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.

Impact of acquisition time and misregistration with CT on data-driven gated PET.

Objective. Data-driven gating (DDG) can address patient motion issues and enhance PET quantification but suffers from increased image noise from utilization of <100% of PET data. Misregistration between DDG-PET and CT may also occur, altering the potential benefits of gating. Here, the effects of PET acquisition time and CT misregistration were assessed with a combined DDG-PET/DDG-CT technique.Approach. In the primary PET bed with lesions of interest and likely respiratory motion effects, PET acquisition time was extended to 12 min and a low-dose cine CT was acquired to enable DDG-CT. Retrospective reconstructions were created for both non-gated (NG) and DDG-PET using 30 s to 12 min of PET data. Both the standard helical CT and DDG-CT were used for attenuation correction of DDG-PET data. SUVmax, SUVpeak, and CNR were compared for 45 lesions in the liver and lung from 27 cases.Main results. For both NG-PET (p= 0.0041) and DDG-PET (p= 0.0028), only the 30 s acquisition time showed clear SUVmaxbias relative to the 3 min clinical standard. SUVpeakshowed no bias at any change in acquisition time. DDG-PET alone increased SUVmaxby 15 ± 20% (p< 0.0001), then was increased further by an additional 15 ± 29% (p= 0.0007) with DDG-PET/CT. Both 3 min and 6 min DDG-PET had lesion CNR statistically equivalent to 3 min NG-PET, but then increased at 12 min by 28 ± 48% (p= 0.0022). DDG-PET/CT at 6 min had comparable counts to 3 min NG-PET, but significantly increased CNR by 39 ± 46% (p< 0.0001).Significance. 50% counts DDG-PET did not lead to inaccurate or biased SUV-increased SUV resulted from gating. Improved registration from DDG-CT was equally as important as motion correction with DDG-PET for increasing SUV in DDG-PET/CT. Lesion detectability could be significantly improved when DDG-PET used equivalent counts to NG-PET, but only when combined with DDG-CT in DDG-PET/CT.

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 (SUVmax and SUVpeak) 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. SUVmax was on average 21% higher than PET/static when either PET/ex or PET/matched was used. SUVpeak 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.

Effects of alterations in positron emission tomography imaging parameters on radiomics features.

Radiomics studies require large patient cohorts, which often include patients imaged using different imaging protocols. We aimed to determine the impact of variability in imaging protocol parameters and interscanner variability using a phantom that produced feature values similar to those of patients. Positron emission tomography (PET) scans of a Hoffman brain phantom were acquired on GE Discovery 710, Siemens mCT, and Philips Vereos scanners. A standard-protocol scan was acquired on each machine, and then each parameter that could be changed was altered individually. The phantom was contoured with 10 regions of interest (ROIs). Values for 45 features with 2 different preprocessing techniques were extracted for each image. To determine the impact of each parameter on the reliability of each radiomics feature, the intraclass correlation coefficient (ICC) was calculated with the ROIs as the subjects and the parameter values as the raters. For interscanner comparisons, we compared the standard deviation of each radiomics feature value from the standard-protocol images to the standard deviation of the same radiomics feature from PET scans of 224 patients with non-small cell lung cancer. When the pixel size was resampled prior to feature extraction, all features had good reliability (ICC > 0.75) for the field of view and matrix size. The time per bed position had excellent reliability (ICC > 0.9) on all features. When the filter cutoff was restricted to values below 6 mm, all features had good reliability. Similarly, when subsets and iterations were restricted to reasonable values used in clinics, almost all features had good reliability. The average ratio of the standard deviation of features on the phantom scans to that of the NSCLC patient scans was 0.73 using fixed-bin-width preprocessing and 0.92 using 64-level preprocessing. Most radiomics feature values had at least good reliability when imaging protocol parameters were within clinically used ranges. However, interscanner variability was about equal to interpatient variability; therefore, caution must be used when combining patients scanned on equipment from different vendors in radiomics data sets.

Radiomics features of the primary tumor fail to improve prediction of overall survival in large cohorts of CT- and PET-imaged head and neck cancer patients.

Radiomics studies require many patients in order to power them, thus patients are often combined from different institutions and using different imaging protocols. Various studies have shown that imaging protocols affect radiomics feature values. We examined whether using data from cohorts with controlled imaging protocols improved patient outcome models. We retrospectively reviewed 726 CT and 686 PET images from head and neck cancer patients, who were divided into training or independent testing cohorts. For each patient, radiomics features with different preprocessing were calculated and two clinical variables-HPV status and tumor volume-were also included. A Cox proportional hazards model was built on the training data by using bootstrapped Lasso regression to predict overall survival. The effect of controlled imaging protocols on model performance was evaluated by subsetting the original training and independent testing cohorts to include only patients whose images were obtained using the same imaging protocol and vendor. Tumor volume, HPV status, and two radiomics covariates were selected for the CT model, resulting in an AUC of 0.72. However, volume alone produced a higher AUC, whereas adding radiomics features reduced the AUC. HPV status and one radiomics feature were selected as covariates for the PET model, resulting in an AUC of 0.59, but neither covariate was significantly associated with survival. Limiting the training and independent testing to patients with the same imaging protocol reduced the AUC for CT patients to 0.55, and no covariates were selected for PET patients. Radiomics features were not consistently associated with survival in CT or PET images of head and neck patients, even within patients with the same imaging protocol.

