Motion of the esophagus due to cardiac motion

Intrafraction Motion Management With MR-Guided Radiation Therapy

High quality radiation therapy requires highly accurate and precise dose delivery. MR-guided radiotherapy (MRgRT), integrating an MRI scanner with a linear accelerator, offers excellent quality images in the treatment room without subjecting patient to ionizing radiation. MRgRT therefore provides a powerful tool for intrafraction motion management. This paper summarizes different sources of intrafraction motion for different disease sites and describes the MR imaging techniques available to visualize and quantify intrafraction motion. It provides an overview of MR guided motion management strategies and of the current technical capabilities of the commercially available MRgRT systems. It describes how these motion management capabilities are currently being used in clinical studies, protocols and provides a future outlook.

Applying a novel two-step deep learning network to improve the automatic delineation of esophagus in non-small cell lung cancer radiotherapy

Purpose

To introduce a model for automatic segmentation of thoracic organs at risk (OARs), especially the esophagus, in non-small cell lung cancer radiotherapy, using a novel two-step deep learning network.

Materials and methods

A total of 59 lung cancer patients' CT images were enrolled, of which 39 patients were randomly selected as the training set, 8 patients as the validation set, and 12 patients as the testing set. The automatic segmentations of the six OARs including the esophagus were carried out. In addition, two sets of treatment plans were made on the basis of the manually delineated tumor and OARs (Plan1) as well as the manually delineated tumor and the automatically delineated OARs (Plan2). The Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and average surface distance (ASD) of the proposed model were compared with those of U-Net as a benchmark. Next, two groups of plans were also compared according to the dose-volume histogram parameters.

Results

The DSC, HD95, and ASD of the proposed model were better than those of U-Net, while the two groups of plans were almost the same. The highest mean DSC of the proposed method was 0.94 for the left lung, and the lowest HD95 and ASD were 3.78 and 1.16 mm for the trachea, respectively. Moreover, the DSC reached 0.73 for the esophagus.

Conclusions

The two-step segmentation method can accurately segment the OARs of lung cancer. The mean DSC of the esophagus realized preliminary clinical significance (>0.70). Choosing different deep learning networks based on different characteristics of organs offers a new option for automatic segmentation in radiotherapy.

CArdiac and REspiratory adaptive Computed Tomography (CARE-CT): a proof-of-concept digital phantom study

Current respiratory 4DCT imaging for high-dose rate thoracic radiotherapy treatments are negatively affected by the complex interaction of cardiac and respiratory motion. We propose an imaging method to reduce artifacts caused by thoracic motion, CArdiac and REspiratory adaptive CT (CARE-CT), that monitors respiratory motion and ECG signals in real-time, triggering CT acquisition during combined cardiac and respiratory bins. Using a digital phantom, conventional 4DCT and CARE-CT acquisitions for nineteen patient-measured physiological traces were simulated. Ten respiratory bins were acquired for conventional 4DCT scans and ten respiratory bins during cardiac diastole were acquired for CARE-CT scans. Image artifacts were quantified for 10 common thoracic organs at risk (OAR) substructures using the differential normalized cross correlation between axial slices (ΔNCC), mean squared error (MSE) and sensitivity. For all images, on average, CARE-CT improved the ΔNCC for 18/19 and the MSE and sensitivity for all patient traces. The ΔNCC was reduced for all cardiac OARs (mean reduction 21%). The MSE was reduced for all OARs (mean reduction 36%). In the digital phantom study, the average scan time was increased from 1.8 ± 0.4 min to 7.5 ± 2.2 min with a reduction in average beam on time from 98 ± 28 s to 45 s using CARE-CT compared to conventional 4DCT. The proof-of-concept study indicates the potential for CARE-CT to image the thorax in real-time during the cardiac and respiratory cycle simultaneously, to reduce image artifacts for common thoracic OARs.

