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

An important consideration in four-dimensional CT scanning is the selection of a breathing metric for sorting the CT data and modeling internal motion. This study compared two noninvasive breathing metrics, spirometry and abdominal height, against internal air content, used as a surrogate for internal motion. Both metrics were shown to be accurate, but the spirometry showed a stronger and more reproducible relationship than the abdominal height in the lung. The abdominal height was known to be affected by sensor placement and patient positioning while the spirometer exhibited signal drift. By combining these two, a normalization of the drift-free metric to tidal volume may be generated and the overall metric precision may be improved.     

A method for the reconstruction of four-dimensional synchronized CT scans acquired during free breathing

Breathing motion is a significant source of error in radiotherapy treatment planning for the thorax and upper abdomen. Accounting for breathing motion has a profound effect on the size of conformal radiation portals employed in these sites. Breathing motion also causes artifacts and distortions in treatment planning computed tomography (CT) scans acquired during free breathing and also causes a breakdown of the assumption of the superposition of radiation portals in intensity-modulated radiation therapy, possibly leading to significant dose delivery errors. Proposed voluntary and involuntary breath-hold techniques have the potential for reducing or eliminating the effects of breathing motion, however, they are limited in practice, by the fact that many lung cancer patients cannot tolerate holding their breath. We present an alternative solution to accounting for breathing motion in radiotherapy treatment planning, where multislice CT scans are collected simultaneously with digital spirometry over many free breathing cycles to create a four-dimensional (4-D) image set, where tidal lung volume is the additional dimension. An analysis of this 4-D data leads to methods for digital-spirometry, based elimination or accounting of breathing motion artifacts in radiotherapy treatment planning for free breathing patients. The 4-D image set is generated by sorting free-breathing multislice CT scans according to user-defined tidal-volume bins. A multislice CT scanner is operated in the ciné mode, acquiring 15 scans per couch position, while the patient undergoes simultaneous digital-spirometry measurements. The spirometry is used to retrospectively sort the CT scans by their correlated tidal lung volume within the patient's normal breathing cycle. This method has been prototyped using data from three lung cancer patients. The actual tidal lung volumes agreed with the specified bin volumes within standard deviations ranging between 22 and 33 cm3. An analysis of sagittal and coronal images demonstrated relatively small (<1 cm) motion artifacts along the diaphragm, even for tidal volumes where the rate of breathing motion is greatest. While still under development, this technology has the potential for revolutionizing the radiotherapy treatment planning for the thorax and upper abdomen.     

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.     

Tracking lung tissue motion and expansion/compression with inverse consistent image registration and spirometry

Breathing motion is one of the major limiting factors for reducing dose and irradiation of normal tissue for conventional conformal radiotherapy. This paper describes a relationship between tracking lung motion using spirometry data and image registration of consecutive CT image volumes collected from a multislice CT scanner over multiple breathing periods. Temporal CT sequences from 5 individuals were analyzed in this study. The couch was moved from 11 to 14 different positions to image the entire lung. At each couch position, 15 image volumes were collected over approximately 3 breathing periods. It is assumed that the expansion and contraction of lung tissue can be modeled as an elastic material. Furthermore, it is assumed that the deformation of the lung is small over one-fifth of a breathing period and therefore the motion of the lung can be adequately modeled using a small deformation linear elastic model. The small deformation inverse consistent linear elastic image registration algorithm is therefore well suited for this problem and was used to register consecutive image scans. The pointwise expansion and compression of lung tissue was measured by computing the Jacobian of the transformations used to register the images. The logarithm of the Jacobian was computed so that expansion and compression of the lung were scaled equally. The log-Jacobian was computed at each voxel in the volume to produce a map of the local expansion and compression of the lung during the breathing period. These log-Jacobian images demonstrate that the lung does not expand uniformly during the breathing period, but rather expands and contracts locally at different rates during inhalation and exhalation. The log-Jacobian numbers were averaged over a cross section of the lung to produce an estimate of the average expansion or compression from one time point to the next and compared to the air flow rate measured by spirometry. In four out of five individuals, the average log-Jacobian value and the air flow rate correlated well (R2 = 0.858 on average for the entire lung). The correlation for the fifth individual was not as good (R2 = 0.377 on average for the entire lung) and can be explained by the small variation in tidal volume for this individual. The correlation of the average log-Jacobian value and the air flow rate for images near the diaphragm correlated well in all five individuals (R2 = 0.943 on average). These preliminary results indicate a strong correlation between the expansion/compression of the lung measured by image registration and the air flow rate measured by spirometry. Predicting the location, motion, and compression/expansion of the tumor and normal tissue using image registration and spirometry could have many important benefits for radiotherapy treatment. These benefits include reducing radiation dose to normal tissue, maximizing dose to the tumor, improving patient care, reducing treatment cost, and increasing patient throughput.     

