Comparison of spirometry and abdominal height as four-dimensional computed tomography metrics in lung

Ventilation measurements using fast-helical free-breathing computed tomography

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 90^th 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.

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.

Image Registration of 18F-FDG PET/CT Using the MotionFree Algorithm and CT Protocols through Phantom Study and Clinical Evaluation

We evaluated the benefits of the MotionFree algorithm through phantom and patient studies. The various sizes of phantom and vacuum vials were linked to RPM moving with or without MotionFree application. A total of 600 patients were divided into six groups by breathing protocols and CT scanning time. Breathing protocols were applied as follows: (a) patients who underwent scanning without any breathing instructions; (b) patients who were instructed to hold their breath after expiration during CT scan; and (c) patients who were instructed to breathe naturally. The length of PET/CT misregistration was measured and we defined the misregistration when it exceeded 10 mm. In the phantom tests, the images produced by the MotionFree algorithm were observed to have excellent agreement with static images. There were significant differences in PET/CT misregistration according to CT scanning time and each breathing protocol. When applying the type (c) protocol, decreasing the CT scanning time significantly reduced the frequency and length of misregistrations (p < 0.05). The MotionFree application is able to correct respiratory motion artifacts and to accurately quantify lesions. The shorter time of CT scan can reduce the frequency, and the natural breathing protocol also decreases the lengths of misregistrations.

Technical Note: Investigating internal-external motion correlation using fast helical CT

PURPOSE

To quantify the use of anterior torso skin surface position measurement as a breathing surrogate.

METHODS

Fourteen patients were scanned 25 times in alternating directions using a free-breathing low-mA fast helical CT protocol. Simultaneously, an abdominal pneumatic bellows was used as a real-time breathing surrogate. The imaged diaphragm dome position was used as a gold standard surrogate, characterized by localizing the most superior points of the diaphragm dome in each lung. These positions were correlated against the bellows signal acquired at the corresponding scan times. The bellows system has been shown to have a slow linear drift, and the bellows-to-CT synchronization process had a small uncertainty, so the drift and time offset were determined by maximizing the correlation coefficient between the craniocaudal diaphragm position and the drift-corrected bellows signal. The corresponding fit was used to model the real-time diaphragm position. To estimate the effectiveness of skin surface positions as surrogates, the anterior torso surface position was measured from the CT scans and correlated against the diaphragm position model. The residual error was defined as the root-mean-square correlation residual with the breathing amplitude normalized to the 5^th to 95^th breathing amplitude percentiles. The fit residual errors were analyzed over the surface for the fourteen studied patients and reported as percentages of the 5^th to 95^th percentile ranges.

RESULTS

A strong correlation was measured between the diaphragm motion and the abdominal bellows signal with an average residual error of 9.21% and standard deviation of 3.77%. In contrast, the correlations between the diaphragm position model and patient surface positions varied throughout the torso and from patient to patient. However, a consistently high correlation was found near the abdomen for each patient, and the average minimum residual error relating the skin surface to the diaphragm was 11.8% with a standard deviation of 4.61%.

CONCLUSIONS

The thoracic patient surface was found to be an accurate surrogate, but the accuracy varied across the surface sufficiently that care would need to be taken to use the surface as an accurate and reliable surrogate. Future studies will use surface imaging to determine surface patch algorithms that utilize the entire chest as well as thoracic and abdominal breathing relationships.

Gated 18F-FDG PET/CT of the Lung Using a Respiratory Spirometric Gating Device: A Feasibility Study