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 SUVmax and SUVpeak 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 SUVmax and SUVpeak range was calculated for the reconstructions and compared between QPG and SWB PET. The mean percentage change in the lesion SUVmax and SUVpeak 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 SUVmax and SUVpeak ranges were 58.5% and 59.0% for QPG, respectively compared to 46.1% and 45.3% for SWB, respectively. The highest SUVmax and SUVpeak 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.

A measurement of the attenuation of radiation from F-18 by a PET/MR scanner.

The attenuation of 511 keV photons by the structure of a PET/MR scanner was measured prior to energizing the magnet. The exposure rate from a source of fluorine-18 was measured in air and, with the source placed at the isocenter of the instrument, at various points outside of the scanner. In an arc from 45 to 135 degrees relative to the long axis of the scanner and at a distance of 1.5 m from the isocenter, the attenuation by the scanner is at least 5.6 half-value layers from the MR component alone and at least 6.6 half-value layers with the PET insert installed. This information could inform better design of the radiation shielding for PET/MR scanners.

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.

HU deviation in lung and bone tissues: Characterization and a corrective strategy.

INTRODUCTION: In the era of precision medicine, quantitative applications of x-ray Computed Tomography (CT) are on the rise. These require accurate measurement of the CT number, also known as the Hounsfield Unit. In this study, we evaluated the effect of patient attenuation-induced beam hardening of the x-ray spectrum on the accuracy of the HU values and a strategy to correct for the resulting deviations in the measured HU values. MATERIALS AND METHODS: A CIRS electron density phantom was scanned on a Siemens Biograph mCT Flow CT scanner and a GE Discovery 710 CT scanner using standard techniques that are employed in the clinic to assess the HU deviation caused by beam hardening in different tissue types. In addition, an anthropomorphic ATOM adult male upper torso phantom was scanned on the GE Discovery 710 scanner. Various amounts of Superflab bolus material were wrapped around the phantoms to simulate different patient sizes. The mean HU values that were measured in the phantoms were evaluated as a function of the water-equivalent area (Aw ), a parameter that is described in the report of AAPM Task Group 220. A strategy by which to correct the HU values was developed and tested. The variation in the HU values in the anthropomorphic ATOM phantom under different simulated body sizes, both before and after correction, were compared, with a focus on the lung and bone tissues. RESULTS: Significant HU deviations that depended on the simulated patient size were observed. A positive correlation between HU and Aw was observed for tissue types that have an HU of less than zero, while a negative correlation was observed for tissue types with HU values that are greater than zero. The magnitude of the difference increases as the underlying attenuation property deviates further away from that of water. In the electron density phantom study, the maximum observed HU differences between the measured and reference values in the cortical bone and lung materials were 426 and 94 HU, respectively. In the anthropomorphic phantom study, the HU difference was as much as -136.7 ± 8.2 HU (or -7.6% ± 0.5% of the attenuation coefficient, AC) in the spine region, and up to 37.6 ± 1.6 HU (or 17.3% ± 0.8% of AC) in the lung region between scenarios that simulated normal and obese patients. Our HU correction method reduced the HU deviations to 8.5 ± 9.1 HU (or 0.5% ± 0.5%) for bone and to -6.4 ± 1.7 HU (or -3.0% ± 0.8%) for lung. The HU differences in the soft tissue materials before and after the correction were insignificant. Visual improvement of the tissue contrast was also achieved in the data of the simulated obese patient. CONCLUSIONS: The effect of a patient's size on the HU values of lung and bone tissues can be significant. The accuracy of those HU values was substantially improved by the correction method that was developed for and employed in this study.

Data-driven optimal binning for respiratory motion management in PET.

PURPOSE: Respiratory gating has been used in PET imaging to reduce the amount of image blurring caused by patient motion. Optimal binning is an approach for using the motion-characterized data by binning it into a single, easy to understand/use, optimal bin. To date, optimal binning protocols have utilized externally driven motion characterization strategies that have been tuned with population-derived assumptions and parameters. In this work, we are proposing a new strategy with which to characterize motion directly from a patient's gated scan, and use that signal to create a patient/instance-specific optimal bin image. METHODS: Two hundred and nineteen phase-gated FDG PET scans, acquired using data-driven gating as described previously, were used as the input for this study. For each scan, a phase-amplitude motion characterization was generated and normalized using principle component analysis. A patient-specific "optimal bin" window was derived using this characterization, via methods that mirror traditional optimal window binning strategies. The resulting optimal bin images were validated by correlating quantitative and qualitative measurements in the population of PET scans. RESULTS: In 53% (n = 115) of the image population, the optimal bin was determined to include 100% of the image statistics. In the remaining images, the optimal binning windows averaged 60% of the statistics and ranged between 20% and 90%. Tuning the algorithm, through a single acceptance window parameter, allowed for adjustments of the algorithm's performance in the population toward conservation of motion or reduced noise-enabling users to incorporate their definition of optimal. In the population of images that were deemed appropriate for segregation, average lesion SUV max were 7.9, 8.5, and 9.0 for nongated images, optimal bin, and gated images, respectively. The Pearson correlation of FWHM measurements between optimal bin images and gated images were better than with nongated images, 0.89 and 0.85, respectively. Generally, optimal bin images had better resolution than the nongated images and better noise characteristics than the gated images. DISCUSSION: We extended the concept of optimal binning to a data-driven form, updating a traditionally one-size-fits-all approach to a conformal one that supports adaptive imaging. This automated strategy was implemented easily within a large population and encapsulated motion information in an easy to use 3D image. Its simplicity and practicality may make this, or similar approaches ideal for use in clinical settings.