Quantitation of the reconstruction quality of a four-dimensional computed tomography process for lung cancer patients

Evaluation of four surface surrogates for modeling lung tumor positions over several fractions in radiotherapy

Patient breathing during lung cancer radiotherapy reduces the ability to keep a sharp dose gradient between tumor and normal tissues. To mitigate detrimental effects, accurate information about the tumor position is required. In this work, we evaluate the errors in modeled tumor positions over several fractions of a simple tumor motion model driven by a surface surrogate measure and its time derivative. The model is tested with respect to four different surface surrogates and a varying number of surrogate and image acquisitions used for model training. Fourteen patients were imaged 100 times with cine CT, at three sessions mimicking a planning session followed by two treatment fractions. Patient body contours were concurrently detected by a body surface laser scanning system BSLS from which four surface surrogates were extracted; thoracic point TP, abdominal point AP, the radial distance mean RDM, and a surface derived volume SDV. The motion model was trained on session 1 and evaluated on sessions 2 and 3 by comparing modeled tumor positions with measured positions from the cine images. The number of concurrent surrogate and image acquisitions used in the training set was varied, and its impact on the final result was evaluated. The use of AP as a surface surrogate yielded the smallest error in modeled tumor positions. The mean deviation between modeled and measured tumor positions was 1.9 mm. The corresponding deviations for using the other surrogates were 2.0 mm (RDM), 2.8 mm (SDV), and 3.0 mm (TP). To produce a motion model that accurately models the tumor position over several fractions requires at least 10 simultaneous surrogate and image acquisitions over 1–2 minutes.

Knowledge Mapping of Acupuncture for Cancer Pain: A Scientometric Analysis (2000-2019)

Objective

This study aimed to demonstrate the state of the present situation and trends concerning the global use of acupuncture for cancer pain in the past 20 years.

Methods

Searched the Web of Science database from 2000 to 2019 related to acupuncture for cancer pain, and then used CiteSpace to conduct scientometric analysis to acquire the knowledge mapping.

Results

Yearly output has increased year by year, and the growth rate has become faster after 2012. According to the cluster analysis of institutions, authors, cited references, and keywords, 4, 4, 15, and 14 categories were obtained, respectively. The most productive countries, institutions, and authors are the USA, Mem Sloan Kettering Cancer Center, and Mao JJ, whose frequencies are 196, 24, and 17, respectively. However, the most important of them are Australia, Univ. Maryland, and Bao T, owing to their highest centrality, they are 0.90, 0.21, and 0.09 separately. Moreover, cited references that contributed to the most co-citations are Crew KD (2010), however, the most key cited reference is Roscoe JA (2003). Keywords such as acupuncture, pain, breast cancer, palliative care, and quality of life are the most frequently used. But auricular acupuncture is the crucial keyword. In the cluster analysis of institutions, authors, cited references, and keywords, the more convincing research categories are multiple myeloma, placebo effect, neck malignancies, and early breast cancer, with S values of 0.990, 0.991, 0.990, and 0.923, respectively. Therefore, they can be regarded as research hotspots in this field.

Conclusion

Based on the scientometric analysis in the past 20 years, the knowledge mapping of the country, institution, author, cited reference, and the keyword is gained, which has an important guiding significance for quickly and accurately positioning the trend in this field.

4D-CT-based motion correction of PET images using 3D iterative deconvolution

Objectives

Positron emission tomography acquisition takes several minutes representing an image averaged over multiple breathing cycles. Therefore, in areas influenced by respiratory movement, PET-positive lesions occur larger, but less intensive than they actually are, resulting in false quantitative assessment. We developed a motion-correction algorithm based on 4D-CT without the need to adapt PET-acquisition.

Methods

The algorithm is based on a full 3D iterative Richardson-Lucy-Deconvolution using a point-spread-function constructed using the motion information obtained from the 4D-CT. In a motion phantom study (3 different hot spheres in background activity), optimal parameters for the algorithm in terms of number of iterations and start image were estimated. Finally, the correction method was applied to 3 patient data sets. In phantom and patient data sets lesions were delineated and compared between motion corrected and uncorrected images for activity uptake and volume.

Results

Phantom studies showed best results for motion correction after 6 deconvolution steps or higher. In phantom studies, lesion volume improved up to 23% for the largest, 43% for the medium and 49% for the smallest sphere due to the correction algorithm. In patient data the correction resulted in a significant reduction of the tumor volume up to 33.3 % and an increase of the maximum and mean uptake of the lesion up to 62.1 and 19.8 % respectively.

Conclusion

In conclusion, the proposed motion correction method showed good results in phantom data and a promising reduction of detected lesion volume and a consequently increasing activity uptake in three patients with lung lesions.

Simulating Developmental Cardiac Morphology in Virtual Reality Using a Deformable Image Registration Approach

While virtual reality (VR) has potential in enhancing cardiovascular diagnosis and treatment, prerequisite labor-intensive image segmentation remains an obstacle for seamlessly simulating 4-dimensional (4-D, 3-D + time) imaging data in an immersive, physiological VR environment. We applied deformable image registration (DIR) in conjunction with 3-D reconstruction and VR implementation to recapitulate developmental cardiac contractile function from light-sheet fluorescence microscopy (LSFM). This method addressed inconsistencies that would arise from independent segmentations of time-dependent data, thereby enabling the creation of a VR environment that fluently simulates cardiac morphological changes. By analyzing myocardial deformation at high spatiotemporal resolution, we interfaced quantitative computations with 4-D VR. We demonstrated that our LSFM-captured images, followed by DIR, yielded average dice similarity coefficients of 0.92 ± 0.05 (n = 510) and 0.93 ± 0.06 (n = 240) when compared to ground truth images obtained from Otsu thresholding and manual segmentation, respectively. The resulting VR environment simulates a wide-angle zoomed-in view of motion in live embryonic zebrafish hearts, in which the cardiac chambers are undergoing structural deformation throughout the cardiac cycle. Thus, this technique allows for an interactive micro-scale VR visualization of developmental cardiac morphology to enable high resolution simulation for both basic and clinical science.

First clinical implementation of real-time, real anatomy tracking and radiation beam control

PURPOSE

We describe the acceptance testing, commissioning, periodic quality assurance, and workflow procedures developed for the first clinically implemented magnetic resonance imaging-guided radiation therapy (MR-IGRT) system for real-time tracking and beam control.

METHODS

The system utilizes real-time cine imaging capabilities at 4 frames per second for real-time tracking and beam control. Testing of the system was performed using an in-house developed motion platform and a commercially available motion phantom. Anatomical tracking is performed by first identifying a target (a region of interest that is either tissue to be treated or a critical structure) and generating a contour around it. A boundary contour is also created to identify tracking margins. The tracking algorithm deforms the anatomical contour (target or a normal organ) on every subsequent cine frame and compares it to the static boundary contour. If the anatomy of interest moves outside the boundary, the radiation delivery is halted until the tracked anatomy returns to treatment portal. The following were performed to validate and clinically implement the system: (a) spatial integrity evaluation; (b) tracking accuracy; (c) latency; (d) relative point dose and spatial dosimetry; (e) development of clinical workflow for gating; and (f) independent verification by an outside credentialing service.

RESULTS

The spatial integrity of the MR system was found to be within 2 mm over a 45-cm diameter field-of-view. The tracking accuracy for geometric targets was within 1.2 mm. The average system latency was measured to be within 394 ms. The dosimetric accuracy using ionization chambers was within 1.3% ± 1.7%, and the dosimetric spatial accuracy was within 2 mm. The phantom irradiation for the outside credentialing service had satisfactory results, as well.

