Phase versus amplitude sorting of 4D-CT data

Embedding-Alignment Fusion-Based Graph Convolution Network With Mixed Learning Strategy for 4D Medical Image Reconstruction

In recent years, 4D medical image involving structural and motion information of tissue has attracted increasing attention. The key to the 4D image reconstruction is to stack the 2D slices based on matching the aligned motion states. In this study, the distribution of the 2D slices with the different motion states is modeled as a manifold graph, and the reconstruction is turned to be the graph alignment. An embedding-alignment fusion-based graph convolution network (GCN) with a mixed-learning strategy is proposed to align the graphs. Herein, the embedding and alignment processes of graphs interact with each other to realize a precise alignment with retaining the manifold distribution. The mixed strategy of self- and semi-supervised learning makes the alignment sparse to avoid the mismatching caused by outliers in the graph. In the experiment, the proposed 4D reconstruction approach is validated on the different modalities including Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Ultrasound (US). We evaluate the reconstruction accuracy and compare it with those of state-of-the-art methods. The experiment results demonstrate that our approach can reconstruct a more accurate 4D image.

A minimally interactive method for labeling respiratory phases in free-breathing thoracic dynamic MRI for constructing 4D images

OBJECTIVE

Determination of end-expiration (EE) and end-inspiration (EI) time points in the respiratory cycle in free-breathing slice image acquisitions of the thorax is one key step needed for 4D image construction via dynamic magnetic resonance imaging. The purpose of this paper is to realize the automation of the labeling process.

METHODS

The diaphragm is used as a surrogate for tracking respiratory motion and determining the state of breathing. Regions of interest (ROIs) containing the hemi-diaphragms are set by human interaction to compute the optical flow matrix between two adjacent 2D time slices. Subsequently, our approach examines the diaphragm speed and direction and by considering the change in the optical flow matrix, the EE or EI points are detected.

RESULTS AND CONCLUSION

The labeling accuracy for the lateral aspect of the left lung and the lateral aspect of the right lung (0.630.71) is significantly lower (P < 0.05) than the accuracy for other positions (0.420.44), but the error in almost all scenarios is less than 1 time point. By comparing between automatic and manual labeling in 12 scenarios, we found out that 9 scenarios showed no significant difference (P > 0.05) between two methods. Overall, our method is found to be highly agreeable with manual labeling and greatly shortens the labeling time, requiring less than 8 minutes/ study compared to 4 hours/ study for manual labeling.

SIGNIFICANCE

Our method achieves automatic labeling of EE and EI points without the need for use of patient internal or external markers.

Strategies to tackle the challenges of external beam radiotherapy for liver tumors

Primary and metastatic liver cancer is an increasingly common and difficult to control disease entity. Radiation offers a non-invasive treatment alternative for these patients who often have few options and a poor prognosis. However, the anatomy and aggressiveness of liver cancer poses significant challenges such as accurate localization at simulation and treatment, management of motion and appropriate selection of dose regimen. This article aims to review the options available and provide information for the practical implementation and/or improvement of liver cancer radiation programs within the context of stereotactic body radiotherapy and image-guided radiotherapy guidelines. Specific patient inclusion and exclusion criteria are presented given the significant toxicity found in certain sub-populations treated with radiation. Indeed, certain sub-populations, such as those with tumor thrombosis or those with larger lesions treated with transarterial chemoembolization, have been shown to have significant improvements in outcome with the addition of radiation and merit special consideration. Implementing a liver radiation program requires three primary challenges to be addressed: (1) immobilization and motion management; (2) localization; and (3) dose regimen and constraint selection. Strategies to deal with motion include simple internal target volume (ITV) expansions, non-gated ITV reduction strategies, breath hold methods, and surrogate marker methods to enable gating or tracking. Localization of the tumor and organs-at-risk are addressed using contrast infusion techniques to take advantage of different normal liver and cancer vascular anatomy, imaging modalities, and margin management. Finally, a dose response has been demonstrated and dose regimens appear to be converging. A more uniform approach to treatment in terms of technique, dose selection and patient selection will allow us to study liver radiation in larger and, hopefully, multicenter randomized studies.

Characterizing spatiotemporal information loss in sparse-sampling-based dynamic MRI for monitoring respiration-induced tumor motion in radiotherapy

PURPOSE

Sparse-sampling and reconstruction techniques represent an attractive strategy to achieve faster image acquisition speeds, while maintaining adequate spatial resolution and signal-to-noise ratio in rapid magnetic resonance imaging (MRI). The authors investigate the use of one such sequence, broad-use linear acquisition speed-up technique (k-t BLAST) in monitoring tumor motion for thoracic and abdominal radiotherapy and examine the potential trade-off between increased sparsification (to increase imaging speed) and the potential loss of "true" information due to greater reliance on a priori information.

METHODS

Lung tumor motion trajectories in the superior-inferior direction, previously recorded from ten lung cancer patients, were replayed using a motion phantom module driven by an MRI-compatible motion platform. Eppendorf test tubes filled with water which serve as fiducial markers were placed in the phantom. The modeled rigid and deformable motions were collected in a coronal image slice using balanced fast field echo in conjunction with k-t BLAST. Root mean square (RMS) error was used as a metric of spatial accuracy as measured trajectories were compared to input data. The loss of spatial information was characterized for progressively increasing acceleration factor from 1 to 16; the resultant sampling frequency was increased approximately from 2.5 to 19 Hz when the principal direction of the motion was set along frequency encoding direction. In addition to the phantom study, respiration-induced tumor motions were captured from two patients (kidney tumor and lung tumor) at 13 Hz over 49 s to demonstrate the impact of high speed motion monitoring over multiple breathing cycles. For each subject, the authors compared the tumor centroid trajectory as well as the deformable motion during free breathing.

RESULTS

In the rigid and deformable phantom studies, the RMS error of target tracking at the acquisition speed of 19 Hz was approximately 0.3-0.4 mm, which was smaller than the reconstructed pixel resolution of 0.67 mm. In the patient study, the dynamic 2D MRI enabled the monitoring of cycle-to-cycle respiratory variability present in the tumor position. It was seen that the range of centroid motion as well as the area covered due to target motion during each individual respiratory cycle was underestimated compared to the entire motion range observed over multiple breathing cycles.

CONCLUSIONS

The authors' initial results demonstrate that sparse-sampling- and reconstruction-based dynamic MRI can be used to achieve adequate image acquisition speeds without significant information loss for the task of radiotherapy guidance. Such monitoring can yield spatial and temporal information superior to conventional offline and online motion capture methods used in thoracic and abdominal radiotherapy.

Pulmonary imaging using respiratory motion compensated simultaneous PET/MR

PURPOSE

Pulmonary positron emission tomography (PET) imaging is confounded by blurring artifacts caused by respiratory motion. These artifacts degrade both image quality and quantitative accuracy. In this paper, the authors present a complete data acquisition and processing framework for respiratory motion compensated image reconstruction (MCIR) using simultaneous whole body PET/magnetic resonance (MR) and validate it through simulation and clinical patient studies.

METHODS

The authors have developed an MCIR framework based on maximum a posteriori or MAP estimation. For fast acquisition of high quality 4D MR images, the authors developed a novel Golden-angle RAdial Navigated Gradient Echo (GRANGE) pulse sequence and used it in conjunction with sparsity-enforcing k-t FOCUSS reconstruction. The authors use a 1D slice-projection navigator signal encapsulated within this pulse sequence along with a histogram-based gate assignment technique to retrospectively sort the MR and PET data into individual gates. The authors compute deformation fields for each gate via nonrigid registration. The deformation fields are incorporated into the PET data model as well as utilized for generating dynamic attenuation maps. The framework was validated using simulation studies on the 4D XCAT phantom and three clinical patient studies that were performed on the Biograph mMR, a simultaneous whole body PET/MR scanner.

