Effect of respiratory gating on reducing lung motion artifacts in PET imaging of lung cancer

Effectiveness of Data-Driven Gating FDG PET/CT for Abdominal Region

This study aimed to validate the effectiveness of MotionFree (MF) in the abdominal region using 2 different PET/CT scanners to determine how to use MF efficiently. Methods: All 198 patients underwent respiratory-gated 18F-FDG PET/CT with MF. Imaging was performed using Discovery MI (DMI) and Discovery IQ (DIQ) PET/CT scanners, and all data were divided into 2 groups in each category (abdominal: upper and lower abdomen, lesion size, <20 mm and ≥20 mm; scanner group: DMI and DIQ). A physician assessed whether the respiratory motion artifacts were reduced with MF. The SUV change rate (ΔSUV) of 80 measurable lesions with and without MF was calculated. The relationship between the ΔSUVs and these groups was compared. Results: Motion artifacts were reduced in 62 of 198 patients (31.3%) in the upper abdomen, in 1 of 198 patients (0.5%) in the lower abdomen, in 51 of 98 patients (52.0%) in the DMI, and in 12 of 100 patients (12.0%) in DIQ with MF. ΔSUVs were significantly higher in the upper abdomen than in the lower abdomen. ΔSUV was up to 58.3% in DMI and up to 47.6% in DIQ. ΔSUVs of lesions with a size of less than 20 mm were significantly higher than those with a lesion size of 20 mm or greater. Although DMI was more effective than DIQ in terms of motion artifacts, both DMI and DIQ have the potential to increase the SUV with MF. MF significantly reduced the respiratory motion artifacts and increased the SUV for lesions smaller than 20 mm in the upper abdomen. Conclusion: MF reduced the motion artifacts in higher-spatial-resolution PET/CT images. In both PET/CT scanners, SUVs in lesions smaller than 20 mm and lesions in the upper abdomen increased significantly with MF. To use MF without increasing the acquisition time, it may be useful to apply it to the upper abdomen.

Impact of Respiratory-gated 4D PET/CT Scan for Motion Correction in Characterizing Lesions Adjacent to the Diaphragm - A Cross-sectional Study at a Tertiary Care Institute

Purpose

The blur introduced by breathing motion degrades the diagnostic accuracy of whole-body F-18 fluorodeoxyglucose positron emission tomography-computed tomography (18F-FDG PET-CT) in lesions adjacent to the diaphragm by increasing the apparent size and by decreasing their metabolic activity. This study aims to evaluate the efficacy of motion correction by four-dimensional phase-based respiratory-gated (RG) 18F-FDG PET-CT in improving metabolic parameters of lesions adjacent to the diaphragm (especially in the lungs or liver).

Materials and Methods

Eighteen patients with known lung or liver lesions underwent conventional 18F-FDG PET-CT and respiratory-gated PET-CT acquisition of the desired region using a pressure-sensing, phase-based respiratory-gating system. Maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were obtained for these lesions from gated and nongated PET-CT images for analysis. Furthermore, a visual analysis of lesions was done.

Statistics

Statistical significance of the RG image parameters was assessed by the two-tailed paired Student's t test and confirmed with the robust nonparametric Wilcoxon's signed-rank test (two-tailed asymptotic).

Results

There was an overall significant increase in SUVmax (P 0.001) in all gating methods with a percentage increase maximum of about 18.13%. On gating methods, MTV decreased significantly (P = 0.001) than that of nongating method (maximum reduction of about 32.9%). There was a significant difference (P = 0.02) in TLG between gated and nongated methods.

Conclusion

Motion correction with phase-based respiratory gating improves the diagnostic value of 18F-FDG PET-CT imaging for lung and liver lesions by more accurate delineation of the lesion volume and quantitation of SUV and can thus impact staging, diagnosis as well as management in selected patients.

Extended MRI-based PET motion correction for cardiac PET/MRI

PURPOSE

A 2D image navigator (iNAV) based 3D whole-heart sequence has been used to perform MRI and PET non-rigid respiratory motion correction for hybrid PET/MRI. However, only the PET data acquired during the acquisition of the 3D whole-heart MRI is corrected for respiratory motion. This study introduces and evaluates an MRI-based respiratory motion correction method of the complete PET data.

METHODS

Twelve oncology patients scheduled for an additional cardiac 18F-Fluorodeoxyglucose (18F-FDG) PET/MRI and 15 patients with coronary artery disease (CAD) scheduled for cardiac 18F-Choline (18F-FCH) PET/MRI were included. A 2D iNAV recorded the respiratory motion of the myocardium during the 3D whole-heart coronary MR angiography (CMRA) acquisition (~ 10 min). A respiratory belt was used to record the respiratory motion throughout the entire PET/MRI examination (~ 30-90 min). The simultaneously acquired iNAV and respiratory belt signal were used to divide the acquired PET data into 4 bins. The binning was then extended for the complete respiratory belt signal. Data acquired at each bin was reconstructed and combined using iNAV-based motion fields to create a respiratory motion-corrected PET image. Motion-corrected (MC) and non-motion-corrected (NMC) datasets were compared. Gating was also performed to correct cardiac motion. The SUVmax and TBRmax values were calculated for the myocardial wall or a vulnerable coronary plaque for the 18F-FDG and 18F-FCH datasets, respectively.

RESULTS

A pair-wise comparison showed that the SUVmax and TBRmax values of the motion corrected (MC) datasets were significantly higher than those for the non-motion-corrected (NMC) datasets (8.2 ± 1.0 vs 7.5 ± 1.0, p < 0.01 and 1.9 ± 0.2 vs 1.2 ± 0.2, p < 0.01, respectively). In addition, the SUVmax and TBRmax of the motion corrected and gated (MC_G) reconstructions were also higher than that of the non-motion-corrected but gated (NMC_G) datasets, although for the TBRmax this difference was not statistically significant (9.6 ± 1.3 vs 9.1 ± 1.2, p = 0.02 and 2.6 ± 0.3 vs 2.4 ± 0.3, p = 0.16, respectively). The respiratory motion-correction did not lead to a change in the signal to noise ratio.

CONCLUSION

The proposed respiratory motion correction method for hybrid PET/MRI improved the image quality of cardiovascular PET scans by increased SUVmax and TBRmax values while maintaining the signal-to-noise ratio. Trial registration METC162043 registered 01/03/2017.

Deep learning-based lung image registration: A review

Lung image registration can effectively describe the relative motion of lung tissues, thereby helping to solve series problems in clinical applications. Since the lungs are soft and fairly passive organs, they are influenced by respiration and heartbeat, resulting in discontinuity of lung motion and large deformation of anatomic features. This poses great challenges for accurate registration of lung image and its applications. The recent application of deep learning (DL) methods in the field of medical image registration has brought promising results. However, a versatile registration framework has not yet emerged due to diverse challenges of registration for different regions of interest (ROI). DL-based image registration methods used for other ROI cannot achieve satisfactory results in lungs. In addition, there are few review articles available on DL-based lung image registration. In this review, the development of conventional methods for lung image registration is briefly described and a more comprehensive survey of DL-based methods for lung image registration is illustrated. The DL-based methods are classified according to different supervision types, including fully-supervised, weakly-supervised and unsupervised. The contributions of researchers in addressing various challenges are described, as well as the limitations of these approaches. This review also presents a comprehensive statistical analysis of the cited papers in terms of evaluation metrics and loss functions. In addition, publicly available datasets for lung image registration are also summarized. Finally, the remaining challenges and potential trends in DL-based lung image registration are discussed.

On the correction of respiratory motion-induced image reconstruction errors in positron-emission tomography-guided radiation therapy

Background and purpose

Free breathing (FB) positron emission tomography (PET) images are routinely used in radiotherapy for lung cancer patients. Respiration-induced artifacts in these images compromise treatment response assessment and obstruct clinical implementation of dose painting and PET-guided radiotherapy. The purpose of this study is to develop a blurry image decomposition (BID) method to correct motion-induced image-reconstruction errors in FB-PETs.