Spirometric gating devices (SGDs) can measure the respiratory signal with high temporal resolution and accuracy. The primary objective of this study was to assess the feasibility and tolerance of a gated lung PET/CT acquisition using an SGD. The secondary objective was to compare the technical quality, accuracy, and interoperability of the SGD with that of a standard respiratory gating device, Real-Time Position Management (RPM), based on measurement of vertical thoracoabdominal displacement. Methods: A prospective phase I monocentric clinical study was performed on patients undergoing ¹⁸F-FDG PET/CT for assessment of a solitary lung nodule, staging of lung malignancy, or planning of radiotherapy. After whole-body PET/CT, a centered gated acquisition of both PET and CT was simultaneously obtained with the SGD and RPM during normal breathing. Results: Of the 46 patients who were included, 6 were prematurely excluded (1 because of hyperglycemia and 5 because of distant metastases revealed by whole-body PET/CT, leading to an unjustified extra gated acquisition). No serious adverse events were observed. Of the 40 remaining patients, the gated acquisition was prematurely stopped in 1 patient because of mask discomfort (2.5%; confidence interval [CI], 0.1%-13.2%). This event was considered patient tolerance failure. The SGD generated accurately gated PET/CT images, with more than 95% of the breathing cycle detected and high temporal resolution, in 34 of the 39 patients (87.2%; 95% CI, 60.0%-100.0%) and failed to generate a biologic tumor volume in 1 of 21 patients with increased ¹⁸F-FDG uptake (4.8%; 95% CI, 0.1%-26.5%). The quality and accuracy of respiratory signal detection and synchronization were significantly better than those obtained with RPM (P < 0.05). Conclusion: This trial supports the use of an SGD for gated lung PET/CT because of its high patient tolerance and accuracy. Although this technique seems to technically outperform RPM for gated PET/CT, further assessment of its superiority and the clinical benefit is warranted. We believe that this technique could be used as a gold standard to develop innovative approaches to eliminate respiration-induced blurring artifacts.

Respiratory motion estimation of the liver with abdominal motion as a surrogate

Background: Respiratory‐induced motion (RIM) causes uncertainties in localizing hepatic lesions, which could lead to inaccurate targeting during interventions. One approach to mitigate the problem is respiratory motion estimation (RME), in which the liver motion is estimated by measuring external signals called surrogates.

Methods: A learning‐based approach has been developed and validated to estimate the RIM of hepatic lesions. External markers placed on the human's abdomen were chosen as surrogates. Accordingly, appropriate motion models (multivariate, Ridge and Lasso regression models) were designed to correlate the liver motion with the abdominal motion, and trained to estimate the superior–inferior (SI) motion of the liver. Three subjects volunteered for 6 sessions of such that liver images acquired by magnetic resonance imaging (MRI) were recorded alongside camera‐tracked external markers.

Results and conclusions: The proposed machine learning approach was validated in MRI on human subjects and the results show that the approach could estimate the respiratory‐induced SI motion of the liver with a mean absolute error (MAE) accuracy below 2 mm.

Using needle orientation sensing as surrogate signal for respiratory motion estimation in percutaneous interventions

PURPOSE

To develop and evaluate an approach to estimate the respiratory-induced motion of lesions in the chest and abdomen.

MATERIALS AND METHODS

The proposed approach uses the motion of an initial reference needle inserted into a moving organ to estimate the lesion (target) displacement that is caused by respiration. The needles position is measured using an inertial measurement unit (IMU) sensor externally attached to the hub of an initially placed reference needle. Data obtained from the IMU sensor and the target motion are used to train a learning-based approach to estimate the position of the moving target. An experimental platform was designed to mimic respiratory motion of the liver. Liver motion profiles of human subjects provided inputs to the experimental platform. Variables including the insertion angle, target depth, target motion velocity and target proximity to the reference needle were evaluated by measuring the error of the estimated target position and processing time.

RESULTS

The mean error of estimation of the target position ranged between 0.86 and 1.29 mm. The processing maximum training and testing time was 5 ms which is suitable for real-time target motion estimation using the needle position sensor.

CONCLUSION

The external motion of an initially placed reference needle inserted into a moving organ can be used as a surrogate, measurable and accessible signal to estimate in real-time the position of a moving target caused by respiration; this technique could then be used to guide the placement of subsequently inserted needles directly into the target.

Lung surface deformation prediction from spirometry measurement and chest wall surface motion

PURPOSE

The authors have developed and evaluated a method to predict lung surface motion based on spirometry measurements, and chest and abdomen motion at selected locations.

METHODS

A patient-specific 3D triangular surface mesh of the lung region was obtained at the end expiratory phase by the threshold-based segmentation method. Lung flow volume changes were recorded with a spirometer for each patient. A total of 192 selected points at a regular spacing of 2 × 2 cm matrix points were used to detect chest wall motion over a total area of 32 × 24 cm covering the chest and abdomen surfaces. QR factorization with column pivoting was employed to remove redundant observations of the chest and abdominal areas. To create a statistical model between the lung surface and the corresponding surrogate signals, the authors developed a predictive model based on canonical ridge regression. Two unique weighting vectors were selected for each vertex on the lung surface; they were optimized during the training process using all other 4D-CT phases except for the test inspiration phase. These parameters were employed to predict the vertex locations of a testing data set.

