TU‐B‐224C‐01: 4D Scanning

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 CTimage. 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 imagingdose,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 CTimages. 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.

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.

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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.

ObjectiveTo study the feasibility and the potential benefits of defining the internal gross tumor volume (IGTV) of hepatocellular carcinoma (HCC) using contrast-enhanced 4D CT images obtained by combining arterial-phase (AP) contrast-enhanced (CE) 3D CT and non-contrast-enhanced (NCE) 4D CT images using deformable registration (DR).MethodsTen HCC patients who had received radiotherapy beforehand were selected for this study. The following CT simulation images were acquired sequentially: NCE 4D CT in free breathing, NCE 3D CT and APCE 3D CT in end-expiration breath holding. All 4D CT images were sorted into ten phases according to breath cycle (CT00 ~ CT90). Gross tumor volumes (GTVs) were contoured on all CT images and the IGTV-1 was obtained by merging the GTVs in each phase of 4D CT images. The GTV on the APCE 3D CT image was deformably registered to each 4D CT phase image according to liver shape using RayStationTM 3.99.0.7 version treatment planning system. The IGTV-DR was obtained by merging the GTVs after DR on the 4D CT images. Volume differences among the GTVs and between the IGTV-1 and the IGTV-DR were compared.ResultsThe edge of most lesions could be definitively identified using APCE 3D CT images compared to NCE 4D and 3D CT images. The GTV volume on APCE 3D CT images increased by an average of 34.79% (P < 0.05). There was no significant difference among the GTV volumes obtained using NCE 4D and 3D CT images (P > 0.05). The GTV volumes after DR on 4D CT different phase images increased by an average of 36.29% (P < 0.05), as was observed using the APCE 3D CT image (P > 0.05). Lastly, the volume of IGTV-DR increased by an average of 19.91% compared to that of IGTV-1 (P < 0.05).ConclusionNCE 4D CT imaging alone has the potential risk of missing a partial volume of the HCC. The combination of APCE 3D CT and NCE 4D CT images using the DR technique improved the accuracy of the definition of the IGTV in HCC.

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

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.

To compare the position, displacement, degree of inclusion (DI) and matching index (MI) of the gross tumor volume (GTV) for peripheral lung cancer based on 4-dimensional CT (4D CT) and 3-dimensional CT (3D CT) assisted with active breathing control (ABC). Eighteen patients with peripheral lung cancer underwent 4D CT simulation scan during free breathing and 3D CT simulation scans in end-inspiratory hold (CTEIH) and end-expiratory hold (CTEEH) in turn. The 4D CT images from each respiratory cycle were sorted into 10 phases. phase 0 was defined as end-inspiratory phase (CT0), and phase 50 was defined as end-expiratory phase (CT50). The GTVs were delineated separately on CT0, CT50, CTEIH and CTEEH images, and then GTV0, GTV50, GTVEIH and GTVEEH were constructed, respectively. The median distances between the centroids of GTV0 and GTVEIH, GTV50 and GTVEEH were 3.9 mm and 3.4 mm in all patients, 3.2 mm and 3.1 mm in the upper lobe group, and 5.0 mm and 4.7 mm in the lower lobe group, respectively. In the upper lobe group, the GTV0 and GTVEIH were 65.9% and 63.1%, and the median mutual DIs of GTV50 and GTVEEH were 67.5%, 63.1%, respectively. In the lower lobe group, the GTV0 and GTVEIH were 35.3% and 21.4%, and the median mutual DIs of GTV50 and GTVEEH were 27.8% and 24.8%, respectively. In the upper lobe group, the median MI of GTV0 and GTVEIH was 0.5, and the median MI of GTV50 and GTVEEH was 0.6. In the lower lobe group, the median MI of GTV0 and GTVEIH was 0.2, and the median MI of GTV50 and GTVEEH was 0.3. Whether in the upper or lower lobe groups, all the differences between displacements of centroid positions of GTVEIH and GTVEEH (ABC displacement) and GTV0 and GTV50 (4D displacement ) were <1 mm in three dimensional directions (all P>0.05). The target displacement of tumors based on 3D CT scanning in end-inspiratory hold and end-expiration hold can be used to construct internal target volume instead of that based on 4D CT scanning in extreme phase for peripheral lung cancers, but spatital mismatches of GTVs are obvious between extreme phases in 4D CT and corresponding phases in 3D CT assisted with ABC, especially for tumors of smaller volume and with larger motion amplitude.

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.

