Diaphragm Tracking Research Articles

Motion analysis comparing surface imaging and diaphragm tracking on kV projections for deep inspiration breath hold (DIBH)

BackgroundSurface-guided imaging (SGI) is increasingly utilized to monitor patient motion during deep inspiration breath hold (DIBH) in radiotherapy. Understanding the association between surface and internal motion is crucial for effective monitoring. PurposeTo investigate the relation between motion detected by SGI using surface-guided radiotherapy (SGRT) and internal motion measured through diaphragm tracking on kV projections acquired with DIBH for online CBCT. MethodsBoth SGI and kV were simultaneously acquired for ten patients over a total of 200 breath holds (BH). Diaphragm tracking was performed using second-degree polynomial curve fitting on the derivative images for each kV projection and high-pass filtering at 1/30 Hz to remove rotational effects. The superior-inferior (SI) and anterior-posterior (AP) motions of SGI were then compared to kV tracking using various statistical measures. ResultsThe correlation (individuals’ median: −0.07 to 0.73) was a suboptimal metric for the BH data. The median and 95th percentile absolute differences between SGI-SI and kV were 0.73 mm and 3.46 mm, respectively, during DIBH. For SGI-AP, the corresponding values were 0.55 mm and 2.80 mm. For inter-BH measurements, the contingency table based on a 3 mm threshold indicated surface/diaphragm motion agreement for SGI-SI/kV and SGI-AP/kV was 61 % and 56 %, respectively. ConclusionBoth intra- and inter-BH measurements indicated a limited association between surface and diaphragm motion, with certain constraints noted due to kV tracking and DIBH data. These findings warrant further investigation into the association between surface and internal motion.

Diaphragm Tracking System That Superimposes Planned Diaphragm Contours on Kilovolt Cine Images During Multiple Breath-Hold Volumetric Modulated Arc Therapy for Abdominal Tumors.

We recently published a phantom validation of our diaphragm tracking system, DiaTrak, on an Elekta linear accelerator with an integrated cone-beam computed tomography (CBCT) unit for multiple breath-hold volumetric modulated arc therapy of abdominal tumors, where the diaphragm position was compared between digitally reconstructed radiography (DRR) and kilovolt (kV) projection streaming images by template matching. In the present report, the visual feedback of the diaphragm position was added to the reported system. DICOM-RT diaphragm contour data were additionally exported from a treatment planning system to the DiaTrak PC. Following phantom localization by registering the CBCT to the planning CT images, a projected diaphragm contour was overlaid on each DRR image, whereas another two projected diaphragm contours were superimposed on each kV projection cine image every 180 ms after shifting ±5 mm (set as breath-hold tolerance) in the craniocaudal direction during gantry rotation. It was visually confirmed that the projected diaphragm surface was observed within the two contour lines on the kV cine window. The diaphragm registration errors of the localized phantom were also calculated based on image cross-correlation between the DRR and the projection cine images every 180 ms. It was found that the mean diaphragm registration error was -0.29 mm with a standard deviation of 0.32 mm during the gantry rotation. In conclusion, a new interface for the 5 mm tolerance check was proposed to provide direct visual feedback, thereby giving a sense of assurance to the attending radiotherapy technologists. The calculated diaphragm registration errors were relatively small compared to the tolerance of 5 mm, and therefore it is considered clinically acceptable.

Novel utilization and quantification of Xsight diaphragm tracking for respiratory motion compensation in Cyberknife Synchrony treatment of liver tumors.

