Paul Schleyer

Quantification of absolute myocardial perfusion in patients with coronary artery disease: comparison between cardiovascular magnetic resonance and positron emission tomography

OBJECTIVES The aim of this study was to compare fully quantitative cardiovascular magnetic resonance (CMR) and positron emission tomography (PET) myocardial perfusion and myocardial perfusion reserve (MPR) measurements in patients with coronary artery disease (CAD). BACKGROUND Absolute quantification of myocardial perfusion and MPR with PET have proven diagnostic and prognostic roles in patients with CAD. Quantitative CMR perfusion imaging has been established more recently and has been validated against PET in normal hearts. However, there are no studies comparing fully quantitative CMR against PET perfusion imaging in patients with CAD. METHODS Forty-one patients with known or suspected CAD prospectively underwent quantitative (13)N-ammonia PET and CMR perfusion imaging before coronary angiography. RESULTS The CMR-derived MPR (MPR(CMR)) correlated well with PET-derived measurements (MPR(PET)) (r = 0.75, p < 0.0001). MPR(CMR) and MPR(PET) for the 2 lowest scoring segments in each coronary territory also correlated strongly (r = 0.79, p < 0.0001). Absolute CMR perfusion values correlated significantly, but weakly, with PET values both at rest (r = 0.32; p = 0.002) and during stress (r = 0.37; p < 0.0001). Area under the receiver-operating characteristic curve for MPR(PET) to detect significant CAD was 0.83 (95% confidence interval: 0.73 to 0.94) and for MPR(CMR) was 0.83 (95% confidence interval: 0.74 to 0.92). An MPR(PET) ≤1.44 predicted significant CAD with 82% sensitivity and 87% specificity, and MPR(CMR) ≤1.45 predicted significant CAD with 82% sensitivity and 81% specificity. CONCLUSIONS There is good correlation between MPR(CMR) and MPR(PET.) For the detection of significant CAD, MPR(PET) and MPR(CMR) seem comparable and very accurate. However, absolute perfusion values from PET and CMR are only weakly correlated; therefore, although quantitative CMR is clinically useful, further refinements are still required.

Fast generation of 4D PET-MR data from real dynamic MR acquisitions

We have implemented and evaluated a framework for simulating simultaneous dynamic PET-MR data using the anatomic and dynamic information from real MR acquisitions. PET radiotracer distribution is simulated by assigning typical FDG uptake values to segmented MR images with manually inserted additional virtual lesions. PET projection data and images are simulated using analytic forward projections (including attenuation and Poisson statistics) implemented within the image reconstruction package STIR. PET image reconstructions are also performed with STIR. The simulation is validated with numerical simulation based on Monte Carlo (GATE) which uses more accurate physical modelling, but has 150× slower computation time compared to the analytic method for ten respiratory positions and is 7000× slower when performing multiple realizations. Results are validated in terms of region of interest mean values and coefficients of variation for 65 million coincidences including scattered events. Although some discrepancy is observed, agreement between the two different simulation methods is good given the statistical noise in the data. In particular, the percentage difference of the mean values is 3.1% for tissue, 17% for the lungs and 18% for a small lesion. The utility of the procedure is demonstrated by simulating realistic PET-MR datasets from multiple volunteers with different breathing patterns. The usefulness of the toolkit will be shown for performance investigations of the reconstruction, motion correction and attenuation correction algorithms for dynamic PET-MR data.