RESULTS

The authors compared MCIR (MC) results with ungated (UG) and one-gate (OG) reconstruction results. The XCAT study revealed contrast-to-noise ratio (CNR) improvements for MC relative to UG in the range of 21%-107% for 14 mm diameter lung lesions and 39%-120% for 10 mm diameter lung lesions. A strategy for regularization parameter selection was proposed, validated using XCAT simulations, and applied to the clinical studies. The authors' results show that the MC image yields 19%-190% increase in the CNR of high-intensity features of interest affected by respiratory motion relative to UG and a 6%-51% increase relative to OG.

CONCLUSIONS

Standalone MR is not the traditional choice for lung scans due to the low proton density, high magnetic susceptibility, and low T2 (∗) relaxation time in the lungs. By developing and validating this PET/MR pulmonary imaging framework, the authors show that simultaneous PET/MR, unique in its capability of combining structural information from MR with functional information from PET, shows promise in pulmonary imaging.

An externally and internally deformable, programmable lung motion phantom

PURPOSE

Most clinically deployed strategies for respiratory motion management in lung radiotherapy (e.g., gating and tracking) use external markers that serve as surrogates for tumor motion. However, typical lung phantoms used to validate these strategies are based on a rigid exterior and a rigid or a deformable-interior. Such designs do not adequately represent respiration because the thoracic anatomy deforms internally as well as externally. In order to create a closer approximation of respiratory motion, the authors describe the construction and experimental testing of an externally as well as internally deformable, programmable lung phantom.

METHODS

The outer shell of a commercially available lung phantom (RS-1500, RSD, Inc.) was used. The shell consists of a chest cavity with a flexible anterior surface, and embedded vertebrae, rib-cage and sternum. A custom-made insert was designed using a piece of natural latex foam block. A motion platform was programmed with sinusoidal and ten patient-recorded lung tumor trajectories. The platform was used to drive a rigid foam "diaphragm" that compressed/decompressed the phantom interior. Experimental characterization comprised of determining the reproducibility and the external-internal correlation of external and internal marker trajectories extracted from kV x-ray fluoroscopy. Experiments were conducted to illustrate three example applications of the phantom-(i) validating the geometric accuracy of the VisionRT surface photogrammetry system; (ii) validating an image registration tool, NiftyReg; and (iii) quantifying the geometric error due to irregular motion in four-dimensional computed tomography (4DCT).

RESULTS

The phantom correctly reproduced sinusoidal and patient-derived motion, as well as realistic respiratory motion-related effects such as hysteresis. The reproducibility of marker trajectories over multiple runs for sinusoidal as well as patient traces, as characterized by fluoroscopy, was within 0.25 mm RMS error. The motion trajectories of internal and external radio-opaque markers as measured by fluoroscopy were found to be highly correlated (R > 0.95). Using the phantom, it was demonstrated that the motion trajectories of regions-of-interest on the surface as measured by VisionRT are highly consistent with corresponding fluoroscopically acquired surface marker trajectories, with RMS errors within 0.26 mm. Furthermore, it was shown that the trajectories of external and internal marker trajectories derived from NiftyReg deformation vector fields were within 1 mm root mean square errors comparing to trajectories obtained by segmenting markers from individual fluoro frames. Finally, it was shown that while 4DCT can be used to localize internal markers for sinusoidal motion with reasonable accuracy, the localization error increases significantly (by a factor of ∼ 2) in the presence of cycle-to-cycle variations that are observed in patient-derived respiratory motion.

CONCLUSIONS

The authors have developed a realistic externally and internally deformable, programmable lung phantom that will serve as a valuable tool for clinical and investigational motion management studies in thoracic and abdominal radiation therapies.

Experimental investigation of irregular motion impact on 4D PET-based particle therapy monitoring

Particle therapy positron emission tomography (PT-PET) is an in vivo and non-invasive imaging technique to monitor treatment delivery in particle therapy. The inevitable patient respiratory motion during irradiation causes artefacts and inaccurate activity distribution in PET images. Four-dimensional (4D) maximum likelihood expectation maximisation (4D MLEM) allows for a compensation of these effects, but has up to now been restricted to regular motion for PT-PET investigations. However, intra-fractional motion during treatment might differ from that during acquisition of the 4D-planning CT (e.g. amplitude variation, baseline drift) and therefore might induce inaccurate 4D PET reconstruction results. This study investigates the impact of different irregular analytical one-dimensional (1D) motion patterns on PT-PET imaging by means of experiments with a radioactive source and irradiated moving phantoms. Three sorting methods, namely phase sorting, equal amplitude sorting and event-based amplitude sorting, were applied to manage the PET list-mode data. The influence of these sorting methods on the motion compensating algorithm has been analysed. The event-based amplitude sorting showed a superior performance and it is applicable for irregular motions with ⩽ 4 mm amplitude elongation and drift. For motion with 10 mm baseline drift, the normalised root mean square error was as high as 10.5% and a 10 mm range deviation was observed.

T2-weighted four dimensional magnetic resonance imaging with result-driven phase sorting

PURPOSE

T2-weighted MRI provides excellent tumor-to-tissue contrast for target volume delineation in radiation therapy treatment planning. This study aims at developing a novel T2-weighted retrospective four dimensional magnetic resonance imaging (4D-MRI) phase sorting technique for imaging organ/tumor respiratory motion.

METHODS

A 2D fast T2-weighted half-Fourier acquisition single-shot turbo spin-echo MR sequence was used for image acquisition of 4D-MRI, with a frame rate of 2-3 frames/s. Respiratory motion was measured using an external breathing monitoring device. A phase sorting method was developed to sort the images by their corresponding respiratory phases. Besides, a result-driven strategy was applied to effectively utilize redundant images in the case when multiple images were allocated to a bin. This strategy, selecting the image with minimal amplitude error, will generate the most representative 4D-MRI. Since we are using a different image acquisition mode for 4D imaging (the sequential image acquisition scheme) with the conventionally used cine or helical image acquisition scheme, the 4D dataset sufficient condition was not obviously and directly predictable. An important challenge of the proposed technique was to determine the number of repeated scans (NR) required to obtain sufficient phase information at each slice position. To tackle this challenge, the authors first conducted computer simulations using real-time position management respiratory signals of the 29 cancer patients under an IRB-approved retrospective study to derive the relationships between NR and the following factors: number of slices (NS), number of 4D-MRI respiratory bins (NB), and starting phase at image acquisition (P0). To validate the authors' technique, 4D-MRI acquisition and reconstruction were simulated on a 4D digital extended cardiac-torso (XCAT) human phantom using simulation derived parameters. Twelve healthy volunteers were involved in an IRB-approved study to investigate the feasibility of this technique.