Optimal criteria for predicting lymph node metastasis in esophageal squamous cell carcinoma by anatomical location using preoperative computed tomography: a retrospective cohort study

PURPOSE

Predicting lymph node metastasis (LNM) in esophageal squamous cell carcinoma (ESCC) is critical for selecting appropriate treatments despite the low accuracy of computed tomography (CT) for detecting LNM. Variation in potential nodal sizes among locations or patients' clinicopathological background factors may impact the diagnostic quality. This study explored the optimal criteria and diagnostic ability of CT by location.

METHODS

We retrospectively reviewed preoperative CT scans of 229 patients undergoing curative esophagectomy. We classified nodal stations into six groups: Cervical (C), Right-upper mediastinal (U_R), Left-upper mediastinal (U_L), Middle mediastinal (M), Lower mediastinal (L), and Abdominal (A). We then measured the short-axial diameter (SAD) of the largest lymph node in each area. We used receiver operating characteristics analyses to evaluate the CT diagnostic ability and determined the cut-off values for the SAD in all groups.

RESULTS

Optimal cut-offs were 6.5 mm (M), 6 mm (C, L, and A), and 5 mm (U_R and U_L). Diagnostic abilities differed among locations, and U_R had the highest sensitivity. A multivariate analysis showed poor differentiation to be an independent risk factor for a false-negative diagnosis (p = 0.044).

CONCLUSIONS

Optimal criteria and diagnostic abilities for predicting LNM in ESCC varied among locations, and poor differentiation might contribute to failure to detect LNM.

Review of Deep Learning Based Automatic Segmentation for Lung Cancer Radiotherapy

Lung cancer is the leading cause of cancer-related mortality for males and females. Radiation therapy (RT) is one of the primary treatment modalities for lung cancer. While delivering the prescribed dose to tumor targets, it is essential to spare the tissues near the targets-the so-called organs-at-risk (OARs). An optimal RT planning benefits from the accurate segmentation of the gross tumor volume and surrounding OARs. Manual segmentation is a time-consuming and tedious task for radiation oncologists. Therefore, it is crucial to develop automatic image segmentation to relieve radiation oncologists of the tedious contouring work. Currently, the atlas-based automatic segmentation technique is commonly used in clinical routines. However, this technique depends heavily on the similarity between the atlas and the image segmented. With significant advances made in computer vision, deep learning as a part of artificial intelligence attracts increasing attention in medical image automatic segmentation. In this article, we reviewed deep learning based automatic segmentation techniques related to lung cancer and compared them with the atlas-based automatic segmentation technique. At present, the auto-segmentation of OARs with relatively large volume such as lung and heart etc. outperforms the organs with small volume such as esophagus. The average Dice similarity coefficient (DSC) of lung, heart and liver are over 0.9, and the best DSC of spinal cord reaches 0.9. However, the DSC of esophagus ranges between 0.71 and 0.87 with a ragged performance. In terms of the gross tumor volume, the average DSC is below 0.8. Although deep learning based automatic segmentation techniques indicate significant superiority in many aspects compared to manual segmentation, various issues still need to be solved. We discussed the potential issues in deep learning based automatic segmentation including low contrast, dataset size, consensus guidelines, and network design. Clinical limitations and future research directions of deep learning based automatic segmentation were discussed as well.

Measuring breathing induced oesophageal motion and its dosimetric impact

PURPOSE

Stereotactic body radiation therapy allows for a precise dose delivery. Organ motion bears the risk of undetected high dose healthy tissue exposure. An organ very susceptible to high dose is the oesophagus. Its low contrast on CT and the oblong shape render motion estimation difficult. We tackle this issue by modern algorithms to measure oesophageal motion voxel-wise and estimate motion related dosimetric impacts.

METHODS

Oesophageal motion was measured using deformable image registration and 4DCT of 11 internal and 5 public datasets. Current clinical practice of contouring the organ on 3DCT was compared to timely resolved 4DCT contours. Dosimetric impacts of the motion were estimated by analysing the trajectory of each voxel in the 4D dose distribution. Finally an organ motion model for patient-wise comparisons was built.