The Xsight lung tracking system (XLTS) utilizes an advanced image processing algorithm to precisely identify the position of a tumor and determine its location in orthogonal x-ray images, instead of finding fiducials, thereby minimizing the risk of fiducial insertion-related side effects. To assess and gauge the effectiveness of CyberKnife Synchrony in treating liver tumors located in close proximity to or within the diaphragm, we employed the Xsight diaphragm tracking system (XDTS), which was based on the XLTS. We looked back at the treatment logs of 11 patients (8/11 [XDTS], 3/11 [Fiducial-based Target Tracking System-FTTS]) who had liver tumors in close proximity to or within the diaphragm. And the results are compared with the patients who undergo the treatment of FTTS. The breathing data information was calculated as a rolling average to reduce the effect of irregular breathing. We tested the tracking accuracy with a dynamic phantom (18023-A) on the basis of patient-specific respiratory curve. The average values for the XDTS and FTTS correlation errors were 1.38±0.65 versus 1.50±0.26mm (superior-inferior), 1.28±0.48 versus 0.40±0.09mm (left-right), and 0.96±0.32 versus 0.47±0.10mm(anterior-posterior), respectively. The prediction errors for two methods of 0.65±0.16 versus 5.48±3.33mm in the S-I direction, 0.34±0.10 versus 1.41±0.76mm in the A-P direction, and 0.22±0.072 versus 1.22±0.48mm in the L-R direction. The coverage rate of FTTS slightly less than that of XDTS, such as 96.53±8.19% (FTTS) versus 98.03±1.54 (XDTS). The prediction error, the motion amplitude, and the variation of the respiratory center phase were strongly related to each other. Especially, the higher the amplitude and the variation, the higher the prediction error. The diaphragm has the potential to serve as an alternative to gold fiducial markers for detecting liver tumors in close proximity or within it. We also found that we needed to reduce the motion amplitude and train the respiration of the patients during liver radiotherapy, as well as control and evaluate their breathing.

Initial validation of a diaphragm tracking system for multiple breath-hold volumetric modulated arc therapy of abdominal tumors: A phantom study.

The breath-hold radiotherapy has been increasingly used to mitigate interfractional and intrafractional breathing impact on treatment planning and beam delivery. Previous techniques include body surface measurements or radiopaque metal markers, each having known disadvantages. We recently proposed a new markerless technique without the disadvantages, where diaphragm was registered between DRR and fluoroscopic x-ray projection images every 180ms during VMAT delivery. An initial validation of the proposed diaphragm tracking system (DiaTrak) was performed using a chest phantom to evaluate its characteristics. Diaphragm registration was performed between DRR and projection streaming kV x-ray images of a chest phantom during VMAT delivery. Streaming data including the projection images and the beam angles were transferred from a linac system to an external PC, where the diaphragm registration accuracy and beam-off latency were measured based on image cross correlation between the DRR and the projection images every 180ms. It was shown that the average of the beam-off latency was 249.5ms and the average of the diaphragm registration error was 0.84mm CONCLUSIONS: Initial validation of the proposed DiaTrak system for multiple breath-hold VMAT of abdominal tumors has been successfully completed with a chest phantom. The resulting beam-off latency and the diaphragm registration error were regarded clinically acceptable.

Markerless dynamic tumor tracking (MDTT) radiotherapy using diaphragm as a surrogate for liver targets.

To assess the feasibility of using the diaphragm as a surrogate for liver targets during MDTT. Diaphragm as surrogate for markers: a dome-shaped phantom with implanted markers was fabricated and underwent dual-orthogonal fluoroscopy sequences on the Vero4DRT linac. Ten patients participated in an IRB-approved, feasibility study to assess the MDTT workflow. All images were analyzed using an in-house program to back-project the diaphragm/markers position to the isocenter plane. ExacTrac imager log files were analyzed. Diaphragm as tracking structure for MDTT: The phantom "diaphragm" was contoured as a markerless tracking structure (MTS) and exported to Vero4DRT/ExacTrac. A single field plan was delivered to the phantom film plane under static and MDTT conditions. In the patient study, the diaphragm tracking structure was contoured on CT breath-hold-exhale datasets. The MDTT workflow was applied until just prior to MV beam-on. Diaphragm as surrogate for markers: phantom data confirmed the in-house 3D back-projection program was functioning as intended. In patients, the diaphragm/marker relative positions had a mean±RMS difference of 0.70±0.89, 1.08±1.26, and 0.96±1.06mm in ML, SI, and AP directions. Diaphragm as tracking structure for MDTT: Building a respiratory-correlation model using the diaphragm as surrogate for the implanted markers was successful in phantom/patients. During the tracking verification imaging step, the phantom mean±SD difference between the image-detected and predicted "diaphragm" position was 0.52±0.18mm. The 2D film gamma (2%/2mm) comparison (static to MDTT deliveries) was 98.2%. In patients, the mean difference between the image-detected and predicted diaphragm position was 2.02±0.92mm. The planning target margin contribution from MDTT diaphragm tracking is 2.2, 5.0, and 4.7mm in the ML, SI, and AP directions. In phantom/patients, the diaphragm motion correlated well with markers' motion and could be used as a surrogate. MDTT workflows using the diaphragm as the MTS is feasible using the Vero4DRT linac and could replace the need for implanted markers for liver radiotherapy.

