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PurposeMotion management of tumors within the lung and abdomen is challenging because it requires balancing tissue sparing with accuracy of hitting the target, while considering treatment delivery efficiency. Physicists can play an important role in analyzing four-dimensional computed tomography (4DCT) data to recommend the optimal respiratory gating parameters for a patient. The goal of this work was to develop a standardized procedure for making recommendations regarding gating parameters and planning margins for lung and gastrointestinal stereotactic body radiotherapy (SBRT) treatments. In doing so, we hoped to simplify decision-making and analysis, and provide a tool for troubleshooting complex cases.MethodsFactors that impact gating decisions and planning target volume (PTV) margins were identified. The gating options included gating on exhale with approximately a 50% duty cycle (Gate3070), exhale gating with a reduced duty cycle (Gate4060), and treating for most of respiration, excluding only extreme inhales and exhales (Gate100). A standard operating procedure was developed, as well as a physics consult document to communicate motion management recommendations to other members of the treatment team. This procedure was implemented clinically for 1 year and results are reported below.ResultsIdentified factors that impact motion management included the magnitude of motion observed on 4DCT, the regularity of breathing and quality of 4DCT data, and ability to observe the target on fluoroscopy. These were collated into two decision tables-one specific to lung tumors and another for gastrointestinal tumors-such that a physicist could answer a series of questions to determine the optimal gating and PTV margin. The procedure was used clinically for 252 sites from 213 patients treated with respiratory-gated SBRT and standardized practice across our 12-member physics team.ConclusionImplementation of a standardized procedure for respiratory gating had a positive impact in our clinic, improving efficiency and ease of 4DCT analysis and standardizing gating decision-making amongst physicists.

PurposeStandard four-dimensional computed tomography (4DCT) cardiac reconstructions typically include spiraling artifacts that depend not only on the motion of the heart but also on the gantry angle range over which the data was acquired. We seek to reduce these motion artifacts and, thereby, improve the accuracy of left ventricular wall positions in 4DCT image series.MethodsWe use a motion artifact reduction approach (ResyncCT) that is based largely on conjugate pairs of partial angle reconstruction (PAR) images. After identifying the key locations where motion artifacts exist in the uncorrected images, paired subvolumes within the PAR images are analyzed with a modified cross-correlation function in order to estimate 3D velocity and acceleration vectors at these locations. A subsequent motion compensation process (also based on PAR images) includes the creation of a dense motion field, followed by a backproject-and-warp style compensation. The algorithm was tested on a 3D printed phantom, which represents the left ventricle (LV) and on challenging clinical cases corrupted by severe artifacts.ResultsThe results from our preliminary phantom test as well as from clinical cardiac scans show crisp endocardial edges and resolved double-wall artifacts. When viewed as a temporal series, the corrected images exhibit a much smoother motion of the LV endocardial boundary as compared to the uncorrected images. In addition, quantitative results from our phantom studies show that ResyncCT processing reduces endocardial surface distance errors from 0.9 ± 0.8 to 0.2 ± 0.1 mm.ConclusionsThe ResyncCT algorithm was shown to be effective in reducing motion artifacts and restoring accurate wall positions. Some perspectives on the use of conjugate-PAR images and on techniques for CT motion artifact reduction more generally are also given.

PURPOSE: To examine the use of multiple fast-helical free breathing computed tomography (FHFBCT) scans for ventilation measurement. METHODS: Ten patients were scanned 25 times in alternating directions using a FHFBCT protocol. Simultaneously, an abdominal pneumatic bellows was used as a real-time breathing surrogate. Regions-of-interest (ROIs) were selected from the upper right lungs of each patient for analysis. The ROIs were first registered using a published registration technique (pTV). A subsequent follow-up registration employed an objective function with two terms, a ventilation-adjusted Hounsfield Unit difference and a conservation-of-mass term labeled ΔΓ that denoted the difference between the deformation Jacobian and the tissue density ratio. The ventilations were calculated voxel-by-voxel as the slope of a first-order fit of the Jacobian as a function of the breathing amplitude. RESULTS: The ventilations of the 10 patients showed different patterns and magnitudes. The average ventilation calculated from the deformation vector fields (DVFs) of the pTV and secondary registration was nearly identical, but the standard deviation of the voxel-to-voxel differences was approximately 0.1. The mean of the 90th percentile values of ΔΓ was reduced from 0.153 to 0.079 between the pTV and secondary registration, implying first that the secondary registration improved the conservation-of-mass criterion by almost 50% and that on average the correspondence between the Jacobian and density ratios as demonstrated by ΔΓ was less than 0.1. This improvement occurred in spite of the average of the 90th percentile changes in the DVF magnitudes being only 0.58 mm. CONCLUSIONS: This work introduces the use of multiple free-breathing CT scans for free-breathing ventilation measurements. The approach has some benefits over the traditional use of 4-dimensional CT (4DCT) or breath-hold scans. The benefit over 4DCT is that FHFBCT does not have sorting artifacts. The benefits over breath-hold scans include the relatively small motion induced by quiet respiration versus deep-inspiration breath hold and the potential for characterizing dynamic breathing processes that disappear during breath hold.

Rationale and objectivesTo establish a proof-of-principle for combined assessment of pulmonary ventilation and perfusion using single-energy computed tomography (CT) and image processing/analysis (denoted as single-energy CT ventilation/perfusion imaging).Materials and methodsBreath-hold CT scans were acquired at end-expiration and end-inspiration before injection of iodinated contrast agents, and repeated at end-inspiration after contrast injection for 17 canines (8 normal and 9 diseased lung subjects). Ventilation images were calculated with deformable image registration to map the end-expiratory and end-inspiratory CT images and quantitative analysis for regional volume changes as surrogates for ventilation. Perfusion images were calculated by subtracting the end-inspiratory precontrast CT from the deformably registered end-inspiratory postcontrast CT, yielding a map of regional Hounsfield unit enhancement as a surrogate for perfusion. Ventilation-perfusion matching, spatial heterogeneity, and gravitationally directed gradients were compared between two groups using a Wilcoxon rank-sum test.ResultsThe normal group had significantly higher Dice similarity coefficients for spatial overlap of segmented functional volumes between ventilation and perfusion (median 0.40 vs. 0.33, p = 0.05), suggesting stronger ventilation-perfusion matching. The normal group also had greater Spearman's correlation coefficients based on 16 regions of interest (median 0.58 vs. 0.40, p = 0.09). The coefficients of variation were comparable (median, ventilation 0.71 vs. 0.91, p = 0.60; perfusion 0.63 vs. 0.75, p = 0.27). The linear regression slopes of gravitationally directed gradient were also comparable for ventilation (median, ventilation -0.26 vs. -0.18, p = 0.19; perfusion -0.17 vs. -0.06, p = 0.11).ConclusionThese findings provide proof-of-principle for single-energy CT ventilation/perfusion imaging.

