WE-SAMS-AUD-01: 4D Scanning: Imaging/Planning

The purpose of CT simulation in radiotherapy is to acquire patient geometrical information and to build a patient geometrical model for treatment planning. Errors in patient model caused by motion artifacts will influence all treatment fractions and therefore should be handled carefully. Due to the tumor respiratory motion, the captured tumor position and shape can be heavily distorted. The distortions along the axis of motion could result in either a lengthening or shortening of the target. The center of the imaged target can be displaced by as much as the amplitude of the motion.A newly developed technique that can reduce motion artifacts and provide patient geometry throughout the whole breathing cycle is called respiration‐correlated or 4D CT scan. The basic idea for 4D CT scan is that, at every position of interest along patient's long axis, images are over‐sampled and each image is tagged with breathing phase information. After the scan is done, images are sorted based on the corresponding breathing phase signals. Thus, many 3D CT sets are obtained, each corresponding to a particular breathing phase, and together constitutes a 4D CT set that covers that the whole breathing cycle. 4D CT scan has been developed at various institutions with slightly different flavors. In this lecture, we will provide an overview of various implementations of 4D CT scan.4D CT scan can be used to account for respiratory motion to generate images with less distortion than 3D CT scan. 4D images also contain respiratory motion information of tumor and organs that is not available in a 3D CT image. This technology can be used for respiratory‐gated treatment to identify the patient‐specific phase of minimum tumor motion, determine residual tumor motion within the gate interval, and compare treatment plans at different phases. It can also be used for non‐gated treatment planning to define ITV by combining gross tumor volume at all breathing phases or using a method called maximum intensity projection. Of course 4D CT will also play a vital role in the futuristic 4D radiotherapy where the tumor is tracked dynamically during the treatment using multi‐leaf collimator.Existing problems for 4D CT scan include the increased imaging dose, CT tube heating, and data management. More importantly, one has to keep in mind that 4D CT scan is not really 4D. Temporal information is mapped into one breathing cycle. Irregular respiration will cause artifacts in 4D CT images. Patient coaching can improve the regularity of breathing pattern and thus reduce the residual artifacts. However this issue still deserves further studies.Educational Objectives:1. Understand the origin and magnitude of motion artifacts in free breathing helical CT scan.2. Understand how 4D CT scan works.3. Understand how 4D CT can be used in radiotherapy.4. Understand the remaining artifacts in 4D CT scan and possible future improvements.

Analysis of Artifacts in Four-dimensional CT Images of 50 Abdominal and Thoracic Radiotherapy Patients

Four-dimensional computed tomography (4D CT) images have been recently adopted in radiation treatment planning for thoracic and abdominal cancers to explicitly define respiratory motion and anatomy deformation. However, significant image distortions (artifacts) exist in 4D CT images that may affect accurate tumor delineation and the shape representation of normal anatomy. In this study, the authors present a patient-specific respiratory motion model, based on principal component analysis (PCA) of motion vectors obtained from deformable image registration, with the main goal of reducing image artifacts caused by irregular motion during 4D CT acquisition. For a 4D CT image set of a specific patient, the authors calculated displacement vector fields relative to a reference phase, using an in-house deformable image registration method. The authors then used PCA to decompose each of the displacement vector fields into linear combinations of principal motion bases. The authors have demonstrated that the regular respiratory motion of a patient can be accurately represented by a subspace spanned by three principal motion bases and their projections. These projections were parameterized using a spline model to allow the reconstruction of the displacement vector fields at any given phase in a respiratory cycle. Finally, the displacement vector fields were used to deform the reference CT image to synthesize CT images at the selected phase with much reduced image artifacts. The authors evaluated the performance of the in-house deformable image registration method using benchmark datasets consisting of ten 4D CT sets annotated with 300 landmark pairs that were approved by physicians. The initial large discrepancies across the landmark pairs were significantly reduced after deformable registration, and the accuracy was similar to or better than that reported by state-of-the-art methods. The proposed motion model was quantitatively validated on 4D CT images of a phantom and a lung cancer patient by comparing the synthesized images and the original images at different phases. The synthesized images matched well with the original images. The motion model was used to reduce irregular motion artifacts in the 4D CT images of three lung cancer patients. Visual assessment indicated that the proposed approach could reduce severe image artifacts. The shape distortions around the diaphragm and tumor regions were mitigated in the synthesized 4D CT images. The authors have derived a mathematical model to represent the regular respiratory motion from a patient-specific 4D CT set and have demonstrated its application in reducing irregular motion artifacts in 4D CT images. The authors' approach can mitigate shape distortions of anatomy caused by irregular breathing motion during 4D CT acquisition.