RESULTS

4D data acquisition completeness (Cp) increases as NR increases in an inverse-exponential fashion (Cp = 100 - 99 × exp(-0.18 × NR), when NB = 6, fitted using 29 patients' data). The NR required for 4D-MRI reconstruction (defined as achieving 95% completeness, Cp = 95%, NR = NR,95) is proportional to NB (NR,95 ∼ 2.86 × NB, r = 1.0), but independent of NS and P0. Simulated XCAT 4D-MRI showed a clear pattern of respiratory motion. Tumor motion trajectories measured on 4D-MRI were comparable to the average input signal, with a mean relative amplitude error of 2.7% ± 2.9%. Reconstructed 4D-MRI for healthy volunteers illustrated clear respiratory motion on three orthogonal planes, with minimal image artifacts. The artifacts were presumably caused by breathing irregularity and incompleteness of data acquisition (95% acquired only). The mean relative amplitude error between critical structure trajectory and average breathing curve for 12 healthy volunteers is 2.5 ± 0.3 mm in superior-inferior direction.

CONCLUSIONS

A novel T2-weighted retrospective phase sorting 4D-MRI technique has been developed and successfully applied on digital phantom and healthy volunteers.

Impact of CT attenuation correction method on quantitative respiratory-correlated (4D) PET/CT imaging

PURPOSE

Respiratory-correlated positron emission tomography (PET/CT) 4D PET/CT is used to mitigate errors from respiratory motion; however, the optimal CT attenuation correction (CTAC) method for 4D PET/CT is unknown. The authors performed a phantom study to evaluate the quantitative performance of CTAC methods for 4D PET/CT in the ground truth setting.

METHODS

A programmable respiratory motion phantom with a custom movable insert designed to emulate a lung lesion and lung tissue was used for this study. The insert was driven by one of five waveforms: two sinusoidal waveforms or three patient-specific respiratory waveforms. 3DPET and 4DPET images of the phantom under motion were acquired and reconstructed with six CTAC methods: helical breath-hold (3DHEL), helical free-breathing (3DMOT), 4D phase-averaged (4DAVG), 4D maximum intensity projection (4DMIP), 4D phase-matched (4DMATCH), and 4D end-exhale (4DEXH) CTAC. Recovery of SUV(max), SUV(mean), SUV(peak), and segmented tumor volume was evaluated as RC(max), RC(mean), RC(peak), and RC(vol), representing percent difference relative to the static ground truth case. Paired Wilcoxon tests and Kruskal-Wallis ANOVA were used to test for significant differences.

RESULTS

For 4DPET imaging, the maximum intensity projection CTAC produced significantly more accurate recovery coefficients than all other CTAC methods (p < 0.0001 over all metrics). Over all motion waveforms, ratios of 4DMIP CTAC recovery were 0.2 ± 5.4, -1.8 ± 6.5, -3.2 ± 5.0, and 3.0 ± 5.9 for RC(max), RC(peak), RC(mean), and RC(vol). In comparison, recovery coefficients for phase-matched CTAC were -8.4 ± 5.3, -10.5 ± 6.2, -7.6 ± 5.0, and -13.0 ± 7.7 for RC(max), RC(peak), RC(mean), and RC(vol). When testing differences between phases over all CTAC methods and waveforms, end-exhale phases were significantly more accurate (p = 0.005). However, these differences were driven by the patient-specific respiratory waveforms; when testing patient and sinusoidal waveforms separately, patient waveforms were significantly different between phases (p < 0.0001) while the sinusoidal waveforms were not significantly different (p = 0.98). When considering only the subset of 4DMATCH images that corresponded to the end-exhale image phase, 4DEXH, mean and interquartile range were similar to 4DMATCH but variability was considerably reduced.

CONCLUSIONS

Comparative advantages in accuracy and precision of SUV metrics and segmented volumes were demonstrated with the use of the maximum intensity projection and end-exhale CT attenuation correction. While respiratory phase-matched CTAC should in theory provide optimal corrections, image artifacts and differences in implementation of 4DCT and 4DPET sorting can degrade the benefit of this approach. These results may be useful to guide the implementation, analysis, and development of respiratory-correlated thoracic PET/CT in the radiation oncology and diagnostic settings.

Reconstruction of four-dimensional computed tomography lung images by applying spatial and temporal anatomical constraints using a Bayesian model

Current four-dimensional computed tomography (4-D CT) lung image reconstruction methods rely on respiratory gating, such as surrogate, to sort the large number of axial images captured during multiple breathing cycles into serial three-dimensional CT images of different respiratory phases. Such sorting methods may be subject to external surrogate signal noises due to poor reproducibility of breathing cycles. New image-matching-based reconstruction algorithms refine the 4-D CT reconstruction by matching neighboring image slices, and they generally work better for the cine mode of 4-D CT acquisition than the helical mode due to different table positions of axial images in the helical mode. We propose a Bayesian model (BM) based automated 4-D CT lung image reconstruction for helical mode scans. BM allows for applying new spatial and temporal anatomical constraints in the optimization procedure. Using an iterative optimization procedure, each axial image is assigned to a respiratory phase to make sure the anatomical structures are spatially and temporally smooth based on the BM framework. In experiments, we visually and quantitatively compared the results of the proposed BM-based 4-D CT reconstruction with the respiratory surrogate and the normalized cross-correlation based image matching method using both simulated and actual 4-D patient scans. The results indicated that the proposed algorithm yielded more accurate reconstruction and fewer artifacts in the 4-D CT image series.

Investigation of sagittal image acquisition for 4D-MRI with body area as respiratory surrogate

PURPOSE

The authors have recently developed a novel 4D-MRI technique for imaging organ respiratory motion employing cine acquisition in the axial plane and using body area (BA) as a respiratory surrogate. A potential disadvantage associated with axial image acquisition is the space-dependent phase shift in the superior-inferior (SI) direction, i.e., different axial slice positions reach the respiratory peak at different respiratory phases. Since respiratory motion occurs mostly in the SI and anterior-posterior (AP) directions, sagittal image acquisition, which embeds motion information in these two directions, is expected to be more robust and less affected by phase-shift than axial image acquisition. This study aims to develop and evaluate a 4D-MRI technique using sagittal image acquisition.

METHODS

The authors evaluated axial BA and sagittal BA using both 4D-CT images (11 cancer patients) and cine MR images (6 healthy volunteers and 1 cancer patient) by comparing their corresponding space-dependent phase-shift in the SI direction (δSPS (SI)) and in the lateral direction (δSPS (LAT)), respectively. To evaluate sagittal BA 4D-MRI method, a motion phantom study and a digital phantom study were performed. Additionally, six patients who had cancer(s) in the liver were prospectively enrolled in this study. For each patient, multislice sagittal MR images were acquired for 4D-MRI reconstruction. 4D retrospective sorting was performed based on respiratory phases. Single-slice cine MRI was also acquired in the axial, coronal, and sagittal planes across the tumor center from which tumor motion trajectories in the SI, AP, and medial-lateral (ML) directions were extracted and used as references from comparison. All MR images were acquired in a 1.5 T scanner using a steady-state precession sequence (frame rate ∼ 3 frames/s).

RESULTS

4D-CT scans showed that δSPS (SI) was significantly greater than δSPS (LAT) (p-value: 0.012); the median phase-shift was 16.9% and 7.7%, respectively. Body surface motion measurement from axial and sagittal MR cines also showed δSPS (SI) was significantly greater than δSPS (LAT). The median δSPS (SI) and δSPS (LAT) was 11.0% and 9.2% (p-value = 0.008), respectively. Tumor motion trajectories from 4D-MRI matched with those from single-slice cine MRI: the mean (±SD) absolute differences in tumor motion amplitude between the two were 1.5 ± 1.6 mm, 2.1 ± 1.9 mm, and 1.1 ± 1.0 mm in the SI, ML, and AP directions from this patient study.