Acquiring 4D thoracic CT scans using a multislice helical method

Respiratory motion degrades anatomic position reproducibility during imaging, necessitates larger margins during radiotherapy planning and causes errors during radiation delivery. Computed tomography (CT) scans acquired synchronously with the respiratory signal can be used to reconstruct 4D CT scans, which can be employed for 4D treatment planning to explicitly account for respiratory motion. The aim of this research was to develop, test and clinically implement a method to acquire 4D thoracic CT scans using a multislice helical method. A commercial position-monitoring system used for respiratory-gated radiotherapy was interfaced with a third generation multislice scanner. 4D cardiac reconstruction methods were modified to allow 4D thoracic CT acquisition. The technique was tested on a phantom under different conditions: stationary, periodic motion and non-periodic motion. 4D CT was also implemented for a lung cancer patient with audio-visual breathing coaching. For all cases, 4D CT images were successfully acquired from eight discrete breathing phases, however, some limitations of the system in terms of respiration reproducibility and breathing period relative to scanner settings were evident. Lung mass for the 4D CT patient scan was reproducible to within 2.1% over the eight phases, though the lung volume changed by 20% between end inspiration and end expiration (870 cm3). 4D CT can be used for 4D radiotherapy, respiration-gated radiotherapy, 'slow' CT acquisition and tumour motion studies.     

An optical flow based method for improved reconstruction of 4D CT data sets acquired during free breathing

Respiratory motion degrades anatomic position reproducibility and leads to issues affecting image acquisition, treatment planning, and radiation delivery. Four-dimensional (4D) computer tomography (CT) image acquisition can be used to measure the impact of organ motion and to explicitly account for respiratory motion during treatment planning and radiation delivery. Modern CT scanners can only scan a limited region of the body simultaneously and patients have to be scanned in segments consisting of multiple slices. A respiratory signal (spirometer signal or surface tracking) is used to reconstruct a 4D data set by sorting the CT scans according to the couch position and signal coherence with predefined respiratory phases. But artifacts can occur if there are no acquired data segments for exactly the same respiratory state for all couch positions. These artifacts are caused by device-dependent limitations of gantry rotation, image reconstruction times and by the variability of the patient's respiratory pattern. In this paper an optical flow based method for improved reconstruction of 4D CT data sets from multislice CT scans is presented. The optical flow between scans at neighboring respiratory states is estimated by a non-linear registration method. The calculated velocity field is then used to reconstruct a 4D CT data set by interpolating data at exactly the predefined respiratory phase. Our reconstruction method is compared with the usually used reconstruction based on amplitude sorting. The procedures described were applied to reconstruct 4D CT data sets for four cancer patients and a qualitative and quantitative evaluation of the optical flow based reconstruction method was performed. Evaluation results show a relevant reduction of reconstruction artifacts by our technique. The reconstructed 4D data sets were used to quantify organ displacements and to visualize the abdominothoracic organ motion.     