RESULTS

Motion analysis showed mean absolute maximal motion amplitudes of 4.55 ± 1.81 mm left-right, 5.29 ± 2.67 mm anterior-posterior and 10.78 ± 5.30 mm superior-inferior. Motion between cohorts differed significantly. In around 50% of the cases the dosimetric passing criteria was violated. Contours created on 3DCT did not cover 14% of the organ for 50% of the respiratory cycle and were around 38% smaller than the union of all 4D contours. The motion model revealed that the maximal motion is not limited to the lower part of the organ. Our results showed motion amplitudes higher than most reported values in the literature and that motion is very heterogeneous across patients.

CONCLUSIONS

Individual motion information should be considered in contouring and planning.

A study to investigate the influence of cardiac motion on the robustness of pencil beam scanning proton plans in oesophageal cancer

While proton therapy offers an excellent dose conformity and sparing of organs at risk, this can be compromised by uncertainties, e.g. organ motion. This study aimed to investigate the influence of cardiac motion on the contoured oesophagus using electrocardiogram-triggered imaging and to assess the impact of this motion on the robustness of proton therapy plans in oesophageal cancer patients. Limited cardiac-induced motion of the oesophagus was observed with a negligible impact on the robustness of proton therapy plans. Therefore, our data suggest that cardiac motion may be safely ignored in the robust optimisation strategy for proton planning in oesophageal cancer.

Tissue-specific deformable image registration using a spatial-contextual filter

Intensity-based deformable registration with spatial-invariant regularization generally fails when distinct motion exists across different types of tissues. The purpose of this work was to develop and validate a new regularization approach for deformable image registration that is tissue-specific and able to handle motion discontinuities. Our approach was built upon a Demons registration framework, and used the image context supplementing the original spatial constraint to regularize displacement vector fields in iterative image registration process. The new regularization was implemented as a spatial-contextual filter, which favors the motion vectors within the same tissue type but penalizes the motion vectors from different tissues. This approach was validated using five public lung cancer patients, each with 300 landmark pairs identified by a thoracic radiation oncologist. The mean and standard deviation of the landmark registration errors were 1.3 ± 0.8 mm, compared with those of 2.3 ± 2.9 mm using the original Demons algorithm. Particularly, for the case with the largest initial landmark displacement of 15 ± 9 mm, the modified Demons algorithm had a registration error of 1.3 ± 1.1 mm, while the original Demons algorithm had a registration error of 3.6 ± 5.9 mm. We also qualitatively evaluated the modified Demons algorithm using two difficult cases in our routine clinic: one lung case with large sliding motion and one head and neck case with large anatomical changes in air cavity. Visual evaluation on the deformed image created by the deformable image registration showed that the modified Demons algorithm achieved reasonable registration accuracy, but the original Demons algorithm produced distinct registration errors.

Automatic registration of 2D MR cine images for swallowing motion estimation

PURPOSE

To automate the estimation of swallowing motion from 2D MR cine images using deformable registration for future applications of personalized margin reduction in head and neck radiotherapy and outcome assessment of radiation-associated dysphagia.

METHODS

Twenty-one patients with serial 2D FSPGR-MR cine scans of the head and neck conducted through the course of definitive radiotherapy for oropharyngeal cancer. Included patients had at least one cine scan before, during, or after radiotherapy, with a total of 52 cine scans. Contours of 7 swallowing related regions-of-interest (ROIs), including pharyngeal constrictor, epiglottis, base of tongue, geniohyoid, hyoid, soft palate, and larynx, were manually delineated from consecutive frames of the cine scan covering at least one swallowing cycle. We applied a modified thin-plate-spline robust-point-matching algorithm to register the point sets of each ROI automatically over frames. The deformation vector fields from the registration were then used to estimate the motion during swallowing for each ROI. Registration errors were estimated by comparing the deformed contours with the manual contours.

