New full-counts phase-matched data-driven gated (DDG) PET/CT.

BACKGROUND: Data-driven gated (DDG) PET has gained clinical acceptance and has been shown to match or outperform external-device gated (EDG) PET. However, in most clinical applications, DDG PET is matched with helical CT acquired in free breathing (FB) at a random respiratory phase, leaving registration, and optimal attenuation correction (AC) to chance. Furthermore, DDG PET requires additional scan time to reduce image noise as it only preserves 35%-50% of the PET data at or near the end-expiratory phase of the breathing cycle. PURPOSE: A new full-counts, phase-matched (FCPM) DDG PET/CT was developed based on a low-dose cine CT to improve registration between DDG PET and DDG CT, to reduce image noise, and to avoid increasing acquisition times in DDG PET. METHODS: A new DDG CT was developed for three respiratory phases of CT images from a low dose cine CT acquisition of 1.35 mSv for a coverage of about 15.4 cm: end-inspiration (EI), average (AVG), and end-expiration (EE) to match with the three corresponding phases of DDG PET data: -10% to 15%; 15% to 30%, and 80% to 90%; and 30% to 80%, respectively. The EI and EE phases of DDG CT were selected based on the physiological changes in lung density and body outlines reflected in the dynamic cine CT images. The AVG phase was derived from averaging of all phases of the cine CT images. The cine CT was acquired over the lower lungs and/or upper abdomen for correction of misregistration between PET and FB CT as well as DDG PET and FB CT. The three phases of DDG CT were used for AC of the corresponding phases of PET. After phase-matched AC of each PET dataset, the EI and AVG PET data were registered to the EE PET data with deformable image registration. The final result was FCPM DDG PET/CT which accounts for all PET data registered at the EE phase. We applied this approach to 14 18F-FDG lung cancer patient studies acquired at 2 min/bed position on the GE Discovery MI (25-cm axial FOV) and evaluated its efficacy in improved quantification and noise reduction. RESULTS: Relative to static PET/CT, the SUVmax increases for the EI, AVG, EE, and FCPM DDG PET/CT were 1.67 ± 0.40, 1.50 ± 0.28, 1.64 ± 0.36, and 1.49 ± 0.28, respectively. There were 10.8% and 9.1% average decreases in SUVmax from EI and EE to FCPM DDG PET/CT, respectively. EI, AVG, and EE DDG PET/CT all maintained increased image noise relative to static PET/CT. However, the noise levels of FCPM and static PET were statistically equivalent, suggesting the inclusion of all counts was able to decrease the image noise relative to EI and EE DDG PET/CT. CONCLUSIONS: A new FCPM DDG PET/CT has been developed to account for 100% of collected PET data in DDG PET applications. Image noise in FCPM is comparable to static PET, while small decreases in SUVmax were also observed in FCPM when compared to either EI or EE DDG PET/CT.

Correcting CT misregistration in data-driven gated (DDG) PET with PET self-gating and deformable image registration.

BACKGROUND: Misregistration between CT and PET data can result in mis-localization and inaccurate quantification of functional uptake in whole body PET/CT imaging. This problem is exacerbated when an abnormal inspiration occurs during the free-breathing helical CT (FB CT) used for attenuation correction of PET data. In data-driven gated (DDG) PET, the data selected for reconstruction is typically derived from the end-expiration (EE) phase of the breathing cycle, making this potential issue worse. PURPOSE: The objective of this study is to develop a deformable image registration (DIR)-based respiratory motion model to improve the registration and quantification between misregistered FB CT and PET. METHODS: Twenty-two whole-body 18 F-FDG PET/CT scans encompassing 48 lesions in misregistered regions were analyzed in this study. End-inspiration (EI) and EE PET data were derived from -10% to 15% and 30% to 80% of the breathing cycle, respectively. DIR was used to estimate a motion model from the EE to EI phase of the PET data. The model was then used to generate PET images at any phase of up to four times the amplitude of motion between EE and EI for correlation with the misregistered FB CT. Once a matched phase of the FB CT was determined, FB CT was deformed to a pseudo CT at the EE phase (DIR CT). DIR CT was compared with the ground truth DDG CT for AC and localization of the DDG PET. RESULTS: Between DDG PET/FB CT and DDG PET/DIR CT, a significant increase in ∆%SUV was observed (p < 0.01), with median values elevating from 26.7% to 42.4%. This new method was most effective for lesions ≤3 cm proximal to the diaphragm (p < 0.001) but showed decreasing efficacy as the distance increased. When FB CT was severely misregistered with DDG PET (>3 cm), DDG PET/DIR CT outperformed DDG PET/FB CT alone (p < 0.05). Even when patients showed varied breathing patterns during the PET/CT scan, DDG PET/DIR CT still surpassed the efficiency of DDG PET/FB CT (p < 0.01). Though DDG PET/DIR CT couldn't match the performance of the DDG PET/CT ground truth (42.4% vs. 53.6%, p < 0.01), it reached 84% of its quantification, demonstrating good agreement and a strong overall correlation (regression coefficient of 0.94, p < 0.0001). In some cases, anatomical distortion and blurring, and misregistration error were observed in DIR CT, rendering it still unable to correct inaccurate localization near the boundaries of two organs. CONCLUSIONS: Based on the motion model derived from gated PET data, DIR CT can significantly improve the quantification and localization of DDG PET. This approach can achieve a performance level of about 84% of the ground truth established by DDG PET/CT. These results show that self-gated PET and DIR CT may offer an alternative clinical solution to DDG PET and FB CT for quantification without the need for additional cine-CT imaging. DIR CT was at times inferior to DDG CT due to some distortion and blurring of anatomy and misregistration error.