Gated CT imaging using a free-breathing respiration signal from flow-volume spirometry

Respiration-induced tumor motion is known to cause artifacts on free-breathing spiral CT images used in treatment planning. This leads to inaccurate delineation of target volumes on planning CT images. Flow-volume spirometry has been used previously for breath-holds during CT scans and radiation treatments using the active breathing control (ABC) system. We have developed a prototype by extending the flow-volume spirometer device to obtain gated CT scans using a PQ 5000 single-slice CT scanner. To test our prototype, we designed motion phantoms to compare image quality obtained with and without gated CT scan acquisition. Spiral and axial (nongated and gated) CT scans were obtained of phantoms with motion periods of 3-5 s and amplitudes of 0.5-2 cm. Errors observed in the volume estimate of these structures were as much as 30% with moving phantoms during CT simulation. Application of motion-gated CT with active breathing control reduced these errors to within 5%. Motion-gated CT was then implemented in patients and the results are presented for two clinical cases: lung and abdomen. In each case, gated scans were acquired at end-inhalation, end-exhalation in addition to a conventional free-breathing (nongated) scan. The gated CT scans revealed reduced artifacts compared with the conventional free-breathing scan. Differences of up to 20% in the volume of the structures were observed between gated and free-breathing scans. A comparison of the overlap of structures between the gated and free-breathing scans revealed misalignment of the structures. These results demonstrate the ability of flow-volume spirometry to reduce errors in target volumes via gating during CT imaging.     

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.     

Four-dimensional computed tomography: image formation and clinical protocol

Respiratory motion can introduce significant errors in radiotherapy. Conventional CT scans as commonly used for treatment planning can include severe motion artifacts that result from interplay effects between the advancing scan plane and object motion. To explicitly include organ/target motion in treatment planning and delivery, time-resolved CT data acquisition (4D Computed Tomography) is needed. 4DCT can be accomplished by oversampled CT data acquisition at each slice. During several CT tube rotations projection data are collected in axial cine mode for the duration of the patient's respiratory cycle (plus the time needed for a full CT gantry rotation). Multiple images are then reconstructed per slice that are evenly distributed over the acquisition time. Each of these images represents a different anatomical state during a respiratory cycle. After data acquisition at one couch position is completed, x rays are turned off and the couch advances to begin data acquisition again until full coverage of the scan length has been obtained. Concurrent to CT data acquisition the patient's abdominal surface motion is recorded in precise temporal correlation. To obtain CT volumes at different respiratory states, reconstructed images are sorted into different spatio-temporally coherent volumes based on respiratory phase as obtained from the patient's surface motion. During binning, phase tolerances are chosen to obtain complete volumetric information since images at different couch positions are reconstructed at different respiratory phases. We describe 4DCT image formation and associated experiments that characterize the properties of 4DCT. Residual motion artifacts remain due to partial projection effects. Temporal coherence within resorted 4DCT volumes is dominated by the number of reconstructed images per slice. The more images are reconstructed, the smaller phase tolerances can be for retrospective sorting. From phantom studies a precision of about 2.5 mm for quasiregular motion and typical respiratory periods could be concluded. A protocol for 4DCT scanning was evaluated and clinically implemented at the MGH. Patient data are presented to elucidate how additional patient specific parameters can impact 4DCT imaging.     

4D MR imaging of respiratory organ motion and its variability

This paper describes a method for 4D imaging, which is used to study respiratory organ motion, a key problem in various treatments. Whilst the commonly used imaging methods rely on simplified breathing patterns to acquire one breathing cycle, the proposed method was developed to study irregularities in organ motion during free breathing over tens of minutes. The method does not assume a constant breathing depth or even strict periodicity and does not depend on an external respiratory signal. Time-resolved 3D image sequences were reconstructed by retrospective stacking of dynamic 2D images using internal image-based sorting. The generic method is demonstrated for the liver and for the lung. Quantitative evaluations of the volume consistency show the advantages over one-dimensional measurements for image sorting. Dense deformation fields describing the respiratory motion were estimated from the reconstructed volumes using non-rigid 3D registration. All obtained motion fields showed variations in the range of minutes such as drifts and deformations, which changed both the exhalation position of the liver and the breathing pattern. The obtained motion data are used in proton therapy planning to evaluate dose delivery methodologies with respect to their motion sensitivity. Besides this application, the new possibilities of studying respiratory motion are valuable for other applications such as the evaluation of gating techniques with respect to residual motion.     