CONCLUSIONS

Space-dependent phase shift is less problematic for sagittal acquisition than for axial acquisition. 4D-MRI using sagittal acquisition was successfully carried out in patients with hepatic tumors.

A pilot evaluation of a 4-dimensional cone-beam computed tomographic scheme based on simultaneous motion estimation and image reconstruction

PURPOSE

To evaluate the performance of a 4-dimensional (4-D) cone-beam computed tomographic (CBCT) reconstruction scheme based on simultaneous motion estimation and image reconstruction (SMEIR) through patient studies.

METHODS AND MATERIALS

The SMEIR algorithm contains 2 alternating steps: (1) motion-compensated CBCT reconstruction using projections from all phases to reconstruct a reference phase 4D-CBCT by explicitly considering the motion models between each different phase and (2) estimation of motion models directly from projections by matching the measured projections to the forward projection of the deformed reference phase 4D-CBCT. Four lung cancer patients were scanned for 4 to 6 minutes to obtain approximately 2000 projections for each patient. To evaluate the performance of the SMEIR algorithm on a conventional 1-minute CBCT scan, the number of projections at each phase was reduced by a factor of 5, 8, or 10 for each patient. Then, 4D-CBCTs were reconstructed from the down-sampled projections using Feldkamp-Davis-Kress, total variation (TV) minimization, prior image constrained compressive sensing (PICCS), and SMEIR. Using the 4D-CBCT reconstructed from the fully sampled projections as a reference, the relative error (RE) of reconstructed images, root mean square error (RMSE), and maximum error (MaxE) of estimated tumor positions were analyzed to quantify the performance of the SMEIR algorithm.

RESULTS

The SMEIR algorithm can achieve results consistent with the reference 4D-CBCT reconstructed with many more projections per phase. With an average of 30 to 40 projections per phase, the MaxE in tumor position detection is less than 1 mm in SMEIR for all 4 patients.

CONCLUSION

The results from a limited number of patients show that SMEIR is a promising tool for high-quality 4D-CBCT reconstruction and tumor motion modeling.

Patient-specific biomechanical model for the prediction of lung motion from 4-D CT images

This paper presents an approach to predict the deformation of the lungs and surrounding organs during respiration. The framework incorporates a computational model of the respiratory system, which comprises an anatomical model extracted from computed tomography (CT) images at end-expiration (EE), and a biomechanical model of the respiratory physiology, including the material behavior and interactions between organs. A personalization step is performed to automatically estimate patient-specific thoracic pressure, which drives the biomechanical model. The zone-wise pressure values are obtained by using a trust-region optimizer, where the estimated motion is compared to CT images at end-inspiration (EI). A detailed convergence analysis in terms of mesh resolution, time stepping and number of pressure zones on the surface of the thoracic cavity is carried out. The method is then tested on five public datasets. Results show that the model is able to predict the respiratory motion with an average landmark error of 3.40 ±1.0 mm over the entire respiratory cycle. The estimated 3-D lung motion may constitute as an advanced 3-D surrogate for more accurate medical image reconstruction and patient respiratory analysis.

Technical evaluation of a laser-based optical surface scanning system for prospective and retrospective breathing adapted computed tomography

BACKGROUND

For breathing adapted radiotherapy, the same motion monitoring system can be used for imaging and triggering of the accelerator.

PURPOSE

To evaluate a new technique for prospective gated computed tomography (CT) and four-dimensional CT (4DCT) using a laser based surface scanning system (Sentinel(™), C-RAD, Uppsala, Sweden). The system was compared to the AZ-733V respiratory gating system (Anzai Medical, Tokyo, Japan) and the Real-Time Position Management System (RPM(™)) (Varian Medical Systems, Palo Alto, CA, USA).

MATERIAL AND METHODS

Temporal accuracy was evaluated using a moving phantom programmed to move a platform along trajectories following a sin(6)(ωt) function with amplitudes from 6 to 20 mm and periods from 2 to 5 s during 120 s while the motion was recorded. The recorded data was Fourier transformed and the peak area at the fundamental and harmonic frequencies compared to data generated using the same sinusoidal function. For verification of the 4DCT reconstruction process, the phantom was programmed to move along a sinusoidal trajectory. Ten phase series were reconstructed. The distance from the couch to the platform was measured in each image. By fitting the function sin(ωt-ϕ) to the values measured in the images corresponding to each slice, the phase of each image was verified.

RESULTS AND CONCLUSION

In the recorded data, the peak area at the fundamental frequency covered on average 104 ± 4%, 102 ± 4% and 91 ± 27% of the peak area in the generated data for the Sentinel(™), RPM(™) and AZ-733V systems, respectively. All systems managed to resolve both harmonic frequencies. The second experiment showed that all images were sorted into the correct series using breathing data recorded by each system. The systems generated very similar results, however, it is preferable to use the same system both for imaging and treatment.

Quantitative and qualitative assessment of Yttrium-90 PET/CT imaging

Yttrium-90 is known to have a low positron emission decay of 32 ppm that may allow for personalized dosimetry of liver cancer therapy with ⁹⁰Y labeled microspheres. The aim of this work was to image and quantify ⁹⁰Y so that accurate predictions of the absorbed dose can be made. The measurements were performed within the QUEST study (University of Sydney, and Sirtex Medical, Australia). A NEMA IEC body phantom containing 6 fillable spheres (10–37 mm ∅) was used to measure the ⁹⁰Y distribution with a Biograph mCT PET/CT (Siemens, Erlangen, Germany) with time-of-flight (TOF) acquisition. A sphere to background ratio of 8∶1, with a total ⁹⁰Y activity of 3 GBq was used. Measurements were performed for one week (0, 3, 5 and 7 d). he acquisition protocol consisted of 30 min-2 bed positions and 120 min-single bed position. mages were reconstructed with 3D ordered subset expectation maximization (OSEM) and point spread function (PSF) for iteration numbers of 1–12 with 21 (TOF) and 24 (non-TOF) subsets and CT based attenuation and scatter correction. Convergence of algorithms and activity recovery was assessed based on regions-of-interest (ROI) analysis of the background (100 voxels), spheres (4 voxels) and the central low density insert (25 voxels). For the largest sphere, the recovery coefficient (RC) values for the 30 min –2-bed position, 30 min-single bed and 120 min-single bed were 1.12±0.20, 1.14±0.13, 0.97±0.07 respectively. For the smaller diameter spheres, the PSF algorithm with TOF and single bed acquisition provided a comparatively better activity recovery. Quantification of Y-90 using Biograph mCT PET/CT is possible with a reasonable accuracy, the limitations being the size of the lesion and the activity concentration present. At this stage, based on our study, it seems advantageous to use different protocols depending on the size of the lesion.

