Dynamic medical imaging sequences, such as cardiac cine MRI and dynamic CT, are vital for capturing organ motion and enabling functional assessment. However, these modalities face an inherent spatiotemporal trade-off: achieving high spatial resolution often necessitates prolonged acquisition times, which compromises temporal resolution and obscures critical motion details. To address this challenge, we introduce DIFA, a novel framework for arbitrary-time frame interpolation in dynamic medical imaging sequences. DIFA integrates two synergistic components: (1) A Bilateral Flow Estimation (BFE) module incorporates a deformable inter-frame attention mechanism, which explicitly models non-rigid pixel trajectories under the guidance of gradient priors, thereby ensuring the accurate capture of large organ motions. (2) A Bilateral Flow Fusion (BFF) module employs a multi-scale pyramid architecture with cross-attention warping to achieve precise integration of motionwarped features across temporal scales. This framework enables the generation of anatomically coherent intermediate frames at any user-specified time point. Extensive experiments on cardiac cine MRI and dynamic lung CT datasets demonstrate that DIFA surpasses state-of-the-art frame interpolation methods in both quantitative metrics (e.g., PSNR and LPIPS) and qualitative visual assessments, offering a robust solution for enhancing temporal resolution in dynamic medical imaging.

Deep unrolling methods have achieved notable success in accelerated cardiac cine MRI reconstruction. However, their effectiveness remains limited by constrained receptive fields and rigid convolutional sampling, which hinder scalability in high-resolution reconstruction tasks. To address these challenges, we propose an Equivariant Deformable Convolutional Unrolling Network (EDCU-Net) that integrates a Spatiotemporal Deformable Module (STDM) and a Rotation Equivariant Module (REM). EDCU-Net effectively enlarges the receptive field while maintaining low computational cost and high parameter efficiency, enabling more effective suppression of large-scale aliasing artifacts. Specifically, STDM adaptively adjusts sampling locations to better capture complex anatomical structures and dynamic cardiac motion, while reducing interpolation overhead. Furthermore, REM embeds deformable convolutions within an equivariant framework, allowing shared parameters across orientations and further promoting parameter efficiency and generalization capability. Extensive experiments on cardiac cine MRI data demonstrate that EDCU-Net consistently outperforms state-of-the-art methods in both reconstruction accuracy and visual quality.

Mainstream signal processing theory and methods are primarily designed for synchronous sampling architectures, where samples are captured at predefined time instants. While this fits well with Shannon’s framework, in the absence of synchronous structure, even fundamental tools like filtering and convolution break down. Alternatively, event-driven or time-encoded sampling offers a more efficient method by capturing signals only when an event occurs. This approach, reminiscent of the "spiking neuron" behavior in the brain, can lead to low-power electronic implementations. Unlike Shannon’s framework, measurements in this scheme are defined by asynchronous sampling, presenting unique challenges. One such open problem is performing spectral estimation from asynchronous samples. In this paper, we propose a novel approach that directly enables spectral estimation from asynchronous measurements. Empirically, our algorithm offers robust, high-resolution spectral information, with a lower sampling rate on trigger times. Beyond numerical experiments, we build an event-driven sampling hardware utilizing asynchronous sigma-delta modulators to validate our approach. These hardware experiments further demonstrate the robustness and practical applicability of our method.

The problem of frequency estimation from sub-Nyquist samples has numerous applications across various disciplines and has been extensively studied in signal processing literature. Despite the existence of several algorithmic approaches, the full potential of these methods has not been realized due to the limitations of analog-to-digital converters (ADCs). In particular, accurately estimating frequency for high-dynamic-range signals that may saturate the ADC is still an interesting problem, regardless of the sub-Nyquist aspect. On a different note, the Unlimited Sensing Framework (USF) focuses on recovering large signals from folded samples but requires oversampling. In this paper, we propose a hardware-software co-design approach that allows for frequency estimation from folded samples at sub-Nyquist rates. Our key insight is that temporal redundancy can be eliminated by introducing channel redundancy. Surprisingly, our recovery guarantees are independent of the sampling rate. To achieve this, we introduce a novel multi-channel sampling pipeline coupled with a reconstruction algorithm. Beyond numerical experiments, we build customized hardware and validate our approach through lab experiments. This demonstrates the capabilities of our method in real-world scenarios while opening up new questions for the field.