Respiratory Motion Compensation Using Xsight Diaphragm Tracking for Liver Tumor in CK Synchrony Treatment: A Feasibility Study

This study investigates the feasibility of using Xsight Diaphragm Tracking (XDT) for liver tumors located near or in the diaphragm during CyberKnife Synchrony Treatment. Eight patients (46 fractions) with liver tumors located near or in the diaphragm were reviewed retrospectively. Prior to motion data analysis, baselines were flattened to remove the portions with significant changes and improve accuracy. To reduce the uncertainty about sudden irregular breathing, respiratory data were calculated as a rolling average. The overall tracking accuracy based on the patient-specific respiratory curve was evaluated using E2E testing with CIRS (18023-A) dynamic phantom. Three main trajectories were observed in this work: Linearity (1/8), Linear radial type (6/8) and Hysteresis (1/1). The mean amplitude was 8.56±4.54 mm, 2.77±2.83 mm, and 4.23±3.92 mm for S-I, L-R, and A-P components. The linear trajectory patient had a more concentrated amplitude distribution. The baseline shifts were 5.88 mm (S-I), 2.53 mm (L-R), and 3.48 mm (A-P). Except for patient 2, all standard deviations of the center phase shift were less than 1 mm. The values of XDT correlation and prediction errors were 1.38±0.65 mm vs. 0.65±0.16 mm (S-I), 1.28±0.48 mm vs. 0.34±0.10 mm (L-R), and 0.96±0.32 mm vs. 0.22±0.072 mm (A-P), respectively. The strong positive correlations were amplitude vs. prediction error, SD of center phase vs. prediction error, and SD of center phase vs. amplitude. The median patient curve-based targeting accuracy was less than 1mm. Additionally, the mean target coverage for all patients with a 3 mm margin was 98.03±1.54%. This study proved that the diaphragm could be used as a tracking surrogate for liver tumors located in or near the diaphragm instead of placing golden fiducial markers. A reduction in motion amplitude and respiration training were necessary during liver SABR treatment, along with respiration control and evaluation.

Proof-of-concept for x-ray based real-time image guidance during cardiac radioablation

Cardiac radioablation offers non-invasive treatments for refractory arrhythmias. However, treatment delivery for this technique remains challenging. In this paper, we introduce the first method for real-time image guidance during cardiac radioablation for refractory atrial fibrillation on a standard linear accelerator. Our proposed method utilizes direct diaphragm tracking on intrafraction images to estimate the respiratory component of cardiac substructure motion. We compare this method to treatment scenarios without real-time image guidance using the 4D-XCAT digital phantom. Pre-treatment and intrafraction imaging was simulated for 8 phantoms with unique anatomies programmed using cardiorespiratory motion from healthy volunteers. As every voxel in the 4D-XCAT phantom is labelled precisely according to the corresponding anatomical structure, this provided ground-truth for quantitative evaluation. Tracking performance was compared to the ground-truth for simulations with and without real-time image guidance using the left atrium as an exemplar target. Differences in target volume size, mean volumetric coverage, minimum volumetric coverage and geometric error were recorded for each simulation. We observed that differences in target volume size were statistically significant (p < 0.001) across treatment scenarios and that real-time image guidance enabled reductions in target volume size ranging from 11% to 24%. Differences in mean and minimum volumetric coverage were statistically insignificant (both p = 0.35) while differences in geometric error were statistically significant (p = 0.039). The results of this study provide proof-of-concept for x-ray based real-time image guidance during cardiac radioablation.

