Data-driven Respiratory Gating Research Articles

Improved [18F]FDG PET/CT Diagnostic Accuracy for Infective Endocarditis Using Conventional Cardiac Gating or Combined Cardiac and Respiratory Motion Correction (CardioFreezeTM).

Infective endocarditis (IE) is a serious and diagnostically challenging condition. [18F]FDG PET/CT is valuable for evaluating suspected IE, but it is susceptible to motion-related artefacts. This study investigated the potential benefits of cardiac motion correction for [18F]FDG PET/CT. In this prospective study, patients underwent [18F]FDG PET/CT for suspected IE, combined with a conventional cardiac gating sequence, a data-driven cardiac and respiratory gating sequence (CardioFreezeTM), or both. Scans were performed in adherence to EANM guidelines and assessors were blinded to patients' clinical contexts. Final diagnosis of IE was established based on multidisciplinary consensus after a minimum of 4 months follow-up and surgical findings, whenever performed. Seven patients participated in the study, undergoing both an ungated [18F] FDG-PET/CT and a scan with either conventional cardiac gating, CardioFreezeTM, or both. Cardiac motion correction improved the interpretability of [18F]FDG PET/CT in four out of five patients with valvular IE lesions, regardless of the method of motion correction used, which was statistically significant by Wilcoxon's signed rank test: p = 0.046. In one patient the motion-corrected sequence confirmed the diagnosis of endocarditis, which had been missed on non-gated PET. The performance of the two gating sequences was comparable. In conclusion, in this exploratory study, cardiac motion correction of [18F]FDG PET/CT improved the interpretability of [18F]FDG PET/CT. This may improve the sensitivity of PET/CT for suspected IE. Further larger comparative studies are necessary to confirm the additive value of these cardiac motion correction methods.

Impact of irregular waveforms on data-driven respiratory gated PET/CT images processed using MotionFree algorithm.

MotionFree® (AMF) is a data-driven respiratory gating (DDG) algorithm for image processing that has recently been introduced into clinical practice. The present study aimed to verify the accuracy of respiratory waveform and the effects of normal and irregular respiratory motions using AMF with the DDG algorithm. We used a NEMA IEC body phantom comprising six spheres (37-, 28-, 22-, 17-, 13-, and 10mm diameter) containing 18F. The sphere-to-background ratio was 4:1 (21.2 and 5.3kBq/mL). We acquired PET/CT images from a stationary or moving phantom placed on a custom-designed motion platform. Respiratory motions were reproduced based on normal (sinusoidal or expiratory-paused waveforms) and irregular (changed amplitude or shifted baseline waveforms) movements. The "width" parameters in AMF were set at 10-60% and extracted data during the expiratory phases of each waveform. We verified the accuracy of the derived waveforms by comparing those input from the motion platform and output determined using AMF. Quantitative accuracy was evaluated as recovery coefficients (RCs), improvement rate, and %change that were calculated based on sphere diameter or width. We evaluated statistical differences in activity concentrations of each sphere between normal and irregular waveforms. Respiratory waveforms derived from AMF were almost identical to the input waveforms on the motion platform. Although the RCs in each sphere for expiratory-paused and ideal stationary waveforms were almost identical, RCs except the expiratory-paused waveform were lower than those for the stationary waveform. The improvement rate decreased more for the irregular, than the normal waveforms with AMF in smaller spheres. The %change was improved by decreasing the width of waveforms with a shifted baseline. Activity concentrations significantly differed between normal waveforms and those with a shifted baseline in spheres < 28mm. The PET images using AMF with the DDG algorithm provided the precise waveform of respiratory motions and the improvement of quantitative accuracy in the four types of respiratory waveforms. The improvement rate was the most obvious in expiratory-paused waveforms, and the most subtle in those with a shifted baseline. Optimizing the width parameter in irregular waveform will benefit patients who breathe like the waveform with the shifted baseline.

Reproducibility of the principal component analysis (PCA)-based data-driven respiratory gating on texture features in non-small cell lung cancer patients with 18 F-FDG PET/CT.

