Statistical shape modeling of the diaphragm for application to Rb-82 cardiac PET-CT studies

It is important when motion-correcting Rb-82 cardiac PET-CT scans that diaphragm motion is accounted for, to avoid attenuation-correction artifacts. In the absence of a gated CT, a model of the diaphragm could assist in identifying the diaphragm position in noisy PET images as a step towards performing respiratory-matched attenuation-correction. To test this, a shape model was constructed from a training set of 10 gated CT datasets, in which the diaphragm was segmented. Principal Component Analysis was performed on corresponding landmarks from all surfaces to extract modes of variation in shape and motion between patients. Fitting the model to a segmented surface was then achieved by weighting each mode to minimize the sum of squared differences between the fitted and original surfaces: this was carried out for datasets used in its construction and previously unseen datasets, using a leave-one-out approach. It was found that 95 of training data variation was described in only 5 modes, indicating that 5 parameters need to be fitted in order to fully describe the diaphragm over the respiratory cycle. Model success was measured in terms of the residual differences after fitting and was found to be 3.8 ± 1.0 mm per landmark for the 10 leave-one-out models. Since the slice thickness in the PET data is 3.3 mm, it is likely that this level of error is tolerable in this application. Furthermore, the overall diaphragm shape was reproduced well in the presence of these errors, further indicating the validity of this approach. These results demonstrate the potential of this technique in benefiting the prediction of the time-varying diaphragm position. This could therefore provide a valuable technique in determining diaphragm motion in cardiac PET-CT studies. ©2008 IEEE.</p

Statistical shape modeling of the diaphragm for application to Rb-82 cardiac PET-CT studies

It is important when motion-correcting Rb-82 cardiac PET-CT scans that diaphragm motion is accounted for, to avoid attenuation-correction artifacts. In the absence of a gated CT, a model of the diaphragm could assist in identifying the diaphragm position in noisy PET images as a step towards performing respiratory-matched attenuation-correction. To test this, a shape model was constructed from a training set of 10 gated CT datasets, in which the diaphragm was segmented. Principal Component Analysis was performed on corresponding landmarks from all surfaces to extract modes of variation in shape and motion between patients. Fitting the model to a segmented surface was then achieved by weighting each mode to minimize the sum of squared differences between the fitted and original surfaces: this was carried out for datasets used in its construction and previously unseen datasets, using a leave-one-out approach. It was found that 95 of training data variation was described in only 5 modes, indicating that 5 parameters need to be fitted in order to fully describe the diaphragm over the respiratory cycle. Model success was measured in terms of the residual differences after fitting and was found to be 3.8 ± 1.0 mm per landmark for the 10 leave-one-out models. Since the slice thickness in the PET data is 3.3 mm, it is likely that this level of error is tolerable in this application. Furthermore, the overall diaphragm shape was reproduced well in the presence of these errors, further indicating the validity of this approach. These results demonstrate the potential of this technique in benefiting the prediction of the time-varying diaphragm position. This could therefore provide a valuable technique in determining diaphragm motion in cardiac PET-CT studies. ©2008 IEEE.</p

The application of a statistical shape model to diaphragm tracking in respiratory-gated cardiac PET images

Respiratory-induced diaphragm mismatch between positron emission tomography (PET) and computed tomography (CT) has been identified as a source of attenuation-correction artifact in cardiac PET. Diaphragm tracking in gated PET could therefore form part of a mismatch correction technique, where a single CT is transformed to match each PET frame. To investigate the feasibility of such a technique, a statistical shape model of the diaphragm was constructed from gated CT and applied to two gated (18)F-FDG PET-CT datasets. A poor level of accuracy was obtained when the model was fitted to landmarks obtained from PET, with errors of 3.6 and 5.0 mm per landmark for the two patients, despite inclusion of the data within the model construction. However, errors were reduced to 2.4 and 1.9 mm with the incorporation of a single frame of CT landmarks. These values are closer to the baseline measure of fitting solely to CT landmarks, found to be 2.2 and 1.2 mm in this case. Excluding the datasets from the model yielded similar trends but with higher overall residual errors, indicating the need for a larger training set. Therefore, a highly trained diaphragm model could negate the need for a gated CT for diaphragm tracking, provided that information from a static CT is incorporated.</p