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.     

Fusion of respiration-correlated PET and CT scans: correlated lung tumour motion in anatomical and functional scans

Lower lobe lung tumours in particular can move up to 2 cm in the cranio-caudal direction during the respiration cycle. This breathing motion causes image artefacts in conventional free-breathing computed tomography (CT) and positron emission tomography (PET) scanning, rendering delineation of structures for radiotherapy inaccurate. The purpose of this study was to develop a method for four-dimensional (4D) respiration-correlated (RC) acquisition of both CT and PET scans and to develop a framework to fuse these modalities. The breathing signal was acquired using a thermometer in the breathing airflow of the patient. Using this breathing signal, the acquired CT and PET data were grouped to the corresponding respiratory phases, thereby obtaining 4D CT and PET scans. Tumour motion curves were assessed in both image modalities. From these tumour motion curves, the deviation with respect to the mean tumour position was calculated for each phase. The absolute position of the centre of the tumour, relative to the bony anatomy, in the RCCT and gated PET scans was determined. This 4D acquisition and 4D fusion methodology was performed for five patients with lower lobe tumours. The peak-to-peak amplitude range in this sample group was 1-2 cm. The 3D tumour motion curve differed less than 1 mm between PET and CT for all phases. The mean difference in amplitude was less than 1 mm. The position of the centre of the tumour (relative to the bony anatomy) in the RCCT and gated PET scan was similar (difference <1 mm) when no atelectasis was present. Based on these results, we conclude that the method described in this study allows for accurate quantification of tumour motion in CT and PET scans and yields accurate respiration-correlated 4D anatomical and functional information on the tumour region.     

Dynamic ventilation imaging from four-dimensional computed tomography

A novel method for dynamic ventilation imaging of the full respiratory cycle from four-dimensional computed tomography (4D CT) acquired without added contrast is presented. Three cases with 4D CT images obtained with respiratory gated acquisition for radiotherapy treatment planning were selected. Each of the 4D CT data sets was acquired during resting tidal breathing. A deformable image registration algorithm mapped each (voxel) corresponding tissue element across the 4D CT data set. From local average CT values, the change in fraction of air per voxel (i.e. local ventilation) was calculated. A 4D ventilation image set was calculated using pairs formed with the maximum expiration image volume, first the exhalation then the inhalation phases representing a complete breath cycle. A preliminary validation using manually determined lung volumes was performed. The calculated total ventilation was compared to the change in contoured lung volumes between the CT pairs (measured volume). A linear regression resulted in a slope of 1.01 and a correlation coefficient of 0.984 for the ventilation images. The spatial distribution of ventilation was found to be case specific and a 30% difference in mass-specific ventilation between the lower and upper lung halves was found. These images may be useful in radiotherapy planning.     

4D medical image computing and visualization of lung tumor mobility in spatio-temporal CT image data

The development of 4D CT imaging has introduced the possibility of measuring breathing motion of tumors and inner organs. Conformal thoracic radiation therapy relies on a quantitative understanding of the position of lungs, lung tumors, and other organs during radiation delivery. Using 4D CT data sets, medical image computing and visualization methods were developed to visualize different aspects of lung and lung tumor mobility during the breathing cycle and to extract quantitative motion parameters. A non-linear registration method was applied to estimate the three-dimensional motion field and to compute 3D point trajectories. Specific visualization techniques were used to display the resulting motion field, the tumor's appearance probabilities during a breathing cycle as well as the volume covered by the moving tumor. Furthermore, trajectories of the tumor center-of-mass and organ specific landmarks were computed for the quantitative analysis of tumor and organ motion. The analysis of 4D data sets of seven patients showed that tumor mobility differs significantly between the patients depending on the individual breathing pattern and tumor location.     