Respiratory motion represents a major problem in radiotherapy of thoracic and abdominal tumors. Methods for compensation require comprehensive knowledge of underlying dynamics. Therefore, 4D (= 3D + t) CT data can be helpful. But modern CT scanners cannot scan a large region of interest simultaneously. So patients have to be scanned in segments. Commonly used approaches for reconstructing the data segments into 4D CT images cause motion artifacts. In order to reduce the artifacts, a new method for 4D CT reconstruction is presented. The resulting data sets are used to analyze respiratory motion. Spatiotemporal CT image sequences of lung cancer patients were acquired using a multi-slice CT in cine mode during free breathing. 4D CT reconstruction was done by optical flow based temporal interpolation. The resulting 4D image data were compared with data generated by the commonly used nearest neighbor reconstruction. Subsequent motion analysis is mainly concerned with tumor mobility. The presented optical flow-based method enables the reconstruction of 3D CT images at arbitrarily chosen points of the patient's breathing cycle. A considerable reduction of motion artifacts has been proven in eight patient data sets. Motion analysis showed that tumor mobility differs strongly between the patients. Due to the proved reduction of motion artifacts, the optical flow-based 4D CT reconstruction offers the possibility of high-quality motion analysis. Because the method is based on an interpolation scheme, it additionally has the potential to enable the reconstruction of 4D CT data from a lesser number of scans.

lower lobe lung tumors move with amplitudes of up to 2 cm due to respiration. To reduce respiration imaging artifacts in planning CT scans, 4D imaging techniques are used. Currently, we use a single (midventilation) frame of the 4D data set for clinical delineation of structures and radiotherapy planning. A single frame, however, often contains artifacts due to breathing irregularities, and is noisier than a conventional CT scan since the exposure per frame is lower. Moreover, the tumor may be displaced from the mean tumor position due to hysteresis. The aim of this work is to develop a framework for the acquisition of a good quality scan representing all scanned anatomy in the mean position by averaging transformed (deformed) CT frames, i.e., canceling out motion. A nonrigid registration method is necessary since motion varies over the lung. 4D and inspiration breath-hold (BH) CT scans were acquired for 13 patients. An iterative multiscale motion estimation technique was applied to the 4D CT scan, similar to optical flow but using image phase (gray-value transitions from bright to dark and vice versa) instead. From the (4D) deformation vector field (DVF) derived, the local mean position in the respiratory cycle was computed and the 4D DVF was modified to deform all structures of the original 4D CT scan to this mean position. A 3D midposition (MidP) CT scan was then obtained by (arithmetic or median) averaging of the deformed 4D CT scan. Image registration accuracy, tumor shape deviation with respect to the BH CT scan, and noise were determined to evaluate the image fidelity of the MidP CT scan and the performance of the technique. Accuracy of the used deformable image registration method was comparable to established automated locally rigid registration and to manual landmark registration (average difference to both methods < 0.5 mm for all directions) for the tumor region. From visual assessment, the registration was good for the clearly visible features (e.g., tumor and diaphragm). The shape of the tumor, with respect to that of the BH CT scan, was better represented by the MidP reconstructions than any of the 4D CT frames (including MidV; reduction of "shape differences" was 66%). The MidP scans contained about one-third the noise of individual 4D CT scan frames. We implemented an accurate method to estimate the motion of structures in a 4D CT scan. Subsequently, a novel method to create a midposition CT scan (time-weighted average of the anatomy) for treatment planning with reduced noise and artifacts was introduced. Tumor shape and position in the MidP CT scan represents that of the BH CT scan better than MidV CT scan and, therefore, was found to be appropriate for treatment planning.