Materials and methods

Assuming a blurry PET is represented as an average of multi-phase PETs. A four-dimensional computed-tomography image is deformably registered from the end-inhalation (EI) phase to other phases. With the registration-derived deformation maps, PETs at other phases can be deformed from a PET at the EI phase. To reconstruct the EI-PET, the difference between the blurry PET and the average of the deformed EI-PETs is minimized using a maximum-likelihood expectation-maximization algorithm. The developed method was evaluated with computational and physical phantoms as well as PET/CT images acquired from three patients.

Results

The BID method increased the signal-to-noise ratio from 1.88 ± 1.05 to 10.5 ± 3.3 and universal-quality index from 0.72 ± 0.11 to 1.0 for the computational phantoms, and reduced the motion-induced error from 69.9% to 10.9% in the maximum of activity concentration and from 317.5% to 8.7% in the full width at half maximum of the physical PET-phantom. The BID-based corrections increased the maximum standardized-uptake values by 17.7 ± 15.4% and reduced tumor volumes by 12.5 ± 10.4% on average for the three patients.

Conclusions

The proposed image-decomposition method reduces respiration-induced errors in PET images and holds potential to improve the quality of radiotherapy for thoracic and abdominal cancer patients.

Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging

Despite technological and medical advances, the detection, interpretation, and treatment of cancer based on imaging data continue to pose significant challenges. These include inter-observer variability, class imbalance, dataset shifts, inter- and intra-tumour heterogeneity, malignancy determination, and treatment effect uncertainty. Given the recent advancements in image synthesis, Generative Adversarial Networks (GANs), and adversarial training, we assess the potential of these technologies to address a number of key challenges of cancer imaging. We categorise these challenges into (a) data scarcity and imbalance, (b) data access and privacy, (c) data annotation and segmentation, (d) cancer detection and diagnosis, and (e) tumour profiling, treatment planning and monitoring. Based on our analysis of 164 publications that apply adversarial training techniques in the context of cancer imaging, we highlight multiple underexplored solutions with research potential. We further contribute the Synthesis Study Trustworthiness Test (SynTRUST), a meta-analysis framework for assessing the validation rigour of medical image synthesis studies. SynTRUST is based on 26 concrete measures of thoroughness, reproducibility, usefulness, scalability, and tenability. Based on SynTRUST, we analyse 16 of the most promising cancer imaging challenge solutions and observe a high validation rigour in general, but also several desirable improvements. With this work, we strive to bridge the gap between the needs of the clinical cancer imaging community and the current and prospective research on data synthesis and adversarial networks in the artificial intelligence community.

18F-FDG gallbladder uptake: observation from a total-body PET/CT scanner

BACKGROUND

Total-body positron emission tomography/computed tomography (PET/CT) scanners are characterized by higher signal collection efficiency and greater spatial resolution compared to conventional scanners, allowing for delayed imaging and improved image quality. These advantages may also lead to better detection of physiological processes that diagnostic imaging professionals should be aware of. The gallbladder (GB) is not usually visualized as an ¹⁸F-2-fluorodeoxyglucose (¹⁸F-FDG)-avid structure in routine clinical PET/CT studies; however, with the total-body PET/CT, we have been increasingly visualizing GB activity without it being involved in an inflammatory or neoplastic process. The aim of this study was to report visualization rates and characteristics of GB ¹⁸F-FDG uptake observed in both healthy and oncological subjects scanned on a total-body PET/CT system.

MATERIALS AND METHODS

Scans from 73 participants (48 healthy and 25 with newly diagnosed lymphoma) who underwent ¹⁸F-FDG total-body PET/CT were retrospectively reviewed. Subjects were scanned at multiple timepoints up to 3 h post-injection. Gallbladder ¹⁸F-FDG activity was graded using liver uptake as a reference, and the pattern was qualified as present in the wall, lumen, or both. Participants' characteristics, such as age, sex, body-mass index, blood glucose, and other clinical parameters, were collected to assess for any significant correlation with GB ¹⁸F-FDG uptake.

RESULTS

All 73 subjects showed GB uptake at one or more imaging timepoints. An increase in uptake intensity overtime was observed up until the 180-min scan, and the visualization rate of GB ¹⁸F-FDG uptake was 100% in the 120- and 180-min post-injection scans. GB wall uptake was detected in a significant number of patients (44/73, 60%), especially at early timepoint scans, whereas luminal activity was detected in 71/73 (97%) subjects, especially at later timepoint scans. No significant correlation was found between GB uptake intensity/pattern and subjects' characteristics.

CONCLUSION

The consistent observation of GB ¹⁸F-FDG uptake recorded in this study in healthy participants and subjects with a new oncological diagnosis indicates that this is a normal physiologic finding rather than representing an exception.

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 with¹⁸F-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 (Q_H); 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 Q_Hand SUVs compared with NM-PET. Compared with NG-PET, DDG-PET has significantly improved Q_Hand SUVs in spheres above 6 mm. The background region showed no significant difference between groups. The SUVmax, SUVpeak, and Q_Hvalues 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

OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Respiratory 4D-Gating F-18 FDG PET/CT Scan for Liver Malignancies: Feasibility in Liver Cancer Patient and Tumor Quantitative Analysis

Purpose

To evaluate the potential clinical role and effectiveness of respiratory 4D-gating F-18 FDG PET/CT scan for liver malignancies, relative to routine (3D) F-18 FDG PET/CT scan.

Materials and Methods

This study presented a prospective clinical study of 16 patients who received F-18 FDG PET/CT scan for known or suspected malignant liver lesions. Ethics approvals were obtained from the ethics committees of the Hong Kong Baptist Hospital and The Hong Kong Polytechnic University. Liver lesions were compared between the gated and ungated image sets, in terms of 1) volume measurement of PET image, 2) accuracy of maximum standardized uptake value (SUV_max), mean standardized uptake value (SUV_mean), and 3) accuracy of total lesion glycoses (TLG). Statistical analysis was performed by using a two-tailed paired Student t-test and Pearson correlation test.

Results

The study population consisted of 16 patients (9 males and 7 females; mean age of 65) with a total number of 89 lesions. The SUV_max and SUV_mean measurement of the gated PET images was more accurate than that of the ungated PET images, compared to the static reference images. An average of 21.48% (p < 0.001) reduction of the tumor volume was also observed. The SUV_max and SUV_mean of the gated PET images were improved by 19.81% (p < 0.001) and 25.53% (p < 0.001), compared to the ungated PET images.

Conclusions

We have demonstrated the feasibility of implementing 4D PET/CT scan for liver malignancies in a prospective clinical study. The 4D PET/CT scan for liver malignancies could improve the quality of PET image by improving the SUV accuracy of the lesions and reducing image blurring. The improved accuracy in the classification and identification of liver tumors with 4D PET image would potentially lead to its increased utilization in target delineation of GTV, ITV, and PTV for liver radiotherapy treatment planning in the future.

PET respiratory motion correction: quo vadis?

Positron emission tomography (PET) respiratory motion correction has been a subject of great interest for the last twenty years, prompted mainly by the development of multimodality imaging devices such as PET/computed tomography (CT) and PET/magnetic resonance imaging (MRI). PET respiratory motion correction involves a number of steps including acquisition synchronization, motion estimation and finally motion correction. The synchronization steps include the use of different external device systems or data driven approaches which have been gaining ground over the last few years. Patient specific or generic motion models using the respiratory synchronized datasets can be subsequently derived and used for correction either in the image space or within the image reconstruction process. Similar overall approaches can be considered and have been proposed for both PET/CT and PET/MRI devices. Certain variations in the case of PET/MRI include the use of MRI specific sequences for the registration of respiratory motion information. The proposed review includes a comprehensive coverage of all these areas of development in field of PET respiratory motion for different multimodality imaging devices and approaches in terms of synchronization, estimation and subsequent motion correction. Finally, a section on perspectives including the potential clinical usage of these approaches is included.

Design of an anthropomorphic PET phantom with elastic lungs and respiration modeling

PURPOSE

Respiratory motion during positron emission tomography (PET) scans can be a major detriment to image quality in oncological imaging. The impact of motion on lesion quantification and detectability can be assessed using phantoms with realistic anatomy representation and motion modeling. In this work, we develop an anthropomorphic phantom for PET imaging that combines anatomic fidelity and a realistic breathing mechanism with deformable lungs.