CONCLUSIONS

The first clinical MR-IGRT system was validated for real-time tracking and gating capabilities and shown to be reliable and accurate. Patient workflow methods were developed for efficient treatment. Periodic quality assurance tests can be efficiently performed with commercially available equipment to ensure accurate system performance.

An efficient model to guide prospective T2-weighted 4D magnetic resonance imaging acquisition

PURPOSE

To establish a mathematical model to guide prospective T2-weighted four-dimensional magnetic resonance imaging (4DMRI) acquisition and to propose an efficient solution to expedite prospective T2-weighted 4DMRI acquisition.

METHODS

Prospective T2-weighted 4DMRI acquisition was characterized by a mathematical model with 4DMRI acquisition time as the objective function and completeness of the image set, acquisition timing, image contrast, and image artifacts as constraints. Given the irregular nature of human respiration, an efficient solution based on the greedy strategy (ESGS) was proposed. The efficiency of the ESGS method was validated using healthy human subjects. Comparisons were made with the previous 4DMRI method incorporating the prefixed-order respiratory state splitting (PO-RSS) technique.

RESULTS

4DMRI image sets acquired using the ESGS and PO-RSS methods had similar image quality. The average time to acquire a 4DMRI image set covering 60 slices at 10 respiratory states was reduced by 30%, from 13.1 min using the PO-RSS method to 9.0 min using the ESGS method. It was demonstrated that high-quality T2-weighted 4DMRI could be obtained within a reasonable amount of time and all slices within each of the three-dimensional volumes were indeed acquired at the same respiratory state.

CONCLUSIONS

The ESGS method substantially reduces the acquisition time for T2-weighted 4DMRI, making it ready for clinical evaluation to obtain abdominal tumor motion for radiotherapy treatment planning.

Characterization of optical-surface-imaging-based spirometry for respiratory surrogating in radiotherapy

PURPOSE

To provide a comprehensive characterization of a novel respiratory surrogate that uses optical surface imaging (OSI) for accurate tidal volume (TV) measurement, dynamic airflow (TV') calculation, and quantitative breathing pattern (BP) estimation during free breathing (FB), belly breathing (BB), chest breathing (CB), and breath hold (BH).

METHODS

Optical surface imaging, which captures all respiration-induced torso surface motion, was applied to measure respiratory TV, TV', and BP in three common breathing patterns. Eleven healthy volunteers participated in breathing experiments with concurrent OSI-based and conventional spirometric measurements under an institutional review board approved protocol. This OSI-based technique measures dynamic TV from torso volume change (ΔVtorso = TV) in reference to full exhalation and airflow (TV' = dTV/dt). Volume conservation, excluding exchanging air, was applied for OSI-based measurements under negligible pleural pressure variation in FB, BB, and CB. To demonstrate volume conservation, a constant TV was measured during BH while the chest and belly are moving ("pretended" respiration). To assess the accuracy of OSI-based spirometry, a conventional spirometer was used as the standard for both TV and TV'. Using OSI, BP was measured as BP(OSI) = ΔVchest/ΔVtorso and BP can be visualized using BP(SHI) = SHIchest/(SHIchest + SHIbelly), where surface height index (SHI) is defined as the mean vertical distance within a region of interest on the torso surface. A software tool was developed for OSI image processing, volume calculation, and BP visualization, and another tool was implemented for data acquisition using a Bernoulli-type spirometer.

RESULTS

The accuracy of the OSI-based spirometry is -21 ± 33 cm(3) or -3.5% ± 6.3% averaged from 11 volunteers with 76 ± 28 breathing cycles on average in FB. Breathing variations between two separate acquisitions with approximate 30-min intervals are substantial: -1% ± 34% (ranging from -64% to 40%) in TV, 4% ± 20% (ranging from -50% to 26%) in breathing period (T), and -1% ± 34% (ranging from -49% to 44%) in BP. The airflow accuracy and variation (between two exercises) are -1 ± 54 cm(3)/s and -5% ± 30%, respectively. The slope of linear regression between OSI-TV and spirometric TV is 0.93 (R(2) = 0.95) for FB, 0.96 (R(2) = 0.98) for BB, and 0.95 (R(2) = 0.95) for CB. The correlation between the two spirometric measurements is 0.98 ± 0.01. BP increases from BB, FB to CB, while TV increases from FB, BB, to CB. Under BH, 4% volume variation (range) on average was observed.

CONCLUSIONS

The OSI-based technique provides an accurate measurement of tidal volume, airflow rate, and breathing pattern; all affect internal organ motion. This technique can be applied to various breathing patterns, including FB, BB, and CB. Substantial breathing irregularities and irreproducibility were observed and quantified with the OSI-based technique. These breathing parameters are useful to quantify breathing conditions, which could be used for effective tumor motion predictions.

Novel spirometry based on optical surface imaging

PURPOSE

To evaluate the feasibility of using optical surface imaging (OSI) to measure the dynamic tidal volume (TV) of the human torso during free breathing.

METHODS

We performed experiments to measure volume or volume change in geometric and deformable phantoms as well as human subjects using OSI. To assess the accuracy of OSI in volume determination, we performed experiments using five geometric phantoms and two deformable body phantoms and compared the values with those derived from geometric calculations and computed tomography (CT) measurements, respectively. To apply this technique to human subjects, an institutional review board protocol was established and three healthy volunteers were studied. In the human experiment, a high-speed image capture mode of OSI was applied to acquire torso images at 4-5 frames per second, which was synchronized with conventional spirometric measurements at 5 Hz. An in-house matlab program was developed to interactively define the volume of interest (VOI), separate the thorax and abdomen, and automatically calculate the thoracic and abdominal volumes within the VOIs. The torso volume change (TV C = ΔVtorso = ΔVthorax + ΔVabdomen) was automatically calculated using full-exhalation phase as the reference. The volumetric breathing pattern (BPv = ΔVthorax/ΔVtorso) quantifying thoracic and abdominal volume variations was also calculated. Under quiet breathing, TVC should equal the tidal volume measured concurrently by a spirometer with a conversion factor (1.08) accounting for internal and external differences of temperature and moisture. Another matlab program was implemented to control the conventional spirometer that was used as the standard.

RESULTS

The volumes measured from the OSI imaging of geometric phantoms agreed with the calculated volumes with a discrepancy of 0.0% ± 1.6% (range -1.9% to 2.5%). In measurements from the deformable torso/thorax phantoms, the volume differences measured using OSI imaging and CT imaging were 1.2% ± 2.1% (range -0.5% to 3.6%), with a linear regression fitting (slope = 1.02 and R(2) = 0.999). In volunteers, the relative error in OSI tidal volume measurement was -2.2% ± 4.9% (range -9.2% to 4.8%) and a correlation of r = 0.98 was found with spirometric measurement. The breathing pattern values of the three volunteers were substantially different from each other (BPv = 0.15, 0.45, and 0.32).

CONCLUSIONS

This study demonstrates the feasibility of using OSI to measure breathing tidal volumes and breathing patterns with adequate accuracy. This is the first time that dynamic breathing tidal volume as well as breathing patterns is measured using optical surface imaging. The OSI-observed movement of the entire torso could serve as a new respiratory surrogate in the treatment room during radiation therapy.