RESULTS

The position of each lung surface mesh vertex was estimated from the motion at selected positions within the chest wall surface and from spirometry measurements in ten lung cancer patients. The average estimation of the 98th error percentile for the end inspiration phase was less than 1 mm (AP = 0.9 mm, RL = 0.6 mm, and SI = 0.8 mm). The vertices located at the lower region of the lung had a larger estimation error as compared with those within the upper region of the lung. The average landmark motion errors, derived from the biomechanical modeling using real surface deformation vector fields (SDVFs), and the predicted SDVFs were 3.0 and 3.1 mm, respectively.

CONCLUSIONS

Our newly developed predictive model provides a noninvasive approach to derive lung boundary conditions. The proposed system can be used with personalized biomechanical respiration modeling to derive lung tumor motion during radiation therapy from noninvasive measurements.

Mooney-Rivlin biomechanical modeling of lung with Inhomogeneous material

In this study, the Mooney-Rivlin material with hyperelastic strain energy was proposed for biomechanical modeling of the lung. We modeled the lung as an inhomogeneous Mooney-Rivlin material with uncoupled deviatoric and volumetric behavior. The proposed method was evaluated on the lungs of eight lung cancer patients. For each patient, the lung was segmented from the 4D-CT images and tetrahedral volume mesh of the lung in phase 50% was created by using the adaptive mesh generation toolkit. The demons deformable registration algorithm was used to extract the displacement vector fields (DVFs). The Jacobian of the deformation gradient was calculated from DVFs, and the lung strain energy function was optimized to improve the tumor center of mass (TCM) motion simulation accuracy between respiratory phase 50% and 0%. The average TCM motion simulation error for the proposed strategy is 1.95 mm for eight patients. We observed 13% improvement in the TCM position prediction compared with the homogeneous Mooney-Rivlin modeling.

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.

Sensitivity of tumor motion simulation accuracy to lung biomechanical modeling approaches and parameters

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.

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.

Optimizing geometric accuracy of four-dimensional CT scans acquired using the wall- and couch-mounted Varian® Real-time Position Management™ camera systems

OBJECTIVE

The aim of this study was to identify sources of anatomical misrepresentation owing to the location of camera mounting, tumour motion velocity and image processing artefacts in order to optimize the four-dimensional CT (4DCT) scan protocol and improve geometrical-temporal accuracy.

METHODS

A phantom with an imaging insert was driven with a sinusoidal superior-inferior motion of varying amplitude and period for 4DCT scanning. The length of a high-density cube within the insert was measured using treatment planning software to determine the accuracy of its spatial representation. Scan parameters were varied, including the tube rotation period and the cine time between reconstructed images. A CT image quality phantom was used to measure various image quality signatures under the scan parameters tested.

RESULTS

No significant difference in spatial accuracy was found for 4DCT scans carried out using the wall- or couch-mounted camera for sinusoidal target motion. Greater spatial accuracy was found for 4DCT scans carried out using a tube rotation speed of 0.5 s rather than 1.0 s. The reduction in image quality when using a faster rotation speed was not enough to require an increase in patient dose.

CONCLUSION

The 4DCT accuracy may be increased by optimizing scan parameters, including choosing faster tube rotation speeds. Peak misidentification in the recorded breathing trace may lead to spatial artefacts, and this risk can be reduced by using a couch-mounted infrared camera.

ADVANCES IN KNOWLEDGE

This study explicitly shows that 4DCT scan accuracy is improved by scanning with a faster CT tube rotation speed.

Physiologically guided approach to characterizing respiratory motion

PURPOSE

To characterize radiation therapy patient breathing patterns based on measured external surrogate information.

METHODS

Breathing 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).

RESULTS

There 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.

CONCLUSIONS

Based 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.

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

PURPOSE

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

METHODS AND MATERIALS

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

RESULTS

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

CONCLUSIONS

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

A new respiratory monitoring and processing system based on Wii remote: proof of principle

PURPOSE

To create a patient respiratory management system and patient self-practice tool using the Wii remote, a widely available consumer hardware product.