METHODS

We start from a previously developed anatomically accurate but static phantom of a human torso, and add elastic lungs with a highly controllable actuation mechanism which replicates the physics of breathing. The space outside the lungs is filled with a radioactive water solution. To maintain anatomical accuracy and realistic gamma ray attenuation in the torso, all motion mechanisms and actuators are positioned outside of the phantom compartment. The actuation mechanism can produce custom respiratory waveforms with breathing rates up to 25 breaths per minute and tidal volumes up to 1200 mL.

RESULTS

Several tests were performed to validate the performance of the phantom assembly, in which the phantom was filled with water and given respiratory waveforms to execute. All parts demonstrated expected performance. Force requirements were not exceeded and no leaks were detected, although continued use of the phantom is required to evaluate wear. The motion of the lungs was determined to be within a reasonable realistic range.

CONCLUSIONS

The full mechanical design is described in this paper, as well as a software application with graphical user interface which was developed to plan and visualize respiratory patterns. Both are available online as open source files. The developed phantom will facilitate future work in evaluating the impact of respiratory motion on lesion quantification and detectability in clinical practice.

4D-CT Attenuation Correction in Respiratory-Gated PET for Hypoxia Imaging: Is It Really Beneficial?

Previous literature has shown that 4D respiratory-gated positron emission tomography (PET) is beneficial for quantitative analysis and defining targets for boosting therapy. However the case for addition of a phase-matched 4D-computed tomography (CT) for attenuation correction (AC) is less clear. We seek to validate the use of 4D-CT for AC and investigate the impact of motion correction for low signal-to-background PET imaging of hypoxia using radiotracers such as FAZA and FMISO. A new insert for the Modus Medicals' QUASAR™ Programmable Respiratory Motion Phantom was developed in which a 3D-printed sphere was placed within the "lung" compartment while an additional compartment is added to simulate muscle/blood compartment required for hypoxia quantification. Experiments are performed at 4:1 or 2:1 signal-to-background ratio consistent with clinical FAZA and FMISO imaging. Motion blur was significant in terms of SUV_max, mean, and peak for motion ≥1 cm and could be significantly reduced (from 20% to 8% at 2-cm motion) for all 4D-PET-gated reconstructions. The effect of attenuation method on precision was significant (σ² hCT-AC = 5.5%/4.7%/2.7% vs σ² 4D-CT-AC = 0.5%/0.6%/0.7% [max%/peak%/mean% variance]). The simulated hypoxic fraction also significantly decreased under conditions of 2-cm amplitude motion from 55% to 20% and was almost fully recovered (HF = 0.52 for phase-matched 4D-CT) using gated PET. 4D-gated PET is valuable under conditions of low radiotracer uptake found in hypoxia imaging. This work demonstrates the importance of using 4D-CT for AC when performing gated PET based on its significantly improved precision over helical CT.

Motion estimation and correction in SPECT, PET and CT

Patient motion impacts single photon emission computed tomography (SPECT), positron emission tomography (PET) and X-ray computed tomography (CT) by giving rise to projection data inconsistencies that can manifest as reconstruction artifacts, thereby degrading image quality and compromising accurate image interpretation and quantification. Methods to estimate and correct for patient motion in SPECT, PET and CT have attracted considerable research effort over several decades. The aims of this effort have been two-fold: to estimate relevant motion fields characterizing the various forms of voluntary and involuntary motion; and to apply these motion fields within a modified reconstruction framework to obtain motion-corrected images. The aims of this review are to outline the motion problem in medical imaging and to critically review published methods for estimating and correcting for the relevant motion fields in clinical and preclinical SPECT, PET and CT. Despite many similarities in how motion is handled between these modalities, utility and applications vary based on differences in temporal and spatial resolution. Technical feasibility has been demonstrated in each modality for both rigid and non-rigid motion, but clinical feasibility remains an important target. There is considerable scope for further developments in motion estimation and correction, and particularly in data-driven methods that will aid clinical utility. State-of-the-art machine learning methods may have a unique role to play in this context.

Phantom and clinical assessment of small pulmonary nodules using Q.Clear reconstruction on a silicon-photomultiplier-based time-of-flight PET/CT system

To evaluate the quantification accuracy of different positron emission tomography-computed tomography (PET/CT) reconstruction algorithms, we measured the recovery coefficient (RC) and contrast recovery (CR) in phantom studies. The results played a guiding role in the partial-volume-effect correction (PVC) for following clinical evaluations. The PET images were reconstructed with four different methods: ordered subsets expectation maximization (OSEM), OSEM with time-of-flight (TOF), OSEM with TOF and point spread function (PSF), and Bayesian penalized likelihood (BPL, known as Q.Clear in the PET/CT of GE Healthcare). In clinical studies, SUVmax and SUVmean (the maximum and mean of the standardized uptake values, SUVs) of 75 small pulmonary nodules (sub-centimeter group: < 10 mm and medium-size group: 10-25 mm) were measured from 26 patients. Results show that Q.Clear produced higher RC and CR values, which can improve quantification accuracy compared with other methods (P < 0.05), except for the RC of 37 mm sphere (P > 0.05). The SUVs of sub-centimeter fludeoxyglucose (FDG)-avid pulmonary nodules with Q.Clear illustrated highly significant differences from those reconstructed with other algorithms (P < 0.001). After performing the PVC, highly significant differences (P < 0.001) still existed in the SUVmean measured by Q.Clear comparing with those measured by the other algorithms. Our results suggest that the Q.Clear reconstruction algorithm improved the quantification accuracy towards the true uptake, which potentially promotes the diagnostic confidence and treatment response evaluations with PET/CT imaging, especially for the sub-centimeter pulmonary nodules. For small lesions, PVC is essential.

Improvement of image quality using amplitude-based respiratory gating in PET-computed tomography scanning

OBJECTIVES

This study sought to provide data supporting the expanded clinical use of respiratory gating by assessing the diagnostic accuracy of breathing motion correction using amplitude-based respiratory gating.

METHODS

A respiratory movement tracking device was attached to a PET-computed tomography scanner, and images were obtained in respiratory gating mode using a motion phantom that was capable of sensing vertical motion. Specifically, after setting amplitude changes and intervals according to the movement cycle using a total of nine combinations of three waveforms and three amplitude ranges, respiratory motion-corrected images were reconstructed using the filtered back projection method. After defining areas of interest in the acquired images in the same image planes, statistical analyses were performed to compare differences in standardized uptake value (SUV), lesion volume, full width at half maximum (FWHM), signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR).

RESULTS

SUVmax increased by 89.9%, and lesion volume decreased by 27.9%. Full width at half maximum decreased by 53.9%, signal-to-noise ratio increased by 11% and contrast-to-noise ratio increased by 16.3%. Optimal results were obtained when using a rest waveform and 35% duty cycle, in which the change in amplitude in the respiratory phase signal was low, and a constant level of long breaths was maintained.

CONCLUSIONS

These results demonstrate that respiratory-gated PET-CT imaging can be used to accurately correct for SUV changes and image distortion caused by respiratory motion, thereby providing excellent imaging information and quality.

Gated Radiotherapy Development and its Expansion

One of the most important challenges in treatment of patients with cancerous tumors of chest and abdominal areas is organ movement. The delivery of treatment radiation doses to tumor tissue is a challenging matter while protecting healthy and radio sensitive tissues. Since the movement of organs due to respiration causes a discrepancy in the middle of planned and delivered dose distributions. The moderation in the fatalistic effect of intra-fractional target travel on the radiation therapy correctness is necessary for cutting-edge methods of motion remote monitoring and cancerous growth irradiancy. Tracking respiratory milling and implementation of breath-hold techniques by respiratory gating systems have been used for compensation of respiratory motion negative effects. Therefore, these systems help us to deliver precise treatments and also protect healthy and critical organs. It seems aspiration should be kept under observation all over treatment period employing tracking seed markers (e.g. fiducials), skin surface scanners (e.g. camera and laser monitoring systems) and aspiration detectors (e.g. spirometers). However, these systems are not readily available for most radiotherapy centers around the word. It is believed that providing and expanding the required equipment, gated radiotherapy will be a routine technique for treatment of chest and abdominal tumors in all clinical radiotherapy centers in the world by considering benefits of respiratory gating techniques in increasing efficiency of patient treatment in the near future. This review explains the different technologies and systems as well as some strategies available for motion management in radiotherapy centers.