Technical Note: Simulation of 4DCT tumor motion measurement errors

PURPOSE

To determine if and by how much the commercial 4DCT protocols under- and overestimate tumor breathing motion.

METHODS

1D 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.

RESULTS

In 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.

CONCLUSIONS

The simulation indicates that commercial low-pitch helical 4DCT processes potentially yield large tumor motion measurement errors, both over- and underestimating the tumor motion.

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.

Reduction of acquisition time in the intersection profile method for four-dimensional magnetic resonance imaging reconstruction of thoracoabdominal organs

We have previously proposed an intersection profile method for reconstructing four-dimensional (4-D) magnetic resonance imaging (MRI) consisting of one breathing cycle of the thoracoabdominal region. This method captures a set of temporal sequence images in a proper sagittal plane and sets of temporal sequence images in continuous coronal slices. The former set is used as a navigator slice and the latter sets are used as data slices. A 4-D MRI is reconstructed by synchronizing the respiratory pattern found in the navigator slice and the data slices. We propose a prospective method to reduce the acquisition time for data slices. During data slice acquisition, the synchronization process between the respiratory pattern found in the navigator slice and one data slice is monitored in real time. Data acquisition will be terminated and moved to the next data slice based on a threshold value. We used 14 data sets (seven patients with certain pulmonary disease and seven healthy volunteers) previously obtained for the original intersection profile method for a simulation using the proposed method to evaluate the time reduction and impact on image quality. Each of the data set was tested using three different threshold values and the acquisition time can be reduced up to 75%. Although the quantitative evaluation of image quality was slightly worse than that by the conventional method, the difference based on the visual inspection was subtle to human eyes.

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.

Multivariate regression approaches for surrogate-based diffeomorphic estimation of respiratory motion in radiation therapy

Breathing-induced location uncertainties of internal structures are still a relevant issue in the radiation therapy of thoracic and abdominal tumours. Motion compensation approaches like gating or tumour tracking are usually driven by low-dimensional breathing signals, which are acquired in real-time during the treatment. These signals are only surrogates of the internal motion of target structures and organs at risk, and, consequently, appropriate models are needed to establish correspondence between the acquired signals and the sought internal motion patterns. In this work, we present a diffeomorphic framework for correspondence modelling based on the Log-Euclidean framework and multivariate regression. Within the framework, we systematically compare standard and subspace regression approaches (principal component regression, partial least squares, canonical correlation analysis) for different types of common breathing signals (1D: spirometry, abdominal belt, diaphragm tracking; multi-dimensional: skin surface tracking). Experiments are based on 4D CT and 4D MRI data sets and cover intra- and inter-cycle as well as intra- and inter-session motion variations. Only small differences in internal motion estimation accuracy are observed between the 1D surrogates. Increasing the surrogate dimensionality, however, improved the accuracy significantly; this is shown for both 2D signals, which consist of a common 1D signal and its time derivative, and high-dimensional signals containing the motion of many skin surface points. Eventually, comparing the standard and subspace regression variants when applied to the high-dimensional breathing signals, only small differences in terms of motion estimation accuracy are found.

Investigation of sliced body volume (SBV) as respiratory surrogate

The purpose of this study was to evaluate the sliced body volume (SBV) as a respiratory surrogate by comparing with the real‐time position management (RPM) in phantom and patient cases. Using the SBV surrogate, breathing signals were extracted from unsorted 4D CT images of a motion phantom and 31 cancer patients (17 lung cancers, 14 abdominal cancers) and were compared to those clinically acquired using the RPM system. Correlation coefficient (R), phase difference (D), and absolute phase difference (DA) between the SBV‐derived breathing signal and the RPM signal were calculated. 4D CT reconstructed based on the SBV surrogate (4D CTSBV) were compared to those clinically generated based on RPM (4D CTRPM). Image quality of the 4D CT were scored (SSBV and SRPM, respectively) from 1 to 5 (1 is the best) by experienced evaluators. The comparisons were performed for all patients, and for the lung cancer patients and the abdominal cancer patients separately. RPM box position (P), breathing period (T), amplitude (A), period variability (VT), amplitude variability (VA), and space‐dependent phase shift (F) were determined and correlated to SSBV. The phantom study showed excellent match between the SBV‐derived breathing signal and the RPM signal (R=0.99, D=−3.0%, DA=4.5%). In the patient study, the mean (± standard deviation (SD)) R, D, DA, T, VT, A, VA, and F were 0.92(±0.05), −3.3% (±7.5%), 11.4% (±4.6%), 3.6 (±  0.8) s, 0.19 (±  0.10), 6.6 (±  2.8) mm, 0.20 (±  0.08), and 0.40 (±  0.18) s, respectively. Significant differences in R and DA (p=0.04 and 0.001, respectively) were found between the lung cancer patients and the abdominal cancer patients. 4D CTRPM slightly outperformed 4D CTSBV: the mean (±  SD) SRPM and SSBV were 2.6 (±  0.6) and 2.9 (±  0.8), respectively, for all patients, 2.5 (±  0.6) and 3.1 (±  0.8), respectively, for the lung cancer patients, and 2.6 (±  0.7) and 2.8 (±  0.9), respectively, for the abdominal cancer patients. The difference between SRPM and SSBV was insignificant for the abdominal patients (p=0.59). F correlated moderately with SSBV (r=0.72). The correlation between SBV‐derived breathing signal and RPM signal varied between patients and was significantly better in the abdomen than in the thorax. Space‐dependent phase shift is a limiting factor of the accuracy of the SBV surrogate.

PACS number: 87.59.bd

Measurement of intra-fraction displacement of the mediastinal metastatic lymph nodes using four-dimensional CT in non-small cell lung cancer

Objective

To measure the intra-fraction displacements of the mediastinal metastatic lymph nodes by using four-dimensional CT (4D-CT) in non-small cell lung cancer (NSCLC).

Materials and Methods

Twenty-four patients with NSCLC, who were to be treated by using three dimensional conformal radiation therapy (3D-CRT), underwent a 4D-CT simulation during free breathing. The mediastinal metastatic lymph nodes were delineated on the CT images of 10 phases of the breath cycle. The lymph nodes were grouped as the upper, middle and lower mediastinal groups depending on the mediastinal regions. The displacements of the center of the lymph node in the left-right (LR), anterior-posterior (AP), and superior-inferior (SI) directions were measured.

Results

The mean displacements of the center of the mediastinal lymph node in the LR, AP, and SI directions were 2.24 mm, 1.87 mm, and 3.28 mm, respectively. There were statistically significant differences between the displacements in the SI and LR, and the SI and AP directions (p < 0.05). For the middle and lower mediastinal lymph nodes, the displacement difference between the AP and SI was statistically significant (p = 0.005; p = 0.015), while there was no significant difference between the LR and AP directions (p < 0.05).

Conclusion

The metastatic mediastinal lymph node movements are different in the LR, AP, and SI directions in patients with NSCLC, particularly for the middle and lower mediastinal lymph nodes. The spatial non-uniform margins should be considered for the metastatic mediastinal lymph nodes in involved-field radiotherapy.

