BPPR: A Framework for Content Navigation in Multi-Contrast Body CT Images using Deep Regression Models

Computed tomography (CT) images obtained in clinical settings are often acquired with diverse scanner types and acquisition parameters. They may exhibit significant variations in fields of view (FOVs) and levels of contrast enhancement. An automated method for navigating the content in these images is therefore essential for effective dataset curation and downstream analyses. This work introduces a framework called Body- Part-Phase Regression (BPPR) to automatically identify regions of interest and determine the contrast enhancement phase of body CT images. The framework consists of two key components: (1) A two-phase body part regression method for predicting the anatomical location of 2D slices within 3D volumes. (2) A circular regression model for predicting the contrast timing of CT images (i.e., the timing of the scan relative to contrast agent injection) from a continuous perspective, providing a fine-grained understanding of contrast differences, particularly in relation to patient-specific vascular effects. These two components are linked via a positional weighting mechanism which enhances volumelevel phase prediction by leveraging slice-level predictions. By unifying the "part" and "phase" regression models, our framework establishes a cohesive approach to continuous content navigation in CT images. We train and evaluate our models on large-scale datasets consisting of multi-contrast images and compare their performance with alternative approaches pursuing similar goals. The experiments demonstrate improvements in both slice localization and contrast phase prediction. In particular, the two-phase training scheme reduces the slice localization error of previous body part regression methods from 9.2 mm to 6.1 mm. We also discuss the distinctive advantages of BPR over segmentationbased approaches and highlight potential clinical applications that may benefit from the proposed BPPR framework.