Texture analysis is one of the lung cancer countermeasures in the field of radiomics. Even though image quality affects texture features, the reproducibility of principal component analysis (PCA)-based data-driven respiratory gating (DDG) on texture features remains poorly understood. Hence, this study aimed to clarify the reproducibility of PCA-based DDG on texture features in non-small cell lung cancer (NSCLC) patients with 18 F-Fluorodeoxyglucose (18 F-FDG) Positron emission tomography/computed tomography (PET/CT). Twenty patients with NSCLC who underwent 18 F-FDG PET/CT in routine clinical practice were retrospectively analyzed. Each patient's PET data were reconstructed in two PET groups of no gating (NG-PET) and PCA-based DDG gating (DDG-PET). Forty-six image features were analyzed using LIFEx software. Reproducibility was evaluated using Lin's concordance correlation coefficient ( ) and percentage difference (%Diff). Non-reproducibility was defined as having unacceptable strength <0.8) and a %Diff of>10%. NG-PET and DDG-PET were compared using the Wilcoxon signed-rank test. A total of 3/46 (6.5%) image features had unacceptable strength, and 9/46 (19.6%) image features had a %Diff of>10%. Significant differences between the NG-PET and DDG-PET groups were confirmed in only 4/46 (8.7%) of the high %Diff image features. Although the DDG application affected several texture features, most image features had adequate reproducibility. PCA-based DDG-PET can be routinely used as interchangeable images for texture feature extraction from NSCLC patients.

Evaluation of data-driven respiratory gating for subcentimeter lesions using digital PET/CT system and three-axis motion phantom

Introduction. The application of data-driven respiratory gating (DDG) for subcentimeter lesions with respiratory movement remains poorly understood. Hence, this study aimed to clarify DDG application for subcentimeter lesions and the ability of digital Positron emission tomography/computed tomography (PET/CT) system combined with DDG to detect these lesions under three-axis respiration. Methods. Discovery MI PET/CT system and National Electrical Manufacturers Association (NEMA) body phantom with Micro Hollow Sphere (4, 5, 6, 8, 10, and 13 mm) were used. The NEMA phantom was filled with 18F-FDG solutions of 42.4 and 5.3 kBq/ml for each hot sphere and background region. The 3.6 s cycles of three-axis respiratory motion were reproduced using the motion platform UniTraQ. The PET data acquisition was performed in stationary and respiratory-moving states. The data were reconstructed in three PET groups: stationary (NM-PET), no gating with respiratory movement (NG-PET), and DDG gating with respiratory movement (DDG-PET) groups. For image quality, percent contrast (QH); maximum, peak, and mean standardized uptake value (SUV); background region; and detectability index (DI) were evaluated in each PET group. Visual assessment was also conducted. Results. The groups with respiratory movement had deteriorated QH and SUVs compared with NM-PET. Compared with NG-PET, DDG-PET has significantly improved QH and SUVs in spheres above 6 mm. The background region showed no significant difference between groups. The SUVmax, SUVpeak, and QH values of 8 mm sphere were highest in NM-PET, followed by DDG-PET and NG-PET. In visual assessment, the spheres above 6 mm were detected in all PET groups. DDG application did not detect new lesions, but it increased DI and visual score. Conclusions. The application of principal component analysis (PCA)-based DDG algorithm improves both image quality and quantitative SUVs in subcentimeter lesions measuring above 6 mm. Although DDG application cannot detect new subcentimeter lesions, it increases the visual indices.

What factors influence the R value in data-driven respiratory gating technique? A phantom study.

The R value is adopted as a metric for the effectiveness of the respiratory waveform in the Advanced Motion Free implemented in the PET scanner as the data-driven respiratory gating (DDG) algorithm. The effects of changes in various factors on R values were evaluated by phantom analysis. We used a programmable respiratory motion phantom QUASAR with a sphere filled with an 18F solution. Respiratory motion simulation was performed by changing the sphere diameter, radioactivity concentration, amplitude, respiratory cycle, and respiratory waveform shape. Three evaluations were performed. (1) The power spectra calculated from the input waveforms were evaluated. (2) The effects of changes in the factors on the R value were evaluated. (3) DDG waveforms and inspiratory peak intervals were compared with the input waveform data set. The R values were increased and converged to a certain value as sphere diameter, radioactivity concentration, and amplitude gradually increased. The respiratory cycle showed the highest R value at 7.5 s, and the graph showed an upward convex pattern. The R value of the sinusoid waveform was higher than that of the typical waveform. There was a relationship between the power spectrum of the input waveform and R value. The visual score was also lower in the condition with a lower R value. In cases of no sphere, radioactivity, or motion, and a fast respiratory cycle, peak intervals were not accurately acquired. Factors affecting the R value were sphere diameter, radioactivity concentration, amplitude, respiratory cycle, and respiratory waveform shape.

