Real-Time Motion Analysis With 4D Deep Learning for Ultrasound-Guided Radiotherapy

Bengs, Marcel; Sprenger, Johanna; Gerlach, Stefan; Neidhardt, Maximilian; Schlaefer, Alexander

doi:10.1109/tbme.2023.3262422

Cited by 1 publication

(2 citation statements)

References 40 publications

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“…In 2015, Z. Liang et al [ 91 ] from Rensselaer Polytechnic Institute proposed a preoperative 4D shape derived from patient-specific breathing patterns to drive intraoperative range imaging (RI)-based real-time respiratory movement analysis. The information is encoded in a surface motion model that obtains 3D body surface data at different breathing states through non-rigid registration, and the patient’s current body surface obtained through multi-view RI registration.…”

Section: Resultsmentioning

confidence: 99%

“…Based on the idea of dense connected convolutional networks (DenseNet), M. Bengs et al [ 91 ] proposed an efficient 4D architecture that can process long-term 4D ultrasound sequences in real time. According to the parameter efficiency and feature propagation intensity of DenseNet, 3D mode is used for the spatial processing of volume ultrasonic data.…”

Section: Resultsmentioning

confidence: 99%

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Current Research Status of Respiratory Motion for Thorax and Abdominal Treatment: A Systematic Review

Wu,

Wang,

Chu

et al. 2024

Biomimetics

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Malignant tumors have become one of the serious public health problems in human safety and health, among which the chest and abdomen diseases account for the largest proportion. Early diagnosis and treatment can effectively improve the survival rate of patients. However, respiratory motion in the chest and abdomen can lead to uncertainty in the shape, volume, and location of the tumor, making treatment of the chest and abdomen difficult. Therefore, compensation for respiratory motion is very important in clinical treatment. The purpose of this review was to discuss the research and development of respiratory movement monitoring and prediction in thoracic and abdominal surgery, as well as introduce the current research status. The integration of modern respiratory motion compensation technology with advanced sensor detection technology, medical-image-guided therapy, and artificial intelligence technology is discussed and analyzed. The future research direction of intraoperative thoracic and abdominal respiratory motion compensation should be non-invasive, non-contact, use a low dose, and involve intelligent development. The complexity of the surgical environment, the constraints on the accuracy of existing image guidance devices, and the latency of data transmission are all present technical challenges.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%