Comparison of Motion Correction Methods Incorporating Motion Modelling for PET/CT Using a Single Breath Hold Attenuation Map

Introducing motion models into respiratory motion correction methods can lead to a reduction in blurring and artefacts. However, the pool of research where motion modelling methods are applied to combined positron emission tomography and computed tomography is relatively shallow. Previous work used non-attenuation corrected time-of-flight data to fit motion models, not only to motion correct the volumes themselves, but also to warp a single attenuation map to the positions of the initial gated data. This work seeks to extend previous work to offer a comparison of respiratory motion correction methods, not only with and without motion models, but also to compare pair-wise and group-wise registration techniques, on simulation data, in a low count scenario, where the attenuation map is from a pseudo-breath hold acquisition. To test the methods, 4-Dimensional Extended Cardiac Torso images are constructed, simulated and reconstructed without attenuation correction, then motion corrected using one of pair-wise, pair-wise with motion model, group-wise and group-wise with motion model registration. Next these motion corrected volumes are registered to the breath hold attenuation map. The positron emission tomography data are then reconstructed using deformed attenuation maps and motion corrected. Evaluation compares the results of these methods against non-motion corrected and motion free examples. Results indicate that the incorporation of motion models and group-wise registration, improves contrast and quantification

Sequential deep learning image enhancement models improve diagnostic confidence, lesion detectability, and image reconstruction time in PET

Background: Investigate the potential benefits of sequential deployment of two deep learning (DL) algorithms namely DL-Enhancement (DLE) and DL-based time-of-flight (ToF) (DLT). DLE aims to enhance the rapidly reconstructed ordered-subset-expectation-maximisation algorithm (OSEM) images towards block-sequential-regularised-expectation-maximisation (BSREM) images, whereas DLT aims to improve the quality of BSREM images reconstructed without ToF. As the algorithms differ in their purpose, sequential application may allow benefits from each to be combined. 20 FDG PET-CT scans were performed on a Discovery 710 (D710) and 20 on Discovery MI (DMI; both GE HealthCare). PET data was reconstructed using five combinations of algorithms:1. ToF-BSREM, 2. ToF-OSEM + DLE, 3. OSEM + DLE + DLT, 4. ToF-OSEM + DLE + DLT, 5. ToF-BSREM + DLT. To assess image noise, 30 mm-diameter spherical VOIs were drawn in both lung and liver to measure standard deviation of voxels within the volume. In a blind clinical reading, two experienced readers rated the images on a five-point Likert scale based on lesion detectability, diagnostic confidence, and image quality. Results: Applying DLE + DLT reduced noise whilst improving lesion detectability, diagnostic confidence, and image reconstruction time. ToF-OSEM + DLE + DLT reconstructions demonstrated an increase in lesion SUVmax of 28 ± 14% (average ± standard deviation) and 11 ± 5% for data acquired on the D710 and DMI, respectively. The same reconstruction scored highest in clinical readings for both lesion detectability and diagnostic confidence for D710. Conclusions: The combination of DLE and DLT increased diagnostic confidence and lesion detectability compared to ToF-BSREM images. As DLE + DLT used input OSEM images, and because DL inferencing was fast, there was a significant decrease in overall reconstruction time. This could have applications to total body PET

PET/MR imaging of bone lesions - implications for PET quantification from imperfect attenuation correction