Impact of low injected activity on data driven respiratory gating for PET/CT imaging with continuous bed motion.

Data driven respiratory gating (DDG) in positron emission tomography (PET) imaging extracts respiratory waveforms from the acquired PET data obviating the need for dedicated external devices. DDG performance, however, degrades with decreasing detected number of coincidence counts. In this paper, we assess the clinical impact of reducing injected activity on a new DDG algorithm designed for PET data acquired with continuous bed motion (CBM_DDG) by evaluating CBM_DDG waveforms, tumor quantification, and physician's perception of motion blur in resultant images. Forty patients were imaged on a Siemens mCT scanner in CBM mode. Reduced injected activity was simulated by generating list mode datasets with 50% and 25% of the original data (100%). CBM_DDG waveforms were compared to that of the original data over the range between the aortic arch and the center of the right kidney using the Pearson correlation coefficient (PCC). Tumor quantification was assessed by comparing the maximum standardized uptake value (SUVmax) and peak SUV (SUVpeak) of reconstructed images from the various list mode datasets using elastic motion deblurring (EMDB) reconstruction. Perceived motion blur was assessed by three radiologists of one lesion per patient on a continuous scale from no motion blur (0) to significant motion blur (3). The mean PCC of the 50% and 25% dataset waveforms was 0.74 ± 0.18 and 0.59 ± 0.25, respectively. In comparison to the 100% datasets, the mean SUVmax increased by 2.25% (p = 0.11) for the 50% datasets and by 3.91% (p = 0.16) for the 25% datasets, while SUVpeak changes were within ±0.25%. Radiologist evaluations of motion blur showed negligible changes with average values of 0.21, 0.3, and 0.28 for the 100%, 50%, and 25% datasets. Decreased injected activities degrades the resultant CBM_DDG respiratory waveforms; however this decrease has minimal impact on quantification and perceived image motion blur.

Primer on Commonly Occurring MRI Artifacts and How to Overcome Them.