PurposeRadiation dose delivered to targets located near the upper abdomen or thorax are significantly affected by respiratory motion, necessitating large margins, limiting dose escalation. Surrogate motion management devices, such as the Real-time Position Management (RPM™) system (Varian Medical Systems, Palo Alto, CA), are commonly used to improve normal tissue sparing. Alternative to current solutions, we have developed and evaluated the feasibility of a real-time position management system that leverages the motion data from the onboard hardware of Apple iOS devices to provide patients with visual coaching with the potential to improve the reproducibility of breathing as well as improve patient compliance and reduce treatment delivery time.Methods and materialsThe iOS application, coined the Instant Respiratory Feedback (IRF) system, was developed in Swift (Apple Inc., Cupertino, CA) using the Core-Motion library and implemented on an Apple iPhone® devices. Operation requires an iPhone®, a three-dimensional printed arm, and a radiolucent projector screen system for feedback. Direct comparison between IRF, which leverages sensor fusion data from the iPhone®, and RPM™, an optical-based system, was performed on multiple respiratory motion phantoms and volunteers. The IRF system and RPM™ camera tracking marker were placed on the same location allowing for simultaneous data acquisition. The IRF surrogate measurement of displacement was compared to the signal trace acquired using RPM™ with univariate linear regressions and Bland-Altman analysis.ResultsPeriodic motion shows excellent agreement between both systems, and subject motion shows good agreement during regular and irregular breathing motion. Comparison of IRF and RPM™ show very similar signal traces that were significantly related across all phantoms, including those motion with different amplitude and frequency, and subjects' waveforms (all r > 0.9, P

PurposeCT ventilation imaging (CTVI) is being used to achieve functional avoidance lung cancer radiation therapy in three clinical trials (NCT02528942, NCT02308709, NCT02843568). To address the need for common CTVI validation tools, we have built the Ventilation And Medical Pulmonary Image Registration Evaluation (VAMPIRE) Dataset, and present the results of the first VAMPIRE Challenge to compare relative ventilation distributions between different CTVI algorithms and other established ventilation imaging modalities.MethodsThe VAMPIRE Dataset includes 50 pairs of 4DCT scans and corresponding clinical or experimental ventilation scans, referred to as reference ventilation images (RefVIs). The dataset includes 25 humans imaged with Galligas 4DPET/CT, 21 humans imaged with DTPA-SPECT, and 4 sheep imaged with Xenon-CT. For the VAMPIRE Challenge, 16 subjects were allocated to a training group (with RefVI provided) and 34 subjects were allocated to a validation group (with RefVI blinded). Seven research groups downloaded the Challenge dataset and uploaded CTVIs based on deformable image registration (DIR) between the 4DCT inhale/exhale phases. Participants used DIR methods broadly classified into B-splines, Free-form, Diffeomorphisms, or Biomechanical modeling, with CT ventilation metrics based on the DIR evaluation of volume change, Hounsfield Unit change, or various hybrid approaches. All CTVIs were evaluated against the corresponding RefVI using the voxel-wise Spearman coefficient rS , and Dice similarity coefficients evaluated for low function lung ( DSClow ) and high function lung ( DSChigh ).ResultsA total of 37 unique combinations of DIR method and CT ventilation metric were either submitted by participants directly or derived from participant-submitted DIR motion fields using the in-house software, VESPIR. The rS and DSC results reveal a high degree of inter-algorithm and intersubject variability among the validation subjects, with algorithm rankings changing by up to ten positions depending on the choice of evaluation metric. The algorithm with the highest overall cross-modality correlations used a biomechanical model-based DIR with a hybrid ventilation metric, achieving a median (range) of 0.49 (0.27-0.73) for rS , 0.52 (0.36-0.67) for DSClow , and 0.45 (0.28-0.62) for DSChigh . All other algorithms exhibited at least one negative rS value, and/or one DSC value less than 0.5.ConclusionsThe VAMPIRE Challenge results demonstrate that the cross-modality correlation between CTVIs and the RefVIs varies not only with the choice of CTVI algorithm but also with the choice of RefVI modality, imaging subject, and the evaluation metric used to compare relative ventilation distributions. This variability may arise from the fact that each of the different CTVI algorithms and RefVI modalities provides a distinct physiologic measurement. Ultimately this variability, coupled with the lack of a "gold standard," highlights the ongoing importance of further validation studies before CTVI can be widely translated from academic centers to the clinic. It is hoped that the information gleaned from the VAMPIRE Challenge can help inform future validation efforts.

Objective:Lung tissue elasticity is an effective spatial representation for Chronic Obstructive Pulmonary Disease phenotypes and pathophysiology. We investigated a novel imaging biomarker based on the voxel-by-voxel distribution of lung tissue elasticity. Our approach combines imaging and biomechanical modeling to characterize tissue elasticity.Methods:We acquired 4DCT images for 13 lung cancer patients with known COPD diagnoses based on GOLD 2017 criteria. Deformation vector fields (DVFs) from the deformable registration of end-inhalation and end-exhalation breathing phases were taken to be the ground-truth. A linear elastic biomechanical model was assembled from end-exhalation datasets with a density-guided initial elasticity distribution. The elasticity estimation was formulated as an iterative process, where the elasticity was optimized based on its ability to reconstruct the ground-truth. An imaging biomarker (denoted YM1-3) derived from the optimized elasticity distribution, was compared with the current gold standard, RA950 using confusion matrix and area under the receiver operating characteristic (AUROC) curve analysis.Results:The estimated elasticity had 90 % accuracy when representing the ground-truth DVFs. The YM1-3 biomarker had higher diagnostic accuracy (86% vs 71 %), higher sensitivity (0.875 vs 0.5), and a higher AUROC curve (0.917 vs 0.875) as compared to RA950. Along with acting as an effective spatial indicator of lung pathophysiology, the YM1-3 biomarker also proved to be a better indicator for diagnostic purposes than RA950.Conclusions:Overall, the results suggest that, as a biomarker, lung tissue elasticity will lead to new end points for clinical trials and new targeted treatment for COPD subgroups.Advances in knowledge:The derivation of elasticity information directly from 4DCT imaging data is a novel method for performing lung elastography. The work demonstrates the need for a mechanics-based biomarker for representing lung pathophysiology.

