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

Device-Less Data-Driven Cardiac and Respiratory Gating Using LAFOV PET Histo Images

Background: The outstanding capabilities of modern Positron Emission Tomography (PET) to highlight small tumor lesions and provide pathological function assessment are at peril from image quality degradation caused by respiratory and cardiac motion. However, the advent of the long axial field-of-view (LAFOV) scanners with increased sensitivity, alongside the precise time-of-flight (TOF) of modern PET systems, enables the acquisition of ultrafast time resolution images, which can be used for estimating and correcting the cyclic motion. Methods: 0.25 s so-called [18F]FDG PET histo image series were generated in the scope of for detecting respiratory and cardiac frequency estimates applicable for performing device-less data-driven gated image reconstructions. The frequencies of the cardiac and respiratory motion were estimated for 18 patients using Short Time Fourier Transform (STFT) with 20 s and 30 s window segments, respectively. Results: The Fourier analysis provided time points usable as input to the gated reconstruction based on eight equally spaced time gates. The cardiac investigations showed estimates in accordance with the measured pulse oximeter references (p = 0.97) and a mean absolute difference of 0.4 ± 0.3 beats per minute (bpm). The respiratory frequencies were within the expected range of 10-20 respirations per minute (rpm) in 16 out of 18 patients. Using this setup, the analysis of three patients with visible lung tumors showed an increase in tumor SUVmax and a decrease in tumor volume compared to the non-gated reconstructed image. Conclusions: The method can provide signals that were applicable for gated reconstruction of both cardiac and respiratory motion, providing a potential increased diagnostic accuracy.

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

OBJECTIVES

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.

METHODS

We used a NEMA IEC body phantom comprising six spheres (37-, 28-, 22-, 17-, 13-, and 10 mm diameter) containing 18F. The sphere-to-background ratio was 4:1 (21.2 and 5.3 kBq/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.

RESULTS

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 < 28 mm.

CONCLUSIONS

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.

Role of breath-hold lung PET in stage IA pulmonary adenocarcinoma

BACKGROUND

Respiratory motion during PET acquisition may result in image blurring and resolution loss, reduced measurement of radiotracer uptake, and consequently, inaccurate lesion quantification and description. With the introduction of the total-body PET system, short-time PET acquisition is feasible due to its high sensitivity and spatial resolution. The purpose of this study was to evaluate the additional value of 20-s breath-hold (BH) lung PET in patients with stage IA pulmonary adenocarcinoma.

METHODS

Forty-seven patients with confirmed stage IA pulmonary adenocarcinoma were enrolled in this retrospective study. All patients underwent a 300-s FB whole-body PET, followed by a BH lung PET. The SUV_max, TBR of the lesions and the percentage difference in nodule SUV_max (%ΔSUV_max) and TBR (%ΔTBR) between the two acquisitions was also calculated. The lesions were further divided by distance from pleura for subgroup analysis. The lesion detectability on PET images was the percentage of FDG-positive lesions.

RESULTS

Among 47 patients, the BH lung PET images identified all lung nodules, and there was a significant difference in overall nodule SUV_max and TBR between BH PET and FB PET (both p < 0.01). The %ΔSUV_max and %ΔTBR were significantly higher in nodules adjacent to pleura (≤ 10 mm in distance) than those away from pleura (both p < 0.05). The lesion detectability of BH lung PET was significantly higher than that of FB PET (p < 0.01).

CONCLUSION

BH PET acquisition is a practical way to minimize motion artifacts in PET which has the potential to improve lesion detection for stage IA pulmonary adenocarcinoma.

CRITICAL RELEVANCE STATEMENT

BH PET acquisition is a practical way to minimize motion artifacts in PET which has the potential to improve lesion detection for stage IA pulmonary adenocarcinoma.

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.

Aortic valve imaging using 18F-sodium fluoride: impact of triple motion correction

BACKGROUND

Current 18F-NaF assessments of aortic valve microcalcification using 18F-NaF PET/CT are based on evaluations of end-diastolic or cardiac motion-corrected (ECG-MC) images, which are affected by both patient and respiratory motion. We aimed to test the impact of employing a triple motion correction technique (3 × MC), including cardiorespiratory and gross patient motion, on quantitative and qualitative measurements.