Artifacts affect 4D CT images due to breathing irregularities or incorrect breathing phase identification. The purpose of this study is the reduction of artifacts in sorted 4D CT images. The assumption is that the use of multiple respiratory related signals may reduce uncertainties and increase robustness in breathing phase identification. Multiple respiratory related signals were provided by infrared 3D localization of a configuration of markers placed on the thoracoabdominal surface. Multidimensional K-means clustering was used for retrospective 4D CT image sorting, which was based on multiple marker variables, in order to identify clusters representing different breathing phases. The proposed technique was tested on computational simulations, phantom experimental acquisitions, and clinical data coming from two patients. Computational simulations provided a controlled and noise-free condition for testing the clustering technique on regular and irregular breathing signals, including baseline drift, time variant amplitude, time variant frequency, and end-expiration plateau. Specific attention was given to cluster initialization. Phantom experiments involved two moving phantoms fitted with multiple markers. Phantoms underwent 4D CT acquisition while performing controlled rigid motion patterns and featuring end-expiration plateau. Breathing cycle period and plateau duration were controlled by means of weights leaned upon the phantom during repeated 4D CT scans. The implemented sorting technique was applied to clinical 4D CT scans acquired on two patients and results were compared to conventional sorting methods. For computational simulations and phantom studies, the performance of the multidimensional clustering technique was evaluated by measuring the repeatability in identifying the breathing phase among adjacent couch positions and the uniformity in sampling the breathing cycle. When breathing irregularities were present, the clustering technique consistently improved breathing phase identification with respect to conventional sorting methods based on monodimensional signals. In patient studies, a qualitative comparison was performed between corresponding breathing phases of 4D CT images obtained by conventional sorting methods and by the described clustering technique. Artifact reduction was clearly observable on both data set especially in the lower lung region. The implemented multiple point method demonstrated the ability to reduce artifacts in 4D CT imaging. Further optimization and development are needed to make the most of the availability of multiple respiratory related variables and to extend the method to 4D CT-PET hybrid scan.

In many patients respiratory motion causes motion artifacts in CT images, thereby inhibiting precise treatment planning and lowering the ability to target radiation to tumors. The 4D Phantom, which includes a 3D stage and a 1D stage that each are capable of arbitrary motion and timing, was developed to serve as an end-to-end radiation therapy QA device that could be used throughout CT imaging, radiation therapy treatment planning, and radiation therapy delivery. The dynamic accuracy of the system was measured with a camera system. The positional error was found to be equally likely to occur in the positive and negative directions for each axis, and the stage was within 0.1 mm of the desired position 85% of the time. In an experiment designed to use the 4D Phantom's encoders to measure trial-to-trial precision of the system, the 4D Phantom reproduced the motion during variable bag ventilation of a transponder that had been bronchoscopically implanted in a canine lung. In this case, the encoder readout indicated that the stage was within 10 microns of the sent position 94% of the time and that the RMS error was 7 microns. Motion artifacts were clearly visible in 3D and respiratory-correlated (4D) CT scans of phantoms reproducing tissue motion. In 4D CT scans, apparent volume was found to be directly correlated to instantaneous velocity. The system is capable of reproducing individual patient-specific tissue trajectories with a high degree of accuracy and precision and will be useful for end-to-end radiation therapy QA.