BackgroundPancreatic ductal adenocarcinoma has dismal prognosis. Most patients receive radiation therapy (RT), which is complicated by respiration induced organ motion in upper abdomen. The purpose of this study is to report our early clinical experience in a novel self-gated k-space sorted four-dimensional magnetic resonance imaging (4D-MRI) with slab-selective (SS) excitation to highlight tumor infiltrating blood vessels for pancreatic RT.MethodsTen consecutive patients with borderline resectable or locally advanced pancreatic cancer were recruited to the study. Non-contrast 4D-MRI with and without slab-selective excitation and 4D-CT with delay contrast were performed on all patients. Vessel-tissue CNR were calculated for aorta and critical vessels (superior mesenteric artery or superior mesenteric vein) encompassed by tumor. Respiratory motion trajectories for tumor, as well as involved vessels were analyzed on SS-4D-MRI. Intra-class cross correlation (ICC) between tumor volume and involved vessels were calculated.ResultsAmong all 4D imaging modalities evaluated, SS-4D-MRI sampling trajectory results in images with highest vessel-tissue CNR comparing to non-slab-selective 4D-MRI and 4D-CT for all patients studied. Average (±standard deviation) CNR for involved vessels are 13.1 ± 8.4 and 3.2 ± 2.7 for SS-4D-MRI and 4D-CT, respectively. The ICC factors comparing tumor and involved vessels motion trajectories are 0.93 ± 0.10, 0.65 ± 0.31 and 0.77 ± 0.23 for superior-inferior, anterior-posterior and medial-lateral directions respectively.ConclusionsA novel 4D-MRI sequence based on 3D-radial sampling and slab-selective excitation has been assessed for pancreatic cancer patients. The non-contrast 4D-MRI images showed significantly better contrast to noise ratio for the vessels that limit tumor resectability compared to 4D-CT with delayed contrast. The sequence has great potential in accurately defining both the tumor and boost volume margins for pancreas RT with simultaneous integrated boost.

Based on 4D-CT, we aimed to characterize the pattern of morphological changes in lung tumors during respiration, and investigated its potential in non-invasively differentiating lung adenocarcinoma (AC) and squamous cell carcinoma (SCC). We applied a 3D surface analysis on 22 tumors (13 AC, 9 SCC) to investigate the tumor regional morphological fluctuations in response to respiration phases. Tumor surface vertices among ten respiratory phases were matched using surface-based registration, and the shape descriptors (ρ and detJ) were calculated and tracked across respiration stages in a regionally aligned scenario. Pair-wise group comparisons were performed between lung AC and SCC subtypes, in terms of ratios of maximal shape changes as well as correlation coefficients between tumor shape and respiratory stage indicators from the lung. AC type tumors had averaged larger surface measurements at exhale than at inhale, and these surface measurements were negatively correlated with lung volumes across respiratory stages. In contrast, SCC type tumors had averaged smaller surface measurements at exhale than at inhale, and the correlations with lung volumes were positive. The group differences in maximal shape changes as well as correlations were both statistically significant (p

BackgroundCT SQUEEZ is a new automated technique to evaluate regional endocardial strain by tracking features on the endocardium from 4D cine CT data. The objective of this study was to measure the range of endocardial regional strain (RSCT) values obtained with CT SQUEEZ in the normal human left ventricle (LV) from standard clinical 4D coronary CTA exams.MethodsRSCT was measured over the heart cycle in 25 humans with normal LV function using cine CT from three vendors. Mean and standard deviation of RSCT values were computed in 16 AHA LV segments to estimate the range of values expected in the normal LV.ResultsCurves describing RSCT vs. time were consistent between subjects. There was a slight gradient of decreasing minimum RSCT value (increased shortening) from the base to the apex of the heart. Mean RSCT values at end-systole were: base = -32% ± 1%, mid = -33% ± 1%, apex = -36% ± 1%. The standard deviation of the minimum systolic RSCT in each segment over all subjects was 5%. The average time to reach maximum shortening was 34% of the RR interval.ConclusionsRegional strain (RSCT) can be rapidly obtained from standard gated coronary CCTA protocols using 4DCT SQUEEZ processing. We estimate that 95% of normal LV end-systolic RSCT values will fall between -23% and -43%; therefore, we hypothesize that an RSCT value higher than -23% will indicate a hypokinetic segment in the human heart.

PurposeIn radiation treatment planning for thoracic and abdominal tumors, 4D-MRI has shown promise in respiratory motion characterization with improved soft-tissue contrast compared to clinical standard, 4D computed tomography (4D-CT). This study aimed to further improve vessel-tissue contrast and overall image quality in 3D radial sampling-based 4D-MRI using a slab-selective (SS) excitation approach.MethodsThe technique was implemented in a 3D radial sampling with self-gating-based k-space sorting sequence. The SS excitation approach was compared to a non-selective (NS) approach in six cancer patients and two healthy volunteers at 3T. Improvements in vessel-tissue contrast ratio (CR) and vessel signal-to-noise ratio (SNR) were analyzed in five of the eight subjects. Image quality was visually assessed in all subjects on a 4-point scale (0: poor; 3: excellent). Tumor (patients) and pancreas (healthy) motion trajectories were compared between the two imaging approaches.ResultsCompared with NS-4D-MRI, SS-4D-MRI significantly improved the overall vessel-tissue CR (2.60 ± 3.97 vs. 1.03 ± 1.44, P < 0.05), SNR (63.33 ± 38.45 vs. 35.74 ± 28.59, P < 0.05), and image quality score (2.6 ± 0.5 vs. 1.4 ± 0.5, P = 0.02). Motion trajectories from the two approaches exhibited strong correlation in the superior-inferior (0.96 ± 0.06), but weaker in the anterior-posterior (0.78 ± 0.24) and medial-lateral directions (0.46 ± 0.44).ConclusionsThe proposed 4D-MRI with slab-selectively excited 3D radial sampling allows for improved blood SNR, vessel-tissue CR, and image quality.

PurposeComputed tomographic (CT) ventilation imaging is a new modality that uses 4-dimensional (4D) CT information to calculate lung ventilation. Although retrospective studies have reported on the reduction in dose to functional lung, no work to our knowledge has been published in which the dosimetric improvements have been translated to a reduction in the probability of pulmonary toxicity. Our work estimates the reduction in toxicity for CT ventilation-based functional avoidance planning.Methods and materialsSeventy previously treated lung cancer patients who underwent 4DCT imaging were used for the study. CT ventilation maps were calculated with 4DCT deformable image registration and a density change-based algorithm. Pneumonitis was graded on the basis of imaging and clinical presentation. Maximum likelihood methods were used to generate normal tissue complication probability (NTCP) models predicting grade 2 or higher (2+) and grade 3+ pneumonitis as a function of dose (V5 Gy, V10 Gy, V20 Gy, V30 Gy, and mean dose) to functional lung. For 30 patients a functional plan was generated with the goal of reducing dose to the functional lung while meeting Radiation Therapy Oncology Group 0617 constraints. The NTCP models were applied to the functional plans and the clinically used plans to calculate toxicity reduction.ResultsBy the use of functional avoidance planning, absolute reductions in grade 2+ NTCP of 6.3%, 7.8%, and 4.8% were achieved based on the mean fV20 Gy, fV30 Gy, and mean dose to functional lung metrics, respectively. Absolute grade 3+ NTCP reductions of 3.6%, 4.8%, and 2.4% were achieved with fV20 Gy, fV30 Gy, and mean dose to functional lung. Maximum absolute reductions of 52.3% and 16.4% were seen for grade 2+ and grade 3+ pneumonitis for individual patients.ConclusionOur study quantifies the possible toxicity reduction from CT ventilation-based functional avoidance planning. Reductions in grades 2+ and 3+ pneumonitis were 7.1% and 4.7% based on mean dose-function metrics, with reductions as high as 52.3% for individual patients. Our work provides seminal data for determining the potential toxicity benefit from incorporating CT ventilation into thoracic treatment planning.