Quantitation of respiratory motion during 4D-PET/CT acquisition

We report on the variability of the respiratory motion during 4D-PET/CT acquisition. The respiratory motion for five lung cancer patients was monitored by tracking external markers placed on the abdomen. CT data were acquired over an entire respiratory cycle at each couch position. The x-ray tube status was recorded by the tracking system, for retrospective sorting of the CT data as a function of respiration phase. Each respiratory cycle was sampled in ten equal bins. 4D-PET data were acquired in gated mode, where each breathing cycle was divided into ten 500 ms bins. For both CT and PET acquisition, patients received audio prompting to regularize breathing. The 4D-CT and 4D-PET data were then correlated according to their respiratory phases. The respiratory periods, and average amplitude within each phase bin, acquired in both modality sessions were then analyzed. The average respiratory motion period during 4D-CT was within 18% from that in the 4D-PET sessions. This would reflect up to 1.8% fluctuation in the duration of each 4D-CT bin. This small uncertainty enabled good correlation between CT and PET data, on a phase-to-phase basis. Comparison of the average-amplitude within the respiration trace, between 4D-CT and 4D- PET, on a bin-by-bin basis show a maximum deviation of approximately 15%. This study has proved the feasibility of performing 4D-PET/CT acquisition. Respiratory motion was in most cases consistent between PET and CT sessions, thereby improving both the attenuation correction of PET images, and co-registration of PET and CT images. On the other hand, in two patients, there was an increased partial irregularity in their breathing motion, which would prevent accurately correlating the corresponding PET and CT images.     

Geometrical differences in target volumes between slow CT and 4D CT imaging in stereotactic body radiotherapy for lung tumors in the upper and middle lobe

Since stereotactic body radiotherapy (SBRT) was started for patients with lung tumor in 1998 in our institution, x-ray fluoroscopic examination and slow computed tomography (CT) scan with a rotation time of 4 s have been routinely applied to determine target volumes. When lung tumor motion observed with x-ray fluoroscopy is larger than 8 mm, diaphragm control (DC) is used to reduce tumor motion during respiration. After the installation of a four-dimensional (4D) CT scanner in 2006, 4D CT images have been supplementarily acquired to determine target volumes. It was found that target volumes based on slow CT images were substantially different from those on 4D CT images, even for patients with lung tumor motion no larger than 8 mm. Although slow CT scan might be expected to fare well for lung tumors with motion range of 8 mm or less, the potential limitations of slow CT scan are unknown. The purpose of this study was to evaluate the geometrical differences in target volumes between slow CT and 4D CT imaging for lung tumors with motion range no larger than 8 mm in the upper and middle lobe. Of the patients who underwent SBR between October 2006 and April 2008, 32 patients who had lung tumor with motion range no larger than 8 mm and did not need to use DC were enrolled in this study. Slow CT and 4D CT images were acquired under free breathing for each patient. Target volumes were manually delineated on slow CT images (TV(slow CT)). Gross tumor volumes were also delineated on each of the 4D CT volumes and their union (TV(4D CT)) was constructed. Volumetric and statistical analyses were performed for each patient. The mean +/- standard deviation (S.D.) of TV(slow CT)/TV(4D CT) was 0.75 +/- 0.17 (range, 0.38-1.10). The difference between sizes of TV(slow CT) and TV(4D CT) was not statistically significant (P = 0.096). A mean of 8% volume of TV(slow CT) was not encompassed in TV(4D CT) (mean +/- S.D. = 0.92 +/- 0.07). The patients were separated into two groups to test whether the quality of target delineation on slow CT scans depends on respiratory periods below or above the CT rotation time of 4 s. No significant difference was observed between these groups (P = 0.229). Even lung tumors with motion range no larger than 8 mm might not be accurately depicted on slow CT images. When only a single slow CT scan was used for lung tumors with motion range of 8 mm or less, 95% confidence values for additional margins for TV(slow CT) to encompass TV(4D CT) were 4.0, 5.4, 4.9, 5.1, 1.8, and 1.7 mm for lateral, medial, ventral, dorsal, cranial, and caudal directions, respectively.     

