In proton-based radiotherapy, proton radiography could allow for direct measurement of the water-equivalent path length (WEPL) in tissue, which can then be used to determine relative stopping power (RSP). Additionally, proton radiographs allow for imaging in the beam's-eye-view. In this work, a proton radiography technique using a flat-panel imager and a pencil-beam scanning (PBS) system is demonstrated in phantom. Proton PBS plans were delivered on a Varian ProBeam system to a flat-panel imager. Each proton plan consisted of energy layers separated by 4.8 MeV, and a field size of 25 cm × 25 cm. All measured data is binned into a layer-by-layer delivery in post processing. To build a calibration curve correlating detector response to WEPL, the plans were delivered to slabs of solid water of increasing thickness. Pixel-by-pixel detector response in the time/energy domain is then fit as a function of WEPL. Tissue equivalent phantoms are imaged for evaluation of WEPL accuracy. A spatial resolution phantom and a head phantom are also imaged. For all experiments, the detector was run with an effective pixel size of 0.4 mm × 0.4 mm. The proposed method reconstructed RSP with mean errors of 2.65%, −0.14%, and 0.61% for lung, soft tissue, and bone, respectively. In a 40 mm thick spatial resolution phantom, a 2 mm deep pinhole with 1 mm diameter can be seen. The accuracy and spatial resolution of the method show that it could be implemented for patient position verification. Further development could lead to patient-specific verification of RSP to be used for treatment guidance.

Ultrasound imaging of the lung has played an important role in managing patients with COVID-19–associated pneumonia and acute respiratory distress syndrome (ARDS). During the COVID-19 pandemic, lung ultrasound (LUS) or point-of-care ultrasound (POCUS) has been a popular diagnostic tool due to its unique imaging capability and logistical advantages over chest X-ray and CT. Pneumonia/ARDS is associated with the sonographic appearances of pleural line irregularities and B-line artefacts, which are caused by interstitial thickening and inflammation, and increase in number with severity. Artificial intelligence (AI), particularly machine learning, is increasingly used as a critical tool that assists clinicians in LUS image reading and COVID-19 decision making. We conducted a systematic review from academic databases (PubMed and Google Scholar) and preprints on arXiv or TechRxiv of the state-of-the-art machine learning technologies for LUS images in COVID-19 diagnosis. Openly accessible LUS datasets are listed. Various machine learning architectures have been employed to evaluate LUS and showed high performance. This paper will summarize the current development of AI for COVID-19 management and the outlook for emerging trends of combining AI-based LUS with robotics, telehealth, and other techniques.

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Purpose: Conventional iterative low-dose CBCT reconstruction techniques are slow and tend to over-smooth edges through uniform weighting of the image penalty gradient. In this study, we present a non-iterative analytical low-dose CBCT reconstruction technique by restoring the noisy low-dose CBCT projection with the non-local total variation (NLTV) method.Methods: We modeled the low-dose CBCT reconstruction as recovering high quality, high-dose CBCT x-ray projections (100 kVp, 1.6 mAs) from low-dose, noisy CBCT x-ray projections (100 kVp, 0.1 mAs). The restoration of CBCT projections was performed using the NLTV regularization method. In NLTV, the x-ray image is optimized by minimizing an energy function that penalizes gray-level difference between pair of pixels between noisy x-ray projection and denoising x-ray projection. After the noisy projection is restored by NLTV regularization, the standard FDK method was applied to generate the final reconstruction output.Results: Significant noise reduction was achieved comparing to original, noisy inputs while maintaining the image quality comparable to the high-dose CBCT projections. The experimental validations show the proposed NLTV algorithm can robustly restore the noise level of x-ray projection images while significantly improving the overall image quality. The improvement in normalized mean square error (NMSE) and peak signal-to-noise ratio (PSNR) measured from the non-local total variation-gradient projection (NLTV-GPSR) algorithm is noticeable compared to that of uncorrected low-dose CBCT images. Moreover, the difference of CNRs from the gains from the proposed algorithm is noticeable and comparable to high-dose CBCT. Conclusion:The proposed method successfully restores noise degraded, low-dose CBCT projections to high-dose projection quality. Such an outcome is a considerable improvement to the reconstruction result compared to the FDK-based method. In addition, a significant reduction in reconstruction time makes the proposed algorithm more attractive. This demonstrates the potential use of the proposed algorithm for clinical practice in radiotherapy.

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