# Task-based Generation of Optimized Projection Sets using Differentiable Ranking

arXiv: https://arxiv.org/abs/2303.11724 · HTML: https://mlsanity.com/paper/2303.11724/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 9 references that are in the map.
- First posted on arXiv 21 Mar 2023.

## Builds on (most cited first, 3 of 3)

- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [Fast Differentiable Sorting and Ranking](https://mlsanity.com/paper/2002.08871/) (2020)
- [A Learning-based Method for Online Adjustment of C-arm Cone-Beam CT Source Trajectories for Artifact Avoidance](https://mlsanity.com/paper/2008.06262/) (2020)

## Built on by (most cited first, 3 of 3)

- [DRACO: Differentiable Reconstruction for Arbitrary CBCT Orbits](https://mlsanity.com/paper/2410.14900/) (2024)
- [Compressibility Analysis for the differentiable shift-variant Filtered Backprojection Model](https://mlsanity.com/paper/2501.11586/) (2025)
- [Gradient-Based Trajectory Optimisation over Continuous Poses for Sparse-View Cone-Beam CT](https://mlsanity.com/paper/2610.09579/) (2026)

## Abstract (arXiv)

We present a method for selecting valuable projections in computed tomography (CT) scans to enhance image reconstruction and diagnosis. The approach integrates two important factors, projection-based detectability and data completeness, into a single feed-forward neural network. The network evaluates the value of projections, processes them through a differentiable ranking function and makes the final selection using a straight-through estimator. Data completeness is ensured through the label provided during training. The approach eliminates the need for heuristically enforcing data completeness, which may exclude valuable projections. The method is evaluated on simulated data in a non-destructive testing scenario, where the aim is to maximize the reconstruction quality within a specified region of interest. We achieve comparable results to previous methods, laying the foundation for using reconstruction-based loss functions to learn the selection of projections.