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Four‐dimensional computed tomography (4D CT), also called respiratory correlated CT, was first published on, and commercially available in 2003. Since then this technology has gained widespread acceptance and clinical use. The 4D CT acquisition concept is relatively simple: acquire CT scans synchronized with the respiratory cycle such that sufficient data exists to reconstruct a volumetric image at or near a number of respiratory phases. There are a variety of commercial implementations of the basic acquisition concept. There are several limitations of 4D CT. One problem is artifacts. Though 4D CT was developed to account for the deleterious effects of respiratory motion on 3D image acquisition techniques, irregular respiratory motion causes artifacts in 4D scans. Free breathing, unlike the cardiac cycle on which the technology is based, is typically irregular and artifacts can be found in nearly all 4D CT scans with current technology. There are several strategies to deal with this irregular signal: (1) improve the regularity of the signal itself, using audio‐visual biofeedback tools, (2) during imaging only acquire data during regular cycles and (3) use post‐processing methods to reduce artifacts. Another limitation of 4D CT, at least in its application to radiotherapy, is that the time interval during which images are acquired over, ∼5 seconds per anatomic location for a ∼1 minute total scan time, is a small sample of the respiratory induced anatomic changes occurring over a course of radiotherapy, which can be between a single fraction to several weeks. Despite these limitations 4D CT has been found to be very useful for a number of applications in radiotherapy planning. 4D CT can be used for measuring target motion, and motion inclusive, respiratory gated and target tracking treatment scenarios. Fully utilizing 4D CTimages for treatment planning requires deformable image registration algorithms for automatic contour propagation and dose summation. For this application, several studies have shown that current algorithms have acceptable geometric performance with respect to expert observers. The dosimetric impact of the geometric uncertainty of deformable registration algorithms appears low. A more recent development in 4D CT is the extension to 4D cone beam CT (4D CBCT) which offers the ability for pre‐treatment anatomic position and motion verification. This application is a major innovation and will increase treatment accuracy. Residual uncertainties from anatomic changes between the time of imaging and time of treatment have been observed, and intra‐fraction position monitoring is desired to complement 4D CBCT. Educational Objectives: 1. Understand the principles of 4D CTimage acquisition and reconstruction. 2. Understand the limitations of current 4D CT technology. 3. Understand the ongoing developments in 4D CT and 4D CBCTimaging. 4. Understand the application of 4D CT to treatment planning.

4DCT is long overdue for improvement

The question remains regarding the dosimetric impact of intrafraction motion in 3D breast treatment. This study was conducted to investigate this issue utilizing the 4DCT scan. The 4D and helical CT scan sets were acquired for 12 breast cancer patients. For each of these patients, based on the helical CT scan, a conventional 3D conformal plan was generated. The breast treatment was then simulated based on the 4DCT scan. In each phase of the 4DCT scan, dose distribution was generated with the same beam parameters as the conventional plan. A software package was developed to compute the cumulative dose distribution from all the phases. Since the intrafraction organ motion is reflected by the 4DCT images, the cumulative dose computed based on the 4DCT images should be closer to what the patient received during treatment. Various dosimetric parameters were obtained from the plan and 4D cumulative dose distribution for the target volume and heart, and were compared to deduce the motion-induced impacts. The studies were performed for both whole breast and partial breast treatment. In the whole breast treatment, the average intrafraction motion induced changes in D95, D90, V100, V95, and V90 of the target volume were -5.4%, -3.1%, -13.4%, -5.1%, and -3.2%, respectively, with the largest values at -26.2%, -14.1%, -91.0%, -15.1%, and -9.0%, respectively. Motion had little impact on the Dmax of the target volume, but its impact on the Dmin of the target volume was significant. For left breast treatment, the motion-induced Dmax change to the heart could be negative or positive, with the largest increase at about 6 Gy. In partial breast treatment, the only non-insignificant impact was in the Dmin of the CTV (ranging from -15.2% to 11.7%). The results showed that the intrafraction motion may compromise target dose coverage in breast treatments and the degree of that compromise was correlated with motion magnitude. However, the dosimetric impact of the motion on the heart dose may be limited.