METHODS

The Wii remote (Wiimote) (Nintendo, Redmond, WA) contains an infrared (IR) camera that can track up to four spots whose coordinates are reported to a host computer via Bluetooth. The Wiimote is capable of tracking a fiducial box currently used by a commercial monitoring system [Real-time Position Management(TM) (RPM) system, Varian Associates, Palo Alto, CA], if the correct IR source is used. The authors validated the Wiimote tracking by comparing the amplitude and frequency of signals among those reported by Wiimote with known movements from an inhouse servo-driven respiratory simulator, as well as with those measured using the RPM. The simulator comparison was done using standard sinusoid signals with amplitude of 2.0 cm as well as recorded patient respiratory traces. The RPM comparisons were done by simultaneously recording the RPM reflective box position with the Wiimote and the RPM. Timing was compared between these two systems by using the digital beam-on signal from the CT scanner, for the 4DCT to synchronize these acquisitions.

RESULTS

The data acquisition rate from the Wiimote was 100.0 ± 0.4 Hz with a version 2.1 Bluetooth adaptor. The standard deviation of the height of the motion extrema was 0.06 and 1.1 mm when comparing those measured by the Wiimote and the servomotor encoder for standard sinusoid signal and prerecorded patient respiratory signal, respectively. The standard deviation of the amplitude of motion extrema between the Wiimote and RPM was 0.9 mm and the timing difference was 253 ms.

CONCLUSION

The performance of Wiimote shows promise for respiratory monitoring for its faster sampling rate as well as the potential optical and GPU abilities. If used with care it can deliver reasonable spatial and temporal accuracy.

Quantification of the thorax-to-abdomen breathing ratio for breathing motion modeling

PURPOSE

The purpose of this study was to develop a methodology to quantitatively measure the thorax-to-abdomen breathing ratio from a 4DCT dataset for breathing motion modeling and breathing motion studies.

METHODS

The thorax-to-abdomen breathing ratio was quantified by measuring the rate of cross-sectional volume increase throughout the thorax and abdomen as a function of tidal volume. Twenty-six 16-slice 4DCT patient datasets were acquired during quiet respiration using a protocol that acquired 25 ciné scans at each couch position. Fifteen datasets included data from the neck through the pelvis. Tidal volume, measured using a spirometer and abdominal pneumatic bellows, was used as breathing-cycle surrogates. The cross-sectional volume encompassed by the skin contour when compared for each CT slice against the tidal volume exhibited a nearly linear relationship. A robust iteratively reweighted least squares regression analysis was used to determine η(i), defined as the amount of cross-sectional volume expansion at each slice i per unit tidal volume. The sum Ση(i) throughout all slices was predicted to be the ratio of the geometric expansion of the lung and the tidal volume; 1.11. The Xiphoid process was selected as the boundary between the thorax and abdomen. The Xiphoid process slice was identified in a scan acquired at mid-inhalation. The imaging protocol had not originally been designed for purposes of measuring the thorax-to-abdomen breathing ratio so the scans did not extend to the anatomy with η(i) = 0. Extrapolation of η(i)-η(i) = 0 was used to include the entire breathing volume. The thorax and abdomen regions were individually analyzed to determine the thorax-to-abdomen breathing ratios. There were 11 image datasets that had been scanned only through the thorax. For these cases, the abdomen breathing component was equal to 1.11 - Ση(i) where the sum was taken throughout the thorax.

RESULTS

The average Ση(i) for thorax and abdomen image datasets was found to be 1.20 ± 0.17, close to the expected value of 1.11. The thorax-to-abdomen breathing ratio was 0.32 ± 0.24. The average Ση(i) was 0.26 ± 0.14 in the thorax and 0.93 ± 0.22 in the abdomen. In the scan datasets that encompassed only the thorax, the average Ση(i) was 0.21 ± 0.11.

CONCLUSIONS

A method to quantify the relationship between abdomen and thoracic breathing was developed and characterized.

Distribution of lung tissue hysteresis during free breathing

PURPOSE

To characterize and quantify free breathing lung tissue motion distributions.