Data-driven gated CT: An automated respiratory gating method to enable data-driven gated PET/CT.

BACKGROUND: The accuracy of positron emission tomography (PET) quantification and localization can be compromised if a misregistered computed tomography (CT) is used for attenuation correction (AC) in PET/CT. As data-driven gating (DDG) continues to grow in clinical use, these issues are becoming more relevant with respect to solutions for gated CT. PURPOSE: In this work, a new automated DDG CT method was developed to provide average CT and DDG CT for AC of PET and DDG PET, respectively. METHODS: An automatic DDG CT was developed to provide the end-expiratory (EE) and end-inspiratory (EI) phases of images from low-dose cine CT images, with all phases being averaged to generate an average CT. The respiratory phases of EE and EI were determined according to lung region Hounsfield unit (HU) values and body outline contours. The average CT was used for AC of baseline PET and DDG CT at EE phase was used for AC of DDG PET at the quiescent or EE phase. The EI and EE phases obtained with DDG CT were used for assessing the magnitude of respiratory motion. The proposed DDG CT was compared to two commercial CT gating methods: (1) 4D CT (external device based) and (2) D4D CT (DDG based) in 38 patient datasets with respect to respiratory phase image selection, lung HU, lung volume, and image artifacts. In a separate set of twenty consecutive PET/CT studies containing a mix of 18 F-FDG, 68 Ga-Dotatate, and 64 Cu-Dotatate scans, the proposed DDG CT was compared with D4D CT for impacts on registration and quantification in DDG PET/CT. RESULTS: In the EE phase, the images selected by DDG CT and 4D CT were identical 62.5% ± 21.6% of the time, whereas DDG CT and D4D CT were 6.5% ± 9.7%, and 4D CT and D4D CT were 8.6% ± 12.2%. These differences in EE phase image selection were significant (p < 0.0001). In the EI phase, the images selected by DDG CT and 4D CT were identical 68.2% ± 18.9% of the time, DDG CT and D4D CT were 63.9% ± 18.8%, and 4D CT and D4D CT were 61.2% ± 19.8%. These differences were not significant. The mean lung HU and volumes were not statistically different (p > 0.1) among the three methods. In some studies, DDG CT was better than D4D or 4D CT in the appropriate selection of the EE and EI phases, and D4D CT was found to reverse the EE and EI phases or not select the correct images by visual inspection. A statistically significant improvement of DDG CT over D4D CT for AC of DDG PET was also demonstrated with PET quantification analysis. When irregular breath cycles were present in the cine CT, DDG CT could be used to replace average CT for the improved AC of baseline PET. CONCLUSION: A new automatic DDG CT was developed to tackle the issues of misregistration and tumor motion in PET/CT imaging. DDG CT was significantly more consistent than D4D CT in selecting the EE phase images as the clinical standard of 4D CT. When compared to both commercial gated CT methods of 4D CT and D4D CT, DDG CT appeared to be more robust in the lower lung and upper diaphragm regions where misregistration and tumor motion often occur. DDG CT offered improved AC for DDG PET relative to D4D CT. In cases with irregular respiratory motion, DDG CT improved AC over average CT for baseline PET. The new DDG CT provides the benefits of 4D CT without the need for external device gating.

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.

Noise reduction profile: A new method for evaluation of noise reduction techniques in CT.