HomeRadioGraphicsVol. 42, No. 3 PreviousNext PhysicsRadioGraphics FundamentalsPrimer on Commonly Occurring MRI Artifacts and How to Overcome ThemChikara Noda, Bharath Ambale Venkatesh, Jennifer D. Wagner, Yoko Kato, Jason M. Ortman, João A. C. Lima Chikara Noda, Bharath Ambale Venkatesh, Jennifer D. Wagner, Yoko Kato, Jason M. Ortman, João A. C. Lima Author AffiliationsFrom the Divisions of Cardiology (C.N., Y.K., J.M.O., J.A.C.L.) and Radiology (B.A.V.), Johns Hopkins University School of Medicine, 600 N Wolfe St, Blalock 524, Baltimore, MD 21287; and Canon Medical Research USA, Cleveland, Ohio (J.D.W.).Address correspondence to J.A.C.L. (e-mail: [email protected]).Chikara NodaBharath Ambale VenkateshJennifer D. WagnerYoko KatoJason M. OrtmanJoão A. C. Lima Published Online:Apr 22 2022https://doi.org/10.1148/rg.210021MoreSectionsFull textPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In AbstractVarious artifacts occur at MRI, and MRI technologists and radiologists must know how to identify and avoid these artifacts so they can consistently produce high-quality images.Suggested Readings Hirokawa Y, Isoda H, Maetani YS, Arizono S, Shimada K, Togashi K. MRI artifact reduction and quality improvement in the upper abdomen with PROPELLER and prospective acquisition correction (PACE) technique. AJR Am J Roentgenol 2008;191(4):1154–1158. Crossref, Medline, Google Scholar Huang SY, Seethamraju RT, Patel P, Hahn PF, Kirsch JE, Guimaraes AR. Body MR Imaging: Artifacts, k-Space, and Solutions. RadioGraphics 2015;35(5):1439–1460. Link, Google Scholar Runge M, Ibrahim EH, Bogun F, et al. Metal Artifact Reduction in Cardiovascular MRI for Accurate Myocardial Scar Assessment in Patients With Cardiac Implantable Electronic Devices. AJR Am J Roentgenol 2019;213(3):555–561. Crossref, Medline, Google Scholar Stadler A, Schima W, Ba-Ssalamah A, Kettenbach J, Eisenhuber E. Artifacts in body MR imaging: their appearance and how to eliminate them. Eur Radiol 2007;17(5):1242–1255. Crossref, Medline, Google Scholar Tsuchihashi T. Artifact of MRI [in Japanese]. Nippon Hoshasen Gijutsu Gakkai Zasshi 2003;59(11):1370–1377. Crossref, Medline, Google ScholarArticle HistoryReceived: Feb 1 2021Revision requested: Apr 20 2021Revision received: June 29 2021Accepted: July 9 2021Published online: Apr 22 2022Published in print: May 2022 FiguresReferencesRelatedDetailsCited ByPostsurgical Evaluation of the Pediatric Foot and AnkleMaria PilarAparisi Gómez, AlbertoBazzocchi, PaoloSimoni, Emilio J.Inarejos Clemente2022 | Seminars in Musculoskeletal Radiology, Vol. 26, No. 06Accompanying This ArticlePrimer on Commonly Occurring MRI Artifacts and How to Overcome ThemApr 22 2022Default Digital Object SeriesRecommended Articles Respiratory Motion Management in Abdominal MRI: Radiology In TrainingRadiology2022Volume: 306Issue: 1pp. 47-53Invited Commentary on “Optimizing Diffusion-Tensor Imaging Acquisition for Spinal Cord Assessment,” with Response from Dr Martín Noguerol et alRadioGraphics2020Volume: 40Issue: 2pp. 428-431Impact of Data-driven Respiratory Gating in Clinical PETRadiology2016Volume: 281Issue: 1pp. 229-238Validation of Software Gating: A Practical Technology for Respiratory Motion Correction in PETRadiology2016Volume: 281Issue: 1pp. 239-248MR Neurography of Peripheral Nerve Injury in the Presence of Orthopedic Hardware: Technical ConsiderationsRadiology2021Volume: 300Issue: 2pp. 246-259See More RSNA Education Exhibits A Primer on Commonly Occurring MRI Artifacts and How to Overcome ThemDigital Posters2020Up-to-Date Technique for Improvement of CT and MRI Imaging Quality in the Head and Neck: Theory, Clinical ApplicationsDigital Posters2019Pulse Sequences Used in Musculoskeletal MRI Exams: A Review for Residents and FellowsDigital Posters2018 RSNA Case Collection Orbital blowout fractureRSNA Case Collection2020Metastatic invasive lobular breast carcinomaRSNA Case Collection2021Pulmonary SequestrationRSNA Case Collection2022 Vol. 42, No. 3 Slide PresentationMetrics Altmetric Score PDF download

Clinical feasibility and impact of data-driven respiratory motion compensation studied in 200 whole-body 18F-FDG PET/CT scans

BackgroundThis study examines the clinical feasibility and impact of implementing a fully automated whole-body PET protocol with data-driven respiratory gating in patients with a broad range of oncological and non-oncological pathologies 592 FDG PET/CT patients were prospectively included. 200 patients with lesions in the torso were selected for further analysis, and ungated (UG), belt gated (BG) and data-driven gating (DDG) images were reconstructed. All images were reconstructed using the same data and without prolonged acquisition time for gated images. Images were quantitatively analysed for lesion uptake and metabolic volume, complemented by a qualitative analysis of visual lesion detection. In addition, the impact of gating on treatment response evaluation was evaluated in 23 patients with malignant lymphoma.ResultsPlacement of the belt needed for BG was associated with problems in 27% of the BG scans, whereas no issues were reported using DDG imaging. For lesion quantification, DDG and BG images had significantly greater SUV values and smaller volumes than UG. The physicians reported notable image blurring in 44% of the UG images that was problematic for clinical evaluation in 4.5% of cases.ConclusionRespiratory motion compensation using DDG is readily integrated into clinical routine and produce images with more accurate and significantly greater SUV values and smaller metabolic volumes. In our broad cohort of patients, the physicians overwhelmingly preferred gated over ungated images, with a slight preference for DDG images. However, even in patients with malignant disease in the torso, no additional diagnostic information was obtained by the gated images that could not be derived from the ungated images.