MO‐D‐ValA‐01: Modeling &amp; Characterization of Respiratory Motion

50. Magnetic Resonance Imaging optimization for liver SBRT: Breath-triggered acquisition in treatment position to improve lesion contouring

Purpose: Lung volumes at different breathing phases are substantially different from each other based on 4D CT. They are also different from the lung volume based on conventional 3D ungated CT. However, the dosimetric criteria and lung toxicity data used in the current planning process are based on the conventional 3D CT lung volume. Determining appropriate lung volume from 4D CT for treatment planning of lung cancer is the purpose of this work. Method and Materials: We obtained 4D CT scans of 10 patients using a GE LightSpeed RT scanner in combination with the Varian RPM respiratory gating system. For two of these patients, we have also obtained conventional 3D CT scans. The 4D CT scans were reconstructed into 10 breathing phases, as well as maximum, minimum, and average intensity projections. The lungs were then contoured for each phase and intensity projection, as well as for the 3D scans, and volumes for these contours were obtained. Results: We find that the average intensity projection (AveIP) is the most consistently close to the 3D CT volume, differing at most by about 3% of the total lung volume. The phase volume most closely approximated by the AveIP is the 20% phase (mid‐exhalation) or the 80% phase (mid‐inhalation), differing from the AveIP on average by 1.8±2.2% and −1.2±1.7%, respectively. The lung volume from the maximum intensity projection (MIP) is on average less than the AveIP volume by 11.4±2.3%. Conclusion: The lung volume from the 20% or 80% phases or the AveIP based on 4D CT should be used in the treatment planning for lung cancer. While the MIP is useful for ITV determination, it underestimates the lung volume compared to the conventional 3D CT.

Background Initial imaging work-up using radiography and CT arthrography sometimes can be insufficient to identify a scapholunate (SL) instability (SLI) in patients suspected of having SL ligament tears. Purpose To determine the diagnostic performance of four-dimensional (4D) CT in the identification of SLI and apply the findings to patients suspected of having SLI and with inconclusive findings on radiographs and CT arthrograms. Materials and Methods This prospective single-center study enrolled participants suspected of having SLI (recent trauma, dorsal pain, positive Watson test results, decreased grip strength) between March 2015 and March 2020. Participants with wrist fractures, substantial joint stiffness, or history of wrist surgery were excluded. Each participant underwent radiography, CT arthrography, and 4D CT on the same day. Participants were divided into three groups: those with no SLI, those with SLI, and those with inconclusive results. SL gap and radioscaphoid and lunocapitate angle were measured using semiautomatic quantitative analysis of 4D CT images by two independent readers. Receiver operating characteristic curves were used to evaluate the diagnostic performance of 4D CT. Thresholds were determined with the Youden index and were applied to the inconclusive group. Results Of the 150 included participants (mean age, 41 years ± 14 [SD]; 102 male, 48 female), there were 63 with no SLI, 48 with SLI, and 39 with inconclusive results. The maximum value and range of SL gap measurements on 4D CT scans showed high sensitivity (83% [40 of 48] and 90% [43 of 48], respectively) and high specificity (95% [59 of 62] and 81% [50 of 62], respectively) in the identification of SLI. At least one of these parameters was abnormal on 4D CT scans in 17 of 39 (44%) participants in the inconclusive group, and 10 of 17 (59%) participants had confirmed SLI. In the 22 participants in the inconclusive group with no indication of SLI at 4D CT, follow-up showed no evidence of SLI in 10 (45%) and enabled confirmation of SLI via arthroscopy in three (14%). Conclusion Scapholunate gap measurements on kinematic 4D CT scans enabled correct identification of SLI in 59% of participants with inconclusive results on conventional images. ClinicalTrials.gov registration no. NCT02401568 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Demehri and Ibad in this issue.