Simulation and experimental study of respiratory motion effect on image quality of single photon emission computed tomography (SPECT)

Interventional respiratory motion compensation by simultaneous fluoroscopic and nuclear imaging: a phantom study

PURPOSE

A compact and mobile hybrid c-arm scanner, capable of simultaneously acquiring nuclear and fluoroscopic projections and SPECT/CBCT, was developed to aid fluoroscopy-guided interventional procedures involving the administration of radionuclides (e.g. hepatic radioembolization). However, as in conventional SPECT/CT, the acquired nuclear images may be deteriorated by patient respiratory motion. We propose to perform compensation for respiratory motion by extracting the motion signal from fluoroscopic projections so that the nuclear counts can be gated into motion bins. The purpose of this study is to quantify the performance of this motion compensation technique with phantom experiments.

METHODS

Anthropomorphic phantom configurations that are representative of distributions obtained during the pre-treatment procedure of hepatic radioembolization were placed on a stage that translated with three different motion patterns. Fluoroscopic projections and nuclear counts were simultaneously acquired under planar and SPECT/CBCT imaging. The planar projections were visually assessed. The SPECT reconstructions were visually assessed and quantitatively assessed by calculating the activity recovery of the spherical inserts in the phantom.

RESULTS

The planar nuclear projections of the translating anthropomorphic phantom were blurry when no motion compensation was applied. With motion compensation, the nuclear projections became representative of the stationary phantom nuclear projection. Similar behavior was observed for the visual quality of SPECT reconstructions. The mean error of the activity recovery in the uncompensated SPECT reconstructions was 15.8% ± 0.9% for stable motion, 11.9% ± 0.9% for small variations, and 11.0% ± 0.9% for large variations. When applying motion compensation, the mean error decreased to 1.8% ± 1.6% for stable motion, 2.2% ± 1.5% for small variations, and 5.2% ± 2.5% for large variations.

CONCLUSION

A compact and mobile hybrid c-arm scanner, capable of simultaneously acquiring nuclear and fluoroscopic projections, can perform compensation for respiratory motion. Such motion compensation results in sharper planar nuclear projections and increases the quantitative accuracy of the SPECT reconstructions.

Improving perfusion defect detection with respiratory motion correction in cardiac SPECT at standard and reduced doses

BACKGROUND

In cardiac SPECT perfusion imaging, respiratory motion can cause non-uniform blurring in the reconstructed myocardium. We investigate the potential benefit of respiratory correction with respiratory-binned acquisitions, both at standard dose and at reduced dose, for defect detection and for left ventricular (LV) wall resolution.

METHODS

We applied two reconstruction methods for respiratory motion correction: post-reconstruction motion correction (PMC) and motion-compensated reconstruction (MCR), and compared with reconstruction without motion correction (Non-MC). We quantified the presence of perfusion defects in reconstructed images by using the total perfusion deficit (TPD) scores and conducted receiver-operating-characteristic (ROC) studies using TPD. We quantified the LV spatial resolution by using the FWHM of its cross-sectional intensity profile.

RESULTS

The values in the area-under-the-ROC-curve (AUC) achieved by MCR, PMC, and Non-MC at standard dose were 0.835, 0.830, and 0.798, respectively. Similar AUC improvements were also obtained by MCR and PMC over Non-MC at 50%, 25%, and 12.5% of full dose. Improvements in LV resolution were also observed with motion correction.

CONCLUSIONS

Respiratory-binned acquisitions can improve perfusion-defect detection accuracy over traditional reconstruction both at standard dose and at reduced dose. Motion correction may contribute to achieving further dose reduction while maintaining the diagnostic accuracy of traditional acquisitions.

The imaging of small pulmonary nodules

Lung cancer is the leading cause of cancer death worldwide. The major goal in lung cancer research is the improvement of long-term survival. Pulmonary nodules have high clinical importance, they may not only prove to be an early manifestation of lung cancer, but decide to choose the right therapy. This review will introduce the development and current situation of several imaging examination methods: computed tomography (CT), positron emission tomography/computed tomography (PET/CT), endobronchial ultrasound (EBUS).