METHODS

Forty seven patient data sets were acquired using a 4DCT protocol consisting of 25 ciné scans at abutting couch positions on a 16-slice scanner. The tidal volume of each scan was measured by simultaneously acquiring spirometry and an abdominal pneumatic bellows. The concept of a characteristic breath was developed to manage otherwise natural breathing pattern variations. The characteristic breath was found by first dividing the breathing traces into individual breaths, from maximum exhalation to maximum exhalation. A linear breathing drift model was assumed and the drift removed for each breath. Breaths that exceeded one standard deviation in period or amplitude were removed from further analysis. A characteristic breath was defined by normalizing each breath to a common amplitude, aligning the peak inhalation times for all of the breaths, and determining the average time at each tidal volume, keeping inhalation and exhalation separate. Breathing motion trajectories were computed using a previously published five-dimensional lung tissue trajectory model which expresses the position of internal lung tissue, X, as: X(v,f:X0)=X0+α(X0)v+β(X0)f, where X0 is the internal lung tissue position at zero tidal volume and zero airflow, the scalar values v and f are the measured tidal volume and airflow, respectively, and the vectors α and β are fitted free parameters. In order to characterize the motion patterns, the trajectory elongations were examined throughout the subject's lungs. Elongation was defined here by generating a rectangular bounding box with one side parallel to the α vector and the box oriented in the plane defined by the α and β motion vectors. Hysteresis motion was defined as the ratio of the box dimensions aligned orthogonal to and parallel to the α vector. The 15th and 85th percentile of the elongation were used to characterize tissue trajectory hysteresis.

RESULTS

The 15th and 85th percentile bounding box elongations were 0.090 ± 0.005 and 0.083 ± 0.013 in the upper left lung and 0.187 ± 0.037 and 0.203 ± 0.053, in the lower left lung. The 15th and 85th percentiles for the upper right lung were 0.092 ± 0.006 and 0.085 ± 0.013, and 0.184 ± 0.038, and 0.196 ± 0.043 in the lower right lung. Both percentiles were calculated for tidal volume displacements between 5 and 15 mm. In the left lung, the average elongations in the upper and lower lung were ζ=0.120 ± 0.064 and ζ=0.090 ± 0.055, respectively. The average elongations in the upper and lower right lung were ζ=0.107 ± 0.060 and ζ=0.082 ± 0.048, respectively. The elongation varied smoothly throughout the lungs.

CONCLUSIONS

The hysteresis motion was relatively small compared to the volume-filling motion, contributing between 8% and 20% of the overall motion. Statistically significant differences were observed in the range of hysteresis contribution for upper and lower lung regions. The characteristic breath process provided an excellent method for defining an average breath. The characteristic breath had continuous tidal volume and airflow characteristics when the breath was continuously repeated,useful for generating patterns representative of realistic motion for breathing motion studies.

Evaluation of the potential of multi-modal sensors for respiratory motion prediction and correlation

In modern robotic radiotherapy, precise radiation of moving tumors is possible by tracking external optical surrogates. The surrogates are used to compensate for time delays and to predict internal landmarks using a correlation model. The correlation depends significantly on the surrogate position and breathing characteristics of the patient. In this context, we aim to increase the accuracy and robustness of prediction and correlation models by using a multi-modal sensor setup. Here, we evaluate the correlation coefficient of a strain belt, an acceleration and temperature sensor (air flow) with respect to external optical sensors and one internal landmark in the liver, measured by 3D ultrasound. The focus of this study is the influence of breathing artefacts, like coughing and harrumphing. Evaluating seven subjects, we found a strong decrease of the correlation for all modalities in case of artefacts. The results indicate that no precise motion compensation during these times is possible. Overall, we found that apart from the optical markers, the strain belt and temperature sensor data show the best correlation to external and internal motion.

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.

Investigation of a breathing surrogate prediction algorithm for prospective pulmonary gating

PURPOSE

A major challenge of four dimensional computed tomography (4DCT) in treatment planning and delivery has been the lack of respiration amplitude and phase reproducibility during image acquisition. The implementation of a prospective gating algorithm would ensure that images would be acquired only during user-specified breathing phases. This study describes the development and testing of an autoregressive moving average (ARMA) model for human respiratory phase prediction under quiet respiration conditions.