4D CT amplitude binning for the generation of a time-averaged 3D mid-position CT scan

The purpose of this study was to develop a method to use amplitude binned 4D-CT (A-4D-CT) data for the construction of mid-position CT data and to compare the results with data created from phase-binned 4D-CT (P-4D-CT) data. For the latter purpose we have developed two measures which describe the regularity of the 4D data and we have tried to correlate these measures with the regularity of the external respiration signal. 4D-CT data was acquired for 27 patients on a combined PET-CT scanner. The 4D data were reconstructed twice, using phase and amplitude binning. The 4D frames of each dataset were registered using a quadrature-based optical flow method. After registration the deformation vector field was repositioned to the mid-position. Since amplitude-binned 4D data does not provide temporal information, we corrected the mid-position for the occupancy of the bins. We quantified the differences between the two mid-position datasets in terms of tumour offset and amplitude differences. Furthermore, we measured the standard deviation of the image intensity over the respiration after registration (σregistration) and the regularity of the deformation vector field (Delta J) to quantify the quality of the 4D-CT data. These measures were correlated to the regularity of the external respiration signal (σsignal).The two irregularity measures, Delta J and σregistration, were dependent on each other (p<0.0001, R2=0.80 for P-4D-CT, R2=0.74 for A-4D-CT). For all datasets amplitude binning resulted in lower Delta J and σregistration and large decreases led to visible quality improvements in the mid-position data. The quantity of artefact decrease was correlated to the irregularity of the external respiratory signal.The average tumour offset between the phase and amplitude binned mid-position without occupancy correction was 0.42 mm in the caudal direction (10.6% of the amplitude). After correction this was reduced to 0.16 mm in caudal direction (4.1% of the amplitude). Similar relative offsets were found at the diaphragm. We have devised a method to use amplitude binned 4D-CT to construct motion model and generate a mid-position planning CT for radiotherapy treatment purposes. We have decimated the systematic offset of this mid-position model with a motion model derived from P-4D-CT. We found that the A-4D-CT led to a decrease of local artefacts and that this decrease was correlated to the irregularity of the external respiration signal.

A novel fast helical 4D-CT acquisition technique to generate low-noise sorting artifact-free images at user-selected breathing phases

PURPOSE

To develop a novel 4-dimensional computed tomography (4D-CT) technique that exploits standard fast helical acquisition, a simultaneous breathing surrogate measurement, deformable image registration, and a breathing motion model to remove sorting artifacts.

METHODS AND MATERIALS

Ten patients were imaged under free-breathing conditions 25 successive times in alternating directions with a 64-slice CT scanner using a low-dose fast helical protocol. An abdominal bellows was used as a breathing surrogate. Deformable registration was used to register the first image (defined as the reference image) to the subsequent 24 segmented images. Voxel-specific motion model parameters were determined using a breathing motion model. The tissue locations predicted by the motion model in the 25 images were compared against the deformably registered tissue locations, allowing a model prediction error to be evaluated. A low-noise image was created by averaging the 25 images deformed to the first image geometry, reducing statistical image noise by a factor of 5. The motion model was used to deform the low-noise reference image to any user-selected breathing phase. A voxel-specific correction was applied to correct the Hounsfield units for lung parenchyma density as a function of lung air filling.

RESULTS

Images produced using the model at user-selected breathing phases did not suffer from sorting artifacts common to conventional 4D-CT protocols. The mean prediction error across all patients between the breathing motion model predictions and the measured lung tissue positions was determined to be 1.19 ± 0.37 mm.

CONCLUSIONS

The proposed technique can be used as a clinical 4D-CT technique. It is robust in the presence of irregular breathing and allows the entire imaging dose to contribute to the resulting image quality, providing sorting artifact-free images at a patient dose similar to or less than current 4D-CT techniques.

Investigating the feasibility of rapid MRI for image-guided motion management in lung cancer radiotherapy

Cycle-to-cycle variations in respiratory motion can cause significant geometric and dosimetric errors in the administration of lung cancer radiation therapy. A common limitation of the current strategies for motion management is that they assume a constant, reproducible respiratory cycle. In this work, we investigate the feasibility of using rapid MRI for providing long-term imaging of the thorax in order to better capture cycle-to-cycle variations. Two nonsmall-cell lung cancer patients were imaged (free-breathing, no extrinsic contrast, and 1.5 T scanner). A balanced steady-state-free-precession (b-SSFP) sequence was used to acquire cine-2D and cine-3D (4D) images. In the case of Patient 1 (right midlobe lesion, ~40 mm diameter), tumor motion was well correlated with diaphragmatic motion. In the case of Patient 2, (left upper-lobe lesion, ~60 mm diameter), tumor motion was poorly correlated with diaphragmatic motion. Furthermore, the motion of the tumor centroid was poorly correlated with the motion of individual points on the tumor boundary, indicating significant rotation and/or deformation. These studies indicate that image quality and acquisition speed of cine-2D MRI were adequate for motion monitoring. However, significant improvements are required to achieve comparable speeds for truly 4D MRI. Despite several challenges, rapid MRI offers a feasible and attractive tool for noninvasive, long-term motion monitoring.

A comparison of amplitude-based and phase-based positron emission tomography gating algorithms for segmentation of internal target volumes of tumors subject to respiratory motion

PURPOSE

To quantitatively compare the accuracy of tumor volume segmentation in amplitude-based and phase-based respiratory gating algorithms in respiratory-correlated positron emission tomography (PET).

METHODS AND MATERIALS

List-mode fluorodeoxyglucose-PET data was acquired for 10 patients with a total of 12 fluorodeoxyglucose-avid tumors and 9 lymph nodes. Additionally, a phantom experiment was performed in which 4 plastic butyrate spheres with inner diameters ranging from 1 to 4 cm were imaged as they underwent 1-dimensional motion based on 2 measured patient breathing trajectories. PET list-mode data were gated into 8 bins using 2 amplitude-based (equal amplitude bins [A1] and equal counts per bin [A2]) and 2 temporal phase-based gating algorithms. Gated images were segmented using a commercially available gradient-based technique and a fixed 40% threshold of maximum uptake. Internal target volumes (ITVs) were generated by taking the union of all 8 contours per gated image. Segmented phantom ITVs were compared with their respective ground-truth ITVs, defined as the volume subtended by the tumor model positions covering 99% of breathing amplitude. Superior-inferior distances between sphere centroids in the end-inhale and end-exhale phases were also calculated.

RESULTS

Tumor ITVs from amplitude-based methods were significantly larger than those from temporal-based techniques (P=.002). For lymph nodes, A2 resulted in ITVs that were significantly larger than either of the temporal-based techniques (P<.0323). A1 produced the largest and most accurate ITVs for spheres with diameters of ≥2 cm (P=.002). No significant difference was shown between algorithms in the 1-cm sphere data set. For phantom spheres, amplitude-based methods recovered an average of 9.5% more motion displacement than temporal-based methods under regular breathing conditions and an average of 45.7% more in the presence of baseline drift (P<.001).

CONCLUSIONS

Target volumes in images generated from amplitude-based gating are larger and more accurate, at levels that are potentially clinically significant, compared with those from temporal phase-based gating.

A novel technique for markerless, self-sorted 4D-CBCT: feasibility study

PURPOSE

Four-dimensional CBCT (4D-CBCT) imaging in the treatment room can provide verification of moving targets, facilitating the potential for margin reduction and consequent dose escalation. Reconstruction of 4D-CBCT images requires correlation of respiratory phase with projection acquisition, which is often achieved with external surrogate measures of respiration. However, external measures may not be a direct representation of the motion of the internal anatomy and it is therefore the aim of this work to develop a novel technique for markerless, self-sorted 4D-CBCT reconstruction.