RESULTS

On average 22 frames of each cine scan were contoured. The registration for one cine scan (7 ROIs over 22 frames) on average took roughly 22 minutes. A number of 8018 registrations were successfully batch processed without human interaction after the contours were drawn. The average registration error for all ROIs and all patients was 0.36 mm (range: 0.06 mm- 2.06 mm). Larynx had the average largest motion in superior direction of all structures under consideration (range: 0.0 mm- 58.7 mm). Geniohyoid had the smallest overall motion of all ROIs under consideration and the superior-inferior motion was larger than the anterior-posterior motion for all ROIs.

CONCLUSION

We developed and validated a deformable registration framework to automate the estimation of swallowing motion from 2D MR cine scans.

Intrafraction esophageal motion in patients with clinical T1N0 esophageal cancer

Aim

To investigate the intrafraction movement of the esophagus using fiducial markers.

Background

Studies on intrafraction esophageal motion using the fiducial markers are scarce.

Materials and methods

We retrospectively analyzed patients with clinical T1N0 esophageal cancer who had received fiducial markers at our hospital between July 2007 and December 2013. Real-Time Position Management System to track the patient's respiration was used, and each patient underwent three-dimensional computed tomography of the resting expiratory and inspiratory level. We used the center of the marker to calculate the distance between the expiratory and inspiratory breath-holds, which were measured with the radiotherapy treatment planning system in three directions: left-right (LR), superior-inferior (SI), and anterior-posterior (AP). The movements at each site were compared with the Kruskal-Wallis analysis and Wilcoxon rank sum test with a Bonferroni correction.

Results

A total of 101 patients with 201 fiducial markers were included. The upper, middle and lower thoracic positions had 40, 77, and 84 markers, respectively. The mean absolute magnitudes of the shifts (standard deviation) were 0.18 (0.19) cm, 0.68 (0.46) cm, and 0.24 (0.24) cm in the LR, SI, and AP directions, respectively. From the cumulative frequency distribution, we assumed that 0.35 cm LR, 0.8 cm SI, and 0.3 cm AP in the upper; 0.5 cm LR, 1.55 cm SI, and 0.55 cm AP in the middle; and 0.75 cm LR, 1.9 cm SI, and 0.95 cm AP in the lower thoracic esophagus covered 95% of the cases.

Conclusions

The internal margin based on the site of esophagus was estimated.

A rapid, computational approach for assessing interfraction esophageal motion for use in stereotactic body radiation therapy planning

Purpose

We present a rapid computational method for quantifying interfraction motion of the esophagus in patients undergoing stereotactic body radiation therapy on a magnetic resonance (MR) guided radiation therapy system.

Methods and materials

Patients who underwent stereotactic body radiation therapy had simulation computed tomography (CT) and on-treatment MR scans performed. The esophagus was contoured on each scan. CT contours were transferred to MR volumes via rigid registration. Digital Imaging and Communications in Medicine files containing contour points were exported to MATLAB. In-plane CT and MR contour points were spline interpolated, yielding boundaries with centroid positions, C_CT and C_MR. MR contour points lying outside of the CT contour were extracted. For each such point, B_MR(j), a segment from C_CT intersecting B_MR(j), was produced; its intersection with the CT contour, B_CT(i), was calculated. The length of the segment S_ij, between B_CT(i) and B_MR(j), was found. The orientation θ was calculated from S_ij vector components:

θ = arctan[(S_ij)_y / (S_ij)_x]

A set of segments {S_ij} was produced for each slice and binned by quadrant with 0° < θ ≤ 90°, 90° < θ ≤ 180°, 180° < θ ≤ 270°, and 270° < θ ≤ 360° for the left anterior, right anterior, right posterior, and left posterior quadrants, respectively. Slices were binned into upper, middle, and lower esophageal (LE) segments.

