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Misregistration between CT and PET in PET/CT is mainly caused by respiratory motion or irregular respiration during the CT scan in PET/CT. Other than repeat CT, repeat PET/CT, or data-driven gated (DDG) CT, there is no practical approach to mitigate the misregistration artifacts and subsequent CT attenuation correction (CTAC) of the PET data. DDG PET derives a respiratory motion model based on the multiple phases of PET images without hardware gating and it allows for a potential correction of the misregistration artifacts based on the respiratory motion model. The purpose of this commentary was to compare the recent two publications on matching the random phase of helical CT with one of the PET phases derived from the motion model of DDG PET and warping the misregistered helical CT for CTAC of and registration with PET or DDG PET. The two publications were similar in methodology. However, the data sets used for the comparison were different and could potentially impact their conclusions.

BackgroundData-driven gating (DDG) can improve PET quantitation and alleviate many issues with patient motion. However, misregistration between DDG-PET and CT may occur due to the distinct temporal resolutions of PET and CT and can be mitigated by DDG-CT. Here, the effects of misregistration and respiratory motion on PET quantitation and lesion segmentation were assessed with a new DDG-PET/CT method.MethodsA low-dose cine-CT was acquired in misregistered regions to enable both average CT (ACT) and DDG-CT. The following were compared: (1) baseline PET/CT, (2) PET/ACT (attenuation correction, AC = ACT), (3) DDG-PET (AC = helical CT), and (4) DDG-PET/CT (AC = DDG-CT). For DDG-PET, end-expiration (EE) data were derived from 50% of the total PET data at 30% from end-inspiration. For DDG-CT, EE phase CT data were extracted from cine-CT data by lung Hounsfield unit (HU) value and body contour. A total of 91 lesions from 16 consecutive patients were assessed for changes in standard uptake value (SUV), lesion glycolysis (LG), lesion volume, centroid-to-centroid distance (CCD), and DICE coefficients.ResultsRelative to baseline PET/CT, median changes in SUVmax ± σ for all 91 lesions were 20 ± 43%, 26 ± 23%, and 66 ± 66%, respectively, for PET/ACT, DDG-PET, and DDG-PET/CT. Median changes in lesion volume were 0 ± 58%, − 36 ± 26%, and − 26 ± 40%. LG for individual lesions increased for PET/ACT and decreased for DDG-PET, but was not different for DDG-PET/CT. Changes in mean HU from baseline PET/CT were dramatic for most lesions in both PET/ACT and DDG-PET/CT, especially for lesions with mean HU < 0 at baseline. CCD and DICE were both affected more by motion correction with DDG-PET than improved registration with ACT or DDG-CT.ConclusionAs misregistration becomes more prominent, the impact of motion correction with DDG-PET is diminished. The potential benefits of DDG-PET toward accurate lesion segmentation and quantitation could only be fully realized when combined with DDG-CT. These results impress upon the necessity of ensuring both misregistration and motion correction are accounted for together to optimize the clinical utility of PET/CT.

BackgroundCoronary PET shows promise in the detection of high-risk atherosclerosis, but there remains a need to optimize imaging and reconstruction techniques. We investigated the impact of reconstruction parameters and cardiac motion-correction in 18F Sodium Fluoride (18F-NaF) PET.MethodsTwenty-two patients underwent 18F-NaF PET within 22 days of an acute coronary syndrome. Optimal reconstruction parameters were determined in a subgroup of six patients. Motion-correction was performed on ECG-gated data of all patients with optimal reconstruction. Tracer uptake was quantified in culprit and reference lesions by computing signal-to-noise ratio (SNR) in diastolic, summed, and motion-corrected images.ResultsReconstruction using 24 subsets, 4 iterations, point-spread-function modelling, time of flight, and 5-mm post-filtering provided the highest median SNR (31.5) compared to 4 iterations 0-mm (22.5), 8 iterations 0-mm (21.1), and 8 iterations 5-mm (25.6; all P < .05). Motion-correction improved SNR of culprit lesions (n = 33) (24.5[19.9-31.5]) compared to diastolic (15.7[12.4-18.1]; P < .001) and summed data (22.1[18.9-29.2]; P < .001). Motion-correction increased the SNR difference between culprit and reference lesions (10.9[6.3-12.6]) compared to diastolic (6.2[3.6-10.3]; P = .001) and summed data (7.1 [4.8-11.6]; P = .001).ConclusionsThe number of iterations and extent of post-filtering has marked effects on coronary 18F-NaF PET quantification. Cardiac motion-correction improves discrimination between culprit and reference lesions.

