The use of deep learning in medical imaging has increased rapidly over the past few years, finding applications throughout the entire radiology pipeline, from improved scanner performance to automatic disease detection and diagnosis. These advancements have resulted in a wide variety of deep learning approaches being developed, solving unique challenges for various imaging modalities. This paper provides a review on these developments from a technical point of view, categorizing the different methodologies and summarizing their implementation. We provide an introduction to the design of neural networks and their training procedure, after which we take an extended look at their uses in medical imaging. We cover the different sections of the radiology pipeline, highlighting some influential works and discussing the merits and limitations of deep learning approaches compared to other traditional methods. As such, this review is intended to provide a broad yet concise overview for the interested reader, facilitating adoption and interdisciplinary research of deep learning in the field of medical imaging.

Objective. We investigate the use of 3D convolutional neural networks for gamma arrival time estimation in monolithic scintillation detectors. Approach. The required data is obtained by Monte Carlo simulation in GATE v8.2, based on a 50 × 50 × 16 mm ³ monolithic LYSO crystal coupled to an 8 × 8 readout array of silicon photomultipliers (SiPMs). The electronic signals are simulated as a sum of bi-exponentional functions centered around the scintillation photon detection times. We include various effects of statistical fluctuations present in non-ideal SiPMs, such as dark counts and limited photon detection efficiency. The data was simulated for two distinct overvoltages of the SensL J-Series 60 035 SiPMs, in order to test the effects of different SiPM parameters. The neural network uses the array of detector waveforms, digitized at 10 GS s ⁻¹ , to predict the time at which the gamma arrived at the crystal. Main results. Best results were achieved for an overvoltage of +6 V, at which point the SiPM reaches its optimal photon detection efficiency, resulting in a coincidence time resolution (CTR) of 141 ps full width at half maximum (FWHM). It is a 26% improvement compared to a simple averaging of the first few SiPM timestamps obtained by leading edge discrimination, which in comparison produced a CTR of 177 ps FWHM. In addition, better detector uniformity was achieved, although some degradation near the corners did remain. Significance. These improvements in time resolution can lead to higher signal-to-noise ratios in time-of-flight positron emission tomography, ultimately resulting in better diagnostic capabilities.

Objective. Time-of-flight positron emission tomography based on bismuth germanate (BGO) detectors is made possible due to fast emission of Cerenkov light. Only around 17 Cerenkov photons are produced per 511 keV photoelectric event, making high photon collection efficiency crucial for obtaining good time-of-flight capabilities. In this study, we investigate how different lateral and back surface finishes affect the photon collection efficiency and Cerenkov based timing performance in monolithic BGO. Approach. The study is performed using GATE for gamma and optical photon modeling, with surface reflections of photons simulated by the LUT Davis model. We compare for different detector configurations (regarding size and surface finishes) the photon collection efficiency, detection delays of the first few optical photons and coincidence time resolution estimations obtained by modeling the SiPM signals and performing leading edge discrimination. An additional comparison is made to LYSO scintillators and pixelated detectors. Main results. Although Cerenkov photon emission is directional, many high incidence angle Cerenkov photons are emitted due to electron scattering in the crystal. Substituting a polished back (photodetector side) surface for a rough surface increases the collection efficiency of these high angle of incidence photons. Results show that for a monolithic 50 × 50 × 12 mm³ BGO detector with reflective side surfaces, this leads to an overall increase in photon collection efficiency of 34%. Cerenkov photon collection efficiency is also improved, resulting in a reduction of the photon detection delays (and the variation therein) of the first few optical photons. This leads to a better coincidence time resolution, primarily achieved by a shortening of the tails in the time-of-flight kernel, with an 18% reduction in full width at tenth maximum. Significance. This study shows the importance of the photon collection efficiency for timing performance in Cerenkov based monolithic detectors, and how it can be improved with different surface finishes.

The novel upright walk-through PET (WT-PET) scanner enhances patient throughput compared to the conventional cylindrical PET systems but introduces unique