METHODS

A novel 4D-CBCT reconstruction technique based on the principles of Fourier transform (FT) theory was investigated for markerless extraction of respiratory phase directly from projection data. In this FT technique, both phase information (FT-phase) and magnitude information (FT-magnitude) were separately implemented in order to discern projections corresponding to peak inspiration, which then facilitated the proceeding sort and bin processes involved in retrospective 4D image reconstruction. In order to quantitatively evaluate the accuracy of the Fourier methods, peak-inspiration projections identified each by FT-phase and FT-magnitude were compared to those manually identified by visual tracking of structures. The average phase difference as assigned by each method vs the manual technique was calculated per projection dataset. The percentage of projections that were assigned within 10% phase of each other was also computed. Both Fourier methods were tested on two phantom datasets, programmed to exhibit sinusoidal respiratory cycles of 2.0 cm in amplitude with respiratory cycle lengths of 3 and 6 s, respectively. Additionally, three sets of patient projections were studied. All of the data were previously acquired at slow-gantry speeds ranging between 0.6°/s and 0.7°/s over a 200° rotation. Ten phase bins with 10% phase windows were selected for 4D-CBCT reconstruction of one phantom and one patient case for visual and quantitative comparison. Line profiles were plotted for the 0% and 50% phase images as reconstructed by the manual technique and each of the Fourier methods.

RESULTS

As compared with the manual technique, the FT-phase method resulted in average phase differences of 1.8% for the phantom with the 3 s respiratory cycle, 3.9% for the phantom with the 6 s respiratory cycle, 2.9% for patient 1, 5.0% for patient 2, and 3.8% for patient 3. For the FT-magnitude method, these numbers were 2.1%, 4.0%, 2.9%, 5.3%, and 3.5%, respectively. The percentage of projections that were assigned within 10% phase by the FT-phase method as compared to the manual technique for the five datasets were 100.0%, 100.0%, 97.6%, 93.4%, and 94.1%, respectively, whereas for the FT-magnitude method these percentages were 98.1%, 92.3%, 98.7%, 87.3%, and 95.7%. Reconstructed 4D phase images for both the phantom and patient case were visually and quantitatively equivalent between each of the Fourier methods vs the manual technique.

CONCLUSIONS

A novel technique employing the basics of Fourier transform theory was investigated and demonstrated to be feasible in achieving markerless, self-sorted 4D-CBCT reconstruction.

A novel CT acquisition and analysis technique for breathing motion modeling

To report on a novel technique for providing artifact-free quantitative four-dimensional computed tomography (4DCT) image datasets for breathing motion modeling. Commercial clinical 4DCT methods have difficulty managing irregular breathing. The resulting images contain motion-induced artifacts that can distort structures and inaccurately characterize breathing motion. We have developed a novel scanning and analysis method for motion-correlated CT that utilizes standard repeated fast helical acquisitions, a simultaneous breathing surrogate measurement, deformable image registration, and a published breathing motion model. The motion model differs from the CT-measured motion by an average of 0.65 mm, indicating the precision of the motion model. The integral of the divergence of one of the motion model parameters is predicted to be a constant 1.11 and is found in this case to be 1.09, indicating the accuracy of the motion model. The proposed technique shows promise for providing motion-artifact free images at user-selected breathing phases, accurate Hounsfield units, and noise characteristics similar to non-4D CT techniques, at a patient dose similar to or less than current 4DCT techniques.

Evaluation of the dose variation for prostate heavy charged particle therapy using four-dimensional computed tomography

We quantified dose variation effects due to respiratory-induced intrafractional motion in conventional carbon-ion prostate treatment by using four-dimensional computed tomography (4DCT). 4DCT scans of 20 patients were acquired under free-breathing conditions using a 256 multi-slice CT scanner. The clinical target volume (CTV) was defined as the prostate and the seminal vesicle. Two types of planning target volumes (PTVs) were defined to minimize excessive dose to the rectum. The first PTV (= PTV1) was calculated by adding a 3D uniform margin to the CTV. The second PTV (= PTV2) was cut in a straight line from the top surface of the rectum from PTV1. Compensating boli were designed for the respective PTVs at the peak-exhalation phase, and carbon-ion dose distributions for a single respiratory cycle were calculated using these boli. Dose conformation to prostate, CTV, PTV1 and PTV2 were unchanged for all respiratory phases. The dose for >95% volume irradiation (D95) was 97.7% for prostate, 92.5% for CTV, 74.1% for PTV1 and 96.1% for PTV2 averaged over all patients. The rectum volume at inhalation phase receiving ≤50% of the prescribed dose was smaller than the planning dose due to the abdominal thickness variation. The target dose is not affected by intrafractional respiration in carbon-ion prostate treatment. Small dose variations, however, were observed due to respiratory-induced abdominal thickness variation; therefore the geometrical changes should be considered for prostate particle therapy.

Helical mode lung 4D-CT reconstruction using Bayesian model

4D computed tomography (CT) has been widely used for treatment planning of thoracic and abdominal cancer radiotherapy. Current 4D-CT lung image reconstruction methods rely on respiratory gating to rearrange the large number of axial images into different phases, which may be subject to external surrogate errors due to poor reproducibility of breathing cycles. New image-matching-based reconstruction works better for the cine mode of 4D-CT acquisition than the helical mode because the table position of each axial image is different in helical mode and image matching might suffer from bigger errors. In helical mode, not only the phases but also the un-uniform table positions of images need to be considered. We propose a Bayesian method for automated 4D-CT lung image reconstruction in helical mode 4D scans. Each axial image is assigned to a respiratory phase based on the Bayesian framework that ensures spatial and temporal smoothness of surfaces of anatomical structures. Iterative optimization is used to reconstruct a series of 3D-CT images for subjects undergoing 4D scans. In experiments, we compared visually and quantitatively the results of the proposed Bayesian 4D-CT reconstruction algorithm with the respiratory surrogate and the image matching-based method. The results showed that the proposed algorithm yielded better 4D-CT for helical scans.

Motion management in positron emission tomography/computed tomography for radiation treatment planning

Hybrid positron emission tomography (PET)/computed tomography (CT) scanners combine, in a unique gantry, 2 of the most important diagnostic imaging systems, a CT and a PET tomograph, enabling anatomical (CT) and functional (PET) studies to be performed in a single study session. Furthermore, as the 2 scanners use the same spatial coordinate system, the reconstructed CT and PET images are spatially co-registered, allowing an accurate localization of the functional signal over the corresponding anatomical structure. This peculiarity of the hybrid PET/CT system results in improved tumor characterization for oncological applications, and more recently, it was found to be also useful for target volume definition (TVD) and treatment planning in radiotherapy (RT) applications. In fact, the use of combined PET/CT information has been shown to improve the RT treatment plan when compared with that obtained by a CT alone. A limiting factor to the accuracy of TVD by PET/CT is organ and tumor motion, which is mainly due to patient respiration. In fact, respiratory motion has a degrading effect on PET/CT image quality, and this is also critical for TVD, as it can lead to possible tumor missing or undertreatment. Thus, the management of respiratory motion is becoming an increasingly essential component in RT treatment planning; indeed, it has been recognized that the use of personalized motion information can improve TVD and, consequently, permit increased tumor dosage while sparing surrounding healthy tissues and organs at risk. This review describes the methods used for motion management in PET/CT for radiation treatment planning. The article covers the following: (1) problems caused by organ and lesion motion owing to respiration, and the artifacts generated on CT, PET, and PET/CT images; (2) data acquisition and processing techniques used to manage respiratory motion in PET/CT studies; and (3) the use of personalized motion information for TVD and radiation treatment planning.

GateCT™ surface tracking system for respiratory signal reconstruction in 4DCT imaging

PURPOSE

To assess the temporal and spatial accuracy of the GateCT™ system (VisionRT, London, UK), a recently released respiratory tracking system for 4DCT, under both ideal and nonideal respiratory conditions.

