Michelle M. Nystrom

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 patients 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.

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

We have developed a four-dimensional computed tomography (4D CT) technique for mapping breathing motion in radiotherapy treatment planning. A multislice CT scanner (1.5 mm slices) operated in ciné mode was used to acquire 12 contiguous slices in each couch position for 15 consecutive scans (0.5 s rotation, 0.25 s between scans) while the patient underwent simultaneous quantitative spirometry measurements to provide a sorting metric. The spirometry-sorted scans were used to reconstruct a 4D data set. A critical factor for 4D CT is quantifying the reconstructed data set quality which we measure by correlating the metric used relative to internal-object motion. For this study, the internal air content within the lung was used as a surrogate for internal motion measurements. Thresholding and image morphological operations were applied to delineate the air-containing tissues (lungs, trachea) from each CT slice. The Hounsfield values were converted to the internal air content (V). The relationship between the air content and spirometer-measured tidal volume (v) was found to be quite linear throughout the lungs and was used to estimate the overall accuracy and precision of tidal volume-sorted 4D CT. Inspection of the CT-scan air content as a function of tidal volume showed excellent correlations (typically r>0.99) throughout the lung volume. Because of the discovered linear relationship, the ratio of internal air content to tidal volume was indicative of the fraction of air change in each couch position. Theoretically, due to air density differences within the lung and in room, the sum of these ratios would equal 1.11. For 12 patients, the mean value was 1.08 +/- 0.06, indicating the high quality of spirometry-based image sorting. The residual of a first-order fit between v and V was used to estimate the process precision. For all patients, the precision was better than 8%, with a mean value of 5.1% +/- 1.9%. This quantitative analysis highlights the value of using spirometry as the metric in sorting CT scans. The 4D reconstruction provides the CT data required to measure the three-dimensional trajectory of tumor and lung tissue during free breathing.

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 semi-automatic method for peak and valley detection in free-breathing respiratory waveforms

The existing commercial software often inadequately determines respiratory peaks for patients in respiration correlated computed tomography. A semi-automatic method was developed for peak and valley detection in free-breathing respiratory waveforms. First the waveform is separated into breath cycles by identifying intercepts of a moving average curve with the inspiration and expiration branches of the waveform. Peaks and valleys were then defined, respectively, as the maximum and minimum between pairs of alternating inspiration and expiration intercepts. Finally, automatic corrections and manual user interventions were employed. On average for each of the 20 patients, 99% of 307 peaks and valleys were automatically detected in 2.8 s. This method was robust for bellows waveforms with large variations.

Automated breathing motion tracking for 4D computed tomography

4D-CT is being developed to provide breathing motion information for radiation therapy treatment planning. Potential applications include optimization of intensity-modulated beams in the presence of breathing motion and intra-fraction target volume margin determination for conformal therapy. A major challenge of this process is the determination of the internal motion (trajectories) from the 4D CT data. Manual identification and tracking of internal landmarks is impractical. For example, in a single couch position, 512 /spl times/ 512 /spl times/ 12 pixel CT scans contains 3.1/spl times/10/sup 5/ voxels. If 15 of these scans are acquired throughout the breathing cycle, there are almost 47 million voxels to evaluate necessitating automation of the registration process. The natural high contrast between bronchi, vessels, other lung tissue offers an excellent opportunity to develop automated deformable registration techniques. We have been investigating the use motion compensated temporal smoothing using optical flow for this purpose. Optical flow analysis uses the CT intensity and temporal (in our case tidal volume) gradients to estimate the motion trajectories. The algorithm is applied to 3D image datasets reconstructed at different percentiles of tidal volumes. The trajectories can be used to interpolate CT datasets between tidal volumes.

Automated 4D lung computed tomography reconstruction during free breathing for conformal radiation therapy

We are developing 4D-CT to provide breathing motion information (trajectories) for radiation therapy treatment planning of lung cancer. Potential applications include optimization of intensity-modulated beams in the presence of breathing motion and intra-fraction target volume margin determination for conformal therapy. The images are acquired using a multi-slice CT scanner while the patient undergoes simultaneous quantitative spirometry. At each couch position, the CT scanner is operated in ciné mode and acquires up to 15 scans of 12 slices each. Each CT scan is associated with the measured tidal volume for retrospective reconstruction of 3D CT scans at arbitrary tidal volumes. The specific tasks of this project involves the development of automated registration of internal organ motion (trajectories) during breathing. A modified least-squares based optical flow algorithm tracks specific features of interest by modifying the eigenvalues of gradient matrix (gradient structural tensor). Good correlations between the measured motion and spirometry-based tidal volume are observed and evidence of internal hysteresis is also detected.

SU‐FF‐J‐04: A Computerized Method for Peak and Valley Detection in Respiratory Waveforms Without Flow Measurement

Purpose: To develop a computerized method for reliable peak (end inspiration) and valley (end expiration) detection in respiratory waveforms for gated radiotherapy and 4D CT. Method and Materials: A computerized method for peak (end inspiration) and valley (end expiration) detection in respiratory waveforms without flow measurement is described. The respiratory period T was estimated by applying a Fast Fourier Transform. The intercepts of the respiratory waveform with a moving average curve were determined with an averaging width of 2T. Peaks and valleys were defined respectively as the maximum and minimum between pairs of interwoven intercepts. While this method worked well the majority of the time, both automatic corrections and manual user interventions were employed to correct errors and adjust the results. Results: The method was implemented in MATLAB on a PC with a 3.0 GHz Pentium IV CPU and 2.0 GB RAM. On average, the respiratory waveform was 575.3 s long and contained a total of 307 peaks and valleys. For each patient, 99% of all peaks and valleys were correctly located by the automatic algorithm in 2.8 s. Only three (1%) points required manual user adjustment. A user spent 66.8 s for reviewing, and manually adding or deleting points. For nine of the 20 patients, all peaks and valleys were automatically detected. The high efficiency of the automatic algorithm is clear. Conclusion: The results demonstrated that this method was reliable and efficient for peak detection in respirator waveforms with noise and large variations in baseline level, amplitude and period.