Four-dimensional (4D) PET/CT imaging of the thorax

We have reported in our previous studies on the methodology, and feasibility of 4D-PET (Gated PET) acquisition, to reduce respiratory motion artifact in PET imaging of the thorax. In this study, we expand our investigation to address the problem of respiration motion in PET/CT imaging. The respiratory motion of four lung cancer patients were monitored by tracking external markers placed on the thorax. A 4D-CT acquisition was performed using a "step-and-shoot" technique, in which computed tomography (CT) projection data were acquired over a complete respiratory cycle at each couch position. The period of each CT acquisition segment was time stamped with an "x-ray ON" signal, which was recorded by the tracking system. 4D-CT data were then sorted into 10 groups, according to their corresponding phase of the breathing cycle. 4D-PET data were acquired in the gated mode, where each breathing cycle was divided into ten 0.5 s bins. For both CT and PET acquisitions, patients received audio prompting to regularize breathing. The 4D-CT and 4D-PET data were then correlated according to respiratory phase. The effect of 4D acquisition on improving the co-registration of PET and CT images, reducing motion smearing, and consequently increase the quantitation of the SUV, were investigated. Also, quantitation of the tumor motions in PET, and CT, were studied and compared. 4D-PET with matching phase 4D-CTAC showed an improved accuracy in PET-CT image co-registration of up to 41%, compared to measurements from 4D-PET with clinical-CTAC. Gating PET data in correlation with respiratory motion reduced motion-induced smearing, thereby decreasing the observed tumor volume, by as much as 43%. 4D-PET lesions volumes showed a maximum deviation of 19% between clinical CT and phase- matched 4D-CT attenuation corrected PET images. In CT, 4D acquisition resulted in increasing the tumor volume in two patients by up to 79%, and decreasing it in the other two by up to 35%. Consequently, these corrections have yielded an increase in the measured SUV by up to 16% over the clinical measured SUV, and 36% over SUV's measured in 4D-PET with clinical-CT Attenuation Correction (CTAC) SUV's. Quantitation of the maximum tumor motion amplitude, using 4D-PET and 4D-CT, showed up to 30% discrepancy between the two modalities. We have shown that 4D PET/CT is clinically a feasible method, to correct for respiratory motion artifacts in PET/CT imaging of the thorax. 4D PET/CT acquisition can reduce smearing, improve the accuracy in PET-CT co-registration, and increase the measured SUV. This should result in an improved tumor assessment for patients with lung malignancies.     

The susceptibility of IMRT dose distributions to intrafraction organ motion: an investigation into smoothing filters derived from four dimensional computed tomography data