Using the Diaphragm as a Tracking Surrogate in CyberKnife Synchrony Treatment.

BackgroundIn this study, we assessed the usefulness of diaphragm surrogate tracking in the design of a respiratory model for CyberKnife Synchrony treatment of lung tumors.Material/MethodsTwenty-four patients with lung cancer who underwent stereotactic body radiotherapy with CyberKnife between April and November 2019 were enrolled. Simulation plans for each patient were designed using Xsight lung tracking (XLT) and diaphragm tracking (DT) methods, and tumor visualization tests were performed. The offset consistency at each respiratory phase was analyzed. The relative distance along the alignment center of the superior-inferior (SI) axis in the 2 projections (dxAB), uncertainty (%), and average standard error (AvgStdErr)/maximum standard error (MAXStdErr) were also analyzed.ResultsBland-Altman analyses revealed that the average differences±standard deviation (SD) between XLT and DT tracking methods were 0.4±2.9 mm, 0.3±4.35 mm, and -1.8±6.8 mm for the SI, left-right (LR), and anterior-posterior (AP) directions, respectively. These results indicated high consistency in the SI and LR directions and poor consistency in the AP direction. Uncertainty differed significantly between XLT and DT (22.813±5.721% vs 9.384±3.799%; t=−5.236; P=0.0008), but we found no significant differences in dxAB, AvgStdErr, or MAXStdErr.ConclusionsIn the majority of cases, motion tracking by XLT and DT was consistent and synchronized in the SI directions, but not in the LR and AP directions. With a boundary margin of 0.3±4.35 mm and 1.8±6.8 mm for the LR and AP directions, DT may contribute to better implementation of CyberKnife Synchrony treatment in patients with lung tumors near the diaphragm that cannot be seen in tumor visualization tests.

Regression model-based real-time markerless tumor tracking with fluoroscopic images for hepatocellular carcinoma

PurposeWe have developed a new method to track tumor position using fluoroscopic images, and evaluated it using hepatocellular carcinoma case data. MethodsOur method consists of a training stage and a tracking stage. In the training stage, the model data for the positional relationship between the diaphragm and the tumor are calculated using four-dimensional computed tomography (4DCT) data. The diaphragm is detected along a straight line, which was chosen to avoid 4DCT artifact. In the tracking stage, the tumor position on the fluoroscopic images is calculated by applying the model to the diaphragm. Using data from seven liver cases, we evaluated four metrics: diaphragm edge detection error, modeling error, patient setup error, and tumor tracking error. We measured tumor tracking error for the 15 fluoroscopic sequences from the cases and recorded the computation time. ResultsThe mean positional error in diaphragm tracking was 0.57 ± 0.62 mm. The mean positional error in tumor tracking in three-dimensional (3D) space was 0.63 ± 0.30 mm by modeling error, and 0.81–2.37 mm with 1–2 mm setup error. The mean positional error in tumor tracking in the fluoroscopy sequences was 1.30 ± 0.54 mm and the mean computation time was 69.0 ± 4.6 ms and 23.2 ± 1.3 ms per frame for the training and tracking stages, respectively. ConclusionsOur markerless tracking method successfully estimated tumor positions. We believe our results will be useful in increasing treatment accuracy for liver cases.

Real-time direct diaphragm tracking using kV imaging on a standard linear accelerator.

