Automatic tracking of an organ section with an ultrasound probe: compensation of respiratory motion

Advancements in Methods and Camera-Based Sensors for the Quantification of Respiration

Assessment of respiratory function allows early detection of potential disorders in the respiratory system and provides useful information for medical management. There is a wide range of applications for breathing assessment, from measurement systems in a clinical environment to applications involving athletes. Many studies on pulmonary function testing systems and breath monitoring have been conducted over the past few decades, and their results have the potential to broadly impact clinical practice. However, most of these works require physical contact with the patient to produce accurate and reliable measures of the respiratory function. There is still a significant shortcoming of non-contact measuring systems in their ability to fit into the clinical environment. The purpose of this paper is to provide a review of the current advances and systems in respiratory function assessment, particularly camera-based systems. A classification of the applicable research works is presented according to their techniques and recorded/quantified respiration parameters. In addition, the current solutions are discussed with regards to their direct applicability in different settings, such as clinical or home settings, highlighting their specific strengths and limitations in the different environments.

Real-Time External Respiratory Motion Measuring Technique Using an RGB-D Camera and Principal Component Analysis

Accurate tracking and modeling of internal and external respiratory motion in the thoracic and abdominal regions of a human body is a highly discussed topic in external beam radiotherapy treatment. Errors in target/normal tissue delineation and dose calculation and the increment of the healthy tissues being exposed to high radiation doses are some of the unsolicited problems caused due to inaccurate tracking of the respiratory motion. Many related works have been introduced for respiratory motion modeling, but a majority of them highly depend on radiography/fluoroscopy imaging, wearable markers or surgical node implanting techniques. We, in this article, propose a new respiratory motion tracking approach by exploiting the advantages of an RGB-D camera. First, we create a patient-specific respiratory motion model using principal component analysis (PCA) removing the spatial and temporal noise of the input depth data. Then, this model is utilized for real-time external respiratory motion measurement with high accuracy. Additionally, we introduce a marker-based depth frame registration technique to limit the measuring area into an anatomically consistent region that helps to handle the patient movements during the treatment. We achieved a 0.97 correlation comparing to a spirometer and 0.53 mm average error considering a laser line scanning result as the ground truth. As future work, we will use this accurate measurement of external respiratory motion to generate a correlated motion model that describes the movements of internal tumors.

Fast and robust extraction of surrogate respiratory signal from intra-operative liver ultrasound images

PURPOSE

   In model-based respiratory motion estimation for the liver or other abdominal organs, the surrogate respiratory signal is usually obtained by using special tracking devices from skin or diaphragm, and subsequently applied to parameterize a 4D motion model for prediction or compensation. However, due to the intrinsic limits and economical costs of these tracking devices, the identification of the respiratory signal directly from intra-operative ultrasound images is a more attractive alternative.

METHODS

   We propose a fast and robust method to extract the respiratory motion of the liver from an intra-operative 2D ultrasound image sequence. Our method employs a preprocess to remove speckle-like noises in the ultrasound images and utilizes the normalized cross-correlation to measure the image similarity fast. More importantly, we present a novel adaptive search strategy, which makes full use of the inter-frame dependency of the image sequence. This search strategy narrows the search range of the optimal matching, thus greatly reduces the search time, and makes the matching process more robust and accurate.

RESULTS

   The experimental results on four volunteers demonstrate that our method is able to extract the respiratory signal from an image sequence of 256 image frames in 5 s. The quantitative evaluation using the correlation coefficient reveals that the respiratory motion, extracted near the liver boundaries and vessels, is highly consistent with the reference motion tracked by an EM device.

CONCLUSIONS

   Our method can use 2D ultrasound to track natural landmarks from the liver as surrogate respiratory signal and hence provide a feasible solution to replace special tracking devices.