Biomechanical interpretation of a free-breathing lung motion model

The purpose of this paper is to develop a biomechanical model for free-breathing motion and compare it to a published heuristic five-dimensional (5D) free-breathing lung motion model. An ab initio biomechanical model was developed to describe the motion of lung tissue during free breathing by analyzing the stress-strain relationship inside lung tissue. The first-order approximation of the biomechanical model was equivalent to a heuristic 5D free-breathing lung motion model proposed by Low et al in 2005 (Int. J. Radiat. Oncol. Biol. Phys. 63 921-9), in which the motion was broken down to a linear expansion component and a hysteresis component. To test the biomechanical model, parameters that characterize expansion, hysteresis and angles between the two motion components were reported independently and compared between two models. The biomechanical model agreed well with the heuristic model within 5.5% in the left lungs and 1.5% in the right lungs for patients without lung cancer. The biomechanical model predicted that a histogram of angles between the two motion components should have two peaks at 39.8° and 140.2° in the left lungs and 37.1° and 142.9° in the right lungs. The data from the 5D model verified the existence of those peaks at 41.2° and 148.2° in the left lungs and 40.1° and 140° in the right lungs for patients without lung cancer. Similar results were also observed for the patients with lung cancer, but with greater discrepancies. The maximum-likelihood estimation of hysteresis magnitude was reported to be 2.6 mm for the lung cancer patients. The first-order approximation of the biomechanical model fit the heuristic 5D model very well. The biomechanical model provided new insights into breathing motion with specific focus on motion trajectory hysteresis.

The correlation of tissue motion within the lung: implications on fiducial based treatments

In radiation therapy many motion management and alignment techniques rely on the accuracy of an internal fiducial acting as a surrogate for target motion within the lung. Although fiducials are routinely used as surrogates for tumor motion, the extent to which varying spatial locations in the lung move similarly to other locations has yet to be quantitatively analyzed. In an attempt to analyze the motion correlation throughout the lung, ten primary lung cancer patients underwent IRB-approved 4DCT scans in the supine position. Deformable registration produced motion vectors for each voxel between exhalation and inhalation. Modeling was performed for each vector and all surrounding vectors within the lung in order to determine the mean 3D Euclidean distance necessary for an implanted fiducial to correlate with surrounding tissue motion to within 3 mm (left lower: 1.7 cm, left upper: 2.1 cm, right lower 1.6 cm, and right upper 2.9 cm). No general implantation rule of where to position a fiducial with respect to the tumor was found as the motion is highly patient and lobe specific. Correlation maps are presented showcasing spatial anisotropy of the motion of tissue surrounding the tumor.

Characterization and identification of spatial artifacts during 4D-CT imaging

PURPOSE

The purpose of this work is twofold: First, to characterize the artifacts occurring in helical 4D-CT imaging; second, to propose a method that can automatically identify the artifacts in 4D-CT images. The authors have designed a process that can automatically identify the artifacts in 4D-CT images, which may be invaluable in quantifying the quality of 4D-CT images and reducing the artifacts from the reconstructed images on a large dataset.

METHODS

Given two adjacent stacks obtained from the same respiration phase, the authors determine if there are artifacts between them. The proposed method uses a "bridge" stack strategy to connect the two stacks. Using normalized cross correlation convolution (NCCC), the two stacks are mapped to the bridge stack and the best matching positions can be located. Using this position information, the authors can then determine if there are artifacts between the two stacks. By combining the matching positions with NCCC values, the performance can be improved.

RESULTS

To validate the method, three expert observers independently labeled over 600 stacks on five patients. The results confirmed that high performance was obtained using the proposed method. The average sensitivity was about 0.87 and the average specificity was 0.82. The proposed method also outperformed the method of using respiratory signal (sensitivity increased from 0.50 to 0.87 and specificity increased from 0.70 to 0.82).

CONCLUSIONS

This study shows that the spatial artifacts during 4D-CT imaging are characterized and can be located automatically by the proposed method. The method is relatively simple but effective. It provides a way to evaluate the artifacts more objectively and accurately. The reported approach has promising potential for automatically identifying the types and frequency of artifacts on large scale 4D-CT image dataset.

Magnetic resonance imaging and computed tomography of respiratory mechanics

Radiotherapy for organs with respiratory motion has motivated the development of dynamic volume lung imaging with computed tomography (4D-CT) or magnetic resonance imaging (4D-MRI). 4D-CT can be realized in helical (continuous couch translation during image acquisition) or cine mode (translation step-by-step), either acquired prospectively or reconstructed retrospectively with temporal resolutions of up to 250 msec. Long exposure times result in high radiation dose and restrict 4D-CT to specific indications (ie, radiotherapy planning). Dynamic MRI accelerated by parallel imaging and echo sharing reaches temporal resolutions of up to 10 images/sec (2D+t) or 1 volume/s (3D+t) that allow analyzing respiratory motion of the lung and its tumors. Near isotropic 4D-MRI can be used to assess tumor displacement, chest wall invasion, and segmental respiratory mechanics. Limited temporal resolution of dynamic volume acquisitions (in their current implementation) may lead to an overestimation of tumor size, as the mass is volume averaged into many voxels during motion. Nevertheless, 4D-MRI allows for repeated and prolonged measurements without radiation exposure and therefore appears to be appropriate for patient selection in motion-adapted radiotherapy as well as for a broad spectrum of scientific applications.

Statistical modeling of 4D respiratory lung motion using diffeomorphic image registration

Modeling of respiratory motion has become increasingly important in various applications of medical imaging (e.g., radiation therapy of lung cancer). Current modeling approaches are usually confined to intra-patient registration of 3D image data representing the individual patient's anatomy at different breathing phases. We propose an approach to generate a mean motion model of the lung based on thoracic 4D computed tomography (CT) data of different patients to extend the motion modeling capabilities. Our modeling process consists of three steps: an intra-subject registration to generate subject-specific motion models, the generation of an average shape and intensity atlas of the lung as anatomical reference frame, and the registration of the subject-specific motion models to the atlas in order to build a statistical 4D mean motion model (4D-MMM). Furthermore, we present methods to adapt the 4D mean motion model to a patient-specific lung geometry. In all steps, a symmetric diffeomorphic nonlinear intensity-based registration method was employed. The Log-Euclidean framework was used to compute statistics on the diffeomorphic transformations. The presented methods are then used to build a mean motion model of respiratory lung motion using thoracic 4D CT data sets of 17 patients. We evaluate the model by applying it for estimating respiratory motion of ten lung cancer patients. The prediction is evaluated with respect to landmark and tumor motion, and the quantitative analysis results in a mean target registration error (TRE) of 3.3 ±1.6 mm if lung dynamics are not impaired by large lung tumors or other lung disorders (e.g., emphysema). With regard to lung tumor motion, we show that prediction accuracy is independent of tumor size and tumor motion amplitude in the considered data set. However, tumors adhering to non-lung structures degrade local lung dynamics significantly and the model-based prediction accuracy is lower in these cases. The statistical respiratory motion model is capable of providing valuable prior knowledge in many fields of applications. We present two examples of possible applications in radiation therapy and image guided diagnosis.

