Author ORCID Identifier
Date of Graduation
12-2019
Document Type
Dissertation (PhD)
Program Affiliation
Medical Physics
Degree Name
Doctor of Philosophy (PhD)
Advisor/Committee Chair
Osama R. Mawlawi, Ph.D.
Committee Member
Jeremy J. Erasmus, M.D.
Committee Member
Tinsu Pan, Ph.D.
Committee Member
Christine B. Peterson, Ph.D.
Committee Member
Richard Wendt, III, Ph.D.
Abstract
In oncological imaging, Positron Emission Tomography/Computed Tomography (PET/CT) is a vital tool used for stating and treatment response assessment of patients due to its ability to visualize and accurately quantify the bio-distribution of radiolabeled pharmaceuticals. However, due to the long acquisition times, respiratory motion blur is unavoidable in PET images especially in the lower lung and upper abdomen. This leads to reductions in measured radiotracer concentration and lesion detectability all of which can potentially result in incorrect management of patients. Multiple methods exist to correct for respiratory motion but are rarely used in the routine clinical setting because of: 1) increased image noise due to the rejection of motion blurred data; 2) burdensome workflows which require setup and troubleshooting of external hardware needed to track patient breathing; 3) and ineffective respiratory motion correction due to irregular patient breathing potentially caused by the abrupt bed transitions during step and shoot (SS) whole body PET acquisition. Our goal of this Ph.D. dissertation is to address these three issues by evaluating 1) a precommercial version of a vendor designed elastic motion correction (EMC) algorithm which uses all of the acquired PET data resulting in reduced image noise; 2) a pre-commercial version of a vendor designed data driven gating (DDG) algorithm, which determines the respiratory waveform from the PET data alone, thereby removing the need for and challenges of external hardware; 3) the effect of using continuous bed motion (CBM) as compared to SS as a means to minimize the irregularity of patient breathing. vii The results of these evaluations showed that the EMC algorithm performed similarly to conventional respiratory motion correction techniques with respect to radiotracer quantification, however, due to using all of the acquired PET data, the EMC algorithm showed improved performance resulting in the lowest amount of image noise, improved contrast to noise ratio, and had the highest overall image quality scores as assessed by independent observers. Evaluation of the CBM DDG algorithm showed that in comparison to an external device, the measured respiratory waveforms, radiotracer quantification, and assessment of the presence of respiratory motion blur were similar, demonstrating that the CBM DDG algorithm holds promise as a replacement to external hardware devices currently needed to measure respiratory waveforms and hence could potentially simplify the data acquisition workflow. Finally, we found no statistically significant differences between the CBM and SS PET acquisition modes with respect to the regularity of respiratory waveforms, radiotracer quantification, contrast to noise ratio and perceptions of respiratory motion blur. In conclusion, although no reductions of irregular breathing were found between CBM and SS, improvements in image quality through the use of EMC and reductions of workflow complexity through the use of DDG will hopefully facilitate the routine adoption of respiratory motion correction in PET/CT.
Keywords
Positron Emission Tomography, Computed Tomography, Respiratory Motion Correction, Elastic Motion Correction, Data Driven Gating, Continuous Bed Motion, Irregular Breathing