Retrospective data-driven respiratory gating for PET/CT

Respiratory motion can adversely affect both PET and CT acquisitions. Respiratory gating allows an acquisition to be divided into a series of motion-reduced bins according to the respiratory signal, which is typically hardware acquired. In order that the effects of motion can potentially be corrected for, we have developed a novel, automatic, data-driven gating method which retrospectively derives the respiratory signal from the acquired PET and CT data. PET data are acquired in listmode and analysed in sinogram space, and CT data are acquired in cine mode and analysed in image space. Spectral analysis is used to identify regions within the CT and PET data which are subject to respiratory motion, and the variation of counts within these regions is used to estimate the respiratory signal. Amplitude binning is then used to create motion-reduced PET and CT frames. The method was demonstrated with four patient datasets acquired on a 4-slice PET/CT system. To assess the accuracy of the data-derived respiratory signal, a hardware-based signal was acquired for comparison. Data-driven gating was successfully performed on PET and CT datasets for all four patients. Gated images demonstrated respiratory motion throughout the bin sequences for all PET and CT series, and image analysis and direct comparison of the traces derived from the data-driven method with the hardware-acquired traces indicated accurate recovery of the respiratory signal.

Differential effects of teriparatide on regional bone formation using 18F-fluoride positron emission tomography

Teriparatide increases skeletal mass, bone turnover markers, and bone strength, but local effects on bone tissue may vary between skeletal sites. We used positron emission tomography (PET) to study 18F‐fluoride plasma clearance (Ki) at the spine and standardized uptake values (SUVs) at the spine, pelvis, total hip, and femoral shaft in 18 postmenopausal women with osteoporosis. Subjects underwent a 1‐hour dynamic scan of the lumbar spine and a 10‐minute static scan of the pelvis and femurs at baseline and after 6 months of treatment with 20 µg/day teriparatide. Blood samples were taken to derive the arterial input function and lumbar spine Ki values evaluated using a three‐compartment model. SUVs were calculated for the spine, pelvis, total hip, and femoral shaft. After 6 months treatment with teriparatide, spine Ki values increased by 24% (p = .0003), while other model parameters were unchanged except for the fraction of tracer going to bone mineral (k3/[k2 + k3]), which increased by 23% (p = .0006). In contrast to Ki, spine SUVs increased by only 3% (p = .84). The discrepancy between changes in Ki and SUVs was explained by a 20% decrease in 18F− plasma concentration. SUVs increased by 37% at the femoral shaft (p = .0019), 20% at the total hip (p = .032), and 11% at the pelvis (p = .070). Changes in bone turnover markers and BMD were consistent with previous trials. We conclude that the changes in bone formation rate during teriparatide treatment as measured by 18F− PET differ at different skeletal sites, with larger increases in cortical bone than at trabecular sites.

Extension of a data-driven gating technique to 3D, whole body PET studies

Respiratory gating can be used to separate a PET acquisition into a series of near motion-free bins. This is typically done using additional gating hardware; however, software-based methods can derive the respiratory signal from the acquired data itself. The aim of this work was to extend a data-driven respiratory gating method to acquire gated, 3D, whole body PET images of clinical patients. The existing method, previously demonstrated with 2D, single bed-position data, uses a spectral analysis to find regions in raw PET data which are subject to respiratory motion. The change in counts over time within these regions is then used to estimate the respiratory signal of the patient. In this work, the gating method was adapted to only accept lines of response from a reduced set of axial angles, and the respiratory frequency derived from the lung bed position was used to help identify the respiratory frequency in all other bed positions. As the respiratory signal does not identify the direction of motion, a registration-based technique was developed to align the direction for all bed positions. Data from 11 clinical FDG PET patients were acquired, and an optical respiratory monitor was used to provide a hardware-based signal for comparison. All data were gated using both the data-driven and hardware methods, and reconstructed. The centre of mass of manually defined regions on gated images was calculated, and the overall displacement was defined as the change in the centre of mass between the first and last gates. The mean displacement was 10.3 mm for the data-driven gated images and 9.1 mm for the hardware gated images. No significant difference was found between the two gating methods when comparing the displacement values. The adapted data-driven gating method was demonstrated to successfully produce respiratory gated, 3D, whole body, clinical PET acquisitions.

