Off-axis holography enables single-shot phase retrieval but reduces spatial bandwidth, while in-line phase-shifting interferometry preserves bandwidth yet requires reference-path stepping and is sensitive to drift, limiting dynamic measurements. Moreover, viscoelastic mapping is rarely available from the same holographic measurement. We propose vibration-encoded in-line Mach-Zehnder holography for simultaneous thickness and viscoelasticity mapping of soft samples. Twelve holograms acquired over one vibration cycle are analyzed using Bessel-based harmonic inversion and robust regression to recover the static phase, modulation depth, and phase lag, yielding thickness and Kelvin-Voigt storage and loss modulus maps ( E ′, E ″). Simulations recover E ′ and E ′′ to within ∼2% across a wide E ′′/ E ′ range and achieve sub-micron thickness error over 20-45 μ m beads. Experiments on calibrated polyacrylamide beads show sub-micron thickness repeatability (median ∼0.57 μ m over 40 repeats) and stiffness estimates typically within 10% of ground truth, and we further demonstrate the approach on adherent MCF-7 cells.

Despite good axial resolution in holographic microscopy, lateral resolution remains constrained by optical limitations, pixel size, and noise. These restrictions hinder accurate reconstruction of fine structural and phase details. To address this, we evaluate three deep-learning super-resolution models (RCAN, SwinIR, and a conditional diffusion network) on 1,440 off-axis digital holograms of microbeads downsampled by 2 ×, 3 ×, and 4 ×. We compare their performance to bicubic spline interpolation using PSNR, SSIM, MSE, and phase-derived depth errors. RCAN and SwinIR yield the most accurate reconstructions, preserving structural and quantitative phase information, and offering guidance on model selection for phase-focused holography.

Single‐cell mechanical properties such as stiffness, elasticity, and viscosity, are crucial in governing biological processes like migration, proliferation, and differentiation. In cancer, the mechanical properties of cells undergo significant alterations, which contribute to tumor growth, metastasis, and resistance to therapy. This review focuses on cancer cell stiffness and explores how its regulation is disrupted by the complex interplay among cytoskeletal remodeling, nuclear mechanics, and extracellular matrix (ECM) interactions. Cancer‐associated fibroblasts (CAFs) and ECM composition within the tumor microenvironment (TME) modulate cellular mechanics via mechanotransduction pathways involving Yes‐associated protein/transcriptional coactivator with PDZ‐binding motif (YAP/TAZ) and integrin‐focal adhesion kinase (FAK) signaling. Increasing evidence supports cell stiffness as a promising diagnostic and prognostic biomarker, as well as a predictor of treatment response. Therefore, advanced techniques for measuring cell stiffness such as atomic force microscopy (AFM), Brillouin microscopy, and acousto‐holography are evaluated with a focus on their potential clinical applicability. However, translation into routine oncology practice remains limited by technical variability, lack of standardized protocols, and the need for large‐scale clinical validation. This review highlights the potential of integrating biomechanical markers into clinical workflows as a means to advance cancer diagnostics and enable more personalized therapeutic strategies.

Significance: Measuring cell stiffness is essential in cellular biomechanics, particularly in understanding disease progression, including cancer metastasis and tissue mechanics. However, conventional techniques such as atomic force microscopy and optical stretching present limitations, including invasiveness, low throughput, and complex sample preparation. These factors restrict their applicability in dynamic and sensitive biological environments.Aim: This study introduces a noninvasive holographic sensor system for evaluating the stiffness of soft microscale samples.Approach: The proposed system integrates holographic imaging with acoustic stimulation using an off-axis Mach–Zehnder interferometer combined with bulk acoustic waves. This setup allows for label-free, high-throughput measurements while preserving sample integrity. The system was validated with polyacrylamide beads engineered to mimic cellular stiffness, ensuring precise and repeatable stiffness assessments.Results: Measurement errors caused by spatial variations were minimized through a structured imaging approach and a calibration strategy, improving uniformity across different regions. These corrections enhanced the consistency and reliability of stiffness assessments. Experimental validation demonstrated stable stiffness measurements regardless of sample size variations. Repeatability tests further confirmed the system’s robustness, producing consistent results across multiple trials.Conclusion: The findings highlight the potential of this holographic sensor system in advancing cell biomechanics research, cancer diagnostics, and mechanobiology. By offering a noninvasive, high-throughput alternative for mechanical property assessments in biological samples, this method contributes to improved characterization of cellular stiffness in biomedical applications.

Off-axis holographic microscopy enables single-shot quantitative phase imaging, but its sensitivity is limited by mixed noise that degrades fringe visibility and biases phase recovery. We compare classical filters (Wiener/median/NLM/BM3D and variance-stabilized variants), a supervised Swin transformer, and a diffusion denoiser trained on synthetic data, using 100,000 physics-based holograms with realistic blur, speckle, shot, and read noise. On a 10,000-hologram test set, the best pipeline (diffusion followed by BM3D) achieves 36.86 dB/0.9964 intensity PSNR/SSIM and 46.48 dB/0.9772 phase PSNR/SSIM, reducing RMSE by 88.5% in intensity and 52.8% in phase, with similar improvements on experimental bead and cell holograms.