Ventilation from four-dimensional computed tomography: density versus Jacobian methods

Two calculation methods to produce ventilation images from four-dimensional computed tomography (4DCT) acquired without added contrast have been reported. We reported a method to obtain ventilation images using deformable image registration (DIR) and the underlying CT density information. A second method performs the ventilation image calculation from the DIR result alone, using the Jacobian determinant of the deformation field to estimate the local volume changes resulting from ventilation. For each of these two approaches, there are variations on their implementation. In this study, two implementations of the Jacobian-based methodology are evaluated, as well as a single density change-based model for calculating the physiologic specific ventilation from 4DCT. In clinical practice, (99m)Tc-labeled aerosol single photon emission computed tomography (SPECT) is the standard method used to obtain ventilation images in patients. In this study, the distributions of ventilation obtained from the CT-based ventilation image calculation methods are compared with those obtained from the clinical standard SPECT ventilation imaging. Seven patients with 4DCT imaging and standard (99m)Tc-labeled aerosol SPECT/CT ventilation imaging obtained on the same day as part of a prospective validation study were selected. The results of this work demonstrate the equivalence of the Jacobian-based methodologies for quantifying the specific ventilation on a voxel scale. Additionally, we found that both Jacobian- and density-change-based methods correlate well with global measurements of the resting tidal volume. Finally, correlation with the clinical SPECT was assessed using the Dice similarity coefficient, which showed statistically higher (p-value < 10(-4)) correlation between density-change-based specific ventilation and the clinical reference than did either Jacobian-based implementation.

Gating and tracking, 4D in thoracic tumours

The limited ability to control for a tumour's location compromises the accuracy with which radiation can be delivered to tumour-bearing tissue. The resultant requirement for larger treatment volumes to accommodate target uncertainty restricts the radiation dose because more surrounding normal tissue is exposed. With image-guided radiation therapy (IGRT), these volumes can be optimized and tumouricidal doses may be delivered, achieving maximum tumour control with minimal complications. Moreover, with the ability of high precision dose delivery and real-time knowledge of the target volume location, IGRT has initiated the exploration of new indications in radiotherapy such as hypofractionated radiotherapy (or stereotactic body radiotherapy), deliberate inhomogeneous dose distributions coping with tumour heterogeneity (dose painting by numbers and biologically conformal radiation therapy), and adaptive radiotherapy. In short: "individualized radiotherapy". Tumour motion management, especially for thoracic tumours, is a particular problem in this context both for the delineation of tumours and organs at risk as well as during the actual treatment delivery. The latter will be covered in this paper with some examples based on the experience of the UZ Brussel. With the introduction of the NOVALIS system (BrainLAB, Feldkirchen, Germany) in 2000 and consecutive prototypes of the ExacTrac IGRT system, gradually a hypofractionation treatment protocol was introduced for the treatment of lung tumours and liver metastases evolving from motion-encompassing techniques towards respiratory-gated radiation therapy with audio-visual feedback and most recently dynamic tracking using the VERO system (BrainLAB, Feldkirchen, Germany). This evolution will be used to illustrate the recent developments in this particular field of research.

Estimation of motion fields by non-linear registration for local lung motion analysis in 4D CT image data

PURPOSE

Motivated by radiotherapy of lung cancer non- linear registration is applied to estimate 3D motion fields for local lung motion analysis in thoracic 4D CT images. Reliability of analysis results depends on the registration accuracy. Therefore, our study consists of two parts: optimization and evaluation of a non-linear registration scheme for motion field estimation, followed by a registration-based analysis of lung motion patterns.

METHODS

The study is based on 4D CT data of 17 patients. Different distance measures and force terms for thoracic CT registration are implemented and compared: sum of squared differences versus a force term related to Thirion's demons registration; masked versus unmasked force computation. The most accurate approach is applied to local lung motion analysis.

RESULTS

Masked Thirion forces outperform the other force terms. The mean target registration error is 1.3 ± 0.2 mm, which is in the order of voxel size. Based on resulting motion fields and inter-patient normalization of inner lung coordinates and breathing depths a non-linear dependency between inner lung position and corresponding strength of motion is identified. The dependency is observed for all patients without or with only small tumors.

CONCLUSIONS

Quantitative evaluation of the estimated motion fields indicates high spatial registration accuracy. It allows for reliable registration-based local lung motion analysis. The large amount of information encoded in the motion fields makes it possible to draw detailed conclusions, e.g., to identify the dependency of inner lung localization and motion. Our examinations illustrate the potential of registration-based motion analysis.

Technical note: development of a tidal volume surrogate that replaces spirometry for physiological breathing monitoring in 4D CT

PURPOSE

Spirometry exhibits baseline drift and frequent measurement errors so it cannot be used by itself to provide tidal volume-based image sorting or breathing motion modeling. Other breathing surrogates, in this study an abdominal bellows system, are drift free but do not measure tidal volume. Simultaneously using spirometry and the bellows system allows the user to convert the recorded bellows signal to tidal volume but still relies on spirometry measurements. The authors therefore propose to use CT-based air content, rather than a spirometer, to convert the bellows signal to tidal volume.

METHODS

41 4D CT data sets are acquired, while the breathing cycle is simultaneously measured using spirometry and an abdominal pressure bellows system. The assumptions underlying the conversion of the bellows measurement to tidal volume by CT-based air content are analyzed. This comprises of detailed correlation studies of the spirometry-measured tidal volume, the bellows signal, and CT-based air content.

RESULTS

For 15/41 patients, the spirometry signals are not consistently acquired during the 4D CT session, so correlating spirometry to bellows measurements and CT-based air content leads to erroneous conversion coefficients. After introducing a minimum correlation threshold to remove these data, good correlations are obtained between the remaining breathing signals. The ratio of CT-based air content to tidal volume is measured to be 1.11 +/- 0.08; the expected value is 1.11 because room air is 11% more dense than air in the lungs.

CONCLUSIONS

The observed problems of spirometry recording illustrate the challenges encountered when using spirometers as breathing surrogate for 4D CT acquisition. The high correlation between spirometry and bellows breathing signals and the verified factor of 1.11 between CT-based air content and tidal volume mean that the bellows measurement (or other equivalent surrogates) can be reliably converted to tidal volume using the CT-based air content, avoiding the need for a spirometer.

Characterization of free breathing patterns with 5D lung motion model

PURPOSE

To determine the quiet respiration breathing motion model parameters for lung cancer and nonlung cancer patients.

METHODS

49 free breathing patient 4DCT image datasets (25 scans, cine mode) were collected with simultaneous quantitative spirometry. A cross-correlation registration technique was employed to track the lung tissue motion between scans. The registration results were applied to a lung motion model: X(-->) = X(-->)0 + alpha(-->)v + beta(-->)f, where X(-->) is the position of a piece of tissue located at reference position X(-->)0 during a reference breathing phase (zero tidal volume v, zero airflow f). alpha(-->) is a parameter that characterizes the motion due to air filling (motion as a function of tidal volume v) and beta(-->) is the parameter that accounts for the motion due to the imbalance of dynamical stress distributions during inspiration and exhalation that causes lung motion hysteresis (motion as a function of airflow f). The parameters alpha(-->) and beta(-->) together provide a quantitative characterization of breathing motion that inherently includes the complex hysteresis interplay. The alpha(-->) and beta(-->) distributions were examined for each patient to determine overall general patterns and interpatient pattern variations.

RESULTS

For 44 patients, the greatest values of /alpha(-->)/ were observed in the inferior and posterior lungs. For the rest of the patients, /alpha(-->)/ reached its maximum in the anterior lung in three patients and the lateral lung in two patients. The hysteresis motion beta(-->) had greater variability, but for the majority of patients, /beta(-->)/ was largest in the lateral lungs.

CONCLUSIONS

This is the first report of the three-dimensional breathing motion model parameters for a large cohort of patients. The model has the potential for noninvasively predicting lung motion. The majority of patients exhibited similar /alpha(-->)/ maps and the /beta(-->)/ maps showed greater interpatient variability. The motion parameter interpatient variability will inform our need for custom radiation therapy motion models. The utility of this model depends on the parameter stability over time, which is still under investigation.