PurposeFour-dimensional (4D) computed tomography (CT) ventilation imaging is increasingly being used to calculate lung ventilation and implement functional-guided radiation therapy in clinical trials. There has been little exhaustive work evaluating which dose-function metrics should be used for treatment planning and plan evaluation. The purpose of our study was to evaluate which dose-function metrics best predict for radiation pneumonitis (RP).Methods and materialsSeventy lung cancer patients who underwent 4D CT imaging and pneumonitis grading were assessed. Pretreatment 4D CT scans of each patient were used to calculate ventilation images. We evaluated 3 types of dose-function metrics that combined the patient's 4D CT ventilation image and treatment planning dose distribution: (1) structure-based approaches; (2) image-based approaches using the dose-function histogram; and (3) nonlinear weighting schemes. Log-likelihood methods were used to generate normal tissue complication probability models predicting grade 3 or higher (ie, grade 3+) pneumonitis for all dose-function schemes. The area under the curve (AUC) was used to assess the predictive power of the models. All techniques were compared with normal tissue complication probability models based on traditional, total lung dose metrics.ResultsThe most predictive models were structure-based approaches that focused on the volume of functional lung receiving ≥20 Gy (AUC, 0.70). Probabilities of grade 3+ RP of 20% and 10% correspond to V20 (percentage of volume receiving ≥20 Gy) to the functional subvolumes of 26.8% and 9.3%, respectively. Imaging-based analysis with the dose-function histogram and nonlinear weighted ventilation values yielded AUCs of 0.66 and 0.67, respectively, when we evaluated the percentage of functionality receiving ≥20 Gy. All dose-function metrics outperformed the traditional dose metrics (mean lung dose, AUC of 0.55).ConclusionsA full range of dose-function metrics and functional thresholds was examined. The calculated AUC values for the most predictive functional models occupied a narrow range (0.66-0.70), and all showed notable improvements over AUC from traditional lung dose metrics (0.55). Identifying the combinations most predictive of grade 3+ RP provides valuable data to inform the functional-guided radiation therapy process.

Surrogate-driven respiratory motion models relate the motion of the internal anatomy to easily acquired respiratory surrogate signals, such as the motion of the skin surface. They are usually built by first using image registration to determine the motion from a number of dynamic images, and then fitting a correspondence model relating the motion to the surrogate signals. In this paper we present a generalized framework that unifies the image registration and correspondence model fitting into a single optimization. This allows the use of 'partial' imaging data, such as individual slices, projections, or k-space data, where it would not be possible to determine the motion from an individual frame of data. Motion compensated image reconstruction can also be incorporated using an iterative approach, so that both the motion and a motion-free image can be estimated from the partial image data. The framework has been applied to real 4DCT, Cine CT, multi-slice CT, and multi-slice MR data, as well as simulated datasets from a computer phantom. This includes the use of a super-resolution reconstruction method for the multi-slice MR data. Good results were obtained for all datasets, including quantitative results for the 4DCT and phantom datasets where the ground truth motion was known or could be estimated.

Background and purposePatients with multigland primary hyperparathyroidism are at higher risk for missed lesions on imaging and failed parathyroidectomy. The purpose of this study was to prospectively validate the ability of previously derived predictive score systems, the composite multigland disease score, and the multiphase multidetector contrast-enhanced CT (4D-CT) composite multigland disease score, to identify patients with a high likelihood of multigland disease.Materials and methodsThis was a prospective study of 71 patients with primary hyperparathyroidism who underwent 4D-CT and successful parathyroidectomy. The size and number of lesions identified on 4D-CT, serum calcium levels, and parathyroid hormone levels were collected. A composite multigland disease score was calculated from 4D-CT imaging findings and the Wisconsin Index (the product of the serum calcium and parathyroid hormone levels). A 4D-CT multigland disease score was obtained by using the CT data alone.ResultsTwenty-eight patients with multigland disease were compared with 43 patients with single-gland disease. Patients with multigland disease had a significantly smaller lesion size (P < .01) and a higher likelihood of having either ≥2 or 0 lesions identified on 4D-CT (P < .01). Composite multigland disease scores of ≥4, ≥5, and 6 had specificities of 72%, 86%, and 100% for multigland disease, respectively. 4D-CT multigland disease scores of ≥3 and 4 had specificities of 74% and 88%.ConclusionsPredictive scoring systems based on 4D-CT data, with or without laboratory data, were able to identify a subgroup of patients with a high likelihood of multigland disease in a prospectively accrued population of patients with primary hyperparathyroidism. These scoring systems can aid in surgical planning.

Background and purposeThe impact of audiovisual (AV) biofeedback on four dimensional (4D) positron emission tomography (PET) and 4D computed tomography (CT) image quality was investigated in a prospective clinical trial (NCT01172041).Material and methods4D-PET and 4D-CT images of ten lung cancer patients were acquired with AV biofeedback (AV) and free breathing (FB). The 4D-PET images were analyzed for motion artifacts by comparing 4D to 3D PET for gross tumor volumes (GTVPET) and maximum standardized uptake values (SUVmax). The 4D-CT images were analyzed for artifacts by comparing normalized cross correlation-based scores (NCCS) and quantifying a visual assessment score (VAS). A Wilcoxon signed-ranks test was used for statistical testing.ResultsThe impact of AV biofeedback varied widely. Overall, the 3D to 4D decrease of GTVPET was 1.2±1.3cm(3) with AV and 0.6±1.8cm(3) for FB. The 4D-PET increase of SUVmax was 1.3±0.9 with AV and 1.3±0.8 for FB. The 4D-CT NCCS were 0.65±0.27 with AV and 0.60±0.32 for FB (p=0.08). The 4D-CT VAS was 0.0±2.7.ConclusionThis study demonstrated a high patient dependence on the use of AV biofeedback to reduce motion artifacts in 4D imaging. None of the hypotheses tested were statistically significant. Future development of AV biofeedback will focus on optimizing the human-computer interface and including patient training sessions for improved comprehension and compliance.