On transcending the impasse of respiratory motion correction applications in routine clinical imaging - a consideration of a fully automated data driven motion control framework

Positron emission tomography (PET) is increasingly used for the detection, characterization, and follow-up of tumors located in the thorax. However, patient respiratory motion presents a unique limitation that hinders the application of high-resolution PET technology for this type of imaging. Efforts to transcend this limitation have been underway for more than a decade, yet PET remains for practical considerations a modality vulnerable to motion-induced image degradation. Respiratory motion control is not employed in routine clinical operations. In this article, we take an opportunity to highlight some of the recent advancements in data-driven motion control strategies and how they may form an underpinning for what we are presenting as a fully automated data-driven motion control framework. This framework represents an alternative direction for future endeavors in motion control and can conceptually connect individual focused studies with a strategy for addressing big picture challenges and goals.

Improved UTE-based attenuation correction for cranial PET-MR using dynamic magnetic field monitoring

PURPOSE Ultrashort echo time (UTE) MRI has been proposed as a way to produce segmented attenuation maps for PET, as it provides contrast between bone, air, and soft tissue. However, UTE sequences require samples to be acquired during rapidly changing gradient fields, which makes the resulting images prone to eddy current artifacts. In this work it is demonstrated that this can lead to misclassification of tissues in segmented attenuation maps (AC maps) and that these effects can be corrected for by measuring the true k-space trajectories using a magnetic field camera. METHODS The k-space trajectories during a dual echo UTE sequence were measured using a dynamic magnetic field camera. UTE images were reconstructed using nominal trajectories and again using the measured trajectories. A numerical phantom was used to demonstrate the effect of reconstructing with incorrect trajectories. Images of an ovine leg phantom were reconstructed and segmented and the resulting attenuation maps were compared to a segmented map derived from a CT scan of the same phantom, using the Dice similarity measure. The feasibility of the proposed method was demonstrated in in vivo cranial imaging in five healthy volunteers. Simulated PET data were generated for one volunteer to show the impact of misclassifications on the PET reconstruction. RESULTS Images of the numerical phantom exhibited blurring and edge artifacts on the bone-tissue and air-tissue interfaces when nominal k-space trajectories were used, leading to misclassification of soft tissue as bone and misclassification of bone as air. Images of the tissue phantom and the in vivo cranial images exhibited the same artifacts. The artifacts were greatly reduced when the measured trajectories were used. For the tissue phantom, the Dice coefficient for bone in MR relative to CT was 0.616 using the nominal trajectories and 0.814 using the measured trajectories. The Dice coefficients for soft tissue were 0.933 and 0.934 for the nominal and measured cases, respectively. For air the corresponding figures were 0.991 and 0.993. Compared to an unattenuated reference image, the mean error in simulated PET uptake in the brain was 9.16% when AC maps derived from nominal trajectories was used, with errors in the SUV max for simulated lesions in the range of 7.17%-12.19%. Corresponding figures when AC maps derived from measured trajectories were used were 0.34% (mean error) and -0.21% to +1.81% (lesions). CONCLUSIONS Eddy current artifacts in UTE imaging can be corrected for by measuring the true k-space trajectories during a calibration scan and using them in subsequent image reconstructions. This improves the accuracy of segmented PET attenuation maps derived from UTE sequences and subsequent PET reconstruction.

Investigation of MR-Based Attenuation Correction and Motion Compensation for Hybrid PET/MR

In hybrid PET/MR systems, attenuation maps can be derived from MR to correct for attenuation in PET. However, MR-based attenuation correction (AC) in abdominal applications remains challenging (i) because of poor signal from important tissue types in common MR sequences (e.g., cortical bone) and (ii) because of respiratory motion which results in misalignments between the derived attenuation maps and the PET emissions. Furthermore, respiratory motion also leads to motion-blurring artefacts in the final PET reconstructions. In this paper, we compute an MR-based 4D attenuation map including cortical bone by combining an Ultrashort Echo Time (UTE) acquisition with a subject-specific motion model derived from a second near real-time 3D MR image acquisition. This model allows us to create attenuation maps at any respiratory position which are used for AC in the reconstruction of different respiratory resolved PET images. The inverse of the model is used for motion compensation (MC) of these images. We demonstrate our approach on MR data from 5 healthy volunteers including 3 manually inserted artificial lesions. The impact of bone tissue and respiratory motion on AC is investigated in PET simulations (i) by misclassifying bone to soft tissue in the attenuation maps leading to errors of up to 26.0% in mean uptake for lesions close to bone, and (ii) by using a non-moving attenuation map leading to errors of up to 24.2%. The impact of respiratory motion on MC showed errors of up to 50.7% in areas of strong motion if MC was not performed. The results show that the effect of motion has to be considered both for attenuation correction and for motion-compensating PET emissions. This additive effect of motion is larger than the effect of a wrong AC.