Evaluation of a novel data-driven respiratory gating technique in PET/CT exams

Evaluation of a novel data-driven respiratory gating technique in PET/CT exams

Data-driven respiratory phase-matched PET attenuation correction without CT

We propose a deep learning-based data-driven respiratory phase-matched gated-PET attenuation correction (AC) method that does not need a gated-CT. The proposed method is a multi-step process that consists of data-driven respiratory gating, gated attenuation map estimation using maximum-likelihood reconstruction of attenuation and activity (MLAA) algorithm, and enhancement of the gated attenuation maps using convolutional neural network (CNN). The gated MLAA attenuation maps enhanced by the CNN allowed for the phase-matched AC of gated-PET images. We conducted a non-rigid registration of the gated-PET images to generate motion-free PET images. We trained the CNN by conducting a 3D patch-based learning with 80 oncologic whole-body 18F-fluorodeoxyglucose (18F-FDG) PET/CT scan data and applied it to seven regional PET/CT scans that cover the lower lung and upper liver. We investigated the impact of the proposed respiratory phase-matched AC of PET without utilizing CT on tumor size and standard uptake value (SUV) assessment, and PET image quality (%STD). The attenuation corrected gated and motion-free PET images generated using the proposed method yielded sharper organ boundaries and better noise characteristics than conventional gated and ungated PET images. A banana artifact observed in a phase-mismatched CT-based AC was not observed in the proposed approach. By employing the proposed method, the size of tumor was reduced by 12.3% and SUV90% was increased by 13.3% in tumors with larger movements than 5 mm. %STD of liver uptake was reduced by 11.1%. The deep learning-based data-driven respiratory phase-matched AC method improved the PET image quality and reduced the motion artifacts.

Prospective data-driven respiratory gating of [68Ga]Ga-DOTATOC PET/CT

AimThe aim of this prospective study was to evaluate a data-driven gating software’s performance, in terms of identifying the respiratory signal, comparing [68Ga]Ga-DOTATOC and [18F]FDG examinations. In addition, for the [68Ga]Ga-DOTATOC examinations, tracer uptake quantitation and liver lesion detectability were assessed.MethodsTwenty-four patients with confirmed or suspected neuroendocrine tumours underwent whole-body [68Ga]Ga-DOTATOC PET/CT examinations. Prospective DDG was applied on all bed positions and respiratory motion correction was triggered automatically when the detected respiratory signal exceeded a certain threshold (R value ≥ 15), at which point the scan time for that bed position was doubled. These bed positions were reconstructed with quiescent period gating (QPG), retaining 50% of the total coincidences. A respiratory signal evaluation regarding the software’s efficacy in detecting respiratory motion for [68Ga]Ga-DOTATOC was conducted and compared to [18F]FDG data. Measurements of SUVmax, SUVmean, and tumour volume were performed on [68Ga]Ga-DOTATOC PET and compared between gated and non-gated images.ResultsThe threshold of R ≥ 15 was exceeded and gating triggered on mean 2.1 bed positions per examination for [68Ga]Ga-DOTATOC as compared to 1.4 for [18F]FDG. In total, 34 tumours were evaluated in a quantitative analysis. An increase of 25.3% and 28.1%, respectively, for SUVmax (P < 0.0001) and SUVmean (P < 0.0001), and decrease of 21.1% in tumour volume (P < 0.0001) was found when DDG was applied.ConclusionsHigh respiratory signal was exclusively detected in bed positions where respiratory motion was expected, indicating reliable performance of the DDG software on [68Ga]Ga-DOTATOC PET/CT. DDG yielded significantly higher SUVmax and SUVmean values and smaller tumour volumes, as compared to non-gated images.

The impact of data-driven respiratory gating in clinical F-18 FDG PET/CT: comparison of free breathing and deep-expiration breath-hold CT protocol.