Patient-specific finite element modeling of respiratory lung motion using 4D CT image data

Development and optimization of methods for adequately accounting for respiratory motion in radiation therapy of thoracic tumors require detailed knowledge of respiratory dynamics and its impact on corresponding dose distributions. Thus, computer aided modeling and simulation of respiratory motion have become increasingly important. In this article a biophysical approach for modeling respiratory lung motion is described: Major aspects of the process of lung ventilation are formulated as a contact problem of elasticity theory which is solved by finite element methods; lung tissue is assumed to be isotropic, homogeneous, and linearly elastic. A main focus of the article is to assess the impact of biomechanical parameters (values of elastic constants) on the modeling process and to evaluate modeling accuracy. Patient-specific models are generated based on 4D CT data of 12 lung tumor patients. Simulated motion patterns of inner lung landmarks are compared with corresponding motion patterns observed in the 4D CT data. Mean absolute differences between model-based predicted landmark motion and corresponding breathing-induced landmark displacements as observed in the CT data sets are in the order of 3 mm (end expiration to end inspiration) and 2 mm (end expiration to midrespiration). Modeling accuracy decreases with increasing tumor size both locally (landmarks close to tumor) and globally (landmarks in other parts of the lung). The impact of the values of the elastic constants appears to be small. Outcomes show that the modeling approach is an adequate strategy in predicting lung dynamics due to lung ventilation. Nevertheless, the decreased prediction quality in cases of large tumors demands further study of the influence of lung tumors on global and local lung elasticity properties.

INVESTIGATION OF RETROSPECTIVE RESPIRATORY GATING TECHNIQUES FOR ACQUISITION OF THIN-SLICE 4D-MULTIDETECTOR-COMPUTED TOMORGRAPHY (MDCT) OF THE LUNG: FEASIBILITY STUDY IN A LARGE ANIMAL MODEL

Respiratory gated 3D-MDCT acquisition of the whole chest over time (4D-MDCT) allow retrospective reconstruction of raw data at any point of the respiratory cycle might be beneficial in severely ill or sedated patients. Aim of this feasibility study was to investigate 2 prototype devices as input for retrospective respiratory gating in order to calculate lung volumes (LVs) and mean lung densities (MLDs) over time. Sixteen-row MDCT data were acquired in 5 ventilated pigs using a laser sensor and charge-coupled devine (CCD) camera and retrospectively reconstructed at every 10% of the respiratory cycle. Semiautomatic segmentation of the 3D data sets was performed, and LV and MLD were calculated. Data acquisition was successful in all cases. The mean difference of LV between maximum inspiration and expiration was 246 and 240 mL (laser and CCD, respectively). The mean difference of MLD between inspiration and expiration was 70 (laser) and 67 (CCD) HU. The lowest MLD was found at the beginning of the respiratory cycle (0%) for laser, and at 90% for CCD. Both gating devices allowed for reliable 4D-MDCT image acquisition. No differences were found for calculated LV and MLD, whereas the respiratory cycle was more precisely detected using the laser based gating device.

A novel analytical approach to the prediction of respiratory diaphragm motion based on external torso volume change

An analytical approach to predict respiratory diaphragm motion should have advantages over a correlation-based method, which cannot adapt to breathing pattern changes without re-calibration for a changing correlation and/or linear coefficient. To quantitatively calculate the diaphragm motion, a new expandable 'piston' respiratory (EPR) model was proposed and tested using 4DCT torso images of 14 patients. The EPR model allows two orthogonal lung motions (with a few volumetric constraints): (1) the lungs expand (DeltaV(EXP)) with the same anterior height variation as the thoracic surface, and (2) the lungs extend (DeltaV(EXT)) with the same inferior distance as the volumetrically equivalent 'piston' diaphragm. A volume conservation rule (VCR) established previously (Li et al 2009 Phys. Med. Biol. 54 1963-78) was applied to link the external torso volume change (TVC) to internal lung volume change (LVC) via lung air volume change (AVC). As the diaphragm moves inferiorly, the vacant space above the diaphragm inside the rib cage should be filled by lung tissue with a volume equal to DeltaV(EXT) (=LVC-DeltaV(EXP)), while the volume of non-lung tissues in the thoracic cavity should conserve. It was found that DeltaV(EXP) accounted for 3-24% of the LVC in these patients. The volumetric shape of the rib cage, characterized by the variation of cavity volume per slice over the piston motion range, deviated from a hollow cylinder by -1.1% to 6.0%, and correction was made iteratively if the variation is >3%. The predictions based on the LVC and TVC (with a conversion factor) were compared with measured diaphragm displacements (averaged from six pivot points), showing excellent agreements (0.2 +/- 0.7 mm and 0.2 +/- 1.2 mm, respectively), which are within clinically acceptable tolerance. Assuming motion synchronization between the piston and points of interest along the diaphragm, point motion was estimated but at higher uncertainty ( approximately 10% +/- 4%). This analytical approach provides a patient-independent technique to calculate the patient-specific diaphragm motion, using the anatomical and respiratory volumetric constraints.

Assessment of two novel ventilatory surrogates for use in the delivery of gated/tracked radiotherapy for non-small cell lung cancer

BACKGROUND

In selected patients with NSCLC the therapeutic index of radical radiotherapy can be improved with gating/tracking technology. Both techniques require real-time information on target location. This is often derived from a surrogate ventilatory signal. We assessed the correlation of two novel surrogate ventilatory signals with a spirometer-derived signal. The novel signals were obtained using the VisionRT stereoscopic camera system. The VisionRT-Tracked-Point (VRT-TP) signal was derived from tracking a point located midway between the umbilicus and xiphisternum. The VisionRT-Surface-Derived-Volume (VRT-SDV) signal was derived from 3D body surface imaging of the torso. Both have potential advantages over the current surrogate signals.

METHODS

Eleven subjects with NSCLC were recruited. Each was positioned as for radiotherapy treatment, and then instructed to breathe in five different modes: normal, abdominal, thoracic, deep and shallow breathing. Synchronous ventilatory signals were recorded for later analysis. The signals were analysed for correlation across all modes of breathing, and phase shifts. The VRT-SDV was also assessed for its ability to determine the mode of breathing.

RESULTS

Both novel respiratory signals showed good correlation (r>0.80) with spirometry in 9 of 11 subjects. For all subjects the correlation with spirometry was better for the VRT-SDV signal than for the VRT-TP signal. Only one subject displayed a phase shift between the VisionRT-derived signals and spirometry. The VRT-SDV signal could also differentiate between different modes of breathing. Unlike the spirometer-derived signal, neither VisionRT-derived signal was subject to drift.

CONCLUSION

Both the VRT-TP and VRT-SDV signals have potential applications in ventilatory-gated and tracked radiotherapy. They can also be used as a signal for sorting 4DCT images, and to drive 4DCT single- and multiple-parameter motion models.