PurposeTo investigate the hypothesis that CT ventilation functional image-based IMRT plans designed to avoid irradiating highly-functional lung regions are comparable to single-photon emission CT (SPECT) ventilation functional image-based plans.Methods and materialsThree IMRT plans were created for eight thoracic cancer patients using: (1) CT ventilation functional images, (2) SPECT ventilation functional images, and (3) anatomic images (no functional images). CT ventilation images were created by deformable image registration of 4D-CT image data sets and quantitative analysis. The resulting plans were analyzed for the relationship between the deviations of CT-functional plan metrics from anatomic plan metrics (ΔCT-anatomic) and those of SPECT-functional plans (ΔSPECT-anatomic), and moreover for agreements of various metrics between the CT-functional and SPECT-functional plans.ResultsThe relationship between ΔCT-anatomic and ΔSPECT-anatomic was strong (e.g., R=0.94; linear regression slope 0.71). The average differences and 95% limits of agreement between the CT-functional and SPECT-functional plan metrics (except for monitor units) for various structures were mostly less than 1% and 2%, respectively.ConclusionsThis study demonstrated a reasonable agreement between the CT ventilation functional image-based IMRT plans and SPECT-functional plans, suggesting the potential for CT ventilation imaging to serve as a surrogate for SPECT ventilation in functional image-guided radiotherapy.

ObjectiveWe evaluated whether patients with early-stage non-small-cell lung cancers (NSCLCs) treated with stereotactic body radiation therapy (SBRT) without full prescription dose coverage of the planning target volume (PTV) had inferior outcomes.MethodsThe SBRT regimen was 54 Gy in three fractions. Dosimetric constraints were as per the Radiation Therapy Oncology Group 0236 guidelines. All patients underwent four-dimensional CT (4D-CT) simulation. The internal target volume (ITV) was defined using 4D-CT, and the PTV was defined as a 6-mm longitudinal and a 3-mm axial expansion from the ITV. If normal tissue constraints were beyond tolerance, ITV-based dosing was employed where priority was made for full ITV coverage at the expense of PTV coverage. Patients with and without full PTV dose coverage were compared, and control rates were estimated using Kaplan-Meier analysis.Results120 NSCLC cases were evaluated with 81% having adequate PTV dose coverage. ITV and PTV were significantly larger in the cohort with inadequate PTV dose coverage (p = 0.0085 and p = 0.0038, respectively), and the mean ITV and PTV doses were higher in patients with adequate PTV dose coverage (p = 0.002 and p

PurposeTo apply a novel self-gating k-space sorted 4-dimensional MRI (SG-KS-4D-MRI) method to overcome limitations due to anisotropic resolution and rebinning artifacts and to monitor pancreatic tumor motion.Methods and materialsTen patients were imaged using 4D-CT, cine 2-dimensional MRI (2D-MRI), and the SG-KS-4D-MRI, which is a spoiled gradient recalled echo sequence with 3-dimensional radial-sampling k-space projections and 1-dimensional projection-based self-gating. Tumor volumes were defined on all phases in both 4D-MRI and 4D-CT and then compared.ResultsAn isotropic resolution of 1.56 mm was achieved in the SG-KS-4D-MRI images, which showed superior soft-tissue contrast to 4D-CT and appeared to be free of stitching artifacts. The tumor motion trajectory cross-correlations (mean ± SD) between SG-KS-4D-MRI and cine 2D-MRI in superior-inferior, anterior-posterior, and medial-lateral directions were 0.93 ± 0.03, 0.83 ± 0.10, and 0.74 ± 0.18, respectively. The tumor motion trajectories cross-correlations between SG-KS-4D-MRI and 4D-CT in superior-inferior, anterior-posterior, and medial-lateral directions were 0.91 ± 0.06, 0.72 ± 0.16, and 0.44 ± 0.24, respectively. The average standard deviation of gross tumor volume calculated from the 10 breathing phases was 0.81 cm(3) and 1.02 cm(3) for SG-KS-4D-MRI and 4D-CT, respectively (P=.012).ConclusionsA novel SG-KS-4D-MRI acquisition method capable of reconstructing rebinning artifact-free, high-resolution 4D-MRI images was used to quantify pancreas tumor motion. The resultant pancreatic tumor motion trajectories agreed well with 2D-cine-MRI and 4D-CT. The pancreatic tumor volumes shown in the different phases for the SG-KS-4D-MRI were statistically significantly more consistent than those in the 4D-CT.

Finite element analysis (FEA)-based biomechanical modeling can be used to predict lung respiratory motion. In this technique, elastic models and biomechanical parameters are two important factors that determine modeling accuracy. We systematically evaluated the effects of lung and lung tumor biomechanical modeling approaches and related parameters to improve the accuracy of motion simulation of lung tumor center of mass (TCM) displacements. Experiments were conducted with four-dimensional computed tomography (4D-CT). A Quasi-Newton FEA was performed to simulate lung and related tumor displacements between end-expiration (phase 50%) and other respiration phases (0%, 10%, 20%, 30%, and 40%). Both linear isotropic and non-linear hyperelastic materials, including the neo-Hookean compressible and uncoupled Mooney-Rivlin models, were used to create a finite element model (FEM) of lung and tumors. Lung surface displacement vector fields (SDVFs) were obtained by registering the 50% phase CT to other respiration phases, using the non-rigid demons registration algorithm. The obtained SDVFs were used as lung surface displacement boundary conditions in FEM. The sensitivity of TCM displacement to lung and tumor biomechanical parameters was assessed in eight patients for all three models. Patient-specific optimal parameters were estimated by minimizing the TCM motion simulation errors between phase 50% and phase 0%. The uncoupled Mooney-Rivlin material model showed the highest TCM motion simulation accuracy. The average TCM motion simulation absolute errors for the Mooney-Rivlin material model along left-right, anterior-posterior, and superior-inferior directions were 0.80 mm, 0.86 mm, and 1.51 mm, respectively. The proposed strategy provides a reliable method to estimate patient-specific biomechanical parameters in FEM for lung tumor motion simulation.