METHODS

Three experiments were performed by benchmarking and comparing its results with the ground-truth input data and those generated by the widely used Varian RPM™ system (Real-time Position Management, Varian, Palo Alto, CA). The first experiment used 10 sinusoidal breathing patterns (constant amplitude and frequency using sin(6)ωt), 10 "consistent" patient breathing patterns, and 10 "sporadic" patient breathing patterns. Motion was simulated with the quasar™ Programmable Respiratory Motion Platform (MODUS, London, Canada) as the surrogate. The GateCT™ and RPM™ systems were used to track the breathing patterns. The data from both systems were then analyzed in the Fourier domain, to evaluate temporal/phase accuracy, using the Pearson's correlation coefficient (PCC). The analysis correlated the ground-truth input data against the GateCT™ and RPM™ tracking results, respectively. The second experiment used 10 ideal sinusoidal breathing patterns, five of period 2.0 s, and five of period 5.0 s, with varying abdominal amplitudes found in clinical cases (peak-to-peak range: 1.67-10 mm) to test the sensitivity of the system to reconstruct various range of motion. And, the third experiment used 12 consecutive clinical patients to track the abdominal motion simultaneously by the GateCT™ and RPM™ systems. The baseline of the tracking results from both the two systems was analyzed via the mean-position-estimate (MPE) calculations. All experiments were tracked for at least 120 s.

RESULTS

In the first experiment, the average PCC values (±SD) of all thirty breathing patterns were 0.9995 ± 0.00035 and 0.9994 ± 0.00041 for the GateCT™ and the RPM™ system, respectively. These nearly identical results demonstrated similar temporal/phase tracking accuracy for the two systems. The results in the second experiment, however, revealed a pattern for the GateCT™ system in which the uncertainty of its mean-position tracking increased as the amplitude of the breathing pattern decreased. For example, a non-negligible baseline drift of up to 29.3% with respect to the peak-to-peak amplitude of 1.67-mm was observed. On the contrary, the RPM™ system displayed a more consistent recording of amplitudes over time with the greatest drift being <7.7%. The third experiment confirmed these findings in the clinical setting. Consistent decrease in PCC values due to the increase in artificial amplitude drifts, as the breathing amplitude decreased, was found. The lowest PCC value was 0.7239 for a patient with 1.57-mm peak-to-peak amplitude.

CONCLUSIONS

The GateCT™ system revealed its consistency in temporal/phase tracking but had limitations in accurately tracking the absolute abdominal positions, thus suggesting its appropriateness for phase-sorting of 4DCT rather than amplitude-sorting. In contrast, the RPM™ system demonstrated stable respiratory signal tracking in all ranges and accurately both in phase and amplitude, and is a robust system to use for both phase-sorting and amplitude-sorting techniques. The impact of the observed mean-position drift in the GateCT™ system on the resulting 4DCT image quality, in amplitude-sorting, needs further investigation.

Comparison of two respiration monitoring systems for 4D imaging with a Siemens CT using a new dynamic breathing phantom

Four-dimensional computed tomography (4D-CT) requires breathing information from the patient, and for this, several systems are available. Testing of these systems, under realistic conditions, requires a phantom with a moving target and an expandable outer contour. An anthropomorphic phantom was developed to simulate patient breathing as well as lung tumor motion. Using the phantom, an optical camera system (GateCT) and a pressure sensor (AZ-733V) were simultaneously operated, and 4D-CTs were reconstructed with a Siemens CT using the provided local-amplitude-based sorting algorithm. The comparison of the tumor trajectories of both systems revealed discrepancies up to 9.7 mm. Breathing signal differences, such as baseline drift, temporal resolution and noise level were shown not to be the reason for this. Instead, the variability of the sampling interval and the accuracy of the sampling rate value written on the header of the GateCT-signal file were identified as the cause. Interpolation to regular sampling intervals and correction of the sampling rate to the actual value removed the observed discrepancies. Consistently, the introduction of sampling interval variability and inaccurate sampling rate values into the header of the AZ-733V file distorted the tumor trajectory for this system. These results underline the importance of testing new equipment thoroughly, especially if components of different manufacturers are combined.

Respiratory motion correction for quantitative PET/CT using all detected events with internal-external motion correlation

PURPOSE

We present a method to correct respiratory motion blurring in PET/CT imaging using internal-external (INTEX) motion correlation. The internal motion of a known tumor is derived from respiratory-gated PET images; this internal motion is then correlated with external respiratory signals to determine the complete information of tumor motion during the scan.

METHODS

For each PET/CT data, PET listmode data were phase-gated into five bins and reconstructed. The centroid of a targeted tumor in each bin was determined and correlated with the corresponding mean displacement of externally monitored respiratory motion signal. Based on this correlation, the external motion signal was converted into internal tumor motion information in the superior-inferior direction. Then, the PET listmode data were binned sequentially to multiple 1-s sinograms. According to the converted internal tumor motion signal, each 1-s sinogram was registered to a reference frame, which best matched the helical CT attenuation map based on consistency conditions. The registered sinograms were summed and reconstructed to form an image, corrected for the motion of the specific tumor. In this study, the proposed INTEX method was evaluated with phantom and patient studies in terms of tracer concentration and volume.

RESULTS

The INTEX method effectively recovered the tracer concentration to the level of the stationary scan data in the phantom experiment. In the patient study, the INTEX method yielded a (17 +/- 22)% tumor volume decrease and a (10 +/- 10)% tumor SUVmax increase compared to non-gated images.

CONCLUSIONS

The proposed INTEX method reduces respiratory motion degradation of PET tumor quantification and delineation in an effective manner. This can be used to improve the assessment of response to therapy for a known tumor by minimizing residual motion and matching the attenuation correction, without increasing image noise.

Optimal gating compared to 3D and 4D PET reconstruction for characterization of lung tumours

PURPOSE

We investigated the added value of a new respiratory amplitude-based PET reconstruction method called optimal gating (OG) with the aim of providing accurate image quantification in lung cancer.

METHODS

FDG-PET imaging was performed in 26 lung cancer patients during free breathing using a 24-min list-mode acquisition on a PET/CT scanner. The data were reconstructed using three methods: standard 3D PET, respiratory-correlated 4D PET using a phase-binning algorithm, and OG. These datasets were compared in terms of the maximum SUV (SUVmax) in the primary tumour (main endpoint), noise characteristics, and volumes using thresholded regions of SUV 2.5 and 40% of the SUVmax.

RESULTS

SUVmax values from the 4D method (13.7 ± 5.6) and the OG method (14.1 ± 6.5) were higher (4.9 ± 4.8%, p < 0.001 and 6.9 ± 8.8%, p < 0.001, respectively) than that from the 3D method (13.1 ± 5.4). SUVmax did not differ between the 4D and OG methods (2.0 ± 8.4%, p = NS). Absolute and relative threshold volumes did not differ between methods, except for the 40% SUVmax volume in which the value from the 3D method was lower than that from the 4D method (-5.3 ± 7.1%, p = 0.007). The OG method exhibited less noise than the 4D method. Variations in volumes and SUVmax of up to 40% and 27%, respectively, of the individual gates of the 4D method were also observed.

CONCLUSION

The maximum SUVs from the OG and 4D methods were comparable and significantly higher than that from the 3D method, yet the OG method was visibly less noisy than the 4D method. Based on the better quantification of the maximum and the less noisy appearance, we conclude that OG PET is a better alternative to both 3D PET, which suffers from breathing averaging, and the noisy images of a 4D PET.

