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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.27143 |
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| _version_ | 1866912986609745920 |
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| author | Guo, Zaiyang Dong, Jessie N. Bellos, Filippos Hao, Jilei MacKay, Emily J. Chan, Trevor Goldfinger, Shir Reddy, Sethu Vance, Steven Corso, Jason J. Pouch, Alison M. |
| author_facet | Guo, Zaiyang Dong, Jessie N. Bellos, Filippos Hao, Jilei MacKay, Emily J. Chan, Trevor Goldfinger, Shir Reddy, Sethu Vance, Steven Corso, Jason J. Pouch, Alison M. |
| contents | Point-of-care transthoracic echocardiography (TTE) makes it possible to assess a patient's cardiac function in almost any setting. A critical step in the TTE exam is acquisition of the apical 4-chamber (A4CH) view, which is used to evaluate clinically impactful measurements such as left ventricular ejection fraction (LVEF). However, optimizing transducer pose for high-quality image acquisition and subsequent measurement is a challenging task, particularly for novice users. In this work, we present a multi-task network that provides feedback cues for A4CH view acquisition and automatically estimates LVEF in high-quality A4CH images. The network cascades a transducer pose scoring module and an uncertainty-aware LV landmark detector with automated LVEF estimation. A strength is that network training and inference do not require cumbersome or costly setups for transducer position tracking. We evaluate performance on point-of-care TTE data acquired with a spatially dense "sweep" protocol around the optimal A4CH view. The results demonstrate the network's ability to determine when the transducer pose is on target, close to target, or far from target based on the images alone, while generating visual landmark cues that guide anatomical interpretation and orientation. In conclusion, we demonstrate a promising strategy to provide guidance for A4CH view acquisition, which may be useful when deploying point-of-care TTE in limited resource settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_27143 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Follow Your Heart: Landmark-Guided Transducer Pose Scoring for Point-of-Care Echocardiography Guo, Zaiyang Dong, Jessie N. Bellos, Filippos Hao, Jilei MacKay, Emily J. Chan, Trevor Goldfinger, Shir Reddy, Sethu Vance, Steven Corso, Jason J. Pouch, Alison M. Computer Vision and Pattern Recognition Machine Learning Point-of-care transthoracic echocardiography (TTE) makes it possible to assess a patient's cardiac function in almost any setting. A critical step in the TTE exam is acquisition of the apical 4-chamber (A4CH) view, which is used to evaluate clinically impactful measurements such as left ventricular ejection fraction (LVEF). However, optimizing transducer pose for high-quality image acquisition and subsequent measurement is a challenging task, particularly for novice users. In this work, we present a multi-task network that provides feedback cues for A4CH view acquisition and automatically estimates LVEF in high-quality A4CH images. The network cascades a transducer pose scoring module and an uncertainty-aware LV landmark detector with automated LVEF estimation. A strength is that network training and inference do not require cumbersome or costly setups for transducer position tracking. We evaluate performance on point-of-care TTE data acquired with a spatially dense "sweep" protocol around the optimal A4CH view. The results demonstrate the network's ability to determine when the transducer pose is on target, close to target, or far from target based on the images alone, while generating visual landmark cues that guide anatomical interpretation and orientation. In conclusion, we demonstrate a promising strategy to provide guidance for A4CH view acquisition, which may be useful when deploying point-of-care TTE in limited resource settings. |
| title | Follow Your Heart: Landmark-Guided Transducer Pose Scoring for Point-of-Care Echocardiography |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2603.27143 |