PURPOSE: Accurate attenuation correction (AC) is essential for quantitative analysis of PET tracer distribution. In MR, the lack of cortical bone signal makes bone segmentation difficult and may require implementation of special sequences. The purpose of this study was to evaluate the need for accurate bone segmentation in MR-based AC for whole-body PET/MR imaging. METHODS: In 22 patients undergoing sequential PET/CT and 3-T MR imaging, modified CT AC maps were produced by replacing pixels with values of >100 HU, representing mostly bone structures, by pixels with a constant value of 36 HU corresponding to soft tissue, thereby simulating current MR-derived AC maps. A total of 141 FDG-positive osseous lesions and 50 soft-tissue lesions adjacent to bones were evaluated. The mean standardized uptake value (SUVmean) was measured in each lesion in PET images reconstructed once using the standard AC maps and once using the modified AC maps. Subsequently, the errors in lesion tracer uptake for the modified PET images were calculated using the standard PET image as a reference. RESULTS: Substitution of bone by soft tissue values in AC maps resulted in an underestimation of tracer uptake in osseous and soft tissue lesions adjacent to bones of 11.2 ± 5.4 % (range 1.5-30.8 %) and 3.2 ± 1.7 % (range 0.2-4 %), respectively. Analysis of the spine and pelvic osseous lesions revealed a substantial dependence of the error on lesion composition. For predominantly sclerotic spine lesions, the mean underestimation was 15.9 ± 3.4 % (range 9.9-23.5 %) and for osteolytic spine lesions, 7.2 ± 1.7 % (range 4.9-9.3 %), respectively. CONCLUSION: CT data simulating treating bone as soft tissue as is currently done in MR maps for PET AC leads to a substantial underestimation of tracer uptake in bone lesions and depends on lesion composition, the largest error being seen in sclerotic lesions. Therefore, depiction of cortical bone and other calcified areas in MR AC maps is necessary for accurate quantification of tracer uptake values in PET/MR imaging

Clinical image quality perception and its relation to NECR measurements in PET

BACKGROUND: The purpose of this study is to describe a clinical relation of noise equivalent count rate (NECR) - an objective measurement of positron emission tomography (PET) systems - measured in a large number of patients, to clinical image quality of PET and their relation to 18F-fluoro-2-deoxyglucose (FDG) activity and patient's weight. METHODS: A total of 71 consecutive patients were evaluated in this retrospective study. All data was automatically analysed using Matlab to estimate the noise equivalent count rate. Then, image quality was evaluated according to two subjective scores: the IQ local score was a 3-point scale assigned to each bed position in all patients and the IQ global score was a 10-point scale assigned after evaluating the coronal whole-body PET. Patient data was also analysed concerning weight, body mass index, FDG dose at the start of acquisition (D Acq), presence of bowel uptake and presence of FDG-positive pathologic lesions. Two additional parameters were defined for each patient: the ratio between D Acq and patient weight (R DW) and the ratio between D Acq and patient BMI (R DBMI). RESULTS: Clinically perceived image quality in PET has a significant positive correlation with NECR measured in patients, R DW, R DBMI and presence of pathologic lesions. Clinical image quality furthermore has significant negative correlation with weight, body mass index (BMI) and presence of bowel uptake. Thresholds of R DW and R DBMI in which clinical IQ is good to excellent in more than 90% of the patients were 2.6 and 8.0, respectively. CONCLUSIONS: Clinically perceived image quality in PET systems is positively and significantly related to NECR measured in patients. An optimal threshold for the R DW and R DBMI was defined in which clinical IQ is good to excellent in more than 90% of patients. With this data, it is possible to extrapolate technical as well as clinical image quality to other PET system and to predict clinical image perception

Clinical evaluation of a block sequential regularized expectation maximization reconstruction algorithm in 18F-FDG PET/CT studies

PURPOSE To investigate the clinical performance of a block sequential regularized expectation maximization (BSREM) penalized likelihood reconstruction algorithm in oncologic PET/computed tomography (CT) studies. METHODS A total of 410 reconstructions of 41 fluorine-18 fluorodeoxyglucose-PET/CT studies of 41 patients with a total of 2010 lesions were analyzed by two experienced nuclear medicine physicians. Images were reconstructed with BSREM (with four different β values) or ordered subset expectation maximization (OSEM) algorithm with/without time-of-flight (TOF/non-TOF) corrections. OSEM reconstruction postfiltering was 4.0 mm full-width at half-maximum; BSREM did not use postfiltering. Evaluation of general image quality was performed with a five-point scale using maximum intensity projections. Artifacts (category 1), image sharpness (category 2), noise (category 3), and lesion detectability (category 4) were analyzed using a four-point scale. Size and maximum standardized uptake value (SUVmax) of lesions were measured by a third reader not involved in the image evaluation. RESULTS BSREM-TOF reconstructions showed the best results in all categories, independent of different body compartments. In all categories, BSREM non-TOF reconstructions were significantly better than OSEM non-TOF reconstructions (P<0.001). In almost all categories, BSREM non-TOF reconstruction was comparable to or better than the OSEM-TOF algorithm (P<0.001 for general image quality, image sharpness, noise, and P=1.0 for artifact). Only in lesion detectability was OSEM-TOF significantly better than BSREM non-TOF (P<0.001). Both BSREM-TOF and BSREM non-TOF showed a decreasing SUVmax with increasing β values (P<0.001) and TOF reconstructions showed a significantly higher SUVmax than non-TOF reconstructions (P<0.001). CONCLUSION The BSREM reconstruction algorithm showed a relevant improvement compared with OSEM reconstruction in PET/CT studies in all evaluated categories. BSREM might be used in clinical routine in conjunction with TOF to achieve better/higher image quality and lesion detectability or in PET/CT-systems without TOF-capability for enhancement of overall image quality as well