This study investigated the sensitivity of static planning of intensity-modulated beams (IMBs) to intrafraction deformable organ motion and assessed whether smoothing of the IMBs at the treatment-planning stage can reduce this sensitivity. The study was performed with a 4D computed tomography (CT) data set for an IMRT treatment of a patient with liver cancer. Fluence profiles obtained from inverse-planning calculations on a standard reference CT scan were redelivered on a CT scan from the 4D data set at a different part of the breathing cycle. The use of a nonrigid registration model on the 4D data set additionally enabled detailed analysis of the overall intrafraction motion effects on the IMRT delivery during free breathing. Smoothing filters were then applied to the beam profiles within the optimization process to investigate whether this could reduce the sensitivity of IMBs to intrafraction organ motion. In addition, optimal fluence profiles from calculations on each individual phase of the breathing cycle were averaged to mimic the convolution of a static dose distribution with a motion probability kernel and assess its usefulness. Results from nonrigid registrations of the CT scan data showed a maximum liver motion of 7 mm in superior-inferior direction for this patient. Dose-volume histogram (DVH) comparison indicated a systematic shift when planning treatment on a motion-frozen, standard CT scan but delivering over a full breathing cycle. The ratio of the dose to 50% of the normal liver to 50% of the planning target volume (PTV) changed up to 28% between different phases. Smoothing beam profiles with a median-window filter did not overcome the substantial shift in dose due to a difference in breathing phase between planning and delivery of treatment. Averaging of optimal beam profiles at different phases of the breathing cycle mainly resulted in an increase in dose to the organs at risk (OAR) and did not seem beneficial to compensate for organ motion compared with using a large margin. Additionally, the results emphasized the need for 4D CT scans when aiming to reduce the internal margin (IM). Using only a single planning scan introduces a systematic shift in the dose distribution during delivery. Smoothing beam profiles either based on a single scan or over the different breathing phases was not beneficial for reducing this shift.     

Planning lung radiotherapy using 4D CT data and a motion model

This work is a feasibility study to use a four-dimensional computed tomography (4D CT) dataset generated by a continuous motion model for treatment planning in lung radiotherapy. The model-based 4D CT data were derived from multiple breathing cycles. Four patients were included in this retrospective study. Treatment plans were optimized at end-exhale for each patient and the effect of respiratory motion on the dose delivery investigated. The accuracy of the delivered dose as determined by the number of intermediate respiratory phases used for the calculation was considered. The time-averaged geometry of the anatomy representing the mid-ventilation phase of the breathing cycle was generated using the motion model and a treatment plan was optimized for this phase for one patient. With respiratory motion included, the mid-ventilation plan achieved better target coverage than the plan optimized at end-exhale when standard margins were used to expand the clinical target volume (CTV) to planning target volume (PTV). Using a margin to account for set-up uncertainty only, resulted in poorer target coverage and healthy tissue sparing. For this patient cohort, the results suggest that conventional three-dimensional treatment planning was sufficient to maintain target coverage despite respiratory motion. The motion model has proved a useful tool in 4D treatment planning.     

Image registration of a moving target phantom with helical tomotherapy: effect of the CT acquisition technique and action level proposal

This study aims to quantify the effects of target motion and resultant motion artifacts in planning and megavoltage CT (MVCT) studies on the automatic registration processes of helical tomotherapy. Clinical and experimental data were used to derive an action level for patient repositioning on helical tomotherapy. Planning CT studies of a respiratory motion phantom were acquired using conventional and four-dimensional CT (4D CT) techniques. MVCT studies were acquired on helical tomotherapy in the presence and absence of target motion and were registered with different planning CT studies. The residual errors of the registration process were calculated from the registration values to quantify the ability of the process to detect 5 or 10 mm translations of the phantom in two directions. Twenty-seven registration combinations of MVCT inter-slice spacing, technique and resolution were investigated. The residual errors were used as an estimate of the localization error of the registration process, and the accuracy of couch repositioning was determined from couch position measurements during 866 treatment fractions. These two parameters were used to calculate the action level for patient repositioning on helical tomotherapy. Automatic registration of an MVCT study with 0% breathing phase, average intensity and maximum intensity 4D CT projections did not differ from that of an MVCT study with a conventional planning CT. Motion artifacts in the MVCT or planning CT studies changed the accuracy of the automatic registration process by less than 2.0%. The action level for patient repositioning using MVCT studies of 6 mm inter-slice spacing was determined to be 0.7, 1.1 and 0.6 mm in the x-, y- and z-directions, respectively. These action levels have the greatest effect on treatments for disease sites in the brain.