Objective To conduct a computer simulation to evaluate the discrepancy between the cumulative doses calculated by four-dimensional computed tomography (4DCT) images and 4DCT scans (for real-time respiratory motions) due to the patient’s irregular breathing. Methods A series of digital phantoms were generated from a patient’s 4DCT images to simulate 4DCT images and 4DCT scans (for real-time respiratory motions) resulting from various irregular breathing curves. A six-field intensity-modulated radiotherapy plan was generated. Two cumulative doses in the target were calculated. The first one, named Dall, was calculated by tracking the point displacements in the target manifested on the 4DCT images; the second one, named D4D, was calculated based on the point displacements along the whole breathing motion during 4DCT scanning. Dose discrepancy between D4D and Dall was calculated to evaluate the correlation between breathing pattern and dose discrepancy in the target. Results The dose discrepancy in the target was correlated with mean motion excursion and the standard deviation of motion excursion.ΔDmin(ΔD99) in the target increased from 2.39%(2.04%) to 11.91%(5.24%) as the mean motion excursion increased from 5 mm to 15 mm, and increased from 5.93%(2.15%) to 14.65%(5.01%) as the standard deviation of motion excursion increased from 15% to 45% of the mean motion excursion. When the mean period increased from 3 s to 5 s or the standard deviation of period increased from 10% to 40% of the mean period, ΔDmin(ΔD99) in the target was greater than 6.0%(2.0%), but less than 9.0%(3.0%). When the target diameter was 2 cm, 3 cm, and 4 cm, ΔDmin(ΔD99) in the target was 11.88%(5.50%), 6.91%(2.42%), and 7.53%(3.62%), respectively. Conclusions There is a large discrepancy between the cumulative doses calculated using 4DCT images and 4DCT scans (for real-time respiratory motions) when the patient has irregular breathing. This dose discrepancy depends on mean motion excursion and the standard deviation of motion excursion, but has little relationship with mean period, the standard deviation of period, and tumor volume. Key words: Tomography, X-ray computed; Respiratory-induced motion; Accumulative dose

Four-dimensional (4D) CT imaging is a central part of current treatment planning workflows in 4D radiotherapy (RT). However, clinical 4D CT image data often suffer from severe artifacts caused by insufficient projection data coverage due to the inability of current commercial 4D CT imaging protocols to adapt to breathing irregularity. We propose an intelligent sequence mode 4D CT imaging protocol (i4DCT) that builds on online breathing curve analysis and respiratory signal-guided selection of beam on/off periods during scan time in order to fulfill projection data coverage requirements. i4DCT performance is evaluated and compared to standard clinical sequence mode 4D CT (seq4DCT) and spiral 4D CT (spiral4DCT) approaches. i4DCT consists of three main blocks: (a) an initial learning period to establish a patient-specific reference breathing cycle representation for data-driven i4DCT parameter selection, (b) online respiratory signal-guided sequence mode scanning (i4DCT core), (c) rapid breathing record analysis and quality control after scanning to trigger potential local rescanning (i4DCT rescan). Based on a phase space representation of the patient's breathing signal, i4DCT core implements real-time analysis of the signal to appropriately switch on and off projection data acquisition even during irregular breathing. Performance evaluation was based on 189 clinical breathing records acquired during spiral 4D CT scanning for RT planning (data acquisition period: 2013-2017; Siemens Somatom with Varian RPM system). For each breathing record, i4DCT, seq4DCT, and spiral4DCT scanning protocol variants were simulated. Evaluation measures were local projection data coverage ; number of local projection data coverage failures; and number of patients with coverage failures; average beam on time as a surrogate for imaging dose and total patient on table time as the time between first and last beam on signal. Using i4DCT, mean inhalation and exhalation projection data coverage increased significantly compared to standard spiral 4D CT scanning as applied for the original clinical data acquisition and conventional 4D CT sequence scanning modes. The improved projection data coverage translated into a reduction of coverage failures by 89% without and 93% when allowing for a rescanning at up to five z-positions compared to spiral scanning and between 76% and 82% without and 85% and 89% with rescanning when compared to seq4DCT. Similar numbers were observed for . Simultaneously, i4DCT (without rescanning) reduced the beam on time on average by 3%-17% compared to standard spiral 4D CT. In turn, the patient on table time increased by between 35% and 66%. Allowing for rescanning led on average to additional 5.9 s beam on and 10.6 s patient on table time. i4DCT outperformed currently implemented clinical fixed beam on period 4D CT scanning approaches by means of a significantly smaller data coverage failure rate without requiring additional beam on time compared to, for example, conventional spiral 4D CT protocols.

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.

4DCT is long overdue for improvement

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.

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.