Purpose Misregistration between CT and PET can result in mis-localization and inaccurate quantification of the tracer uptake in PET. Data-driven gated (DDG) CT can correct registration and quantification but requires a radiation dose of 1.3 mSv and 1 min of acquisition time. AI registration (AIR) does not require an additional CT and has been validated to improve registration and reduce the ‘banana’ misregistration artifacts around the diaphragm. We aimed to compare a validated AIR and DDG CT in registration and quantification of avid thoracic lesions misregistered in DDG PET scans. Methods Thirty PET/CT patient data (23 with 18 F-FDG, 4 with 68 Ga-Dotatate, and 3 with 18 F-PSMA piflufolastat) with at least one misregistered avid lesion in the thorax were recruited. Patient studies were conducted using DDG CT to correct misregistration with DDG PET data of the phases 30 to 80% on GE Discovery MI PET/CT scanners. Non-attenuation correction DDG PET and misregistered CT were input to AIR and the AIR-corrected CT data were output to register and quantify the DDG PET data. Registration and quantification of lesion SUV max and signal-to-background ratio (SBR) of the lesion SUV max to the 2-cm background mean SUV were compared for each of the 51 avid lesions. Results DDG CT outperformed AIR in misregistration correction and quantification of avid thoracic lesions (1.16 ± 0.45 cm). Most lesions (46/51, 90%) showed improved registration from DDG CT relative to AIR, with 10% (5/51) being similar between AIR and DDG CT. The lesions in the baseline CT were an average of 2.06 ± 1.0 cm from their corresponding lesions in the DDG CT, while those in the AIR CT were an average of 0.97 ± 0.54 cm away. AIR significantly improved lesion registration compared to the baseline CT ( P  < 0.0001). SUV max increased by 18.1 ± 15.3% with AIR, but a statistically significantly larger increase of 34.4 ± 25.4% was observed with DDG CT ( P  < 0.0001). A statistically significant increase in SBR was also observed, rising from 10.5 ± 12.1% of AIR to 21.1 ± 20.5% of DDG CT ( P  < 0.0001). Many registration improvements by AIR were still left with misregistration. AIR could mis-localize a lymph node to the lung parenchyma or the ribs, and could also mis-localize a lung nodule to the left atrium. AIR could also distort the rib cage and the circular shape of the aorta cross section. Conclusions DDG CT outperformed AIR in both localization and quantification of the thoracic avid lesions. AIR improved registration of the misregistered PET/CT. Registered lymph nodes could be falsely misregistered by AIR. AIR-induced distortion of the rib cage can also negatively impact image quality. Further research on AIR’s accuracy in modeling true patient respiratory motion without introducing new misregistration or anatomical distortion is warranted.

Chemoradiotherapy (CRT) has been associated with increased incidence of cardiovascular (CV) adverse events (CVAE). Coronary artery calcium scoring (CAC) has shown to predict coronary events beyond the traditional CV risk factors. This study examines whether CAC, measured on standard of care, non-contrast chest CT (NCCT) imaging, predicts the development of CVAE in patients with non-small cell lung cancer (NSCLC) treated with CRT. Patients with NSCLC treated with CRT at MD Anderson Cancer Center from 7/2009 until 4/2014 and who had at least one NCCT scan within 6 months from their first CRT were identified. CAC scoring was performed on NCCT scans by an expert cardiologist and a cardiac radiologist following the 2016 SCCT/STR guidelines. CVAE were graded based on the most recent Common Terminology Criteria for Adverse Events (CTCAE) version 5.0. CVAE were also grouped into (i) coronary/vascular events, (ii) arrhythmias, or (iii) heart failure. All CVAE were adjudicated by a board-certified cardiologist. Out of a total of 193 patients, 45% were female and 91% Caucasian. Mean age was 64 ± 9 years and mean BMI 28 ± 6 kg/m . Of 193 patients, 74% had CAC >0 Agatston units (AU), 49% CAC ≥100 AU and 36% CAC ≥300 AU. Twenty-nine patients (15%) developed a grade ≥2 CVAE during a median follow-up of 24.3 months (IQR: 10.9-51.7). Of those, 11 (38%) were coronary/vascular events. In the multivariate cox regression analysis, controlling for mean heart dose and pre-existing CV disease, higher CAC score was independently associated with development of a grade ≥2 CVAE [HR: 1.04 (per 100 AU), 95% CI: 1.01-1.08, = 0.022] and with worse overall survival (OS; CAC ≥100 vs. <100 AU, HR: 1.64, 95% CI: 1.11-2.44, = 0.013). In a sub-analysis evaluating the type of the CVAE, it was the coronary/vascular events that were significantly associated with higher baseline CAC (median: 676 AU vs. 73 AU, = 0.035). Cardiovascular adverse events are frequent in patients with NSCLC treated with CRT. CAC calculated on \"standard of care\" NCCT can predict the development of CVAEs and specifically coronary/vascular events, as well as OS, independently from other traditional risk factors and radiation mean heart dose. [https://clinicaltrials.gov/ct2/show/NCT00915005], identifier [NCT00915005].