TU-D-J-6C-07: A Method for Acquiring PET Images Without Breathing Motion Artifacts

Purpose: PET images typically require many minutes to acquire, so breathing motion can cause the tumor shape to be inaccurately reconstructed. Using a multislice PET/CT scanner to quantitatively acquire 4-dimensional computed tomography (4DCT) and gated PET, a breathing motion-artifact free PET image study can be generated achieving statistical precision as if the patient underwent breath-hold throughout the PET-scan procedure. Method and Materials: The motion of lung structures, including tumors, is mapped by 4DCT as a function of tidal volume using our novel 5-dimensional breathing motion model. The PET scan is performed using spirometry-measured tidal volume and the PET data is stored using list mode. The user selects the phase of breathing for which the PET image is to be reconstructed and the corresponding list-mode data is extracted to reconstruct an image. The gated-CT scans provide quantitative attenuation correction that is accurate with respect to breathing phase. This process is repeated for all breathing phases and using the trajectory maps the reconstructed images are deformably mapped to a reference-breathing phase. This process was tested using a computer-controlled phantom moving in a motion pattern mimicking breathing motion. Three target spheres (1cm, 2cm, 3cm diameters) filled with 11C solution were embedded into a cylindrical phantom filled with 18F solution to provide a series of relative target-to-background activities. Results: Without gating, the target spheres were deformed and the low target-to-background small-target image was lost in the background noise. These problems were alleviated when the images were mapped to a common motion “phase”. Conclusion: The phantom data showed that motion artifacts can be quantitatively removed yielding a high statistics image dataset without compromising PET acquisition time or patient dose. This process will provide the radiation-oncology clinician with PET images having unrivaled spatial resolution and sensitivity for target definition in the thoracic and abdominal regions.

SU‐FF‐J‐121: Patterns of Intraabdominal Organ Motion as Measured by Quantitative 4D CT

Purpose: Many clinics are investigating the use of IMRT for intraabdominal malignancies. Respiratory motion must be measured to determine optimal target and normal tissue margins. The purpose of this study was to quantify the movement of abdominal organs with non‐coached respiration.Method and Materials: Ten patients with hepatobiliary malignancies underwent quantitative spirometry during a multislice‐CT following standard helical‐CT simulation (Philips Brilliance 16‐slice). Abdominal CTimages were reconstructed by tidal volume, capturing end‐expiration, mid‐inspiration, end inspiration, and mid‐expiration. Each CTreconstruction was fused with the standard helical‐CT simulation. The liver, spleen, stomach, pancreas, kidneys and surgical clips were contoured for each patient when applicable. The organ motion was determined by measuring the distance between the geometric center of end‐expiration and end‐inspiration contours. Hysteresis was determined by measuring the distance between the geometric centers of the mid‐inspiration and mid‐expiration contours. Results: A total of 72 structures have been contoured in eight patients. Five patients were status‐post pancreatic resection. Hysteresis in organ movement was demonstrated when comparing mid‐inspiratory and mid‐expiratory contours, with differences in mid‐inspiration and mid‐expiration contours of up to 1.59cm. Total max‐inspiratory to max‐expiratory movement for the liver, stomach, kidneys, spleen, and pancreas were 1.00, 1.46, 1.29, 1.52, and 1.16cm. Total mid‐inspiratory to mid‐expiratory movement for the liver, stomach, kidneys, spleen, and pancreas were 0.51, 0.76, 0.71, 0.78, and 1.12cm. The maximal movement for the liver, stomach, kidneys, spleen and pancreas were 1.47, 2.47, 1.90, 2.29 and 1.73cm. Results for the surgical clips were similar. Conclusion: Despite the anatomic variation expected in a diverse population, all the upper abdominal organs moved inferiorly and anteriorly with inspiration. Each organ moved at least 1cm on average, and more than 2cm in certain patients. Hysteresis was significant in some patients. Further work to investigate changes in dose distribution from this movement is ongoing.

SU‐FF‐J‐32: Use of Internal Body‐Area as a Metric for Retrospective 4D CT Gating

Purpose: Current 4D CT acquisition techniques require the use of an external breathing metric, which adds time, cost and complexity to the procedure. If the use of the metric is not required after the imaging session, an internal metric may be adequate. The purpose of this study was to evaluate the use of the cross-sectional body area as an imaging-based internal metric for breathing motion using CT images acquired during free breathing. Method and Materials: A 16-slice CT scanner (Philips Brilliance) was operated in cine mode to acquire 25 scans consecutively at each couch position while patients underwent simultaneous quantitative spirometry. The cross-sectional body area was computed by automated image segmentation. The body areas for the 16 slices within each 2.4 cm-thick couch position were summed and compared to the corresponding tidal volume. The correlation between tidal volume and body area were examined to evaluate the quality of body area as a metric. Results: Three patients were analyzed. The body area consistently show a high correlation with the tidal volume in the abdomen (correlation coefficients > 0.8, and residual error < 10% of the total tidal volume). In the upper lung region, the correlation coefficients varied in a range of 0.2 to 0.99, and the residual errors were 5% to 30%. For each patient there was a transition region near the mid sternum where the correlation degraded dramatically. Conclusion: For imaging of the upper abdomen, the body area appears to be a good breathing metric for generating 4D imaging studies. One method for improving this process may be to overlap successive couch positions by one slice location, thereby providing a CT slice with overlapping body-area measurements.