PURPOSE: Noise power spectrum (NPS) is a commonly used performance metric to evaluate noise-reduction techniques (NRT) in imaging systems. The images reconstructed with and without an NRT can be compared via their NPS to better understand the NRT's effects on image noise. However, when comparing NPSs, simple visual assessments or a comparison of NPS peaks or medians are often used. These assessments make it difficult to objectively evaluate the effect of noise reduction across all spatial frequencies. In this work, we propose a new noise reduction profile (NRP) to facilitate a more complete and objective evaluation of NPSs for a range of NRTs used specifically in computed tomography (CT). METHODS AND MATERIALS: The homogeneous section of the ACR or Catphan phantoms was scanned on different CT scanners equipped with the following NRTs: AIDR3D, AiCE, ASiR, ASiR-V, TrueFidelity, iDose, SAFIRE, and ADMIRE. The images were then reconstructed with all strengths of each NRT in reference to the baseline filtered back projection (FBP) images. One set of the baseline FBP images was also processed with PixelShine, an NRT based on artificial intelligence. The NPSs of the images before and after noise reduction were calculated in both the xy-plane and along the z-direction. The difference in the logarithmic scale between each NPS (baseline FBP and NRT) was then calculated and deemed the NRP. Furthermore, the relationship between the NRP and NPS peak positions was mathematically analyzed. RESULTS: Each NRT has its own unique NRP. By comparing the NPS and NRP for each NRT, it was found that NRP is related to the peak shift of NPS. Additionally, under the assumption that the NPS has one peak and is differentiable, a relationship was mathematically derived between the slope of the NRP at the peak position of the NPS before noise reduction and the shift of the NPS peak position after noise reduction. CONCLUSIONS: A new metric, NRP, was proposed based on NPS to objectively evaluate and compare methods for noise reduction in CT. The NRP can be used to compare the effects of various NRTs on image noise in both the xy-plane and z-direction. It also enables unbiased assessment of the detailed noise reduction properties of each NRT over all relevant spatial frequencies.

Data-driven gated PET/CT: implications for lesion segmentation and quantitation.

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

A Benchmark for automatic noise measurement in clinical computed tomography.

PURPOSE: Assessment of image quality directly in clinical image data is an important quality control objective as phantom-based testing does not fully represent image quality across patient variation. Computer algorithms for automatically measuring noise in clinical computed tomography (CT) images have been introduced, but the accuracy of these algorithms is unclear. This work benchmarks the accuracy of the global noise (GN) algorithm for automatic noise measurement in contrast-enhanced abdomen CT exams in comparison to precise reference noise measurements. The GN algorithm was further optimized compared to the previous report in the literature. METHODS: Reference values of noise were established in a public image dataset of 82 contrast-enhanced abdomen CT exams. The reference noise values were obtained by manual regions-of-interest measurements of pixel standard deviation in the liver parenchyma according to an instruction protocol. Noise measurements taken by six observers were averaged together to improve reference noise statistical precision. The GN algorithm was used to automatically measure noise in each image set. The accuracy of the GN algorithm was determined in terms of RMS error compared to reference noise. The GN algorithm was optimized by conducting 1000 trials with random algorithm parameter values. The trial with the lowest RMS error was used to select optimum algorithm parameters. RESULTS: The range of noise across CT image sets was 8.8-28.8 HU. Reference noise measurements were made with a precision of ±0.78 HU (95% confidence interval). The RMS error of automatic noise measurement was 0.93 HU (0.77-1.19 HU 95% confidence interval). The automatic noise measurements were equally accurate across image sets of varying noise magnitude. Optimum GN algorithm parameter values were: a kernel size of 7 pixels, and soft tissue lower and upper thresholds of 0 and 170 HU, respectively. CONCLUSIONS: The performance of automatic noise measurement was benchmarked in a large clinical CT dataset. The study provides a framework for thorough validation of automatic clinical image quality measurement methods. The GN algorithm was optimized and validated for automatic measurement of soft-tissue noise in abdomen CT exams.

New Data-Driven Gated PET/CT Free of Misregistration Artifacts.