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

Aim

The 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.

Methods

Twenty-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 SUV_max, SUV_mean, and tumour volume were performed on [⁶⁸Ga]Ga-DOTATOC PET and compared between gated and non-gated images.

Results

The 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 SUV_max (P < 0.0001) and SUV_mean (P < 0.0001), and decrease of 21.1% in tumour volume (P < 0.0001) was found when DDG was applied.

Conclusions

High 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 SUV_max and SUV_mean values and smaller tumour volumes, as compared to non-gated images.

Automated approach for segmenting gross tumor volumes for lung cancer stereotactic body radiation therapy using CT-based dense V-networks

The aim of this study was to develop an automated segmentation approach for small gross tumor volumes (GTVs) in 3D planning computed tomography (CT) images using dense V-networks (DVNs) that offer more advantages in segmenting smaller structures than conventional V-networks. Regions of interest (ROI) with dimensions of 50 × 50 × 6-72 pixels in the planning CT images were cropped based on the GTV centroids when applying stereotactic body radiotherapy (SBRT) to patients. Segmentation accuracy of GTV contours for 192 lung cancer patients [with the following tumor types: 118 solid, 53 part-solid types and 21 pure ground-glass opacity (pure GGO)], who underwent SBRT, were evaluated based on a 10-fold cross-validation test using Dice's similarity coefficient (DSC) and Hausdorff distance (HD). For each case, 11 segmented GTVs consisting of three single outputs, four logical AND outputs, and four logical OR outputs from combinations of two or three outputs from DVNs were obtained by three runs with different initial weights. The AND output (combination of three outputs) achieved the highest values of average 3D-DSC (0.832 ± 0.074) and HD (4.57 ± 2.44 mm). The average 3D DSCs from the AND output for solid, part-solid and pure GGO types were 0.838 ± 0.074, 0.822 ± 0.078 and 0.819 ± 0.059, respectively. This study suggests that the proposed approach could be useful in segmenting GTVs for planning lung cancer SBRT.

A patient-specific approach for quantitative and automatic analysis of computed tomography images in lung disease: Application to COVID-19 patients

PURPOSE

Quantitative metrics in lung computed tomography (CT) images have been widely used, often without a clear connection with physiology. This work proposes a patient-independent model for the estimation of well-aerated volume of lungs in CT images (WAVE).

METHODS

A Gaussian fit, with mean (Mu.f) and width (Sigma.f) values, was applied to the lower CT histogram data points of the lung to provide the estimation of the well-aerated lung volume (WAVE.f). Independence from CT reconstruction parameters and respiratory cycle was analysed using healthy lung CT images and 4DCT acquisitions. The Gaussian metrics and first order radiomic features calculated for a third cohort of COVID-19 patients were compared with those relative to healthy lungs. Each lung was further segmented in 24 subregions and a new biomarker derived from Gaussian fit parameter Mu.f was proposed to represent the local density changes.

RESULTS

WAVE.f resulted independent from the respiratory motion in 80% of the cases. Differences of 1%, 2% and up to 14% resulted comparing a moderate iterative strength and FBP algorithm, 1 and 3 mm of slice thickness and different reconstruction kernel. Healthy subjects were significantly different from COVID-19 patients for all the metrics calculated. Graphical representation of the local biomarker provides spatial and quantitative information in a single 2D picture.

CONCLUSIONS

Unlike other metrics based on fixed histogram thresholds, this model is able to consider the inter- and intra-subject variability. In addition, it defines a local biomarker to quantify the severity of the disease, independently of the observer.

Gyrocardiography: A Review of the Definition, History, Waveform Description, and Applications

Gyrocardiography (GCG) is a non-invasive technique of analyzing cardiac vibrations by a MEMS (microelectromechanical system) gyroscope placed on a chest wall. Although its history is short in comparison with seismocardiography (SCG) and electrocardiography (ECG), GCG becomes a technique which may provide additional insight into the mechanical aspects of the cardiac cycle. In this review, we describe the summary of the history, definition, measurements, waveform description and applications of gyrocardiography. The review was conducted on about 55 works analyzed between November 2016 and September 2020. The aim of this literature review was to summarize the current state of knowledge in gyrocardiography, especially the definition, waveform description, the physiological and physical sources of the signal and its applications. Based on the analyzed works, we present the definition of GCG as a technique for registration and analysis of rotational component of local cardiac vibrations, waveform annotation, several applications of the gyrocardiography, including, heart rate estimation, heart rate variability analysis, hemodynamics analysis, and classification of various cardiac diseases.

[Examination of Optimal Window Size and Acquisition Time of Respiratory-gated PET Image: Phantom Study with a SiPM-based PET/CT Scanner]

PURPOSE

This phantom study aimed to determine the optimal acquisition window size for phase-based respiratory gating in silicon photomultiplier (SiPM)-based fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) and its acquisition time in respiratory-gated imaging with the optimal window size.

METHODS

Images of a moving NEMA IEC Body Phantom Set^TM with hot spheres were acquired. First, the tumor volume and the maximum standardized uptake value (SUV_max) of images reconstructed using a different window size were evaluated to define the optimal window size. Second, the quality of the images reconstructed using the optimal window size and different acquisition times was evaluated using the detectability score of the 10-mm hot sphere and physical indices.

RESULTS

The volume and the SUV_max of the 10-mm hot sphere were improved when the window size was narrow, and there were no significant differences among images reconstructed using a window size narrower than 20%. To reconstruct an image using the 20% window size, an acquisition time of 5 min was required to visualize the 10-mm hot sphere.

CONCLUSIONS

The optimal window size for phase-based respiratory gating is 20%. Further, an acquisition time of 5 min should be taken for respiratory-gated imaging with the 20% window size on SiPM-based FDG-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.

A modern review of the uncertainties in volumetric imaging of respiratory-induced target motion in lung radiotherapy

Radiotherapy has become a critical component for the treatment of all stages and types of lung cancer, often times being the primary gateway to a cure. However, given that radiation can cause harmful side effects depending on how much surrounding healthy tissue is exposed, treatment of the lung can be particularly challenging due to the presence of moving targets. Careful implementation of every step in the radiotherapy process is absolutely integral for attaining optimal clinical outcomes. With the advent and now widespread use of stereotactic body radiation therapy (SBRT), where extremely large doses are delivered, accurate, and precise dose targeting is especially vital to achieve an optimal risk to benefit ratio. This has largely become possible due to the rapid development of image-guided technology. Although imaging is critical to the success of radiotherapy, it can often be plagued with uncertainties due to respiratory-induced target motion. There has and continues to be an immense research effort aimed at acknowledging and addressing these uncertainties to further our abilities to more precisely target radiation treatment. Thus, the goal of this article is to provide a detailed review of the prevailing uncertainties that remain to be investigated across the different imaging modalities, as well as to highlight the more modern solutions to imaging motion and their role in addressing the current challenges.

Motion correction of respiratory-gated PET images using deep learning based image registration framework

Artifacts caused by patient breathing and movement during PET data acquisition affect image quality. Respiratory gating is commonly used to gate the list-mode PET data into multiple bins over a respiratory cycle. Non-rigid registration of respiratory-gated PET images can reduce motion artifacts and preserve count statistics, but it is time consuming. In this work, we propose an unsupervised non-rigid image registration framework using deep learning for motion correction. Our network uses a differentiable spatial transformer layer to warp the moving image to the fixed image and uses a stacked structure for deformation field refinement. Estimated deformation fields were incorporated into an iterative image reconstruction algorithm to perform motion compensated PET image reconstruction. We validated the proposed method using simulation and clinical data and implemented an iterative image registration approach for comparison. Motion compensated reconstructions were compared with ungated images. Our simulation study showed that the motion compensated methods can generate images with sharp boundaries and reveal more details in the heart region compared with the ungated image. The resulting normalized root mean square error (NRMS) was 24.3 ± 1.7% for the deep learning based motion correction, 31.1 ± 1.4% for the iterative registration based motion correction, and 41.9 ± 2.0% for ungated reconstruction. The proposed deep learning based motion correction reduced the bias compared with the ungated image without increasing the noise level and outperformed the iterative registration based method. In the real data study, both motion compensated images provided higher lesion contrast and sharper liver boundaries than the ungated image and had lower noise than the reference gate image. The contrast of the proposed method based on the deep neural network was higher than the ungated image and iterative registration method at any matched noise level.