Deep learning-based time-of-flight (ToF) image enhancement of non-ToF PET scans

PURPOSE To improve the quantitative accuracy and diagnostic confidence of PET images reconstructed without time-of-flight (ToF) using deep learning models trained for ToF image enhancement (DL-ToF). METHODS A total of 273 [$^{18}$F]-FDG PET scans were used, including data from 6 centres equipped with GE Discovery MI ToF scanners. PET data were reconstructed using the block-sequential-regularised-expectation-maximisation (BSREM) algorithm with and without ToF. The images were then split into training (n = 208), validation (n = 15), and testing (n = 50) sets. Three DL-ToF models were trained to transform non-ToF BSREM images to their target ToF images with different levels of DL-ToF strength (low, medium, high). The models were objectively evaluated using the testing set based on standardised uptake value (SUV) in 139 identified lesions, and in normal regions of liver and lungs. Three radiologists subjectively rated the models using testing sets based on lesion detectability, diagnostic confidence, and image noise/quality. RESULTS The non-ToF, DL-ToF low, medium, and high methods resulted in - 28 ± 18, - 28 ± 19, - 8 ± 22, and 1.7 ± 24% differences (mean; SD) in the SUV$_{max}$ for the lesions in testing set, compared to ToF-BSREM image. In background lung VOIs, the SUV$_{mean}$ differences were 7 ± 15, 0.6 ± 12, 1 ± 13, and 1 ± 11% respectively. In normal liver, SUV$_{mean}$ differences were 4 ± 5, 0.7 ± 4, 0.8 ± 4, and 0.1 ± 4%. Visual inspection showed that our DL-ToF improved feature sharpness and convergence towards ToF reconstruction. Blinded clinical readings of testing sets for diagnostic confidence (scale 0-5) showed that non-ToF, DL-ToF low, medium, and high, and ToF images scored 3.0, 3.0, 4.1, 3.8, and 3.5 respectively. For this set of images, DL-ToF medium therefore scored highest for diagnostic confidence. CONCLUSION Deep learning-based image enhancement models may provide converged ToF-equivalent image quality without ToF reconstruction. In clinical scoring DL-ToF-enhanced non-ToF images (medium and high) on average scored as high as, or higher than, ToF images. The model is generalisable and hence, could be applied to non-ToF images from BGO-based PET/CT scanners

Characterization of the impact to PET quantification and image quality of an anterior array surface coil for PET/MR imaging

OBJECT: The aim of this study was to determine the impact to PET quantification, image quality and possible diagnostic impact of an anterior surface array used in a combined PET/MR imaging system. MATERIALS AND METHODS: An extended oval phantom and 15 whole-body FDG PET/CT subjects were re-imaged for one bed position following placement of an anterior array coil at a clinically realistic position. The CT scan, used for PET attenuation correction, did not include the coil. Comparison, including liver SUVmean, was performed between the coil present and absent images using two methods of PET reconstruction. Due to the time delay between PET scans, a model was used to account for average physiologic time change of SUV. RESULTS: On phantom data, neglecting the coil caused a mean bias of -8.2 % for non-TOF/PSF reconstruction, and -7.3 % with TOF/PSF. On clinical data, the liver SUV neglecting the coil presence fell by -6.1 % (±6.5 %) for non-TOF/PSF reconstruction; respectively -5.2 % (±5.3 %) with TOF/PSF. All FDG-avid features seen with TOF/PSF were also seen with non-TOF/PSF reconstruction. CONCLUSION: Neglecting coil attenuation for this anterior array coil results in a small but significant reduction in liver SUVmean but was not found to change the clinical interpretation of the PET images