Respiratory motion can diminish PET image quality and lead to inaccurate lesion quantifications. Data-driven gating (DDG) was recently introduced as an effective respiratory gating technique for PET. In the current study, we investigated the clinical impact of DDG on respiratory movement in 18F-FDG PET/CT. PET list-mode data were collected for each subject and DDG software was utilized for extracting respiratory waveforms. PET images was reconstructed using Q.clear and Q.clear + DDG, respectively. We evaluated SUVmax, SUVmean, the coefficient of variance (CoV), metabolic tumor volume (MTV), and tumor heterogeneity using the area under the curve of cumulative SUV histogram (AUC-CSH). Metabolic parameter changes were compared between each reconstruction method. The Deep-Expiration Breath Hold (DEBH) protocol was introduced for CT scans to correct spatial misalignment between PET and CT and compared with conventional free breathing. The DEBH and free breathing (FB) protocol comparison was made in a separate matching cohort using propensity core matching rather than the same patient. Total 147 PET/CT scans with excessive respiratory movements were used to study DDG-mediated correction. After DDG application, SUVmax (P < 0.0001; 8.15 ± 4.77 vs. 9.03 ± 5.02) and SUVmean (P < 0.0001; 4.91 ± 2.44 vs. 5.49 ± 2.68) of lung and upper abdomen lesions increased, while MTV significantly decreased (P < 0.0001; 7.07 ± 15.46 vs. 6.58 ± 15.14). In addition, the percent change of SUVs was greater in lower lung lesions compared to upper lobe lesions. Likewise, the MTV reduction was significantly greater in lower lobe lesions. No significant difference dependent on location was observed in liver lesions. DEBH-mediated CT breathing correction did not make a significant difference in lesion metabolic parameters compared to conventional free breathing. These results suggest that DDG correction enables more corrected quantification from respiratory movements for lesions located in the lung and upper abdomen. Therefore, we suggest that DDG is worth using as a standard protocol during 18F-FDG PET/CT imaging.

Effects of CT-based Attenuation Correction on PET Images Using Data-driven Respiratory Gating

We applied deviceless, positron emission tomography/computed tomography(PET/CT) data-driven respiratory gating (DDG) to validate the effects of misalignment between PET and CT at various respiratory phases. A lung lesion was simulated using an NEMA IEC body phantom in which the background comprised hot spheres containing polystyrene foam beads. We acquired PET images as the phantom moved downwards and then stopped. Attenuation on computed tomography images acquired at the inspiratory, stationary, and expiratory phases was corrected after the phantom stopped moving. Normalized mean square error (NMSE), recovery coefficients (RCmax and RCmean) and volume were analyzed on DDG-PET images using CT-based attenuation correction. The NMSE was closest to 0 in PET images corrected using the expiratory CT image. The RCmax was<1.0, and the RCmean was closest to 1.0 only in PET images corrected using the expiratory CT image. Volume was either underestimated or overestimated more according to the size of the spheres when the alignment of CT and PET images was greater. We recommend using the expiratory but not the inspiratory phase when using DDG for PET/CT correction.

Management of Respiratory Motion in PET/CT: Data-driven Respiratory Gating PET/CT

Management of Respiratory Motion in PET/CT: Data-driven Respiratory Gating PET/CT

Data-driven respiratory gating based on localized diaphragm sensing in TOF PET

It is important to measure the respiratory cycle in positron emission tomography (PET) to enhance the contrast of the tumor as well as the accuracy of its localization in organs such as the lung and liver. Several types of data-driven respiratory gating methods, such as center of mass and principal component analysis, have been developed to directly measure the breathing cycle from PET images and listmode data. However, the breathing cycle is still hard to detect in low signal-to-noise ratio (SNR) data, particularly in low dose PET/CT scans. To address this issue, a time-of-flight (TOF) PET is currently utilized for the data-driven respiratory gating because of its higher SNR and better localization of the region of interest. To further improve the accuracy of respiratory gating with TOF information, we propose an accurate data-driven respiratory gating method, which retrospectively derives the respiratory signal using a localized sensing method based on a diaphragm mask in TOF PET data. To assess the accuracy of the proposed method, the performance is evaluated with three patient datasets, and a pressure-belt signal as the ground truth is compared. In our experiments, we validate that the respiratory signal using the proposed data-driven gating method is well matched to the pressure-belt respiratory signal with less than 5% peak time errors and over 80% trace correlations. Based on gated signals, the respiratory-gated image of the proposed method provides more clear edges of organs compared to images using conventional non-TOF methods. Therefore, we demonstrate that the proposed method can achieve improvements for the accuracy of gating signals and image quality.

Data-Driven Respiratory Gating Outperforms Device-Based Gating for Clinical 18F-FDG PET/CT.