Comparison of two elastic motion correction approaches for whole-body PET/CT: motion deblurring vs gate-to-gate motion correction

Background

Respiratory motion in PET/CT leads to well-known image degrading effects commonly compensated using elastic motion correction approaches. Gate-to-gate motion correction techniques are promising tools for improving clinical PET data but suffer from relatively long reconstruction times. In this study, the performance of a fast elastic motion compensation approach based on motion deblurring (DEB-MC) was evaluated on patient and phantom data and compared to an EM-based fully 3D gate-to-gate motion correction method (G2G-MC) which was considered the gold standard.

Methods

Twenty-eight patients were included in this study with suspected or confirmed malignancies in the thorax or abdomen. All patients underwent whole-body [¹⁸F]FDG PET/CT examinations applying hardware-based respiratory gating. In addition, a dynamic anthropomorphic thorax phantom was studied with PET/CT simulating tumour motion under controlled but realistic conditions. PET signal recovery values were calculated from phantom scans by comparing lesion activities after motion correction to static ground truth data. Differences in standardized uptake values (SUV) and metabolic volume (MV) between both reconstruction methods as well as between motion-corrected (MC) and non motion-corrected (NOMC) results were statistically analyzed using a Wilcoxon signed-rank test.

Results

Phantom data analysis showed high lesion recovery values of 91% (2 cm motion) and 98% (1 cm) for G2G-MC and 83% (2 cm) and 90% (1 cm) for DEB-MC. The statistical analysis of patient data found significant differences between NOMC and MC reconstructions for SUV _max, SUV _mean, MV, and contrast-to-noise ratio (CNR) for both reconstruction algorithms. Furthermore, both methods showed similar increases of 11–12% in SUV _max and SUV _mean after MC. The statistical analysis of the MC/NOMC ratio found no significant differences between the methods.

Conclusion

Both motion correction techniques deliver comparable improvements of SUV _max, SUV _mean, and CNR after MC on clinical and phantom data. The fast elastic motion compensation technique DEB-MC may thereby be a valuable alternative to state-of-the art motion correction techniques.

Clinical comparison of the positron emission tracking (PeTrack) algorithm with the real-time position management system for respiratory gating in cardiac positron emission tomography

PURPOSE

A data-driven motion tracking system was developed for respiratory gating in positron emission tomography (PET)/computed tomography (CT) studies. The positron emission tracking system (PeTrack) estimates the position of a low-activity fiducial marker placed on the patient during imaging. The aim of this study was to compare the performance of PeTrack against that of the real-time position management (RPM) system as applied to respiratory gating in cardiac PET/CT studies.

METHODS

The list-mode data of 35 patients that were referred for ⁸² Rb myocardial perfusion studies were retrospectively processed with PeTrack to generate respiratory motion signals and triggers. Fifty acquisitions from the initial cohort, conducted under physiologic rest and stress, were considered for analysis. Respiratory-gated reconstructions were performed using reconstruction software provided by the vendor. The respiratory signals and triggers of the gating systems were compared using quantitative measurements of the respiratory signal correlation, median, and interquartiles range (IQR) of observed respiratory rates and the relative frequencies of respiratory cycle outliers. Quantitative measurements of left-ventricular wall thicknesses and motion due to respiration were also compared. Real-time position management signals were also retrospectively processed using the trigger detection method of PeTrack for a third comparator ("RPMretro") that allowed direct comparison of the motion tracking quality independently of differences in the trigger detection methods. The comparison of PeTrack to the original RPM data represent a practical comparison of the two systems, whereas that of PeTrack and RPMretro represents an equal comparison of the two. Nongated images were also reconstructed to provide reference left-ventricular wall thicknesses. LV wall thickness and motion measurements were repeated for a subset of cases with motion ≥7 mm as image artifacts were expected to be more severe in these cases.

RESULTS

A significant correlation (P < 0.05) was observed between the RPM and PeTrack respiratory signals in 45/50 acquisitions; the mean correlation coefficient was 0.43. Similar results were found between PeTrack and RPMretro. No significant difference was observed between the RPM and PeTrack with respect to median respiratory rates and the percentage of respiratory cycles outliers. Respiratory rate variability (IQR) was significantly higher with PeTrack vs RPM (P = 0.002) and RPMretro (P = 0.04). Both PeTrack and RPM had a significant increase in the percentage of respiratory rate outliers compared to RPMretro (P < 0.001 and P = 0.001, respectively). All methods indicated significant differences in LV thickness compared to nongated images (P < 0.02). LV thickness was significantly larger for PeTrack compared to RPMretro in the highest motion subset (P = 0.009). Images gated with RPMretro showed significant increases in motion compared to both PeTrack (P < 0.001) and prospective RPM (P = 0.002). In the subset of highest motion cases, the difference between RPM and RPMretro was no longer present.

CONCLUSIONS

The data-driven PeTrack algorithm performed similarly to the well-established RPM system for respiratory gating of ⁸² Rb cardiac perfusion PET/CT studies. Real-time position management performance improved after retrospective processing and led to enhanced performance compared to both PeTrack and prospective RPM. With further development PeTrack has the potential to reduce the need for ancillary hardware systems to monitor respiratory motion.

Improved Alignment of PET and CT Images in Whole-Body PET/CT in Cases of Respiratory Motion During CT

Respiratory motion during the CT and PET parts of a PET/CT scan leads to imperfect alignment of anatomic features seen by the 2 modalities. In this work, we concentrate on the effects of motion during CT. We propose a novel approach for improving the alignment. Methods: Respiratory waveform data were gathered during the CT and PET parts of 28 PET/CT scans of cancer patients with 40 lesions up to 3 cm in size in the lung or upper abdomen. PET list-mode data were reconstructed by 3 reconstruction methods: PET/static (the standard method with no motion correction); PET/ex (a method that calculates a range of expiratory amplitudes from the lowest one to the highest one); and PET/matched (a novel method that uses both waveforms). The 3 methods were compared. The distance between tumor positions in PET and CT were characterized in visual interpretation by physicians as well as quantitatively. Tumor SUVs (SUV_max and SUV_peak) were determined relative to SUV based on the static method. Image noise was evaluated in the liver and compared with PET/static. Results: In visual interpretation, the rate of good alignment was 13 of 21, 13 of 23, and 18 of 21 for the PET/static, PET/ex, and PET/matched methods, respectively, and the mean PET/CT distances were 3.5, 5.1, and 2.8 mm. In visual comparison with PET/ex, the rate of good alignment was increased in 1 of 10 and 7 of 10 cases for PET/static and PET/matched, respectively. SUV_max was on average 21% higher than PET/static when either PET/ex or PET/matched was used. SUV_peak was 12% higher. Image noise in the liver was 15% higher than PET/static for the PET/ex method, and 40% higher for PET/matched; that is, noise was much lower than in gated PET. Conclusion: Acquiring respiratory waveforms both in PET (as in the current state of the art) and in CT (an unusual key step in this approach) has the potential to improve the alignment of PET and CT images. A proposed method for using this information was tested. Improved alignment was demonstrated.

Characterization of continuous bed motion effects on patient breathing and respiratory motion correction in PET/CT imaging

Continuous bed motion (CBM) was recently introduced as an alternative to step‐and‐shoot (SS) mode for PET/CT data acquisition. In CBM, the patient is continuously advanced into the scanner at a preset speed, whereas in SS, the patient is imaged in overlapping bed positions. Previous investigations have shown that patients preferred CBM over SS for PET data acquisition. In this study, we investigated the effect of CBM versus SS on patient breathing and respiratory motion correction. One hundred patients referred for PET/CT were scanned using a Siemens mCT scanner. Patient respiratory waveforms were recorded using an Anzai system and analyzed using four methods: Methods 1 and 2 measured the coefficient of variation (COV) of the respiratory cycle duration (RCD) and amplitude (RCA). Method 3 measured the respiratory frequency signal prominence (RSP) and method 4 measured the width of the HDChest optimal gate (OG) window when using a 35% duty cycle. Waveform analysis was performed over the abdominothoracic region which exhibited the greatest respiratory motion and the results were compared between CBM and SS. Respiratory motion correction was assessed by comparing the ratios of SUVmax, SUVpeak, and CNR of focal FDG uptake, as well as Radiologists’ visual assessment of corresponding image quality of motion corrected and uncorrected images for both acquisition modes. The respiratory waveforms analysis showed that the RCD and RCA COV were 3.7% and 33.3% lower for CBM compared to SS, respectively, while the RSP and OG were 30.5% and 2.0% higher, respectively. Image analysis on the other hand showed that SUVmax, SUVpeak, and CNR were 8.5%, 4.5%, and 3.4% higher for SS compared to CBM, respectively, while the Radiologists’ visual comparison showed similar image quality between acquisition modes. However, none of the results showed statistically significant differences between SS and CBM, suggesting that motion correction is not impacted by acquisition mode.