Modeling the respiratory motion of solitary pulmonary nodules and determining the impact of respiratory motion on their detection in SPECT imaging

The objectives of this investigation were to model the respiratory motion of solitary pulmonary nodules (SPN) and then use this model to determine the impact of respiratory motion on the localization and detection of small SPN in SPECT imaging for four reconstruction strategies. The respiratory motion of SPN was based on that of normal anatomic structures in the lungs determined from breath-held CT images of a volunteer acquired at two different stages of respiration. End-expiration (EE) and time-averaged (Frame Av) non-uniform-B-spline cardiac torso (NCAT) digital-anthropomorphic phantoms were created using this information for respiratory motion within the lungs. SPN were represented as 1 cm diameter spheres which underwent linear motion during respiration between the EE and end-inspiration (EI) time points. The SIMIND Monte Carlo program was used to produce SPECT projection data simulating Tc-99m depreotide (NeoTect) imaging. The projections were reconstructed using 1) no correction (NC), 2) attenuation correction (AC), 3) resolution compensation (RC), and 4) attenuation correction, scatter correction, and resolution compensation (AC_SC_RC). A human-observer localization receiver operating characteristics (LROC) study was then performed to determine the difference in localization and detection accuracy with and without the presence of respiratory motion. The LROC comparison determined that respiratory motion degrades tumor detection for all four reconstruction strategies, thus correction for SPN motion would be expected to improve detection accuracy. The inclusion of RC in reconstruction improved detection accuracy for both EE and Frame Av over NC and AC. Also the magnitude of the impact of motion was least for AC_SC_RC.

LROC Investigation of Three Strategies for Reducing the Impact of Respiratory Motion on the Detection of Solitary Pulmonary Nodules in SPECT

The objective of this investigation was to determine the effectiveness of three motion reducing strategies in diminishing the degrading impact of respiratory motion on the detection of small solitary pulmonary nodules (SPN) in single photon emission computed tomographic (SPECT) imaging in comparison to a standard clinical acquisition and the ideal case of imaging in the absence of respiratory motion. To do this non-uniform rational B-spline cardiac-torso (NCAT) phantoms based on human-volunteer CT studies were generated spanning the respiratory cycle for a normal background distribution of Tc-99m NeoTect. Similarly, spherical phantoms of 1.0 cm diameter were generated to model small SPN for each of 150 uniquely located sites within the lungs whose respiratory motion was based on the motion of normal structures in the volunteer CT studies. The SIMIND Monte Carlo program was used to produce SPECT projection data from these. Normal and single-lesion containing SPECT projection sets with a clinically realistic Poisson noise level were created for the cases of: 1) the end-expiration (EE) frame with all counts, 2) respiration-averaged motion with all counts, 3) one-fourth of the 32 frames centered around EE (Quarter-Binning), 4) one-half of the 32 frames centered around EE (Half-Binning), and 5) eight temporally binned frames spanning the respiratory cycle. Each of the sets of combined projection data were reconstructed with RBI-EM with system spatial-resolution compensation (RC). Based on the known motion for each of the 150 different lesions, the reconstructed volumes of respiratory bins were shifted so as to superimpose the locations of the SPN onto that in the first bin (Reconstruct and Shift). Five human-observers performed localization receiver operating characteristics (LROC) studies of SPN detection. The observer results were analyzed for statistical significance differences in SPN detection accuracy among the three correction strategies, the standard acquisition, and the ideal case of the absence of respiratory motion. Our human-observer LROC determined that Quarter-Binning and Half-Binning strategies resulted in SPN detection accuracy statistically significantly below (P < 0.05) that of standard clinical acquisition, whereas the Reconstruct and Shift strategy resulted in a detection accuracy not statistically significantly different from that of the ideal case. This investigation demonstrates that tumor detection based on acquisitions associated with less than all the counts which could potentially be employed may result in poorer detection despite limiting the motion of the lesion. The Reconstruct and Shift method results in tumor detection that is equivalent to ideal motion correction.