A data-driven method for respiratory gating in PET has recently been commercially developed. We sought to compare the performance of the algorithm with an external, device-based system for oncologic 18F-FDG PET/CT imaging. Methods: In total, 144 whole-body 18F-FDG PET/CT examinations were acquired, with a respiratory gating waveform recorded by an external, device-based respiratory gating system. In each examination, 2 of the bed positions covering the liver and lung bases were acquired with a duration of 6 min. Quiescent-period gating retaining approximately 50% of coincidences was then able to produce images with an effective duration of 3 min for these 2 bed positions, matching the other bed positions. For each examination, 4 reconstructions were performed and compared: data-driven gating (DDG) (we use the term DDG-retro to distinguish that we did not use the real-time R-threshold-based application of DDG that is available within the manufacturer's product), external device-based gating (real-time position management (RPM)-gated), no gating but using only the first 3 min of data (ungated-matched), and no gating retaining all coincidences (ungated-full). Lesions in the images were quantified and image quality scored by a radiologist who was masked to the method of data processing. Results: Compared with the other reconstruction options, DDG-retro increased the SUVmax and decreased the threshold-defined lesion volume. Compared with RPM-gated, DDG-retro gave an average increase in SUVmax of 0.66 ± 0.1 g/mL (n = 87, P < 0.0005). Although the results from the masked image evaluation were most commonly equivalent, DDG-retro was preferred over RPM-gated in 13% of examinations, whereas the opposite occurred in just 2% of examinations. This was a significant preference for DDG-retro (P = 0.008, n = 121). Liver lesions were identified in 23 examinations. Considering this subset of data, DDG-retro was ranked superior to ungated-full in 6 of 23 (26%) cases. Gated reconstruction using the external device failed in 16% of examinations, whereas DDG-retro always provided a clinically acceptable image. Conclusion: In this clinical evaluation, DDG-retro provided performance superior to that of the external device-based system. For most examinations the performance was equivalent, but DDG-retro had superior performance in 13% of examinations, leading to a significant preference overall.

Clinical Evaluation of a Data-Driven Respiratory Gating Algorithm for Whole-Body PET with Continuous Bed Motion

Respiratory gating is the standard to prevent respiration effects from degrading image quality in PET. Data-driven gating (DDG) using signals derived from PET raw data is a promising alternative to gating approaches requiring additional hardware (e.g., pressure-sensitive belt gating [BG]). However, continuous-bed-motion (CBM) scans require dedicated DDG approaches for axially extended PET, compared with DDG for conventional step-and-shoot scans. In this study, a CBM-capable DDG algorithm was investigated in a clinical cohort and compared with BG using optimally gated (OG) and fully motion-corrected (elastic motion correction [EMOCO]) reconstructions. Methods: Fifty-six patients with suspected malignancies in the thorax or abdomen underwent whole-body 18F-FDG CBM PET/CT using DDG and BG. Correlation analyses were performed on both gating signals. Besides static reconstructions, OG and EMOCO reconstructions were used for BG and DDG. The metabolic volume, SUVmax, and SUVmean of lesions were compared among the reconstructions. Additionally, the quality of lesion delineation in the different PET reconstructions was independently evaluated by 3 experts. Results: The global correlation coefficient between BG and DDG signals was 0.48 ± 0.11, peaking at 0.89 ± 0.07 when scanning the kidney and liver region. In total, 196 lesions were analyzed. SUV measurements were significantly higher in BG-OG, DDG-OG, BG-EMOCO, and DDG-EMOCO than in static images (P < 0.001; median SUVmax: static, 14.3 ± 13.4; BG-EMOCO, 19.8 ± 15.7; DDG-EMOCO, 20.5 ± 15.6; BG-OG, 19.6 ± 17.1; and DDG-OG, 18.9 ± 16.6). No significant differences between BG-OG and DDG-OG or between BG-EMOCO and DDG-EMOCO were found. Visual lesion delineation was significantly better in BG-EMOCO and DDG-EMOCO than in static reconstructions (P < 0.001); no significant difference was found when comparing BG and DDG for either EMOCO or OG reconstruction. Conclusion: DDG-based motion compensation of CBM PET acquisitions outperforms static reconstructions, delivering qualities comparable to BG approaches. The new algorithm may be a valuable alternative for CBM PET systems.