MATERIALS AND METHODS

Fourteen patients with aortic stenosis underwent two repeat 30-min PET aortic valve scans within (29 ± 24) days. We considered three different image reconstruction protocols; an end-diastolic reconstruction protocol (standard) utilizing 25% of the acquired data, an ECG-gated (four ECG gates) reconstruction (ECG-MC), and a triple motion-corrected (3 × MC) dataset which corrects for both cardiorespiratory and patient motion. All datasets were compared to aortic valve calcification scores (AVCS), using the Agatston method, obtained from CT scans using correlation plots. We report SUVmax values measured in the aortic valve and maximum target-to-background ratios (TBRmax) values after correcting for blood pool activity.

RESULTS

Compared to standard and ECG-MC reconstructions, increases in both SUVmax and TBRmax were observed following 3 × MC (SUVmax: Standard = 2.8 ± 0.7, ECG-MC = 2.6 ± 0.6, and 3 × MC = 3.3 ± 0.9; TBRmax: Standard = 2.7 ± 0.7, ECG-MC = 2.5 ± 0.6, and 3 × MC = 3.3 ± 1.2, all p values ≤ 0.05). 3 × MC had improved correlations (R2 value) to the AVCS when compared to the standard methods (SUVmax: Standard = 0.10, ECG-MC = 0.10, and 3 × MC = 0.20; TBRmax: Standard = 0.20, ECG-MC = 0.28, and 3 × MC = 0.46).

CONCLUSION

3 × MC improves the correlation between the AVCS and SUVmax and TBRmax and should be considered in PET studies of aortic valves using 18F-NaF.

Multimodal phantoms for clinical PET/MRI

Phantoms are commonly used throughout medical imaging and medical physics for a multitude of applications, the designs of which vary between modalities and clinical or research requirements. Within positron emission tomography (PET) and nuclear medicine, phantoms have a well-established role in the validation of imaging protocols so as to reduce the administration of radioisotope to volunteers. Similarly, phantoms are used within magnetic resonance imaging (MRI) to perform quality assurance on clinical scanners, and gel-based phantoms have a longstanding use within the MRI research community as tissue equivalent phantoms. In recent years, combined PET/MRI scanners for simultaneous acquisition have entered both research and clinical use. This review explores the designs and applications of phantom work within the field of simultaneous acquisition PET/MRI as published over the period of a decade. Common themes in the design, manufacture and materials used within phantoms are identified and the solutions they provided to research in PET/MRI are summarised. Finally, the challenges remaining in creating multimodal phantoms for use with simultaneous acquisition PET/MRI are discussed. No phantoms currently exist commercially that have been designed and optimised for simultaneous PET/MRI acquisition. Subsequently, commercially available PET and nuclear medicine phantoms are often utilised, with CT-based attenuation maps substituted for MR-based attenuation maps due to the lack of MR visibility in phantom housing. Tissue equivalent and anthropomorphic phantoms are often developed by research groups in-house and provide customisable alternatives to overcome barriers such as MR-based attenuation correction, or to address specific areas of study such as motion correction. Further work to characterise materials and manufacture methods used in phantom design would facilitate the ability to reproduce phantoms across sites.

Radiotherapy Planning and Molecular Imaging in Lung Cancer

INTRODUCTION

In patients suitable for radical chemoradiotherapy for lung cancer, 18F-FDGPET/ CT is a proposed management to improve the accuracy of high dose radiotherapy. However, there is a high rate of locoregional failure in patients with locally advanced non-small cell lung cancer (NSCLC), probably due to the fact that standard dosing may not be effective in all patients. The aim of the present review was to address some criticisms associated with the radiotherapy image-guided in NSCLC.

MATERIALS AND METHODS

A systematic literature search was conducted. Only published articles that met the following criteria were included: articles, only original papers, radiopharmaceutical ([18F]FDG and any tracer other than [18F]FDG), target, only specific for lung cancer radiotherapy planning, and experimental design (eventually "in vitro" studies were excluded). Peer-reviewed indexed journals, regardless of publication status (published, ahead of print, in press, etc.) were included. Reviews, case reports, abstracts, editorials, poster presentations, and publications in languages other than English were excluded. The decision to include or exclude an article was made by consensus and any disagreement was resolved through discussion.