Digital anthropomorphic phantoms of non-rigid human respiratory and voluntary body motion for investigating motion correction in emission imaging

The development of methods for correcting patient motion in emission tomography has been receiving increased attention. Often the performance of these methods is evaluated through simulations using digital anthropomorphic phantoms, such as the commonly used extended cardiac torso (XCAT) phantom, which models both respiratory and cardiac motion based on human studies. However, non-rigid body motion, which is frequently seen in clinical studies, is not present in the standard XCAT phantom. In addition, respiratory motion in the standard phantom is limited to a single generic trend. In this work, to obtain a more realistic representation of motion, we developed a series of individual-specific XCAT phantoms, modeling non-rigid respiratory and non-rigid body motions derived from the magnetic resonance imaging (MRI) acquisitions of volunteers. Acquisitions were performed in the sagittal orientation using the Navigator methodology. Baseline (no motion) acquisitions at end-expiration were obtained at the beginning of each imaging session for each volunteer. For the body motion studies, MRI was again acquired only at end-expiration for five body motion poses (shoulder stretch, shoulder twist, lateral bend, side roll, and axial slide). For the respiratory motion studies, an MRI was acquired during free/regular breathing. The magnetic resonance slices were then retrospectively sorted into 14 amplitude-binned respiratory states, end-expiration, end-inspiration, six intermediary states during inspiration, and six during expiration using the recorded Navigator signal. XCAT phantoms were then generated based on these MRI data by interactive alignment of the organ contours of the XCAT with the MRI slices using a graphical user interface. Thus far we have created five body motion and five respiratory motion XCAT phantoms from the MRI acquisitions of six healthy volunteers (three males and three females). Non-rigid motion exhibited by the volunteers was reflected in both respiratory and body motion phantoms with a varying extent and character for each individual. In addition to these phantoms, we recorded the position of markers placed on the chest of the volunteers for the body motion studies, which could be used as external motion measurement. Using these phantoms and external motion data, investigators will be able to test their motion correction approaches for realistic motion obtained from different individuals. The non-uniform rational B-spline data and the parameter files for these phantoms are freely available for downloading and can be used with the XCAT license.

Impact of respiratory motion on the detection of small pulmonary nodules in SPECT imaging

The objective of this investigation is to determine the impact of respiratory motion on the detection of small solitary pulmonary nodules (SPN) in single photon emission computed tomographic (SPECT) imaging. We have previously modeled the respiratory motion of SPN based on the change of location of anatomic structures within the lungs identified on breath-held CT images of volunteers acquired at two different stages of respiration. This information on respiratory motion within the lungs was combined with the end-expiration and time-averaged NCAT phantoms to allow the creation of source and attenuation maps for the normal background distribution of Tc-99m NeoTect. With the source and attenuation distribution thus defined, the SIMIND Monte Carlo program was used to produce SPECT projection data for the normal background and separately for each of 150 end-expiration and time-averaged simulated 1.0 cm tumors. Normal and tumor SPECT projection sets each containing one lesion were combined with a clinically realistic noise level and counts. These were reconstructed with RBI-EM using 1) no correction (NC), 2) attenuation correction (AC), 3) detector response correction (RC), and 4) attenuation correction, detector response correction, and scatter correction (AC_RC_SC). The post-reconstruction parameters of number of iterations and 3-D Gaussian filtering were optimized by human-observer studies. Comparison of lesion detection by human-observer LROC studies reveals that respiratory motion degrades tumor detection for all four reconstruction strategies, and that the magnitude of this effect is greatest for NC and RC, and least for AC_RC_SC. Additionally, the AC_RC_SC strategy results in the best detection of lesions.