Evaluation of data-driven respiratory gating waveforms for clinical PET imaging

BackgroundWe aimed to evaluate the clinical robustness of a commercially developed data-driven respiratory gating algorithm based on principal component analysis, for use in routine PET imaging.MethodsOne hundred fifty-seven adult FDG PET examinations comprising a total of 1149 acquired bed positions were used for the assessment. These data are representative of FDG scans currently performed at our institution. Data were acquired for 4 min/bed position (3 min/bed for legs). The data-driven gating (DDG) algorithm was applied to each bed position, including those where minimal respiratory motion was expected. The algorithm provided a signal-to-noise measure of respiratory-like frequencies within the data, denoted as R. Qualitative evaluation was performed by visual examination of the waveforms, with each waveform scored on a 3-point scale by two readers and then averaged (score S of 0 = no respiratory signal, 1 = some respiratory-like signal but indeterminate, 2 = acceptable signal considered to be respiratory). Images were reconstructed using quiescent period gating and compared with non-gated images reconstructed with a matched number of coincidences. If present, the SUVmax of a well-defined lesion in the thorax or abdomen was measured and compared between the two reconstructions.ResultsThere was a strong (r = 0.86) and significant correlation between R and scores S. Eighty-six percent of waveforms with R ≥ 15 were scored as acceptable for respiratory gating. On average, there were 1.2 bed positions per patient examination with R ≥ 15. Waveforms with high R and S were found to originate from bed positions corresponding to the thorax and abdomen: 90% of waveforms with R ≥ 15 had bed centres in the range 5.6 cm superior to 27 cm inferior from the dome of the liver. For regions where respiratory motion was expected to be minimal, R tended to be < 6 and S tended to be 0. The use of DDG significantly increased the SUVmax of focal lesions, by an average of 11% when considering lesions in bed positions with R ≥ 15.ConclusionsThe majority of waveforms with high R corresponded to the part of the patient where respiratory motion was expected. The waveforms were deemed suitable for respiratory gating when assessed visually, and when used were found to increase SUVmax in focal lesions.

Evaluation of principal component analysis-based data-driven respiratory gating for positron emission tomography.

Objective:Respiratory motion can degrade PET image quality and lead to inaccurate quantification of lesion uptake. Such motion can be mitigated via respiratory gating. Our objective was to evaluate a data-driven gating (DDG) technique that is being developed commercially for clinical PET/CT.Methods:A data-driven respiratory gating algorithm based on principal component analysis (PCA) was applied to phantom and FDG patient data. An anthropomorphic phantom and a NEMA IEC Body phantom were filled with 18F, placed on a respiratory motion platform, and imaged using a PET/CT scanner. Motion waveforms were measured using an infrared camera [the Real-time Position Management™ system (RPM)] and also extracted from the PET data using the DDG algorithm. The waveforms were compared via calculation of Pearson’s correlation coefficients. PET data were reconstructed using quiescent period gating (QPG) and compared via measurement of recovery percentage and background variability.Results:Data-driven gating had similar performance to the external gating system, with correlation coefficients in excess of 0.97. Phantom and patient images were visually clearer with improved contrast when QPG was applied as compared to no motion compensation. Recovery coefficients in the phantoms were not significantly different between DDG- and RPM-based QPG, but were significantly higher than those found for no motion compensation (p < 0.05).Conclusion:A PCA-based DDG algorithm was evaluated and found to provide a reliable respiratory gating signal in anthropomorphic phantom studies and in example patients.Advances in knowledge:The prototype commercial DDG algorithm may enable reliable respiratory gating in routine clinical PET-CT.

Data-driven respiratory gating for ventilation/perfusion lung scan.

Ventilation/perfusion lung scan is subject to blur due to respiratory motion whether with planar acquisition or single photon emission computed tomography (SPECT). We propose a data-driven gating method for extracting different respiratory phases from lung scan list-mode or dynamic data. The algorithm derives a surrogate respiratory signal from an automatically detected diaphragmatic region of interest. The time activity curve generated is then filtered using a Savitzky-Golay filter. We tested this method on an oscillating phantom in order to evaluate motion blur decrease and on one lung SPECT. Our algorithm reduced motion blur on phantom acquisition: mean full width at half maximum 8.1 pixels on non-gated acquisition versus 5.3 pixels on gated acquisition and 4.1 pixels on reference image. Automated detection of the diaphragmatic region and time-activity curves generation were successful on patient acquisition. This algorithm is compatible with a clinical use considering its runtime. Further studies will be needed in order to validate this method.