Quantitative prediction of respiratory tidal volume based on the external torso volume change: a potential volumetric surrogate

An external respiratory surrogate that not only highly correlates with but also quantitatively predicts internal tidal volume should be useful in guiding four-dimensional computed tomography (4DCT), as well as 4D radiation therapy (4DRT). A volumetric surrogate should have advantages over external fiducial point(s) for monitoring respiration-induced motion of the torso, which deforms in synchronization with a patient-specific breathing pattern. This study establishes a linear relationship between the external torso volume change (TVC) and lung air volume change (AVC) by validating a proposed volume conservation hypothesis (TVC = AVC) throughout the respiratory cycle using 4DCT and spirometry. Fourteen patients' torso 4DCT images and corresponding spirometric tidal volumes were acquired to examine this hypothesis. The 4DCT images were acquired using dual surrogates in ciné mode and amplitude-based binning in 12 respiratory stages, minimizing residual motion artifacts. Torso and lung volumes were calculated using threshold-based segmentation algorithms and volume changes were calculated relative to the full-exhalation stage. The TVC and AVC, as functions of respiratory stages, were compared, showing a high correlation (r = 0.992 +/- 0.005, p < 0.0001) as well as a linear relationship (slope = 1.027 +/- 0.061, R(2) = 0.980) without phase shift. The AVC was also compared to the spirometric tidal volumes, showing a similar linearity (slope = 1.030 +/- 0.092, R(2) = 0.947). In contrast, the thoracic and abdominal heights measured from 4DCT showed relatively low correlation (0.28 +/- 0.44 and 0.82 +/- 0.30, respectively) and location-dependent phase shifts. This novel approach establishes the foundation for developing an external volumetric respiratory surrogate.

Lung motion and volume measurement by dynamic 3D MRI using a 128-channel receiver coil

RATIONALE AND OBJECTIVES

The authors present their initial experience using a 3-T whole-body scanner equipped with a 128-channel coil applied to lung motion assessment. Recent improvements in fast magnetic resonance imaging (MRI) technology have enabled several trials of free-breathing three-dimensional (3D) imaging of the lung. A large number of image frames necessarily increases the difficulty of image analysis and therefore warrants automatic image processing. However, the intensity homogeneities of images of prior dynamic 3D lung MRI studies have been insufficient to use such methods. In this study, initial data were obtained at 3 T with a 128-channel coil that demonstrate the feasibility of acquiring multiple sets of 3D pulmonary scans during free breathing and that have sufficient quality to be amenable to automatic segmentation.

MATERIALS AND METHODS

Dynamic 3D images of the lungs of two volunteers were acquired with acquisition times of 0.62 to 0.76 frames/s and an image matrix of 128 x 128, with 24 to 30 slice encodings. The volunteers were instructed to take shallow and deep breaths during the scans. The variation of lung volume was measured from the segmented images.

RESULTS

Dynamic 3D images were successfully acquired for both respiratory conditions for each subject. The images showed whole-lung motion, including lifting of the chest wall and the displacement of the diaphragm, with sufficient contrast to distinguish these structures from adjacent tissues. The average time to complete segmentation for one 3D image was 4.8 seconds. The tidal volume measured was consistent with known tidal volumes for healthy subjects performing deep-breathing maneuvers. The temporal resolution was insufficient to measure tidal volumes for shallow breathing.

CONCLUSION

This initial experience with a 3-T whole-body scanner and a 128-channel coil showed that the scanner and imaging protocol provided dynamic 3D images with spatial and temporal resolution sufficient to delineate the diaphragmatic domes and chest wall during active breathing. In addition, the intensity homogeneities and signal-to-noise ratio were adequate to perform automatic segmentation.

The use of CT density changes at internal tissue interfaces to correlate internal organ motion with an external surrogate

The purpose of this paper is to describe a non-invasive method to monitor the motion of internal organs affected by respiration without using external markers or spirometry, to test the correlation with external markers, and to calculate any time shift between the datasets. Ten lung cancer patients were CT scanned with a GE LightSpeed Plus 4-Slice CT scanner operating in a ciné mode. We retrospectively reconstructed the raw CT data to obtain consecutive 0.5 s reconstructions at 0.1 s intervals to increase image sampling. We defined regions of interest containing tissue interfaces, including tumour/lung interfaces that move due to breathing on multiple axial slices and measured the mean CT number versus respiratory phase. Tumour motion was directly correlated with external marker motion, acquired simultaneously, using the sample coefficient of determination, r(2). Only three of the ten patients showed correlation higher than r(2) = 0.80 between tumour motion and external marker position. However, after taking into account time shifts (ranging between 0 s and 0.4 s) between the two data sets, all ten patients showed correlation better than r(2) = 0.8. This non-invasive method for monitoring the motion of internal organs is an effective tool that can assess the use of external markers for 4D-CT imaging and respiratory-gated radiotherapy on a patient-specific basis.

Iterative sorting for four-dimensional CT images based on internal anatomy motion

Current four-dimensional (4D) computed tomography (CT) imaging techniques using multislice CT scanners require retrospective sorting of the reconstructed two-dimensional (2D) CT images. Most existing sorting methods depend on externally monitored breathing signals recorded by extra instruments. External signals may not always accurately capture the breathing status and may lead to severe discontinuity artifacts in the sorted CT volumes. This article describes a method to find the temporal correspondences for the free-breathing multislice CT images acquired at different table positions based on internal anatomy movement. The algorithm iteratively sorts the CT images using estimated internal motion indices. It starts from two imperfect reference volumes obtained from the unsorted CT images; then, in each iteration, thorax motion is estimated from the reference volumes and the free-breathing CT images. Based on the estimated motion, the breathing indices as well as the reference volumes are refined and fed into the next iteration. The algorithm terminates when two successive iterations attain the same sorted reference volumes. In three out of five patient studies, our method attained comparable image quality with that using external breathing signals. For the other two patient studies, where the external signals poorly reflected the internal motion, the proposed method significantly improved the sorted 4D CT volumes, albeit with greater computation time.

Inference of hysteretic respiratory tumor motion from external surrogates: a state augmentation approach

It is important to monitor tumor movement during radiotherapy. Respiration-induced motion affects tumors in the thorax and abdomen (in particular, those located in the lung region). For image-guided radiotherapy (IGRT) systems, it is desirable to minimize imaging dose, so external surrogates are used to infer the internal tumor motion between image acquisitions. This process relies on consistent correspondence between the external surrogate signal and the internal tumor motion. Respiratory hysteresis complicates the external/internal correspondence because two distinct tumor positions during different breathing phases can yield the same external observation. Previous attempts to resolve this ambiguity often subdivided the data into inhale/exhale stages and restricted the estimation to only one of these directions. In this study, we propose a new approach to infer the internal tumor motion from external surrogate signal using state augmentation. This method resolves the hysteresis ambiguity by incorporating higher-order system dynamics. It circumvents the segmentation of the internal/external trajectory into different phases, and estimates the inference map based on all the available external/internal correspondence pairs. Optimization of the state augmentation is investigated. This method generalizes naturally to adaptive on-line algorithms.

