# AcquisitionFocus: Joint Optimization of Acquisition Orientation and Cardiac Volume Reconstruction Using Deep Learning

## TL;DR

This paper introduces a deep learning model that improves cardiac imaging by optimizing slice acquisition and reconstructing heart volumes accurately.

## Contribution

The novelty lies in jointly optimizing acquisition orientation and volume reconstruction using deep learning for cardiac imaging.

## Key findings

- The model achieves <13 mm HD95 errors in shape reconstruction.
- Dice scores exceed 80%, showing high accuracy in multi-chamber reconstructions.
- It performs well in both simulated and clinical cardiac MRI with various pathologies.

## Abstract

In cardiac cine imaging, acquiring high-quality data is challenging and time-consuming due to the artifacts generated by the heart’s continuous movement. Volumetric, fully isotropic data acquisition with high temporal resolution is, to date, intractable due to MR physics constraints. To assess whole-heart movement under minimal acquisition time, we propose a deep learning model that reconstructs the volumetric shape of multiple cardiac chambers from a limited number of input slices while simultaneously optimizing the slice acquisition orientation for this task. We mimic the current clinical protocols for cardiac imaging and compare the shape reconstruction quality of standard clinical views and optimized views. In our experiments, we show that the jointly trained model achieves accurate high-resolution multi-chamber shape reconstruction with errors of <13 mm HD95 and Dice scores of >80%, indicating its effectiveness in both simulated cardiac cine MRI and clinical cardiac MRI with a wide range of pathological shape variations.