PurposeTo develop a technique that assesses the accuracy of the breathing phase-specific volume image generation process by patient-specific breathing motion model using the original free-breathing computed tomographic (CT) scans as ground truths.MethodsSixteen lung cancer patients underwent a previously published protocol in which 25 free-breathing fast helical CT scans were acquired with a simultaneous breathing surrogate. A patient-specific motion model was constructed based on the tissue displacements determined by a state-of-the-art deformable image registration. The first image was arbitrarily selected as the reference image. The motion model was used, along with the free-breathing phase information of the original 25 image datasets, to generate a set of deformation vector fields that mapped the reference image to the 24 nonreference images. The high-pitch helically acquired original scans served as ground truths because they captured the instantaneous tissue positions during free breathing. Image similarity between the simulated and the original scans was assessed using deformable registration that evaluated the pointwise discordance throughout the lungs.ResultsQualitative comparisons using image overlays showed excellent agreement between the simulated images and the original images. Even large 2-cm diaphragm displacements were very well modeled, as was sliding motion across the lung-chest wall boundary. The mean error across the patient cohort was 1.15 ± 0.37 mm, and the mean 95th percentile error was 2.47 ± 0.78 mm.ConclusionThe proposed ground truth-based technique provided voxel-by-voxel accuracy analysis that could identify organ-specific or tumor-specific motion modeling errors for treatment planning. Despite a large variety of breathing patterns and lung deformations during the free-breathing scanning session, the 5-dimensionl CT technique was able to accurately reproduce the original helical CT scans, suggesting its applicability to a wide range of patients.

PurposeTo determine if and by how much the commercial 4DCT protocols under- and overestimate tumor breathing motion.Methods1D simulations were conducted that modeled a 16-slice CT scanner and tumors moving proportionally to breathing amplitude. External breathing surrogate traces of at least 5-min duration for 50 patients were used. Breathing trace amplitudes were converted to motion by relating the nominal tumor motion to the 90th percentile breathing amplitude, reflecting motion defined by the more recent 5DCT approach. Based on clinical low-pitch helical CT acquisition, the CT detector moved according to its velocity while the tumor moved according to the breathing trace. When the CT scanner overlapped the tumor, the overlapping slices were identified as having imaged the tumor. This process was repeated starting at successive 0.1 s time bin in the breathing trace until there was insufficient breathing trace to complete the simulation. The tumor size was subtracted from the distance between the most superior and inferior tumor positions to determine the measured tumor motion for that specific simulation. The effect of the scanning parameter variation was evaluated using two commercial 4DCT protocols with different pitch values. Because clinical 4DCT scan sessions would yield a single tumor motion displacement measurement for each patient, errors in the tumor motion measurement were considered systematic. The mean of largest 5% and smallest 5% of the measured motions was selected to identify over- and underdetermined motion amplitudes, respectively. The process was repeated for tumor motions of 1-4 cm in 1 cm increments and for tumor sizes of 1-4 cm in 1 cm increments.ResultsIn the examined patient cohort, simulation using pitch of 0.06 showed that 30% of the patients exhibited a 5% chance of mean breathing amplitude overestimations of 47%, while 30% showed a 5% chance of mean breathing amplitude underestimations of 36%; with a separate simulation using pitch of 0.1 showing, respectively, 37% overestimation and 61% underestimation.ConclusionsThe simulation indicates that commercial low-pitch helical 4DCT processes potentially yield large tumor motion measurement errors, both over- and underestimating the tumor motion.

PurposeMRI is increasingly being used for radiotherapy planning, simulation, and in-treatment-room motion monitoring. To provide more detailed temporal and spatial MR data for these tasks, we have recently developed a novel self-gated (SG) MRI technique with advantage of k-space phase sorting, high isotropic spatial resolution, and high temporal resolution. The current work describes the validation of this 4D-MRI technique using a MRI- and CT-compatible respiratory motion phantom and comparison to 4D-CT.MethodsThe 4D-MRI sequence is based on a spoiled gradient echo-based 3D projection reconstruction sequence with self-gating for 4D-MRI at 3 T. Respiratory phase is resolved by using SG k-space lines as the motion surrogate. 4D-MRI images are reconstructed into ten temporal bins with spatial resolution 1.56 × 1.56 × 1.56 mm(3). A MRI-CT compatible phantom was designed to validate the performance of the 4D-MRI sequence and 4D-CT imaging. A spherical target (diameter 23 mm, volume 6.37 ml) filled with high-concentration gadolinium (Gd) gel is embedded into a plastic box (35 × 40 × 63 mm(3)) and stabilized with low-concentration Gd gel. The phantom, driven by an air pump, is able to produce human-type breathing patterns between 4 and 30 respiratory cycles/min. 4D-CT of the phantom has been acquired in cine mode, and reconstructed into ten phases with slice thickness 1.25 mm. The 4D images sets were imported into a treatment planning software for target contouring. The geometrical accuracy of the 4D MRI and CT images has been quantified using target volume, flattening, and eccentricity. The target motion was measured by tracking the centroids of the spheres in each individual phase. Motion ground-truth was obtained from input signals and real-time video recordings.ResultsThe dynamic phantom has been operated in four respiratory rate (RR) settings, 6, 10, 15, and 20/min, and was scanned with 4D-MRI and 4D-CT. 4D-CT images have target-stretching, partial-missing, and other motion artifacts in various phases, whereas the 4D-MRI images are visually free of those artifacts. Volume percentage difference for the 6.37 ml target ranged from 5.3% ± 4.3% to 10.3% ± 5.9% for 4D-CT, and 1.47 ± 0.52 to 2.12 ± 1.60 for 4D-MRI. With an increase of respiratory rate, the target volumetric and geometric deviations increase for 4D-CT images while remaining stable for the 4D-MRI images. Target motion amplitude errors at different RRs were measured with a range of 0.66-1.25 mm for 4D-CT and 0.2-0.42 mm for 4D-MRI. The results of Mann-Whitney tests indicated that 4D-MRI significantly outperforms 4D-CT in phase-based target volumetric (p = 0.027) and geometric (p < 0.001) measures. Both modalities achieve equivalent accuracy in measuring motion amplitude (p = 0.828).ConclusionsThe k-space self-gated 4D-MRI technique provides a robust method for accurately imaging phase-based target motion and geometry. Compared to 4D-CT, the current 4D-MRI technique demonstrates superior spatiotemporal resolution, and robust resistance to motion artifacts caused by fast target motion and irregular breathing patterns. The technique can be used extensively in abdominal targeting, motion gating, and toward implementing MRI-based adaptive radiotherapy.