Advances in 4D medical imaging and 4D radiation therapy

This paper reviews recent advances in 4D medical imaging (4DMI) and 4D radiation therapy (4DRT), which study, characterize, and minimize patient motion during the processes of imaging and radiotherapy. Patient motion is inevitably present in these processes, producing artifacts and uncertainties in target (lesion) identification, delineation, and localization. 4DMI includes time-resolved volumetric CT, MRI, PET, PET/CT, SPECT, and US imaging. To enhance the performance of these volumetric imaging techniques, parallel multi-detector array has been employed for acquiring image projections and the volumetric image reconstruction has been advanced from the 2D to the 3D tomography paradigm. The time information required for motion characterization in 4D imaging can be obtained either prospectively or retrospectively using respiratory gating or motion tracking techniques. The former acquires snapshot projections for reconstructing a motion-free image. The latter acquires image projections continuously with an associated timestamp indicating respiratory phases using external surrogates and sorts these projections into bins that represent different respiratory phases prior to reconstructing the cyclical series of 3D images. These methodologies generally work for all imaging modalities with variations in detailed implementation. In 4D CT imaging, both multi-slice CT (MSCT) and cone-beam CT (CBCT) are applicable in 4D imaging. In 4D MR imaging, parallel imaging with multi-coil-detectors has made 4D volumetric MRI possible. In 4D PET and SPECT, rigid and non-rigid motions can be corrected with aid of rigid and deformable registration, respectively, without suffering from low statistics due to signal binning. In 4D PET/CT and SPECT/CT, a single set of 4D images can be utilized for motion-free image creation, intrinsic registration, and attenuation correction. In 4D US, volumetric ultrasonography can be employed to monitor fetal heart beating with relatively high temporal resolution. 4DRT aims to track and compensate for target motion during radiation treatment, minimizing normal tissue injury, especially critical structures adjacent to the target, and/or maximizing radiation dose to the target. 4DRT requires 4DMI, 4D radiation treatment planning (4D RTP), and 4D radiation treatment delivery (4D RTD). Many concepts in 4DRT are borrowed, adapted and extended from existing image-guided radiation therapy (IGRT) and adaptive radiation therapy (ART). The advantage of 4DRT is its promise of sparing additional normal tissue by synchronizing the radiation beam with the moving target in real-time. 4DRT can be implemented differently depending upon how the time information is incorporated and utilized. In an ideal situation, the motion adaptive approach guided by 4D imaging should be applied to both RTP and RTD. However, until new automatic planning and motion feedback tools are developed for 4DRT, clinical implementation of ideal 4DRT will meet with limited success. However, simplified forms of 4DRT have been implemented with minor modifications of existing planning and delivery systems. The most common approach is the use of gating techniques in both imaging and treatment, so that the planned and treated target localizations are identical. In 4D planning, the use of a single planning CT image, which is representative of the statistical respiratory mean, seems preferable. In 4D delivery, on-site CBCT imaging or 3D US localization imaging for patient setup and internal fiducial markers for target motion tracking can significantly reduce the uncertainty in treatment delivery, providing improved normal tissue sparing. Most of the work on 4DRT can be regarded as a proof-of-principle and 4DRT is still in its early stage of development.

Respiratory-gated helical computed tomography of lung: reproducibility of small volumes in an ex vivo model

PURPOSE

Motion-adapted radiotherapy with gated irradiation or tracking of tumor positions requires dedicated imaging techniques such as four-dimensional (4D) helical computed tomography (CT) for patient selection and treatment planning. The objective was to evaluate the reproducibility of spatial information for small objects on respiratory-gated 4D helical CT using computer-assisted volumetry of lung nodules in a ventilated ex vivo system.

METHODS AND MATERIALS

Five porcine lungs were inflated inside a chest phantom and prepared with 55 artificial nodules (mean diameter, 8.4 mm +/- 1.8). The lungs were respirated by a flexible diaphragm and scanned with 40-row detector CT (collimation, 24 x 1.2 mm; pitch, 0.1; rotation time, 1 s; slice thickness, 1.5 mm; increment, 0.8 mm). The 4D-CT scans acquired during respiration (eight per minute) and reconstructed at 0-100% inspiration and equivalent static scans were scored for motion-related artifacts (0 or absent to 3 or relevant). The reproducibility of nodule volumetry (three readers) was assessed using the variation coefficient (VC).

RESULTS

The mean volumes from the static and dynamic inspiratory scans were equal (364.9 and 360.8 mm3, respectively, p = 0.24). The static and dynamic end-expiratory volumes were slightly greater (371.9 and 369.7 mm3, respectively, p = 0.019). The VC for volumetry (static) was 3.1%, with no significant difference between 20 apical and 20 caudal nodules (2.6% and 3.5%, p = 0.25). In dynamic scans, the VC was greater (3.9%, p = 0.004; apical and caudal, 2.6% and 4.9%; p = 0.004), with a significant difference between static and dynamic in the 20 caudal nodules (3.5% and 4.9%, p = 0.015). This was consistent with greater motion-related artifacts and image noise at the diaphragm (p <0.05). The VC for interobserver variability was 0.6%.

CONCLUSION

Residual motion-related artifacts had only minimal influence on volumetry of small solid lesions. This indicates a high reproducibility of spatial information for small objects in low pitch helical 4D-CT reconstructions.

A quality assurance phantom for the performance evaluation of volumetric micro-CT systems

Small-animal imaging has recently become an area of increased interest because more human diseases can be modeled in transgenic and knockout rodents. As a result, micro-computed tomography (micro-CT) systems are becoming more common in research laboratories, due to their ability to achieve spatial resolution as high as 10 microm, giving highly detailed anatomical information. Most recently, a volumetric cone-beam micro-CT system using a flat-panel detector (eXplore Ultra, GE Healthcare, London, ON) has been developed that combines the high resolution of micro-CT and the fast scanning speed of clinical CT, so that dynamic perfusion imaging can be performed in mice and rats, providing functional physiological information in addition to anatomical information. This and other commercially available micro-CT systems all promise to deliver precise and accurate high-resolution measurements in small animals. However, no comprehensive quality assurance phantom has been developed to evaluate the performance of these micro-CT systems on a routine basis. We have designed and fabricated a single comprehensive device for the purpose of performance evaluation of micro-CT systems. This quality assurance phantom was applied to assess multiple image-quality parameters of a current flat-panel cone-beam micro-CT system accurately and quantitatively, in terms of spatial resolution, geometric accuracy, CT number accuracy, linearity, noise and image uniformity. Our investigations show that 3D images can be obtained with a limiting spatial resolution of 2.5 mm(-1) and noise of +/-35 HU, using an acquisition interval of 8 s at an entrance dose of 6.4 cGy.

Influence of retrospective sorting on image quality in respiratory correlated computed tomography

PURPOSE

To evaluate the influence of retrospective sorting on image quality in four-dimensional respiratory correlated CT.

MATERIALS AND METHODS

Twelve patients with intrapulmonary tumors were examined using a 24-slice CT-scanner in helical mode. Images were reconstructed after retrospective sorting based on five algorithms: amplitude-based sorting with definition of peak-exhalation and peak-inhalation separately/locally for all breathing cycles (LAS) and globally for the time of image acquisition (GAS). Drifts of the breathing signal were corrected in dc-GAS. In phase-based (PS) and cycle-based (CS) algorithm the projections were sorted relative to time. Motion artifacts were scored by a radiologist. The tumor volumes were measured using automatic image segmentation.

RESULTS

Averaged over all breathing phases, LAS and PS achieved significantly improved image quality and lowest tumor volume variability compared to GAS, dc-GAS and CS. Imaging redundancy of 5s was not sufficient for GAS and dc-GAS: missing corresponding amplitude positions in one or several breathing cycles resulted in incomplete reconstruction of peak-ventilation images in 11/12 and 10/12 patients with GAS and dc-GAS, respectively. Limiting the analysis to mid-ventilation phases showed GAS and dc-GAS as the most reliable algorithms.

CONCLUSIONS

LAS and PS are suggested as a compromise between image quality and radiation dose.