Detection and compensation of organ/lesion motion using 4D-PET/CT respiratory gated acquisition techniques

PURPOSE

To describe the degradation effects produced by respiratory organ and lesion motion on PET/CT images and to define the role of respiratory gated (RG) 4D-PET/CT techniques to compensate for such effects.

METHODS

Based on the literature and on our own experience, technical recommendations and clinical indications for the use of RG 4D PET/CT have been outlined.

RESULTS

RG 4D-PET/CT techniques require a state of the art PET/CT scanner, a respiratory monitoring system and dedicated acquisition and processing protocols. Patient training is particularly important to obtain a regular breathing pattern. An adequate number of phases has to be selected to balance motion compensation and statistical noise. RG 4D PET/CT motion free images may be clinically useful for tumour tissue characterization, monitoring patient treatment and target definition in radiation therapy planning.

CONCLUSIONS

RG 4D PET/CT is a valuable tool to improve image quality and quantitative accuracy and to assess and measure organ and lesion motion for radiotherapy planning.

A proto-type design of a real-tissue phantom for the validation of deformation algorithms and 4D dose calculations

The purpose of this study is to design a real-tissue phantom for use in the validation of deformation algorithms. A phantom motion controller that runs sinusoidal and non-regular patient-based breathing pattern, via a piston, was applied to porcine liver tissue. It was regulated to simulate movement ranges similar to recorded implanted liver markers from patients. 4D CT was applied to analyze deformation. The suitability of various markers in the liver and the position reproducibility of markers and of reference points were studied. The similarity of marker motion pattern in the liver phantom and in real patients was evaluated. The viability of the phantom over time and its use with electro-magnetic tracking devices were also assessed. High contrast markers, such as carbon markers, implanted in the porcine liver produced less image artifacts on CT and were well visualized compared to metallic ones. The repositionability of markers was within a measurement accuracy of +/-2 mm. Similar anatomical patient motions were reproducible up to elongations of 3 cm for a time period of at least 90 min. The phantom is compatible with electro-magnetic tracking devices and 4D CT. The phantom motion is reproducible and simulates realistic patient motion and deformation. The ability to carry out voxel-based tracking allows for the evaluation of deformation algorithms in a controlled environment with recorded patient traces. The phantom is compatible with all therapy devices clinically encountered in our department.

A study on the dosimetric accuracy of treatment planning for stereotactic body radiation therapy of lung cancer using average and maximum intensity projection images

PURPOSE

To assess the accuracy of current stereotactic body radiation therapy (SBRT) lung treatment planning methodologies on irregular breathing patterns, we have performed a systematic dosimetric evaluation in phantoms by utilizing maximum intensity projection (MIP) and average (AVG) images generated from four dimensional computed tomography (4DCT).

METHODS

A custom built programmable lung phantom was used to simulate tumor motions due to various breathing patterns of patients. 4DCT scans were obtained in helical mode, and reconstructed AVG and MIP datasets were imported into the Pinnacle 8.0 h treatment planning system. SBRT plans were generated and executed, and delivered doses were measured by radiochromic film for analysis.

RESULTS

For targets moving regularly or irregularly within a small range (7.0+/-1.8 mm, n=6), we observed good agreement between the measured and computed dose distributions. However, for targets moving irregularly with a larger range (20.8+/-2.6 mm, n=4), the measured isodose lines were found to be shifted relative to the planned distribution, resulting in an under-dosing (over 10%) in a portion of the PTV. We further observed that the discrepancy between planned and measured dose distribution is due to the inaccurate representation of irregular target motion in the MIP images generated from 4DCT.

CONCLUSIONS

Caution should be used when planning from 4DCT images in the presence of large and irregular target motion. The inaccuracy inherent in 4DCT MIP and AVG images can be mitigated through the application of methodologies to reduce respiratory motion, such as abdominal compression, and through the use of volumetric image guidance (e.g., cone beam CT-CBCT) to assure precise targeting with minimal shifts.

The impact of respiratory motion on tumor quantification and delineation in static PET/CT imaging

Our aim is to investigate the impact of respiratory motion on tumor quantification and delineation in static PET/CT imaging using a population of patient respiratory traces. A total of 1295 respiratory traces acquired during whole body PET/CT imaging were classified into three types according to the qualitative shape of their signal histograms. Each trace was scaled to three diaphragm motion amplitudes (6 mm, 11 mm and 16 mm) to drive a whole body PET/CT computer simulation that was validated with a physical phantom experiment. Three lung lesions and one liver lesion were simulated with diameters of 1 cm and 2 cm. PET data were reconstructed using the OS-EM algorithm with attenuation correction using CT images at the end-expiration phase and respiratory-averaged CT. The errors of the lesion maximum standardized uptake values (SUV(max)) and lesion volumes between motion-free and motion-blurred PET/CT images were measured and analyzed. For respiration with 11 mm diaphragm motion and larger quiescent period fraction, respiratory motion can cause a mean lesion SUV(max) underestimation of 28% and a mean lesion volume overestimation of 130% in PET/CT images with 1 cm lesions. The errors of lesion SUV(max) and volume are larger for patient traces with larger motion amplitudes. Smaller lesions are more sensitive to respiratory motion than larger lesions for the same motion amplitude. Patient respiratory traces with relatively larger quiescent period fraction yield results less subject to respiratory motion than traces with long-term amplitude variability. Mismatched attenuation correction due to respiratory motion can cause SUV(max) overestimation for lesions in the lower lung region close to the liver dome. Using respiratory-averaged CT for attenuation correction yields smaller mismatch errors than those using end-expiration CT. Respiratory motion can have a significant impact on static oncological PET/CT imaging where SUV and/or volume measurements are important. The impact is highly dependent upon motion amplitude, lesion location and size, attenuation map and respiratory pattern. To overcome the motion effect, motion compensation techniques may be necessary in clinical practice to improve the tumor quantification for determining the response to therapy or for radiation treatment planning.

4D-CT motion estimation using deformable image registration and 5D respiratory motion modeling

Four-dimensional computed tomography (4D-CT) imaging technology has been developed for radiation therapy to provide tumor and organ images at the different breathing phases. In this work, a procedure is proposed for estimating and modeling the respiratory motion field from acquired 4D-CT imaging data and predicting tissue motion at the different breathing phases. The 4D-CT image data consist of series of multislice CT volume segments acquired in ciné mode. A modified optical flow deformable image registration algorithm is used to compute the image motion from the CT segments to a common full volume 3D-CT reference. This reference volume is reconstructed using the acquired 4D-CT data at the end-of-exhalation phase. The segments are optimally aligned to the reference volume according to a proposed a priori alignment procedure. The registration is applied using a multigrid approach and a feature-preserving image downsampling maxfilter to achieve better computational speed and higher registration accuracy. The registration accuracy is about 1.1 +/- 0.8 mm for the lung region according to our verification using manually selected landmarks and artificially deformed CT volumes. The estimated motion fields are fitted to two 5D (spatial 3D+tidal volume+airflow rate) motion models: forward model and inverse model. The forward model predicts tissue movements and the inverse model predicts CT density changes as a function of tidal volume and airflow rate. A leave-one-out procedure is used to validate these motion models. The estimated modeling prediction errors are about 0.3 mm for the forward model and 0.4 mm for the inverse model.