RESULTS

Hundred eligible full-text articles were assessed. Diverse information is now available in the literature about the role of FDG and new alternative radiopharmaceuticals for the planning of radiotherapy in NSCLC. In particular, the role of alternative technologies for the segmentation of FDG uptake is essential, although indeterminate for RT planning. The pros and cons of the available techniques have been extensively reported.

CONCLUSION

PET/CT has a central place in the planning of radiotherapy for lung cancer and, in particular, for NSCLC assuming a substantial role in the delineation of tumor volume. The development of new radiopharmaceuticals can help overcome the problems related to the disadvantage of FDG to accumulate also in activated inflammatory cells, thus improving tumor characterization and providing new prognostic biomarkers.

Body motion detection and correction in cardiac PET: Phantom and human studies

PURPOSE

Patient body motion during a cardiac positron emission tomography (PET) scan can severely degrade image quality. We propose and evaluate a novel method to detect, estimate, and correct body motion in cardiac PET.

METHODS

Our method consists of three key components: motion detection, motion estimation, and motion-compensated image reconstruction. For motion detection, we first divide PET list-mode data into 1-s bins and compute the center of mass (COM) of the coincidences' distribution in each bin. We then compute the covariance matrix within a 25-s sliding window over the COM signals inside the window. The sum of the eigenvalues of the covariance matrix is used to separate the list-mode data into "static" (i.e., body motion free) and "moving" (i.e. contaminated by body motion) frames. Each moving frame is further divided into a number of evenly spaced sub-frames (referred to as "sub-moving" frames), in which motion is assumed to be negligible. For motion estimation, we first reconstruct the data in each static and sub-moving frame using a rapid back-projection technique. We then select the longest static frame as the reference frame and estimate elastic motion transformations to the reference frame from all other static and sub-moving frames using nonrigid registration. For motion-compensated image reconstruction, we reconstruct all the list-mode data into a single image volume in the reference frame by incorporating the estimated motion transformations in the PET system matrix. We evaluated the performance of our approach in both phantom and human studies.

RESULTS

Visually, the motion-corrected (MC) PET images obtained using the proposed method have better quality and fewer motion artifacts than the images reconstructed without motion correction (NMC). Quantitative analysis indicates that MC yields higher myocardium to blood pool concentration ratios. MC also yields sharper myocardium than NMC.

CONCLUSIONS

The proposed body motion correction method improves image quality of cardiac PET.

Real-time data-driven motion correction in PET

PET imaging has been, and continues to be, an evolving diagnostic technology. In recent years, the modernizing digital landscape has opened new opportunities for data-driven innovation. One such facet has been data-driven motion correction (DDMC) in PET. As both research and industry propel this technology forward, we can recognize prospects and opportunities for further development. The concept of clinical practicality is supported by DDMC approaches—it is what sets them apart from traditional hardware-driven motion correction strategies that have largely not gained acceptance in routine diagnostic PET; the ease of use of DDMC may help propel acceptance of motion correction solutions in clinical practice. As we reflect on the present field, we should consider that DDMC can be made even more practical, and likely more impactful, if further developed to fit within a real-time acquisition framework. This vision for development is not new, but has been made more feasible with contemporary electronics, and has begun to be revisited in contemporary literature. The opportunities for development lie on a new forefront of innovation where medical physics integrates with engineering, data science, and modern computing capacities. Real-time DDMC is a systems integration challenge, and achieving it will require cooperation between hardware and software developers, and likely academia and industry. While challenges for development do exist, it is likely that we will see real-time DDMC come to fruition in the coming years. This effort may establish groundwork for developing similar innovations in the emerging digital innovation age.

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

BACKGROUND

We 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.

METHODS

One 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 SUV_max of a well-defined lesion in the thorax or abdomen was measured and compared between the two reconstructions.

RESULTS

There 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 SUV_max of focal lesions, by an average of 11% when considering lesions in bed positions with R ≥ 15.

CONCLUSIONS

The 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 SUV_max in focal lesions.