Background and purposeMultigland disease represents a challenging group of patients with primary hyperparathyroidism. Additional lesions may be missed on imaging because they are not considered or are too small to be seen. The aim of this is study was to identify 4D-CT imaging and biochemical predictors of multigland disease.Materials and methodsThis was a retrospective study of 155 patients who underwent 4D-CT and successful surgery with a biochemical cure that compared patients with multigland and single-gland disease. Variables studied included the size of the largest lesion on 4D-CT, the number of lesions prospectively identified on 4D-CT, serum calcium levels, serum parathyroid hormone levels, and the Wisconsin Index (the product of serum calcium and parathyroid hormone levels). Imaging findings and the Wisconsin Index were used to calculate a composite multigland disease scoring system. We evaluated the predictive value of individual variables and the scoring system for multigland disease.ResultsThirty-six patients with multigland disease were compared with 119 patients with single-gland disease. Patients with multigland disease had significantly lower Wisconsin Index scores, smaller lesion size, and a higher likelihood of having either multiple or zero lesions identified on 4D-CT (P ≤ .01). Size cutoff of

PurposeTo develop objective functions for selecting multiple representative respiratory waveforms. A specific application considered is to reduce the number of swiping scans in a novel helical CT scan protocol to harvest efficiency and dose reduction benefit.MethodsThe authors consider a general class of potential objective functions consisting of weighted norms on pointwise profile differentials. The authors utilize the Lagrangian approach and derive proper conditions on the formulation based on first and second order optimality conditions. The derived objective functions are applied to clinically acquired respiratory trajectories for swipe subset selection to verify the validity and generality of the proposed rationale. An end-to-end 4DCT reconstruction comparison is performed using a swipe subset of data corresponding to 3 out of the full 25 waveforms to assess the consequence in image quality and dose.ResultsTheir results show that maximizing the proposed objective function with the suggested parameters yields maximal spread of trajectories among the selected subset. 4DCT Reconstruction using the chosen subset of data indicates the potential for further dose reduction by about 5 to 10 folds without significant sacrifice in image quality. Experimental results also support further generalization to include slice prioritization.ConclusionsThe authors have derived a formulation that is both simple and general as a metric to quantify the spread of a set of respiratory trajectories, which can be used for subset selection with potential computation and dose reduction benefit when applied to a newly developed helical 4DCT scan protocol.

Purpose: 4-dimensional computed tomography (4D-CT)-based pulmonary ventilation imaging is an emerging functional imaging modality. The purpose of this study was to investigate the physiological significance of 4D-CT ventilation imaging by comparison with pulmonary function test (PFT) measurements and single-photon emission CT (SPECT) ventilation images, which are the clinical references for global and regional lung function, respectively. Methods and Materials: In an institutional review board-approved prospective clinical trial, 4D-CT imaging and PFT and/or SPECT ventilation imaging were performed in thoracic cancer patients. Regional ventilation (V 4DCT) was calculated by deformable image registration of 4D-CT images and quantitative analysis for regional volume change. V 4DCT defect parameters were compared with the PFT measurements (forced expiratory volume in 1second (FEV1; % predicted) and FEV1/forced vital capacity (FVC; %). V 4DCT was also compared with SPECT ventilation (V SPECT) to (1) test whether V 4DCT in V SPECT defect regions is significantly lower than in nondefect regions by using the 2-tailed t test; (2) to quantify the spatial overlap between V 4DCT and V SPECT defect regions with Dice similarity coefficient (DSC); and (3) to test ventral-to-dorsal gradients by using the 2-tailed t test. Results: Of 21 patients enrolled in the study, 18 patients for whom 4D-CT and either PFT or SPECT were acquired were included in the analysis. V 4DCT defect parameters were found to have significant, moderate correlations with PFT measurements. For example, V4DCTHU defect volume increased significantly with decreasing FEV1/FVC (R=-0.65, P

PurposeTo 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 materialsTen 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.ResultsImages 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.ConclusionsThe 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.

Background and purposeThe "polar vessel" sign has been previously described on sonography of parathyroid adenomas. We estimated the 4D CT prevalence of the polar vessel sign and determined features of parathyroid adenomas that are associated with this sign.Materials and methodsTwenty-eight consecutive patients with parathyroid adenomas underwent 4D CT between 2008 and 2012 at 2 institutions. 4D CT images were reviewed for the presence of the polar vessel sign and a second vascular finding of an enlarged ipsilateral inferior thyroid artery. The polar vessel sign was correlated with adenoma weight and size and arterial phase CT attenuation.ResultsThirty-two parathyroid adenomas in 28 patients were studied, with a mean adenoma weight of 0.66 ± 0.65 g, a mean maximal CT diameter of 11.1 ± 4.9 mm, and a mean arterial attenuation of 148 ± 47 HU. The polar vessel sign was seen in 20/32 (63%) adenomas. Adenomas with a polar vessel had higher arterial phase attenuation than adenomas without a polar vessel (163 and 122 HU, respectively, P < .01). Size and weight were not significantly different for adenomas with and without polar vessels. An enlarged inferior thyroid artery was seen in only 2/28 (7%) patients with unilateral disease.ConclusionsThe polar vessel sign was present in nearly two-thirds of parathyroid adenomas on 4D CT and was more likely to be present in adenomas that had greater arterial phase enhancement. This sign can be used along with enhancement characteristics to increase the radiologist's confidence that a visualized lesion is a parathyroid adenoma rather than a thyroid nodule or lymph node.

PurposeTo characterize radiation therapy patient breathing patterns based on measured external surrogate information.MethodsBreathing surrogate data were collected during 4DCT from a cohort of 50 patients including 28 patients with lung cancer and 22 patients without lung cancer. A spirometer and an abdominal pneumatic bellows were used as the surrogates. The relationship between these measurements was assumed to be linear within a small phase difference. The signals were correlated and drift corrected using a previously published method to convert the signal into tidal volume. The airflow was calculated with a first order time derivative of the tidal volume using a window centered on the point of interest and with a window length equal to the CT gantry rotation period. The airflow was compared against the tidal volume to create ellipsoidal patterns that were binned into 25 ml × 25 ml∕s bins to determine the relative amount of time spent in each bin. To calculate the variability of the maximum inhalation tidal volume within a free-breathing scan timeframe, a metric based on percentile volume ratios was defined. The free breathing variability metric (κ) was defined as the ratio between extreme inhalation tidal volumes (defined as >93 tidal volume percentile of the measured tidal volume) and normal inhalation tidal volume (defined as >80 tidal volume percentile of the measured tidal volume).ResultsThere were three observed types of volume-flow curves, labeled Types 1, 2, and 3. Type 1 patients spent a greater duration of time during exhalation with κ = 1.37 ± 0.11. Type 2 patients had equal time duration spent during inhalation and exhalation with κ = 1.28 ± 0.09. The differences between the mean peak exhalation to peak inhalation tidal volume, breathing period, and the 85th tidal volume percentile for Type 1 and Type 2 patients were statistically significant at the 2% significance level. The difference between κ and the 98th tidal volume percentile for Type 1 and Type 2 patients was found to be statistically significant at the 1% significance level. Three patients did not display a breathing stability curve that could be classified as Type 1 or Type 2 due to chaotic breathing patterns. These patients were classified as Type 3 patients.ConclusionsBased on an observed volume-flow curve pattern, the cohort of 50 patients was divided into three categories called Type 1, Type 2, and Type 3. There were statistically significant differences in breathing characteristics between Type 1 and Type 2 patients. The use of volume-flow curves to classify patients has been demonstrated as a physiological characterization metric that has the potential to optimize gating windows in radiation therapy.

