Journal of Physics D: Applied Physics最新文献

Radiation therapy (RT) employs ionizing radiation to kill cancerous cells. However, delivering radiation to tumors, typically embedded within normal tissues, inevitably exposes healthy organs to radiation, leading to collateral damage. This creates a tradeoff between the tumor control probability and normal tissue complication probability, ultimately limiting the dose that can be safely administered. While highly conformal RT techniques have improved tumor targeting and treatment efficacy, they remain inadequate for treating large and radioresistant tumors, pointing out the need for alternative strategies. Spatially fractionated RT, ultra-high dose rate RT, and nanoparticle-enhanced RT are emerging techniques with promise in enhancing tumor control while minimizing normal tissue toxicity. Successful clinical translation of these advanced techniques requires cross-disciplinary efforts aimed at technological innovation, a deeper understanding of the underlying radiobiological mechanisms, and the development of early-phase clinical trials. This paper provides an overview of these techniques and their associated challenges and opportunities.

Strain imaging using conventional x-ray tomography is a widely established technique for investigating the mechanical deformation of materials, including cement and batteries. However, its biomedical applications are primarily restricted to bone tissue due to the low contrast of soft tissues. X-ray phase contrast imaging, offering superior contrast-to-noise ratios in soft tissues, can in principle overcome this limitation. This study explores the feasibility of x-ray strain imaging for soft tissues using edge illumination (EI), a laboratory-based x-ray phase contrast technique. A phantom mimicking the mechanical properties of healthy and tumorous soft tissues, with a stiff inclusion invisible to conventional x-ray imaging, was tested alongside chicken soft tissue fixed in ethanol. While our study confirmed that EI phase contrast imaging provides improved contrast for such samples compared to absorption imaging, it also revealed a reduction in strain retrieval precision. Artefacts caused by absorbing bridges in the mask design and errors arising from differential phase signal integration, which vary spatially between scans, were identified as key limiting factors. Consequently, EI phase contrast strain imaging was unable to locate phantom inclusions based on mechanical contrast. However, EI's capability to increase spatial sampling frequency without compromising the field of view improved strain retrieval precision using its absorption contrast beyond that achieved with conventional x-ray strain imaging. These findings highlight the potential and challenges of applying EI to strain analysis in soft tissues, providing insights into its limitations and opportunities for further improvement.

Magnetic particle imaging (MPI) is a new tomographic imaging technique that can quantitatively correlate MPI signal intensity to the spatial distribution of magnetic nanoparticle (MNP) tracers. Due to its non-ionizing nature, low background signal from biological matrices, high contrast, and relatively good spatial and temporal resolution, MPI has been actively studied and applied to biomedical imaging and is expected to reach the clinical stage soon. To further improve the spatial resolution limit in MPI, researchers have been working towards optimizing the image reconstruction algorithms, magnetic field profiles, tracer designs, circuitry, etc. Recent studies reported that lower excitation field amplitudes can improve spatial resolution, though this comes at the expense of lower MPI signal and tracer sensitivity. Different excitation field profiles directly affect the collective dynamic magnetizations of tracers recorded by the receiver coil in MPI. However, there is a gap between understanding the relaxation dynamics of MNP tracers, the signal-to-noise ratio (SNR) of MPI signals, and the MPI spatial resolution. In this work, we used a stochastic Langevin equation with coupled Brownian and Néel relaxations to model the magnetic dynamics of different MNP tracers subjected to varying excitation fields. We analyzed the collective time-domain dynamic magnetizations (M-t curves), magnetic-field hysteresis loops (M-H curves), point spread functions (PSFs), higher harmonics, and SNR of the third harmonic to understand how the excitation field affects MPI performance. We employed Full Width at Half Maximum and SNR as evaluation metrics for imaging resolution and signal quality, respectively. Our study supports previous findings on the impact of excitation field amplitude on MPI performance while offering more profound insights into the interplay of nonequilibrium Néel and Brownian relaxation, tracer core size, and SNR.

Phase-sensitive Fourier-domain optical coherence tomography (FD-OCT) enables in-vivo, label-free imaging of cellular movements with detection sensitivity down to the nanometer scale, and it is widely employed in emerging functional imaging modalities, such as optoretinography (ORG), Doppler OCT, and optical coherence elastography. However, when imaging tissue dynamics in vivo, inter-frame displacement introduces decorrelation noise that compromises motion detection performance, particularly in terms of sensitivity and accuracy. Here, we demonstrate that the displacement-related decorrelation noise in FD-OCT can be accurately corrected by restoring the initial sampling points using our proposed Phase-Restoring Subpixel Image Registration (PRESIR) method. Derived from a general FD-OCT model, the PRESIR method enables translational shifting of complex-valued OCT images over arbitrary displacements with subpixel precision, while accurately restoring phase components. Unlike conventional approaches that shift OCT images either in the spatial domain at the pixel level or in the spatial frequency domain for subpixel correction, our method reconstructs OCT images by correcting axial displacement in the spectral domain (k domain) and lateral displacement in the spatial frequency domain. We validated the PRESIR method through simulations, phantom experiments, and in-vivo ORG in both rodents and human subjects. Our approach significantly reduced decorrelation noise during the imaging of moving samples, achieving phase sensitivity close to the fundamental limit determined by the signal-to-noise ratio.