As the predominant driver of respiratory motion, the diaphragm represents a key surrogate for motion management during the irradiation of thoracic cancers. Existing approaches to diaphragm tracking often produce phase-based estimates, suffer from lateral side failures or are not executable in real-time. In this paper, we present an algorithm that continuously produces real-time estimates of three-dimensional (3D) diaphragm position using kV images acquired on a standard linear accelerator. Patient-specific 3D diaphragm models were generated via automatic segmentation on end-exhale four-dimensional-computed tomography (4D-CT) images. The estimated trajectory of diaphragmatic motion, referred to as the principal motion vector, was obtained by registering end-exhale to end-inhale 4D-CT images. Two-dimensional (2D) diaphragm masks were generated by forward-projecting 3D models over the complement of angles spanned during kV image acquisition. For each kV image, diaphragm position was determined by shifting angle-matched 2D masks along the principal motion vector and selecting the position of highest contrast on a vertical difference image. Retrospective analysis was performed using 22 cone beam CT (CBCT) image sequences for six lung cancer patients across two datasets. Given the current lack of objective ground truth for diaphragm position, our algorithm was evaluated by examining its ability to track implanted markers. Simple linear regression was used to construct 3D marker motion models and estimation errors were computed as the difference between estimated and ground truth marker positions. Additionally, Pearson correlation coefficients were used to characterize diaphragm-marker correlation. The mean±standard deviation of the estimation errors across all image sequences was -0.1±0.7mm, -0.1±1.8mm and 0.2±1.4mm in the LR, SI, and AP directions respectively. The 95th percentile of the absolute errors ranged over 0.5-3.1mm, 1.6-6.7mm, and 1.2-4.0mm in the LR, SI, and AP directions, respectively. The mean±standard deviation of diaphragm-marker correlations over all image sequences was -0.07±0.57, 0.67±0.49, and 0.29±0.52 in the LR, SI, and AP directions, respectively. Diaphragm-marker correlation was observed to be highly dependent on marker position. Mean correlation along the SI axis ranged over 0.91-0.93 for markers situated in the lower lobes of the lung, while correlations ranging over -0.51-0.79 were observed for markers situated in the upper and middle lobes. This work advances a new approach to real-time direct diaphragm tracking in realistic treatment scenarios. By achieving continuous estimates of diaphragmatic motion, the proposed method has applications for both markerless tumor tracking and respiratory binning in 4D-CBCT.

2D/4D marker-free tumor tracking using 4D CBCT as the reference image

Tumor motion caused by respiration is an important issue in image-guided radiotherapy. A 2D/4D matching method between 4D volumes derived from cone beam computed tomography (CBCT) and 2D fluoroscopic images was implemented to track the tumor motion without the use of implanted markers. In this method, firstly, 3DCBCT and phase-rebinned 4DCBCT are reconstructed from cone beam acquisition. Secondly, 4DCBCT volumes and a streak-free 3DCBCT volume are combined to improve the image quality of the digitally reconstructed radiographs (DRRs). Finally, the 2D/4D matching problem is converted into a 2D/2D matching between incoming projections and DRR images from each phase of the 4DCBCT. The diaphragm is used as a target surrogate for matching instead of using the tumor position directly. This relies on the assumption that if a patient has the same breathing phase and diaphragm position as the reference 4DCBCT, then the tumor position is the same. From the matching results, the phase information, diaphragm position and tumor position at the time of each incoming projection acquisition can be derived. The accuracy of this method was verified using 16 candidate datasets, representing lung and liver applications and one-minute and two-minute acquisitions. The criteria for the eligibility of datasets were described: 11 eligible datasets were selected to verify the accuracy of diaphragm tracking, and one eligible dataset was chosen to verify the accuracy of tumor tracking. The diaphragm matching accuracy was 1.88 ± 1.35 mm in the isocenter plane and the 2D tumor tracking accuracy was 2.13 ± 1.26 mm in the isocenter plane. These features make this method feasible for real-time marker-free tumor motion tracking purposes.