The effect of inaccurate bone attenuation coefficient and segmentation on reconstructed PET images

ObjectiveCombined PET/MRI scanners will require the use of MR images to attenuation correct the PET acquisition. A recognised issue with this is the lack of bone signal in conventional MR images. One approach is to segment the MRI and use a constant attenuation coefficient in place of the bone. The aim of this study was to investigate the effect of replacing the attenuation coefficients of the bone with various constant values on PET. MethodsBone segmentation was performed on the computed tomogrpahy (CT) components of PET/CT images from nine patients, and the bone replaced with three patient-specific values and three generic values. Attenuation-corrected PET images were reconstructed using the modified CT data and compared with the PET images reconstructed using the original CT data. The resulting effects on regions of interest measurements and in all chest voxels were evaluated. ResultsReplacing the bone with the mean bone value (patient specific) produced the least error with, on an average, a maximum error in the lung of 5%. Of the generic bone replacement values tested, an overestimated bone volume with soft tissue values produced the lowest error with, on an average, up to 36, 20 and 10% error in the bone, soft tissue and lung, respectively. ConclusionWhen the bones are substituted with attenuation coefficients that are higher than the soft-tissue, variations in bone classification can significantly degrade the PET images. Using an overestimated bone volume consisting of soft tissue equivalent attenuation coefficients presents a simple, robust method that is less sensitive to segmentation errors.

The Precision and Sensitivity of 18F-Fluoride PET for Measuring Regional Bone Metabolism: A Comparison of Quantification Methods

The planning of research studies requires an understanding of the minimum number of subjects required. The aim of this study was to evaluate different methods of analyzing 18F-fluoride PET (18F− PET) dynamic spine scans to find the approach that requires the smallest sample size to detect a statistically significant response to treatment. Methods: Eight different approaches to 18F− PET analysis (3 variants of the Hawkins 3-tissue compartmental model, 3 variants of spectral analysis, deconvolution, and Patlak analysis) were used to evaluate the fluoride plasma clearance to bone mineral (Ki). Standardized uptake values (SUVs) were also studied. Data for 20 women who had 18F− PET spine scans at 0, 6, and 12 mo after stopping long-term bisphosphonate treatment were used to compare precision errors. Data for 18 women who had scans at baseline and 6 mo after starting teriparatide treatment were used to compare response to treatment. Results: The 4 approaches that fitted the rate constant k4 describing the reverse flow of 18F from bone as a free variable showed close agreement in Ki values, with correlation coefficients greater than 0.97. Their %CVs were 14.4%–14.8%, and treatment response to teriparatide was 23.2%–23.8%. The 3 methods that assumed k4 = 0 gave Ki values 20%–25% lower than the other methods, with correlation coefficients of 0.83–0.94, percentage coefficients of variation (%CVs) of 12.9%–13.3%, and treatment response of 25.2%–28.3%. A Hawkins model with k4 = 0.01 min−1 did not perform any better (%CV, 14.2%; treatment response, 26.1%). Correlation coefficients between SUV and the different Ki methods varied between 0.60 and 0.65. Although SUV gave the best precision (%CV, 10.1%), the treatment response (3.1%) was not statistically significant. Conclusion: Methods that calculated Ki assuming k4 = 0 required fewer subjects to demonstrate a statistically significant response to treatment than methods that fitted k4 as a free variable. Although SUV gave the smallest precision error, the absence of any significant changes make it unsuitable for examining response to treatment in this study.