Results

Seven patients, each having 3 MR scans, were evaluated, yielding 1629 axial slices and 84,716 measurements. The LE segment exhibited the greatest magnitude of motion. The mean LE measurements in the left anterior, left posterior, right anterior, and right posterior were 5.2 ± 0.07 mm, 6.0 ± 0.09 mm, 4.8 ± 0.08 mm, and 5.1 ± 0.08 mm, respectively. There was considerable interpatient variability.

Conclusions

The LE segment exhibited the greatest magnitude of mobility compared with the middle and upper esophageal segments. A novel computational method enables personalized, nonuniform esophageal margins to be tailored to individual patients.

Motion correction options in PET/MRI

Subject motion is unavoidable in clinical and research imaging studies. Breathing is the most important source of motion in whole-body PET and MRI studies, affecting not only thoracic organs but also those in the upper and even lower abdomen. The motion related to the pumping action of the heart is obviously relevant in high-resolution cardiac studies. These two sources of motion are periodic and predictable, at least to a first approximation, which means certain techniques can be used to control the motion (eg, by acquiring the data when the organ of interest is relatively at rest). Additionally, nonperiodic and unpredictable motion can also occur during the scan. One obvious limitation of methods relying on external devices (eg, respiratory bellows or the electrocardiogram signal to monitor the respiratory or cardiac cycle, respectively) to trigger or gate the data acquisition is that the complex motion of internal organs cannot be fully characterized. However, detailed information can be obtained using either the PET or MRI data (or both) allowing the more complete characterization of the motion field so that a motion model can be built. Such a model and the information derived from simple external devices can be used to minimize the effects of motion on the collected data. In the ideal case, all the events recorded during the PET scan would be used to generate a motion-free or corrected PET image. The detailed motion field can be used for this purpose by applying it to the PET data before, during, or after the image reconstruction. Integrating all these methods for motion control, characterization, and correction into a workflow that can be used for routine clinical studies is challenging but could potentially be extremely valuable given the improvement in image quality and reduction of motion-related image artifacts.

Prospective observer and software-based assessment of magnetic resonance imaging quality in head and neck cancer: Should standard positioning and immobilization be required for radiation therapy applications?

PURPOSE

The purpose of this study was to investigate the potential of a head and neck magnetic resonance simulation and immobilization protocol on reducing motion-induced artifacts and improving positional variance for radiation therapy applications.

METHODS AND MATERIALS

Two groups (group 1, 17 patients; group 2, 14 patients) of patients with head and neck cancer were included under a prospective, institutional review board-approved protocol and signed informed consent. A 3.0-T magnetic resonance imaging (MRI) scanner was used for anatomic and dynamic contrast-enhanced acquisitions with standard diagnostic MRI setup for group 1 and radiation therapy immobilization devices for group 2 patients. The impact of magnetic resonance simulation/immobilization was evaluated qualitatively by 2 observers in terms of motion artifacts and positional reproducibility and quantitatively using 3-dimensional deformable registration to track intrascan maximum motion displacement of voxels inside 7 manually segmented regions of interest.

RESULTS

The image quality of group 2 (29 examinations) was significantly better than that of group 1 (50 examinations) as rated by both observers in terms of motion minimization and imaging reproducibility (P < .0001). The greatest average maximum displacement was at the region of the larynx in the posterior direction for patients in group 1 (17 mm; standard deviation, 8.6 mm), whereas the smallest average maximum displacement was at the region of the posterior fossa in the superior direction for patients in group 2 (0.4 mm; standard deviation, 0.18 mm). Compared with group 1, maximum regional motion was reduced in group 2 patients in the oral cavity, floor of mouth, oropharynx, and larynx regions; however, the motion reduction reached statistical significance only in the regions of the oral cavity and floor of mouth (P < .0001).

CONCLUSIONS

The image quality of head and neck MRI in terms of motion-related artifacts and positional reproducibility was greatly improved by use of radiation therapy immobilization devices. Consequently, immobilization with external and intraoral fixation in MRI examinations is required for radiation therapy application.