Multivariate regression approaches for surrogate-based diffeomorphic estimation of respiratory motion in radiation therapy

Breathing-induced location uncertainties of internal structures are still a relevant issue in the radiation therapy of thoracic and abdominal tumours. Motion compensation approaches like gating or tumour tracking are usually driven by low-dimensional breathing signals, which are acquired in real-time during the treatment. These signals are only surrogates of the internal motion of target structures and organs at risk, and, consequently, appropriate models are needed to establish correspondence between the acquired signals and the sought internal motion patterns. In this work, we present a diffeomorphic framework for correspondence modelling based on the Log-Euclidean framework and multivariate regression. Within the framework, we systematically compare standard and subspace regression approaches (principal component regression, partial least squares, canonical correlation analysis) for different types of common breathing signals (1D: spirometry, abdominal belt, diaphragm tracking; multi-dimensional: skin surface tracking). Experiments are based on 4D CT and 4D MRI data sets and cover intra- and inter-cycle as well as intra- and inter-session motion variations. Only small differences in internal motion estimation accuracy are observed between the 1D surrogates. Increasing the surrogate dimensionality, however, improved the accuracy significantly; this is shown for both 2D signals, which consist of a common 1D signal and its time derivative, and high-dimensional signals containing the motion of many skin surface points. Eventually, comparing the standard and subspace regression variants when applied to the high-dimensional breathing signals, only small differences in terms of motion estimation accuracy are found.

Manifold learning for image-based breathing gating in ultrasound and MRI

Respiratory motion is a challenging factor for image acquisition and image-guided procedures in the abdominal and thoracic region. In order to address the issues arising from respiratory motion, it is often necessary to detect the respiratory signal. In this article, we propose a novel, purely image-based retrospective respiratory gating method for ultrasound and MRI. Further, we apply this technique to acquire breathing-affected 4D ultrasound with a wobbler probe and, similarly, to create 4D MR with a slice stacking approach. We achieve the gating with Laplacian eigenmaps, a manifold learning technique, to determine the low-dimensional manifold embedded in the high-dimensional image space. Since Laplacian eigenmaps assign to each image frame a coordinate in low-dimensional space by respecting the neighborhood relationship, they are well suited for analyzing the breathing cycle. We perform the image-based gating on several 2D and 3D ultrasound datasets over time, and quantify its very good performance by comparing it to measurements from an external gating system. For MRI, we perform the manifold learning on several datasets for various orientations and positions. We achieve very high correlations by a comparison to an alternative gating with diaphragm tracking.

Clinical Evaluation of Respiratory Motion Compensation for Anatomical Roadmap Guided Cardiac Electrophysiology Procedures

X-ray fluoroscopically guided cardiac electrophysiological procedures are routinely carried out for diagnosis and treatment of cardiac arrhythmias. X-ray images have poor soft tissue contrast and, for this reason, overlay of static 3-D roadmaps derived from preprocedural volumetric data can be used to add anatomical information. However, the registration between the 3-D roadmap and the 2-D X-ray image can be compromised by patient respiratory motion. Three methods were designed and evaluated to correct for respiratory motion using features in the 2-D X-ray images. The first method is based on tracking either the diaphragm or the heart border using the image intensity in a region of interest. The second method detects the tracheal bifurcation using the generalized Hough transform and a 3-D model derived from 3-D preoperative volumetric data. The third method is based on tracking the coronary sinus (CS) catheter. This method uses blob detection to find all possible catheter electrodes in the X-ray image. A cost function is applied to select one CS catheter from all catheter-like objects. All three methods were applied to X-ray images from 18 patients undergoing radiofrequency ablation for the treatment of atrial fibrillation. The 2-D target registration errors (TRE) at the pulmonary veins were calculated to validate the methods. A TRE of 1.6 mm ± 0.8 mm was achieved for the diaphragm tracking; 1.7 mm ± 0.9 mm for heart border tracking, 1.9 mm ± 1.0 mm for trachea tracking, and 1.8 mm ± 0.9 mm for CS catheter tracking. We present a comprehensive comparison between the techniques in terms of robustness, as computed by tracking errors, and accuracy, as computed by TRE using two independent approaches.

