Ultrasound (US) is a flexible imaging modality used globally as a first-line medical exam procedure in many different clinical cases. It benefits from the continued evolution of ultrasonic technologies and a well-established US-based digital health system. Nevertheless, its diagnostic performance still presents challenges due to the inherent characteristics of US imaging, such as manual operation and significant operator dependence. Artificial intelligence (AI) has proven to recognize complicated scan patterns and provide quantitative assessments for imaging data. Therefore, AI technology has the potential to help physicians get more accurate and repeatable outcomes in the US. In this article, we review the recent advances in AI-assisted US scanning. We have identified the main areas where AI is being used to facilitate US scanning, such as standard plane recognition and organ identification, the extraction of standard clinical planes from 3D US volumes, and the scanning guidance of US acquisitions performed by humans or robots. In general, the lack of standardization and reference datasets in this field makes it difficult to perform comparative studies among the different proposed methods. More open-access repositories of large US datasets with detailed information about the acquisition are needed to facilitate the development of this very active research field, which is expected to have a very positive impact on US imaging.

Objective. The GPU-based Ultra-fast Monte Carlo positron emission tomography simulator (UMC-PET) incorporates the physics of the emission, transport and detection of radiation in PET scanners. It includes positron range, non-colinearity, scatter and attenuation, as well as detector response. The objective of this work is to present and validate UMC-PET as a a multi-purpose, accurate, fast and flexible PET simulator. Approach. We compared UMC-PET against PeneloPET, a well-validated MC PET simulator, both in preclinical and clinical scenarios. Different phantoms for scatter fraction (SF) assessment following NEMA protocols were simulated in a 6R-SuperArgus and a Biograph mMR scanner, comparing energy histograms, NEMA SF, and sensitivity for different energy windows. A comparison with real data reported in the literature on the Biograph scanner is also shown. Main results. NEMA SF and sensitivity estimated by UMC-PET where within few percent of PeneloPET predictions. The discrepancies can be attributed to small differences in the physics modeling. Running in a 11 GB GeForce RTX 2080 Ti GPU, UMC-PET is ∼1500 to ∼2000 times faster than PeneloPET executing in a single core Intel(R) Xeon(R) CPU W-2155 @ 3.30 GHz. Significance. UMC-PET employs a voxelized scheme for the scanner, patient adjacent objects (such as shieldings or the patient bed), and the activity distribution. This makes UMC-PET extremely flexible. Its high simulation speed allows applications such as MC scatter correction, faster SRM estimation for complex scanners, or even MC iterative image reconstruction.

Despite its success in many biomedical applications, Positron Emission Tomography (PET) has the drawback of typically having lower spatial resolution and higher noise respect to other medical imaging techniques. The best achievable spatial resolution in PET scanners is limited by factors such as the positron range, non-collinearity and the size of the detector crystals. In this work, we present a novel method that uses series of image reconstructions (super-iterations) to go beyond the expected resolution-noise limits for a given PET acquisition. The image quality improvement is achieved using the projections of the previous image reconstruction to redistribute the measured counts of each line-of-response (LOR) into several subLORs, from which a new activity distribution with better quality is reconstructed. The method was evaluated with data from the preclinical scanner 4R-SuperArgus PET/CT, using the NEMA NU4-2008 image quality phantom, a cold Derenzo phantom, and an in-vivo FDG cardiac study on a rat. Resolution and recovery coefficient (RC) improvement of ~10% was achieved while keeping the same noise level. Qualitative results from the in-vivo study also confirm this improvement in image quality. The proposed method is able to achieve significantly better images at the expense of a modest increase of the computational time, and it could be also applied to other modalities, such as SPECT and Compton Cameras.

Among other factors such as random, attenuation and scatter corrections, uniform spatial resolution is key to performing accurate quantitative studies in Positron emission tomography (PET). Particularly in preclinical PET studies involving simultaneous acquisition of multiple animals, the degradation of image resolution due to the depth of interaction (DOI) effect far from the center of the Field of View (FOV) becomes a significant concern. In this work, we incorporated a spatially-variant resolution model into a real time iterative reconstruction code to obtain accurate images of multi-animal acquisition. We estimated the spatially variant point spread function (SV-PSF) across the FOV using measurements and Monte Carlo (MC) simulations. The SV-PSF obtained was implemented in a GPU-based Ordered subset expectation maximization (OSEM) reconstruction code, which includes scatter, attenuation and random corrections. The method was evaluated with acquisitions from two preclinical PET/CT scanners of the SEDECAL Argus family: a Derenzo phantom placed 2 cm off center in the 4R-SuperArgus, and a multi-animal study with 4 mice in the 6R-SuperArgus. The SV-PSF reconstructions showed uniform spatial resolution without significant increase in reconstruction time, with superior image quality compared to the uniform PSF model.

Objective. Clinical implementation of in-beam positron emission tomography (PET) monitoring in proton therapy (PT) requires the integration of an online fast and reliable dose calculation engine. This manuscript reports on the achievement of real-time reconstruction of 3D dose and activity maps with proton range verification from experimental in-beam PET measurements. Approach. Several cylindrical homogeneous PMMA phantoms were irradiated with a monoenergetic 70 MeV proton beam in a clinical facility. Additionally, PMMA range-shifting foils of varying thicknesses were placed at the proximal surface of the phantom to investigate range shift prediction capabilities. PET activity was measured using a state-of-the-art in-house developed six-module PET scanner equipped with online PET reconstruction capabilities. For real-time dose estimation, we integrated this system with an in-beam dose estimation algorithm, which combines a graphical processing unit-based 3D reconstruction algorithm with a dictionary-based software, capable of estimating deposited doses from the 3D PET activity images. The range shift prediction performance has been quantitatively studied in terms of the minimum dose to be delivered and the maximum acquisition time. Main results. With this framework, 3D dose maps were accurately reconstructed and displayed with a delay as short as one second. For a dose fraction of 8.4 Gy at the Bragg peak maximum, range shifts as small as 1 mm could be detected. The quantitative analysis shows that accumulating 20 s of statistics from the start of the irradiation, doses down to 1 Gy could be estimated online with total uncertainties smaller than 2 mm. Significance. The hardware and software combination employed in this work can deliver dose maps and accurately predict range shifts after short acquisition times and small doses, suggesting that real-time monitoring and dose reconstruction during PT are within reach. Future work will focus on testing the methodology in more complex clinical scenarios and on upgrading the PET prototype for increased sensitivity.

Preclinical PET animal studies require immobilization of the animal, typically accomplished through the administration of anesthesia, which may affect the radiotracer biodistribution. The use of ${}^{18}$F point sources attached to the rat head is one of the most promising methods for motion compensation in awake rat PET studies. However, the presence of radioactive markers may degrade image quality. In this study, we aimed to investigate the most favorable conditions for preclinical PET studies using awake rats with attached point sources. Firstly, we investigate the optimal activity conditions for the markers and rat-injected tracer using Monte Carlo simulations to determine the parameters of maximum detectability without compromising image quality. Additionally, we scrutinize the impact of delayed window correction for random events on marker detectability and overall image quality within these studies. Secondly, we present a method designed to mitigate the influence of rapid rat movements, which resulted in a medium loss of events of around 30%, primarily observed during the initial phase of the data acquisition. We validated our study with PET acquisitions from an awake rat within the acceptable conditions of activity and motion compensation parameters. This acquisition revealed an 8% reduction in resolution compared to a sedated animal, along with a 6% decrease in signal-to-noise ratio (SNR). These outcomes affirm the viability of our method for conducting awake preclinical brain studies.