Respiratory Motion Correction Using A Novel Positron Emission Particle Tracking Technique: A Framework Towards Individual Lesion-Based Motion Correction

Respiratory motion during PET/CT imaging is a matter of concern due to degraded image quality and reduced quantitative accuracy caused by motion artifacts. One class of motion correction methods relies on hardware-based respiratory motion tracking systems in order to use respiratory cycles for correcting motion artifacts. Another class of hardware-free methods extract motion information from the reconstructed images or sinograms. Hardware-based methods, however, are limited by calibration requirement, patient discomfort, lack of adaptability during scanning, presence of electronic drift during respiratory monitoring etc. Extracting motion information from reconstructed images is also limited by the fact that the original raw information requires significant processing before it can be used. Hence the motivation behind this work is to introduce a software-based approach that can be applied on raw 64-bit listmode data. The basic design of the proposed method is based on the fundamentals of Positron Emission Particle Tracking (PEPT) with additional incorporation of Time of Flight (TOF) information. Respiratory motion of patients has been extracted from the raw PET data by tracking a point source attached to the patient in areas on and near the chest. The key objective of this work is to describe a new process by which this particle tracking based motion correction system can eventually be lesion specific and correct the motion for a particular lesion within the patient. This work thus serves as a framework for lesion specific motion correction.

Task Group 174 Report: Utilization of [18 F]Fluorodeoxyglucose Positron Emission Tomography ([18 F]FDG-PET) in Radiation Therapy

The use of positron emission tomography (PET) in radiation therapy (RT) is rapidly increasing in the areas of staging, segmentation, treatment planning, and response assessment. The most common radiotracer is ¹⁸ F-fluorodeoxyglucose ([¹⁸ F]FDG), a glucose analog with demonstrated efficacy in cancer diagnosis and staging. However, diagnosis and RT planning are different endeavors with unique requirements, and very little literature is available for guiding physicists and clinicians in the utilization of [¹⁸ F]FDG-PET in RT. The two goals of this report are to educate and provide recommendations. The report provides background and education on current PET imaging systems, PET tracers, intensity quantification, and current utilization in RT (staging, segmentation, image registration, treatment planning, and therapy response assessment). Recommendations are provided on acceptance testing, annual and monthly quality assurance, scanning protocols to ensure consistency between interpatient scans and intrapatient longitudinal scans, reporting of patient and scan parameters in literature, requirements for incorporation of [¹⁸ F]FDG-PET in treatment planning systems, and image registration. The recommendations provided here are minimum requirements and are not meant to cover all aspects of the use of [¹⁸ F]FDG-PET for RT.

A Computational Framework for Data Fusion in MEMS-Based Cardiac and Respiratory Gating

Dual cardiac and respiratory gating is a well-known technique for motion compensation in nuclear medicine imaging. In this study, we present a new data fusion framework for dual cardiac and respiratory gating based on multidimensional microelectromechanical (MEMS) motion sensors. Our approach aims at robust estimation of the chest vibrations, that is, high-frequency precordial vibrations and low-frequency respiratory movements for prospective gating in positron emission tomography (PET), computed tomography (CT), and radiotherapy. Our sensing modality in the context of this paper is a single dual sensor unit, including accelerometer and gyroscope sensors to measure chest movements in three different orientations. Since accelerometer- and gyroscope-derived respiration signals represent the inclination of the chest, they are similar in morphology and have the same units. Therefore, we use principal component analysis (PCA) to combine them into a single signal. In contrast to this, the accelerometer- and gyroscope-derived cardiac signals correspond to the translational and rotational motions of the chest, and have different waveform characteristics and units. To combine these signals, we use independent component analysis (ICA) in order to obtain the underlying cardiac motion. From this cardiac motion signal, we obtain the systolic and diastolic phases of cardiac cycles by using an adaptive multi-scale peak detector and a short-time autocorrelation function. Three groups of subjects, including healthy controls (n = 7), healthy volunteers (n = 12), and patients with a history of coronary artery disease (n = 19) were studied to establish a quantitative framework for assessing the performance of the presented work in prospective imaging applications. The results of this investigation showed a fairly strong positive correlation (average r = 0.73 to 0.87) between the MEMS-derived (including corresponding PCA fusion) respiration curves and the reference optical camera and respiration belt sensors. Additionally, the mean time offset of MEMS-driven triggers from camera-driven triggers was 0.23 to 0.3 ± 0.15 to 0.17 s. For each cardiac cycle, the feature of the MEMS signals indicating a systolic time interval was identified, and its relation to the total cardiac cycle length was also reported. The findings of this study suggest that the combination of chest angular velocity and accelerations using ICA and PCA can help to develop a robust dual cardiac and respiratory gating solution using only MEMS sensors. Therefore, the methods presented in this paper should help improve predictions of the cardiac and respiratory quiescent phases, particularly with the clinical patients. This study lays the groundwork for future research into clinical PET/CT imaging based on dual inertial sensors.

Simultaneous respiratory motion correction and image reconstruction in 4D-multi pinhole small animal SPECT

PURPOSE

Respiratory motion in the chest region during single photon emission computed tomography (SPECT) is a major degrading factor that reduces the accuracy of image quantification. This effect is more notable when the tumor is very small, or the spatial resolution of the imaging system is less than the respiratory motion amplitude. Small animals imaging systems with sub-millimeter spatial resolution need more attention to the respiratory motion for quantitative studies. We developed a motion-embedded four-dimensional (4D)-multi pinhole SPECT (MPS) reconstruction algorithm for respiratory motion correction. This algorithm makes full use of projection statistics for reconstruction of every individual frame.

METHODS

The ROBY phantom with small tumors in liver was generated in eight different phases during one respiratory cycle. The MPS projections were modeled using a fast ray tracing method simulating an MPS acquisition. Individual frames were reconstructed and used for motion estimation. The Demons non-rigid registration algorithm was used to calculate deformation vector fields (DVFs) for simultaneous motion correction and image reconstruction. A motion-embedded 4D-MPS method was used to reconstruct images using all the projections and corresponding DVFs, simultaneously. The 4D-MPS reconstructed images were compared to the low-count single frame (LCSF) reconstructed image, the three-dimensional (3D)-MPS images reconstructed using individual frames, and post reconstruction registration (PRR) that aligns all individual phases to a reference frame using Demons-derived DVFs. The tumor volume relative error (TVE), tumor contrast relative error (TCE), and dice index (DI) for 2, 3, and 4 mm liver were calculated and compared for different reconstruction methods.

RESULTS

For the 4D-MPS reconstruction method, TVE was reduced and DI was higher compared to PRR, 3D-MPS, and LCSF. The extent of the improvement was higher for the small tumor size (i.e. 2 mm). For the biggest tumor in contrast 3 (i.e. 4 mm) TVE for 4D-MPS, PRR, 3D-MPS and, LCSF were 1.33%, 8%, 8%, and 14.67%, respectively.

CONCLUSIONS

The results suggest that motion-embedded 4D-MPS method is an effective and practical way for respiratory motion correction. It reconstructs high quality gated frames while using all projection data to reconstruct each frame.