SU‐E‐J‐158: Diaphragm‐Based and Direct Tumor Tracking in KV Rotational Cone‐Beam X‐Ray Projections Using Templates Derived from 4DCT

Purpose: To develop an algorithm for tracking lung tumors in rotational cone‐beam KV x‐ray projections based on combined markerless diaphragm and tumor tracking. Methods: A combined tracking algorithm which uses surrogate‐based tracking for angles at which the tumor is difficult to track directly is implemented. Direct tumor and diaphragm tracking are performed using template matching based on templates from 4DCT. A volume of interest is defined around the diaphragm apex and used to develop diaphragm templates. A linear model relating diaphragm apex and tumor positions is developed based on 4DCT. The tracked position of the diaphragm apex is used to calculate tumor positions based on the linear models. The two tracking signals are combined by selecting the method expected to be more accurate at each angle. Since direct tumor tracking is often difficult when the tumor is occluded by the spine and central structures in projections, diaphragm‐based tracking is used when the tumor is expected to overlap with these structures in the projections. Direct tracking is used when the tumor is expected to be more visible. The methods are applied to anatomical digital phantom and patient cases. Results: For the digital phantom, the e95 was 1.1 mm with direct tracking, 0.9 mm with diaphragm‐based tracking, and 1.1 mm with combined tracking. The correspond erms values were (0.6, 0.4, 0.6) mm. In patient 1, the e95 was 1.9 mm with direct tracking, 1.7 mm with diaphragm‐based tracking, and 1.7 mm with combined tracking. Corresponding erms values were (1.0, 0.9, 0.8) mm. In patient 2, the e95 was 4.6 mm with direct tracking, 3.0 mm with diaphragm‐based tracking, and 2.9 mm with combined tracking. The corresponding erms values were 2.5, 1.7, 1.6) mm.Conclusion: A combined tracking algorithm was developed which incorporates benefits of both diaphragm‐based and direct tumor tracking.

The Application of a Statistical Shape Model to Diaphragm Tracking in Respiratory-Gated Cardiac PET Images

Respiratory-induced diaphragm mismatch between positron emission tomography (PET) and computed tomography (CT) has been identified as a source of attenuation-correction artifact in cardiac PET. Diaphragm tracking in gated PET could therefore form part of a mismatch correction technique, where a single CT is transformed to match each PET frame. To investigate the feasibility of such a technique, a statistical shape model of the diaphragm was constructed from gated CT and applied to two gated 18F-FDG PET-CT datasets. A poor level of accuracy was obtained when the model was fitted to landmarks obtained from PET, with errors of 3.6 and 5.0 mm per landmark for the two patients, despite inclusion of the data within the model construction. However, errors were reduced to 2.4 and 1.9 mm with the incorporation of a single frame of CT landmarks. These values are closer to the baseline measure of fitting solely to CT landmarks, found to be 2.2 and 1.2 mm in this case. Excluding the datasets from the model yielded similar trends but with higher overall residual errors, indicating the need for a larger training set. Therefore, a highly trained diaphragm model could negate the need for a gated CT for diaphragm tracking, provided that information from a static CT is incorporated.

Motion compensated coronary interventional navigation by means of diaphragm tracking and elastic motion models

Current catheter tracking in the x-ray catheter laboratory during coronary interventions is performed using 2D fluoroscopy. Although this features real-time navigation on high-resolution images, drawbacks such as overlap and foreshortening exist and hamper the diagnosis and treatment process. An alternative to fluoroscopy-based tracking is device tracking by means of a magnetic tracking system (MTS). Having measured the 3D location of the interventional device, its position can be reconstructed on 3D images or virtual roadmaps of the organ or vessel structure under examination. In this paper, a method is presented which compensates the interventional device location measured by the MTS for organ motion and thus registers it dynamically to a 3D virtual roadmap. The motion compensation is accomplished by using an elastic motion model which is driven by the ECG signal and a respiratory sensor signal derived from ultrasonic diaphragm tracking. The model is updated during the intervention itself, thus allowing for a local refinement in regions which bear a complex geometric structure, such as stenoses and bifurcations. The evaluation is done by means of a phantom-based study using a dynamic heart-phantom. The mean displacement caused by the overall motion of the heart is improved from 10.4 ± 4.8 mm in the uncompensated case to 2.1 ± 1.2 mm in the motion compensated case.