamforming, clutter filtering and denoising, localization, and tracking. Although DL shows great potential in improving ULM imaging quality and efficiency, current research mainly focuses on imaging algorithmic improvements rather than in-depth image analysis. In the future, with the accumulation of ULM image data, the powerful feature extraction capability of DL is expected to further advance ULM applications in disease prediction and diagnosis.

Artificial intelligent (AI) based on deep learning has been used in medical imaging analysis for years. Improvements have been made in the diagnosis of various diseases with the help of deep learning. Multimodal medical imaging combines two or more imaging modalities, providing comprehensive diagnostic information of the diseases. However, some modality problems always exist in clinical practice. Recently, AI-based deep learning technologies have realized the modality conversion. Investigations on modality conversion have gradually been reported in order to acquire multimodal information. MRI images could be generated from CT images while ultrasound elastography could be generated from B mode ultrasonography. Continuous researches and development of new technologies around deep learning are still under investigation and provide huge clinical potentials in the future. The purpose of this review is to summarize an overview of the current applications and prospects of deep learning-based modality conversion of medical imaging.

The current medical ultrasound imaging device is mainly divided into console ultrasound device and portable ultrasound device. In this article, a new concept along with an innovative pathway to develop ultrasound imaging devices, namely distributed cloud-based ultrasound system (DCUS), was proposed. In DCUS, the ultrasound probes from multiple terminals are used to complete the transmission and reception as well as analog-to-digital conversion of ultrasonic signals, and upload the original radio frequency (RF) signals or in-phase and quadrature (IQ) signals to the cloud server through ultra-bandwidth high-speed communication technology, while the centralized cloud server platform finishes processing of ultrasonic signals and transmits and distributes ultrasound imaging to each related terminal in real time. Various artificial intelligence (AI) algorithms can also be deployed on the cloud-based platform to achieve AI-powered imaging optimization, protocol standardization, and assisted diagnosis. Thus, by utilizing new cloud-based platform and super-high transmission technology and combining the advantages of console ultrasound and portable ultrasound systems with flexibility, high imaging quality and intelligent features, DCUS could become whole new ultrasound system for medical imaging applications in foreseeable future.