Background and purposeTo quantify the benefit of motion-specific internal target volumes for FDG-avid mediastinal and hilar lymph nodes generated using 4D-PET, vs. conventional internal target volumes generated using non-respiratory gated PET and 4D-CT scans.Materials and methodsFive patients with FDG-avid tumors metastatic to 11 hilar or mediastinal lymph nodes were imaged with respiratory-correlated FDG-PET (4D-PET) and 4D-CT. FDG-avid nodes were contoured by a radiation oncologist in two ways. Standard-of-care volumes were contoured using conventional un-gated PET, 4D-CT, and breath-hold CT. A second, motion-specific, set of volumes were contoured using 4D-PET.Contours based on 4D-PET corresponded directly to an internal target volume (ITV(4D)), whereas contours based on un-gated PET were expanded by a series of exploratory isotropic margins (from 5 to 13 mm) based on literature recommendations on lymph node motion to form internal target volumes (ITV(3D)).ResultsA 13 mm expansion of the un-gated PET nodal volume was needed to cover the ITV(4D) for 10 of 11 nodes studied. The ITV(3D) based on a 13 mm expansion included on average 45 cm(3) of tissue that was not included in the ITV(4D).ConclusionsMotion-specific lymph-node internal target volumes generated from 4D-PET imaging could be used to improve accuracy and/or reduce normal-tissue irradiation compared to the standard-of-care un-gated PET based internal target volumes.

PurposeFour-dimensional (4D) computed tomography (CT) ventilation imaging is a novel promising technique for lung functional imaging. The current standard 4D CT technique using phase-based sorting frequently results in artifacts, which may deteriorate the accuracy of ventilation imaging. The purpose of this study was to quantify the variability of 4D CT ventilation imaging due to 4D CT sorting.Methods4D CT image sets from nine lung cancer patients were each sorted by the phase-based method and anatomic similarity-based method, designed to reduce artifacts, with corresponding ventilation images created for each method. Artifacts in the resulting 4D CT images were quantified with the artifact score which was defined based on the difference between the normalized cross correlation for CT slices within a CT data segment and that for CT slices bordering the interface between adjacent CT data segments. The ventilation variation was quantified using voxel-based Spearman rank correlation coefficients for all lung voxels, and Dice similarity coefficients (DSC) for the spatial overlap of low-functional lung volumes. Furthermore, the correlations with matching single-photon emission CT (SPECT) ventilation images (assumed ground truth) were evaluated for three patients to investigate which sorting method provides higher physiologic accuracy.ResultsAnatomic similarity-based sorting reduced 4D CT artifacts compared to phase-based sorting (artifact score, 0.45 ± 0.14 vs 0.58 ± 0.24, p = 0.10 at peak-exhale; 0.63 ± 0.19 vs 0.71 ± 0.31, p = 0.25 at peak-inhale). The voxel-based correlation between the two ventilation images was 0.69 ± 0.26 on average, ranging from 0.03 to 0.85. The DSC was 0.71 ± 0.13 on average. Anatomic similarity-based sorting yielded significantly fewer lung voxels with paradoxical negative ventilation values than phase-based sorting (5.0 ± 2.6% vs 9.7 ± 8.4%, p = 0.05), and improved the correlation with SPECT ventilation regionally.ConclusionsThe variability of 4D CT ventilation imaging due to 4D CT sorting was moderate overall and substantial in some cases, suggesting that 4D CT artifacts are an important source of variations in 4D CT ventilation imaging. Reduction of 4D CT artifacts provided more physiologically convincing and accurate ventilation estimates. Further studies are needed to confirm this result.

Rationale and objectivesA novel ventilation imaging method based on four-dimensional (4D) computed tomography (CT) has been applied to the field of radiation oncology. Understanding its reproducibility is a prerequisite for clinical applications. The purpose of this study was to quantify the reproducibility of 4D CT ventilation imaging over different days and the same session.Materials and methodsTwo ventilation images were created from repeat 4D CT scans acquired over the average time frames of 15 days for 6 lung cancer patients and 5 minutes for another 6 patients. The reproducibility was quantified using the voxel-based Spearman rank correlation coefficients for all lung voxels and Dice similarity coefficients (DSC) for the spatial overlap of segmented high-, moderate-, and low-functional lung volumes. Furthermore, the relationship between the variation in abdominal motion range as a measure of the depth of breathing and variation in ventilation was evaluated using linear regression.ResultsThe voxel-based correlation between the two ventilation images was moderate on average (0.50 ± 0.15). The DSCs were also moderate for the high- (0.60 ± 0.08), moderate- (0.46 ± 0.06), and low-functional lung (0.58 ± 0.09). No patients demonstrated strong correlations. The relationship between the motion range variation and ventilation variation was found to be moderate and significant.ConclusionsWe investigated the reproducibility of 4D CT ventilation imaging over the time frames of 15 days and 5 minutes and found that it was only moderately reproducible. Respiratory variation during 4D CT scans was found to deteriorate the reproducibility. Improvement of 4D CT imaging is necessary to increase the reproducibility of 4D CT ventilation imaging.

The aim of this study is to develop an automated method to objectively compare motion artifacts in two four-dimensional computed tomography (4D CT) image sets, and identify the one that would appear to human observers with fewer or smaller artifacts. Our proposed method is based on the difference of the normalized correlation coefficients between edge slices at couch transitions, which we hypothesize may be a suitable metric to identify motion artifacts. We evaluated our method using ten pairs of 4D CT image sets that showed subtle differences in artifacts between images in a pair, which were identifiable by human observers. One set of 4D CT images was sorted using breathing traces in which our clinically implemented 4D CT sorting software miscalculated the respiratory phase, which expectedly led to artifacts in the images. The other set of images consisted of the same images; however, these were sorted using the same breathing traces but with corrected phases. Next we calculated the normalized correlation coefficients between edge slices at all couch transitions for all respiratory phases in both image sets to evaluate for motion artifacts. For nine image set pairs, our method identified the 4D CT sets sorted using the breathing traces with the corrected respiratory phase to result in images with fewer or smaller artifacts, whereas for one image pair, no difference was noted. Two observers independently assessed the accuracy of our method. Both observers identified 9 image sets that were sorted using the breathing traces with corrected respiratory phase as having fewer or smaller artifacts. In summary, using the 4D CT data of ten pairs of 4D CT image sets, we have demonstrated proof of principle that our method is able to replicate the results of two human observers in identifying the image set with fewer or smaller artifacts.