METHODS

A total of 47 4DCT patient datasets and synchronized respiration records was utilized in this study. Three datasets were used in model development and were removed from further evaluation of the ARMA model. The remaining 44 patient datasets were evaluated with the ARMA model for prediction time steps from 50 to 1000 ms in increments of 50 and 100 ms. Thirty-five of these datasets were further used to provide a comparison between the proposed ARMA model and a commercial algorithm with a prediction time step of 240 ms.

RESULTS

The optimal number of parameters for the ARMA model was based on three datasets reserved for model development. Prediction error was found to increase as the prediction time step increased. The minimum prediction time step required for prospective gating was selected to be half of the gantry rotation period. The maximum prediction time step with a conservative 95% confidence criterion was found to be 0.3 s. The ARMA model predicted peak inhalation and peak exhalation phases significantly better than the commercial algorithm. Furthermore, the commercial algorithm had numerous instances of missed breath cycles and falsely predicted breath cycles, while the proposed model did not have these errors.

CONCLUSIONS

An ARMA model has been successfully applied to predict human respiratory phase occurrence. For a typical CT scanner gantry rotation period of 0.4 s (0.2 s prediction time step), the absolute error was relatively small, 0.06 +/- 0.02 s at peak inhalation and 0.05 +/- 0.04 s at peak exhalation. The application of the ARMA model for prospective pulmonary gating has been demonstrated.

Feasibility study of multi-pass respiratory-gated helical tomotherapy of a moving target via binary MLC closure

Gated radiotherapy of lung lesions is particularly complex for helical tomotherapy, due to the simultaneous motions of its three subsystems (gantry, couch and collimator). We propose a new way to implement gating for helical tomotherapy, namely multi-pass respiratory gating. In this method, gating is achieved by delivering only the beam projections that occur within a respiratory gating window, while blocking the rest of the beam projections by fully closing all collimator leaves. Due to the continuous couch motion, the planned beam projections must be delivered over multiple passes of radiation deliveries. After each pass, the patient couch is reset to its starting position, and the treatment recommences at a different phase of tumour motion to 'fill in' the previously blocked beam projections. The gating process may be repeated until the plan dose is delivered (full gating), or halted after a certain number of passes, with the entire remaining dose delivered in a final pass without gating (partial gating). The feasibility of the full gating approach was first tested for sinusoidal target motion, through experimental measurements with film and computer simulation. The optimal gating parameters for full and partial gating methods were then determined for various fractionation schemes through computer simulation, using a patient respiratory waveform. For sinusoidal motion, the PTV dose deviations of -29 to 5% observed without gating were reduced to range from -1 to 3% for a single fraction, with a 4 pass full gating. For a patient waveform, partial gating required fewer passes than full gating for all fractionation schemes. For a single fraction, the maximum allowed residual motion was only 4 mm, requiring large numbers of passes for both full (12) and partial (7 + 1) gating methods. The number of required passes decreased significantly for 3 and 30 fractions, allowing residual motion up to 7 mm. Overall, the multi-pass gating technique was shown to be a promising way to reduce the impact of lung tumour motion during helical tomotherapy.

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.

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.

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.

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

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

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.

A comparison between amplitude sorting and phase-angle sorting using external respiratory measurement for 4D CT

Respiratory motion can cause significant dose delivery errors in conformal radiation therapy for thoracic and upper abdominal tumors. Four-dimensional computed tomography (4D CT) has been proposed to provide the image data necessary to model tumor motion and consequently reduce these errors. The purpose of this work was to compare 4D CT reconstruction methods using amplitude sorting and phase angle sorting. A 16-slice CT scanner was operated in ciné mode to acquire 25 scans consecutively at each couch position through the thorax. The patient underwent synchronized external respiratory measurements. The scans were sorted into 12 phases based, respectively, on the amplitude and direction (inhalation or exhalation) or on the phase angle (0-360 degrees) of the external respiratory signal. With the assumption that lung motion is largely proportional to the measured respiratory amplitude, the variation in amplitude corresponds to the variation in motion for each phase. A smaller variation in amplitude would associate with an improved reconstructed image. Air content, defined as the amount of air within the lungs, bronchi, and trachea in a 16-slice CT segment and used by our group as a surrogate for internal motion, was correlated to the respiratory amplitude and phase angle throughout the lungs. For the 35 patients who underwent quiet breathing, images (similar to those used for treatment planning) and animations (used to display respiratory motion) generated using amplitude sorting displayed fewer reconstruction artifacts than those generated using phase angle sorting. The variations in respiratory amplitude were significantly smaller (P < 0.001) with amplitude sorting than those with phase angle sorting. The subdivision of the breathing cycle into more (finer) phases improved the consistency in respiratory amplitude for amplitude sorting, but not for phase angle sorting. For 33 of the 35 patients, the air content showed significantly improved (P < 0.001) correlation with the respiratory amplitude than with the phase angle, suggesting a stronger relationship between internal motion and amplitude. Overall, amplitude sorting performed better than phase angle sorting for 33 of the 35 patients and equally well for two patients who were immobilized with a stereotactic body frame and an abdominal compression plate.

