Effectiveness of external respiratory surrogates for in vivo liver motion estimation

Abstract

Purpose: Due to low frame rate of MRI and high radiation damage from fluoroscopy and CT, liver motion estimation using external respiratory surrogate signals seems to be a better approach to track liver motion in real-time for liver tumor treatments in radiotherapy and thermotherapy. This work proposes a liver motion estimation method based on external respiratory surrogate signals. Animal experiments are also conducted to investigate related issues, such as the sensor arrangement, multisensor fusion, and the effective time period. Methods: Liver motion and abdominal motion are both induced by respiration and are proved to be highly correlated. Contrary to the difficult direct measurement of the liver motion, the abdominal motion can be easily accessed. Based on this idea, our study is split into the model-fitting stage and the motion estimation stage. In the first stage, the correlation between the surrogates and the liver motion is studied and established via linear regression method. In the second stage, the liver motion is estimated by the surrogate signals with the correlation model. Animal experiments on cases of single surrogate signal, multisurrogate signals, and long-term surrogate signals are conducted and discussed to verify the practical use of this approach. Results:The results show that the best single sensor location is at the middle of the upper abdomen, while multisurrogate models are generally better than the single ones. The estimation error is reduced from 0.6 mm for the single surrogate models to 0.4 mm for the multisurrogate models. The long-term validity of the estimation models is quite satisfactory within the period of 10 min with the estimation error less than 1.4 mm. Conclusions: External respiratory surrogate signals from the abdomen motion produces good performance for liver motion estimation in real-time. Multisurrogate signals enhance estimation accuracy, and the estimation model can maintain its accuracy for at least 10 min. This approach can be used in practical applications such as the liver tumor treatment in radiotherapy and thermotherapy.

Intraoperative liver deformation and organ motion caused by ventilation, laparotomy, and pneumoperitoneum in a porcine model for image-guided liver surgery

“…The changes in lung and ventilation mechanics under pneumoperitoneum are well documented, including basal atelectasis, compression of the lung basis, and a raised diaphragmatic dome [ 49 – 52 ]. A potential explanation for this is that the physiological and mechanical changes during pneumoperitoneum cause this significant decrease in liver motion due to decreased diaphragmatic motion, which has been shown to be highly correlated to liver motion [ 53 ]. Earlier studies report on liver motion due to introduction of pneumoperitoneum.…”

Motion compensation for MRI-compatible patient-mounted needle guide device: estimation of targeting accuracy in MRI-guided kidney cryoablations

“…There have been several approaches to estimating the patient motion during thermal ablations using additional imaging scanners or sensors including ultrasound imaging [37], surrogate sensors [38], and ultrasound sensors [39]. Those approaches enable compensation of the patient motion with a temporal resolution higher than the MR scan.…”

Automatic liver tumor localization using deep learning‐based liver boundary motion estimation and biomechanical modeling (DL‐Bio)

“…16 Moreover, marker migration is possible and leads to tumor localization errors. Noninvasive surrogates such as breathing signals from surface imaging can also be used for real-time tumor tracking, [17][18][19][20] but the motion correlation between the external markers and internal target motions may not be perfect 21 and is usually patient-specific. Markerless tracking using online magnetic resonance imaging has been used to track tumors.…”