Impact of free-breathing CT on quantitative measurements of static and quiescent period-gated PET Images

Measurements of standardized uptake values (SUV) can vary due to many causes, including respiratory motion. Various methodologies have been introduced to correct for motion in PET, with quiescent-period-gated (QPG) PET being the most popular approach. QPG has been shown to improve PET image quantification compared to static-whole-body (SWB) PET. However, to achieve this improvement, QPG PET requires CT attenuation correction data that matches the QPG PET data. In this paper we investigated the effect of using free-breathing CT for attenuation correction of QPG PET on SUV_max and SUV_peak and compared the results to those of SWB PET. 34 lesions in 27 patients were included. All patients were injected with F-18 FDG. 4D-CT datasets representing all possible phases of respiration that could result from a free-breathing CT were acquired. The 4D-CT datasets were used for attenuation correction of the QPG and SWB PET data. Percentage change in the SUV_max and SUV_peak range was calculated for the reconstructions and compared between QPG and SWB PET. The mean percentage change in the lesion SUV_max and SUV_peak ranges were 19.1% (p   =  0.0178) and 25.2% (p   =  0.0002) higher for QPG compared to SWB, respectively. The maximum percent change in SUV_max and SUV_peak ranges were 58.5% and 59.0% for QPG, respectively compared to 46.1% and 45.3% for SWB, respectively. The highest SUV_max and SUV_peak measurements corresponded to the CT phase that matched the QPG phase. Utilizing free-breathing CT for attenuation correction can lead to large changes in quantification due to misalignment with PET data. This misalignment has a large quantitative impact on QPG PET as compared to SWB PET. When interpreting quantitative changes in lesions, it is critical to consider the influences of free-breathing CT-based attenuation correction.

4D-CT-based motion correction of PET images using 3D iterative deconvolution

Objectives

Positron emission tomography acquisition takes several minutes representing an image averaged over multiple breathing cycles. Therefore, in areas influenced by respiratory movement, PET-positive lesions occur larger, but less intensive than they actually are, resulting in false quantitative assessment. We developed a motion-correction algorithm based on 4D-CT without the need to adapt PET-acquisition.

Methods

The algorithm is based on a full 3D iterative Richardson-Lucy-Deconvolution using a point-spread-function constructed using the motion information obtained from the 4D-CT. In a motion phantom study (3 different hot spheres in background activity), optimal parameters for the algorithm in terms of number of iterations and start image were estimated. Finally, the correction method was applied to 3 patient data sets. In phantom and patient data sets lesions were delineated and compared between motion corrected and uncorrected images for activity uptake and volume.

Results

Phantom studies showed best results for motion correction after 6 deconvolution steps or higher. In phantom studies, lesion volume improved up to 23% for the largest, 43% for the medium and 49% for the smallest sphere due to the correction algorithm. In patient data the correction resulted in a significant reduction of the tumor volume up to 33.3 % and an increase of the maximum and mean uptake of the lesion up to 62.1 and 19.8 % respectively.

Conclusion

In conclusion, the proposed motion correction method showed good results in phantom data and a promising reduction of detected lesion volume and a consequently increasing activity uptake in three patients with lung lesions.

Evaluation of a novel elastic respiratory motion correction algorithm on quantification and image quality in abdomino-thoracic PET/CT

Our aim is to evaluate in phantom and patient studies a recently developed elastic motion debluring (EMDB) technique which makes use of all the acquired PET data and compare its performance to other conventional techniques such as phase based gating (PBG) and HDChest (HDC) both of which use fractions of the acquired data. Comparisons were made with respect to static whole-body (SWB) images with no motion correction. Methods: A phantom simulating respiratory motion of the thorax with lung lesions (5 spheres with ID=10- 28 mm) was scanned with 0, 1, 2, and 3 cm motion. Four reconstructions were performed: SWB, PBG, HDC, and EMDB. For PBG, the average (PBGave) and maximum bin (PBGmax) were used. To compare the reconstructions, the ratios of SUVmax (RSmax), SUVpeak (RSpeak), and CNR (RCNR) were calculated with respect to SWB. Additionally, 46 patients with lung or liver tumors < 3 cm diameter were also studied. Measurements of SUVmax, SUVpeak, and contrast-to-noise ratio (CNR) were made for 46 lung and 19 liver lesions. To evaluate image noise, the SUV standard deviation was measured in healthy lung and liver tissue and in the phantom background. Finally, subjective image quality of patient exams was scored on a 5 point scale by four radiologists. Results: In the phantom, EMDB increased SUVmax/SUVpeak over SWB but to a lesser extent than the other reconstruction methodologies. The RCNR for EMDB however was higher than all other reconstructions (0.68 EMDB > 0.54 HDC > 0.41 PBGmax > 0.31 PBGave). Similar results were seen in patient studies. The SUVmax/SUVpeak were higher by 19.3/11.1% EMDB, 21.6/13.9% HDC, 22.8/12.8% PBGave, and 45.6/26.8% PBGmax compared to SWB. Lung/liver noise increased EMDB (3/15%), HDC (35/56%), PBGave (100/170%), and PBGmax (146/219%). CNR increased in lung/liver tumors only for EMDB (18/13%), and decreased for HDC (-14/-23%), PBGave (-39/-63%), and PBGmax (-18/-46%). Average radiologist scores of image quality were SWB (4.0 ± 0.8) > EMDB (3.7 ± 1.0) > HDC (3.1 ± 1.0) > PBG (1.5 ± 0.7). Conclusion: The EMDB algorithm had the least increase in image noise, improved lesion CNR, and had the highest overall image quality score.

Data-driven gating in PET: Influence of respiratory signal noise on motion resolution

PURPOSE

Data-driven gating (DDG) approaches for positron emission tomography (PET) are interesting alternatives to conventional hardware-based gating methods. In DDG, the measured PET data themselves are utilized to calculate a respiratory signal, that is, subsequently used for gating purposes. The success of gating is then highly dependent on the statistical quality of the PET data. In this study, we investigate how this quality determines signal noise and thus motion resolution in clinical PET scans using a center-of-mass-based (COM) DDG approach, specifically with regard to motion management of target structures in future radiotherapy planning applications.

METHODS

PET list mode datasets acquired in one bed position of 19 different radiotherapy patients undergoing pretreatment [¹⁸ F]FDG PET/CT or [¹⁸ F]FDG PET/MRI were included into this retrospective study. All scans were performed over a region with organs (myocardium, kidneys) or tumor lesions of high tracer uptake and under free breathing. Aside from the original list mode data, datasets with progressively decreasing PET statistics were generated. From these, COM DDG signals were derived for subsequent amplitude-based gating of the original list mode file. The apparent respiratory shift d from end-expiration to end-inspiration was determined from the gated images and expressed as a function of signal-to-noise ratio SNR of the determined gating signals. This relation was tested against additional 25 [¹⁸ F]FDG PET/MRI list mode datasets where high-precision MR navigator-like respiratory signals were available as reference signal for respiratory gating of PET data, and data from a dedicated thorax phantom scan.

RESULTS

All original 19 high-quality list mode datasets demonstrated the same behavior in terms of motion resolution when reducing the amount of list mode events for DDG signal generation. Ratios and directions of respiratory shifts between end-respiratory gates and the respective nongated image were constant over all statistic levels. Motion resolution d/d_max could be modeled as d/dmax=1-e-1.52(SNR-1)0.52, with d_max as the actual respiratory shift. Determining d_max from d and SNR in the 25 test datasets and the phantom scan demonstrated no significant differences to the MR navigator-derived shift values and the predefined shift, respectively.

CONCLUSIONS

The SNR can serve as a general metric to assess the success of COM-based DDG, even in different scanners and patients. The derived formula for motion resolution can be used to estimate the actual motion extent reasonably well in cases of limited PET raw data statistics. This may be of interest for individualized radiotherapy treatment planning procedures of target structures subjected to respiratory motion.