Real-time respiration monitoring using the radiotherapy treatment beam and four-dimensional computed tomography (4DCT)—a conceptual study

Real-time knowledge of intra-fraction motion, such as respiration, is essential for four-dimensional (4D) radiotherapy. Surrogate-based and internal-fiducial-based methods may suffer from one or many drawbacks such as false correlation, being invasive, delivering extra patient radiation, and requiring complicated hardware and software development and implementation. In this paper we develop a simple non-surrogate, non-invasive method to monitor respiratory motion during radiotherapy treatments in real time. This method directly utilizes the treatment beam and thus imposes no additional radiation to the patient. The method requires a pre-treatment 4DCT and a real-time detector system. The method combines off-line processes with on-line processes. The off-line processes include 4DCT imaging and pre-calculating detector signals at each phase of the 4DCT based on the planned fluence map and the detector response function. The on-line processes include measuring detector signal from the treatment beam, and correlating the measured detector signal with the pre-calculated signals. The respiration phase is determined as the position of peak correlation. We tested our method with extensive simulations based on a TomoTherapy machine and a 4DCT of a lung cancer patient. Three types of simulations were implemented to mimic the clinical situations. Each type of simulation used three different TomoTherapy delivery sinograms, each with 800 to 1000 projections, as input fluences. Three arbitrary breathing patterns were simulated and two dose levels, 2 Gy/fraction and 2 cGy/fraction, were used for simulations to study the robustness of this method against detector quantum noise. The algorithm was used to determine the breathing phases and this result was compared with the simulated breathing patterns. For the 2 Gy/fraction simulations, the respiration phases were accurately determined within one phase error in real time for most projections of the treatment, except for a few projections at the start and end of the treatment in which beam intensities were extremely low. At 2 cGy/fraction dose level, the method can still determine the respiration phase very well with less than 10% of projections having more than two phases (approximately 1 s) error. This technique can also be applied in other delivery systems such as orthogonal x-ray systems, although in those cases it would entail the delivery of additional non-treatment radiation.

Free-Breathing Three-Dimensional Computed Tomography of the Lung Using Prospective Respiratory Gating

PURPOSE

The aim was to investigate the feasibility and image quality of prospective respiratory gating for 3-D computed tomography (CT) of the lung.

MATERIAL AND METHODS

Eight anesthetized pigs underwent prospectively gated multidetector computed tomography using 2 devices: a charge-coupled device (CCD) camera and a laser sensor. The output signal of both gating devices was connected to the scanner instead of ECG unit. Inspiratory and expiratory images were obtained during "free-breathing" and analyzed in MPR mode for sharpness of bronchi, diaphragm and lung using a 4-point-score (1, excellent to 4, severe artifacts).

RESULTS

The CCD camera worked in all animals. Using the laser sensor, only 50% of expiratory scans could be acquired. All acquired images showed excellent sharpness (CCD camera vs. laser sensor) for trachea (1.1 +/- 0.3 vs. 1.3 +/- 0.5), bronchi (1.4 +/- 0.7 vs. 1.8 +/- 0.6), lung fissures (1.0 vs. 1.1 +/- 0.3), and lung parenchyma (1.0 +/- 0.2 vs. 1.4 +/- 0.6), and minor to major artifacts for diaphragm (1.5 +/- 0.8 vs. 2.0 +/- 1.0, P < 0.05) and pericardial lung structures (1.9 +/- 0.7 vs. 2.3 +/- 0.5).