When imaging studies (e.g. CT) are used to quantify morphological changes in an anatomical structure, it is necessary to understand the extent and source of motion which can give imaging artifacts (e.g. blurring or local distortion). The objective of this study was to assess the magnitude of esophageal motion due to cardiac motion. We used retrospective electrocardiogram-gated contrast-enhanced computed tomography angiography images for this study. The anatomic region from the carina to the bottom of the heart was taken at deep-inspiration breath hold with the patients' arms raised above their shoulders, in a position similar to that used for radiation therapy. The esophagus was delineated on the diastolic phase of cardiac motion, and deformable registration was used to sequentially deform the images in nearest-neighbor phases among the 10 cardiac phases, starting from the diastolic phase. Using the 10 deformation fields generated from the deformable registration, the magnitude of the extreme displacements was then calculated for each voxel, and the mean and maximum displacement was calculated for each computed tomography slice for each patient. The average maximum esophageal displacement due to cardiac motion for all patients was 5.8 mm (standard deviation: 1.6 mm, maximum: 10.0 mm) in the transverse direction. For 21 of 26 patients, the largest esophageal motion was found in the inferior region of the heart; for the other patients, esophageal motion was approximately independent of superior-inferior position. The esophagus motion was larger at cardiac phases where the electrocardiogram R-wave occurs. In conclusion, the magnitude of esophageal motion near the heart due to cardiac motion is similar to that due to other sources of motion, including respiratory motion and intra-fraction motion. A larger cardiac motion will result into larger esophagus motion in a cardiac cycle.

Purpose Due to various physical degradation factors and limited counts received, PET image quality needs further improvements. The denoising diffusion probabilistic model (DDPM) was a distribution learning-based model, which tried to transform a normal distribution into a specific data distribution based on iterative refinements. In this work, we proposed and evaluated different DDPM-based methods for PET image denoising. Methods Under the DDPM framework, one way to perform PET image denoising was to provide the PET image and/or the prior image as the input. Another way was to supply the prior image as the network input with the PET image included in the refinement steps, which could fit for scenarios of different noise levels. 150 brain [ 18 F]FDG datasets and 140 brain [ 18 F]MK-6240 (imaging neurofibrillary tangles deposition) datasets were utilized to evaluate the proposed DDPM-based methods. Results Quantification showed that the DDPM-based frameworks with PET information included generated better results than the nonlocal mean, Unet and generative adversarial network (GAN)-based denoising methods. Adding additional MR prior in the model helped achieved better performance and further reduced the uncertainty during image denoising. Solely relying on MR prior while ignoring the PET information resulted in large bias. Regional and surface quantification showed that employing MR prior as the network input while embedding PET image as a data-consistency constraint during inference achieved the best performance. Conclusion DDPM-based PET image denoising is a flexible framework, which can efficiently utilize prior information and achieve better performance than the nonlocal mean, Unet and GAN-based denoising methods.

Cardiac imaging is a promising application for combined PET/MR imaging. However, current MR imaging protocols for whole‐body attenuation correction can produce spatial mismatch between PET and MR‐derived attenuation data owing to a disparity between the two modalities' imaging speeds. We assessed the feasibility of using a respiration‐averaged MR (AMR) method for attenuation correction of cardiac PET data in PET/MR images. First, to demonstrate the feasibility of motion imaging with MR, we used a 3T MR system and a two‐dimensional fast spoiled gradient‐recalled echo (SPGR) sequence to obtain AMR images of a moving phantom. Then, we used the same sequence to obtain AMR images of a patient's thorax under free‐breathing conditions. MR images were converted into PET attenuation maps using a three‐class tissue segmentation method with two sets of predetermined CT numbers, one calculated from the patient‐specific (PS) CT images and the other from a reference group (RG) containing 54 patient CT datasets. The MR‐derived attenuation images were then used for attenuation correction of the cardiac PET data, which were compared to the PET data corrected with average CT (ACT) images. In the myocardium, the voxel‐by‐voxel differences and the differences in mean slice activity between the AMR‐corrected PET data and the ACT‐corrected PET data were found to be small (less than 7%). The use of AMR‐derived attenuation images in place of ACT images for attenuation correction did not affect the summed stress score. These results demonstrate the feasibility of using the proposed SPGR‐based MR imaging protocol to obtain patient AMR images and using those images for cardiac PET attenuation correction. Additional studies with more clinical data are warranted to further evaluate the method. PACS number: 87.57.uk