Robust real-time extraction of respiratory signals from PET list-mode data

Respiratory motion, which typically cannot simply be suspended during PET image acquisition, affects lesions' detection and quantitative accuracy inside or in close vicinity to the lungs. Some motion compensation techniques address this issue via pre-sorting ('binning') of the acquired PET data into a set of temporal gates, where each gate is assumed to be minimally affected by respiratory motion. Tracking respiratory motion is typically realized using dedicated hardware (e.g. using respiratory belts and digital cameras). Extracting respiratory signals directly from the acquired PET data simplifies the clinical workflow as it avoids handling additional signal measurement equipment. We introduce a new data-driven method 'combined local motion detection' (CLMD). It uses the time-of-flight (TOF) information provided by state-of-the-art PET scanners in order to enable real-time respiratory signal extraction without additional hardware resources. CLMD applies center-of-mass detection in overlapping regions based on simple back-positioned TOF event sets acquired in short time frames. Following a signal filtering and quality-based pre-selection step, the remaining extracted individual position information over time is then combined to generate a global respiratory signal. The method is evaluated using seven measured FDG studies from single and multiple scan positions of the thorax region, and it is compared to other software-based methods regarding quantitative accuracy and statistical noise stability. Correlation coefficients around 90% between the reference and the extracted signal have been found for those PET scans where motion affected features such as tumors or hot regions were present in the PET field-of-view. For PET scans with a quarter of typically applied radiotracer doses, the CLMD method still provides similar high correlation coefficients which indicates its robustness to noise. Each CLMD processing needed less than 0.4 s in total on a standard multi-core CPU and thus provides a robust and accurate approach enabling real-time processing capabilities using standard PC hardware.

Self-Gating: An Adaptive Center-of-Mass Approach for Respiratory Gating in PET

The goal is to develop an adaptive center-of-mass (COM)-based approach for device-less respiratory gating of list-mode positron emission tomography (PET) data. Our method contains two steps. The first is to automatically extract an optimized respiratory motion signal from the list-mode data during acquisition. The respiratory motion signal was calculated by tracking the location of COM within a volume of interest (VOI). The signal prominence (SP) was calculated based on Fourier analysis of the signal. The VOI was adaptively optimized to maximize SP. The second step is to automatically correct signal-flipping effects. The sign of the signal was determined based on the assumption that the average patient spends more time during expiration than inspiration. To validate our methods, thirty-one ¹⁸F-FDG patient scans were included in this paper. An external device-based signal was used as the gold standard, and the correlation coefficient of the data-driven signal with the device-based signal was measured. Our method successfully extracted respiratory signal from 30 out of 31 datasets. The failure case was due to lack of uptake in the field of view. Moreover, our sign determination method obtained correct results for all scans excluding the failure case. Quantitatively, the proposed signal extraction approach achieved a median correlation of 0.85 with the device-based signal. Gated images using optimized data-driven signal showed improved lesion contrast over static image and were comparable to those using device-based signal. We presented a new data-driven method to automatically extract respiratory motion signal from list-mode PET data by optimizing VOI for COM calculation, as well as determine motion direction from signal asymmetry. Successful application of the proposed method on most clinical datasets and comparison with device-based signal suggests its potential of serving as an alternative to external respiratory monitors.

Retrospective data-driven respiratory gating for PET using TOF information

Traditional data-driven respiratory gating method is capable of detecting breathing cycles directly from positron emission tomography (PET) data, but usually fails at low SNR, particularly at low dose PET/CT study. Time-of-flight (TOF) PET has the potential to improve the SNR. In order for TOF information to reduce the statistical noise and boost the performance of respiratory gating, we present a robust data-driven respiratory gating method using TOF information, which retrospectively derived the respiratory signal from the acquired TOF-PET data. The PET data was acquired in list mode format and analyzed in sinogram space. The method was demonstrated with patient datasets acquired on a TOF PET/CT system. Data-driven gating methods by center of mass (COM) and principle component analysis (PCA) algorithm were successfully performed on nonTOF PET and TOF PET dataset. To assess the accuracy of the data-driven respiratory signal, a hardware-based signal was acquired for comparison. The study showed that retrospectively respiratory gating using TOF sinograms has improved the SNR, and outperforms the non-TOF gating under both COM and PCA algorithms.

Impact of respiratory motion and acquisition settings on SPECT liver dosimetry for radioembolization

PURPOSE

Respiratory motion may impose significant inaccuracies on emission activity estimation in quantitative SPECT. This effect may be a major issue in dosimetry as used in the management of liver radioembolization. The purpose of this study was to investigate the impact of respiratory motion on radioembolization liver dosimetry for different SPECT acquisition settings.

METHODS

In a series of SPECT/CT Monte Carlo simulations using several digital XCAT phantoms, the following parameters were varied: breathing/nonbreathing, liver tumor size (0.3-35 ml) and location, patient properties (body mass index ranging from underweight to obese; male and female), acquisition time (10-30 s/view), collimator setup (High Sensitivity, High Resolution, Ultra High Resolution) and tumor VOI. The effect of applying a respiratory gating scheme was examined as well.

RESULTS

Breathing decreased activity recovery and tumor/non-tumor (T/N) ratios on average from 90% to 66%. VOIs based on SPECT images instead of breath-hold CT improved T/N values significantly. The most accurate results were obtained using a gating scheme combined with SPECT-based VOIs. Scan duration, body mass index, sex, and location all had a minor effect. Lung shunt fraction estimations were relatively unaffected by any of the varied parameters.

CONCLUSIONS

Respiratory motion has a large effect on SPECT activity quantitation of liver tumors as used in radioembolization treatment planning and assessment. As compared with the other parameters that were varied in this study, respiration is the predominant degrading effect on image quantitation. Gating alleviates much of this detrimental effect.

Added diagnostic value of respiratory-gated 4D 18F-FDG PET/CT in the detection of liver lesions: a multicenter study

PURPOSE

The aim of the present study was to evaluate the added diagnostic value of respiratory-gated 4D18F-FDG PET/CT in liver lesion detection and characterization in a European multicenter retrospective study.

METHODS

Fifty-six oncological patients (29 males and 27 females, mean age, 61.2 ± 11.2 years) from five European centers, submitted to standard 3D-PET/CT and liver 4D-PET/CT were retrospectively evaluated. Based on visual analysis, liver PET/CT findings were scored as positive, negative, or equivocal both in 3D and 4D PET/CT. The impact of 4D-PET/CT on the confidence in classifying liver lesions was assessed. PET/CT findings were compared to histology and clinical follow-up as standard reference and diagnostic accuracy was calculated for both techniques. At semi-quantitative analysis, SUVmax was calculated for each detected lesion in 3D and 4D-PET/CT.

RESULTS

Overall, 72 liver lesions were considered for the analysis. Based on visual analysis in 3D-PET/CT, 32/72 (44.4%) lesions were considered positive, 21/72 (29.2%) negative, and 19/72 (26.4%) equivocal, while in 4D-PET/CT 48/72 (66.7%) lesions were defined positive, 23/72 (31.9%) negative, and 1/72 (1.4%) equivocal. 4D-PET/CT findings increased the confidence in lesion definition in 37/72 lesions (51.4%). Considering 3D equivocal lesions as positive, sensitivity, specificity, and accuracy were 88.9, 70.0, and 83.1%, respectively, while the same figures were 67.7, 90.0, and 73.8% if 3D equivocal findings were included as negative. 4D-PET/CT sensitivity, specificity, and accuracy were 97.8, 90.0, and 95.4%, respectively, considering equivocal lesions as positive and 95.6, 90.0, and 93.8% considering equivocal lesions as negative. The SUVmax of the liver lesions in 4D-PET (mean ± SD, 6.9 ± 3.2) was significantly higher (p < 0.001) than SUVmax in 3D-PET (mean ± SD, 5.2 ± 2.3).

CONCLUSIONS

Respiratory-gated PET/CT technique is a valuable clinical tool in diagnosing liver lesions, reducing 3D undetermined findings, improving diagnostic accuracy, and confidence in reporting. 4D-PET/CT also improved the quantification of SUVmax of liver lesions.

Motion management strategies and technical issues associated with stereotactic body radiotherapy of thoracic and upper abdominal tumors: A review from NRG oncology

The efficacy of stereotactic body radiotherapy (SBRT) has been well demonstrated. However, it presents unique challenges for accurate planning and delivery especially in the lungs and upper abdomen where respiratory motion can be significantly confounding accurate targeting and avoidance of normal tissues. In this paper, we review the current literature on SBRT for lung and upper abdominal tumors with particular emphasis on addressing respiratory motion and its affects. We provide recommendations on strategies to manage motion for different, patient-specific situations. Some of the recommendations will potentially be adopted to guide clinical trial protocols.