The application of a statistical shape model to diaphragm tracking in respiratory-gated cardiac PET images

Respiratory-induced diaphragm mismatch between positron emission tomography (PET) and computed tomography (CT) has been identified as a source of attenuation-correction artifact in cardiac PET. Diaphragm tracking in gated PET could therefore form part of a mismatch correction technique, where a single CT is transformed to match each PET frame. To investigate the feasibility of such a technique, a statistical shape model of the diaphragm was constructed from gated CT and applied to two gated (18)F-FDG PET-CT datasets. A poor level of accuracy was obtained when the model was fitted to landmarks obtained from PET, with errors of 3.6 and 5.0 mm per landmark for the two patients, despite inclusion of the data within the model construction. However, errors were reduced to 2.4 and 1.9 mm with the incorporation of a single frame of CT landmarks. These values are closer to the baseline measure of fitting solely to CT landmarks, found to be 2.2 and 1.2 mm in this case. Excluding the datasets from the model yielded similar trends but with higher overall residual errors, indicating the need for a larger training set. Therefore, a highly trained diaphragm model could negate the need for a gated CT for diaphragm tracking, provided that information from a static CT is incorporated.</p

Imaging biomarker roadmap for cancer studies.

Imaging biomarkers (IBs) are integral to the routine management of patients with cancer. IBs used daily in oncology include clinical TNM stage, objective response and left ventricular ejection fraction. Other CT, MRI, PET and ultrasonography biomarkers are used extensively in cancer research and drug development. New IBs need to be established either as useful tools for testing research hypotheses in clinical trials and research studies, or as clinical decision-making tools for use in healthcare, by crossing 'translational gaps' through validation and qualification. Important differences exist between IBs and biospecimen-derived biomarkers and, therefore, the development of IBs requires a tailored 'roadmap'. Recognizing this need, Cancer Research UK (CRUK) and the European Organisation for Research and Treatment of Cancer (EORTC) assembled experts to review, debate and summarize the challenges of IB validation and qualification. This consensus group has produced 14 key recommendations for accelerating the clinical translation of IBs, which highlight the role of parallel (rather than sequential) tracks of technical (assay) validation, biological/clinical validation and assessment of cost-effectiveness; the need for IB standardization and accreditation systems; the need to continually revisit IB precision; an alternative framework for biological/clinical validation of IBs; and the essential requirements for multicentre studies to qualify IBs for clinical use

Imaging biomarker roadmap for cancer studies.

Imaging biomarkers (IBs) are integral to the routine management of patients with cancer. IBs used daily in oncology include clinical TNM stage, objective response and left ventricular ejection fraction. Other CT, MRI, PET and ultrasonography biomarkers are used extensively in cancer research and drug development. New IBs need to be established either as useful tools for testing research hypotheses in clinical trials and research studies, or as clinical decision-making tools for use in healthcare, by crossing 'translational gaps' through validation and qualification. Important differences exist between IBs and biospecimen-derived biomarkers and, therefore, the development of IBs requires a tailored 'roadmap'. Recognizing this need, Cancer Research UK (CRUK) and the European Organisation for Research and Treatment of Cancer (EORTC) assembled experts to review, debate and summarize the challenges of IB validation and qualification. This consensus group has produced 14 key recommendations for accelerating the clinical translation of IBs, which highlight the role of parallel (rather than sequential) tracks of technical (assay) validation, biological/clinical validation and assessment of cost-effectiveness; the need for IB standardization and accreditation systems; the need to continually revisit IB precision; an alternative framework for biological/clinical validation of IBs; and the essential requirements for multicentre studies to qualify IBs for clinical use