CONCLUSION

High image quality for inspiratory and expiratory scans was achieved by free-breathing 3-D CT of the lung using noncontact prospective respiratory gating.

Novel breathing motion model for radiotherapy

PURPOSE

An accurate model of breathing motion under quiet respiration is desirable to obtain the most accurate and conformal dose distributions for mobile lung cancer lesions. On the basis of recent lung motion measurements and the physiologic functioning of the lungs, we have determined that the motion of lung and lung tumor tissues can be modeled as a function of five degrees of freedom, the position of the tissues at a user-specified reference breathing phase, tidal volume and its temporal derivative airflow (tidal volume phase space). Time is an implicit variable in this model.

METHODS AND MATERIALS

To test this hypothesis, a mathematical model of motion was developed that described the motion of objects p in the lungs as linear functions of tidal volume and airflow. The position of an object was described relative to its position -->P0 at the reference tidal volume and zero airflow, and the motion of the object was referenced to this position. Hysteresis behavior was hypothesized to be caused by pressure imbalances in the lung during breathing and was, in this model, a function of airflow. The motion was modeled as independent tidal volume and airflow displacement vectors, with the position of the object at time t equal to the vector sum -->rP(t) = -->rv(t) + -->rf(t) where -->rv(t) and -->rf(t) were displacement vectors with magnitudes approximated by linear functions of the tidal volume and airflow. To test this model, we analyzed five-dimensional CT scans (CT scans acquired with simultaneous real-time monitoring of the tidal volume) of 4 patients. The scans were acquired throughout the lungs, but the trajectories were analyzed in the couch positions near the diaphragm. A template-matching algorithm was implemented to identify the positions of the points throughout the 15 scans. In total, 76 points throughout the 4 patients were tracked. The lateral motion of these points was minimal; thus, the model was described in two spatial dimensions, with a total of six parameters necessary to describe the 30 degrees of freedom inherent in the 15 positions.

RESULTS

For the 76 evaluated points, the average discrepancy (the distance between the measured and prediction positions) of the 15 locations for each tracked point was 0.75 +/- 0.25 mm, with an average maximal discrepancy of 1.55 +/- 0.54 mm. The average discrepancy was also tabulated as a fraction of the breathing motion. Discrepancies of <10% and 15% of the overall motion occurred in 73% and 95% of the tracked points, respectively.

CONCLUSION

The motion tracking algorithms are being improved and automated to provide more motion data to test the models. This may allow a measurement of the motion-fitting parameters throughout the lungs. If the parameters vary smoothly, interpolation may be possible, yielding a continuous mathematical model of the breathing motion throughout the lungs. The utility of the model will depend on its stability as a function of time. If the model is only robust during the measurement session, it may be useful for determining lung function. If it is robust for weeks, it may be useful for treatment planning and gating of lung treatments. The use of tidal volume phase space for characterizing breathing motion appears to have provided, for the first time, the potential for a patient-specific mathematical model of breathing motion.

Reduction of motion blurring artifacts using respiratory gated CT in sinogram space: A quantitative evaluation

Techniques have been developed for reducing motion blurring artifacts by using respiratory gated computed tomography (CT) in sinogram space and quantitatively evaluating the artifact reduction. A synthetic sinogram was built from multiple scans intercepting a respiratory gating window. A gated CT image was then reconstructed using the filtered back-projection algorithm. Wedge phantoms, developed for quantifying the motion artifact reduction, were scanned while being moved using a computer-controlled linear stage. The resulting artifacts appeared between the high and low density regions as an apparent feature with a Hounsfield value that was the average of the two regions. A CT profile through these regions was fit using two error functions, each modeling the partial-volume averaging characteristics for the unmoving phantom. The motion artifact was quantified by determining the apparent distance between the two functions. The blurring artifact had a linear relationship with both the speed and the tangent of the wedge angles. When gating was employed, the blurring artifact was reduced systematically at the air-phantom interface. The gated image of phantoms moving at 20 mm/s showed similar blurring artifacts as the nongated image of phantoms moving at 10 mm/s. Nine patients were also scanned using the synchronized respiratory motion technique. Image artifacts were evaluated in the diaphragm, where high contrast interfaces intercepted the imaging plane. For patients, this respiratory gating technique reduced the blurring artifacts by 9%-41% at the lung-diaphragm interface.