Machine learning using supervised approaches has been widely applied to ultrasound image analysis. In contrast, reinforcement learning (RL), which is well suited for sequential decision-making, is underexplored. Ultrasound workflows involve sequential subtasks like image acquisition, quality assessment, summarization, and interpretation that can be integrated into RL frameworks. This scoping review examined RL applications in ultrasound. A comprehensive search was conducted in Scopus, PubMed, Embase, and MEDLINE for studies published between 2015 and 2026. Eligible studies used RL with clinical ultrasound data, and the data were summarized by application area, methods, and anatomical targets. The review also provides a brief overview of RL concepts as foundational knowledge for understanding various RL formulations used. From 326 records retrieved, 39 studies were included. Most studies used model-free deep RL, with value-based methods being the most common, particularly Deep Q-Network (DQN) and its variants, on retrospective data across diverse anatomical targets, with breast, fetal, and uterine ultrasound being the most frequently represented categories. RL was used to automate tasks including navigation, plane localization, landmark detection, and video summarization. RL in ultrasound imaging is an emerging field of research and has advantages for sequential workflow optimization tasks. Most approaches are at an early stage and have been tested on small datasets, lack consistent evaluation protocols, and report limited clinical translation.

Non-destructively reading text on a sheet of paper with characters on both sides, when those characters might be obscured, unreadable visually, and the paper cannot be turned over, remains an open problem in document analysis. This problem has practical relevance to archival characterisation, fragmentology, and forgery detection. Terahertz (THz) time-of-flight tomography (TOFT) offers the ability to penetrate paper and parchment as well as sensitivity to the composition of inks. However, progress in data-driven THz analysis has been hindered by the absence of publicly available, labelled reference datasets. While a range of historical materials have been employed in manuscripts and printed books, it is favorable to phrase the problem in a way that is accessible to numerous laboratories to develop techniques to extract such texts. In view of this aim, we introduce a curated THz-TOFT dataset comprising raster-scan measurements of four distinct print/writing media (henceforth ink), namely toner, inkjet, iron gall ink, and graphite, deposited on standard copy paper. Samples were prepared from contemporary materials consisting of one sheet of standard copy paper with overlapping test patterns or characters on each side. THz-TOFT data were acquired in reflection mode under controlled, nitrogen-purged conditions, yielding per-pixel time-domain waveforms. Multiple checkerboard-style test patterns are provided per ink type, each covering four ink configuration classes: no ink, ink on the front, back, and on both surfaces. Raw data is distributed as CSV files encoding spatial coordinates, full time-domain waveforms (reflected THz electric field), and manually validated class labels. To demonstrate the suitability of the dataset for machine-learning applications, a convolutional neural network baseline is provided together with a PyTorch-compatible dataloader. This baseline is trained and tested separately for each of the ink datasets, and the predictions on unknown data are qualitatively evaluated to compare the characteristic media properties.