PURPOSE: We developed a new data-driven gated (DDG) positron emission tomography (PET)/computed tomography (CT) to improve the registration of CT and DDG PET. METHODS: We acquired 10 repeat PET/CT and 35 cine CT scans for the mitigation of misregistration between CT and PET data. We also derived end-expiration phase CT as DDG CT for attenuation correction of DDG PET. Radiation exposure, body mass index (BMI), scan coverage, and effective radiation dose were compared between repeat PET/CT and cine CT. Of the 35 cine CT patients, 14 (capturing 59 total tumors) were compared among average PET/CT (baseline PET attenuation correction by average CT), DDG PET (DDG PET attenuation correction by baseline CT), and DDG PET/CT (DDG PET attenuation correction by DDG CT) for registration and quantification without increasing the scan time for DDG PET. RESULTS: Compared with repeat PET/CT, cine CT had significantly lower scan coverage (32.5 ± 11.5 cm vs 15.4 ± 4.7 cm; P < .001) and effective radiation dose (3.7 ± 2.6 mSv vs 1.3 ± 0.6 mSv; P < .01). Repeat PET/CT and cine CT did not differ significantly in BMI or radiation exposure (P > .1). Cine CT saved the scan time for not needing a repeat PET. The SUV ratios of average PET/CT, DDG PET, and DDG PET/CT to baseline PET/CT were 1.14 ± 0.28, 1.28 ± 0.20, and 1.63 ± 0.64, respectively (P < .0001), suggesting that the SUVmax increased consecutively from baseline PET/CT to average PET/CT, DDG PET, and DDG PET/CT. Motion correction with DDG PET had a larger impact on quantification than registration improvement with average CT did. The biggest improvement in quantification was from DDG PET/CT, in which both registration was improved and motion was mitigated. CONCLUSION: Our new DDG PET/CT approach alleviates misregistration artifacts and, compared with DDG PET, improves quantification and registration. The use of cine CT in our DDG PET/CT method also reduces the effective radiation dose and scan coverage compared with repeat CT.

Planning dosimetry for 90 Y radioembolization with glass microspheres: Evaluating the fidelity of 99m Tc-MAA and partition model predictions.

PURPOSE: 99m Tc-MAA-SPECT/CT may be used in 90 Y-glass microsphere radioembolization treatment planning to assess perfused liver volumes and absorbed dose distributions. The partition model (PM) offers a more detailed planning dosimetry option beyond the single-compartment model more traditionally used in 90 Y radioembolization. As 90 Y radioembolization treatments shift toward activities and doses that aim to achieve tumor control, accurate and reliable treatment planning dosimetry for both tumors and normal liver (NL) becomes more critical. In this work, we explore the accuracy and precision of 90 Y dosimetry predictions from pretherapy 99m Tc-MAA and PM. METHODS: Both PM and voxel dosimetry models were used to calculate tumor and NL mean doses using both planning 99m Tc-MAA and verification 90 Y-SPECT/CT in this retrospective analysis of hepatocellular carcinoma cases treated with glass microspheres (NCT01900002, n = 32). Linear regression models were developed at first access, and then later correct, the estimates by (a) 99m Tc-MAA for 90 Y voxel dosimetry and (b) 99m Tc-MAA PM for voxel dosimetry, separately for both tumors and NL. Bland-Altman analysis was then used to evaluate the accuracy and precision of the regression model predictions with the mean bias and 95% prediction intervals (PI, ±1.96σ). Two categories of cases were stratified (catheter matched vs catheter unmatched) by establishing the level of 99m Tc-MAA and 90 Y catheter position alignment. Only catheter-matched cases were included in the 99m Tc-MAA vs 90 Y voxel dosimetry comparison, while all cases were used to compare dosimetry models (PM vs voxel). RESULTS: Half (16/32) of cases were deemed catheter matched. 99m Tc-MAA could reliably predict NL doses in catheter-matched cases after application of the linear model, with mean bias (PI) of -1% (±31%). PM was equivalent to voxel dosimetry for NL doses with mean bias (PI) of 0% (±1%). Even among catheter-matched cases, 99m Tc-MAA planning for 90 Y tumor voxel doses was poor, overestimating dose by an average of nearly 40%. Upon application of the linear model, 99m Tc-MAA predictions for 90 Y tumor voxel dose were only minimally biased (-4%) but possessed very large PI (±104%). PM predictions for tumor voxel dose using the linear model also showed small bias (-6%) but maintained similarly high PI of ±90%. Cases with tumors representing a large majority (>80%) of the total tumor volume demonstrated the best scenarios for 99m Tc-MAA and PM tumor dose predictions, with mean biases (PI) of -3% (±53%) and -4% (±21%), respectively. CONCLUSION: The unconditional use of 99m Tc-MAA to predict 90 Y dosimetry across all cases is not recommended due to: (a) demonstrated the risk of unmatched catheter positions between procedures, and (b) large bias and uncertainty in 99m Tc-MAA predictions in cases with matched catheter locations. However, NL voxel dose predictions with 99m Tc-MAA are clinically viable and either PM or voxel dosimetry can be used to produce equivalent predictions. Both 99m Tc-MAA and PM can provide tumor dose predictions with potential clinical utility, but only in catheter-matched cases and with tumors comprising a clear majority (>80%) of the total tumor volume. These findings stratify the predictive fidelity of 99m Tc-MAA- and PM-based treatment planning for 90 Y dosimetry in improving treatment outcomes.

Development and evaluation of machine learning models for voxel dose predictions in online adaptive magnetic resonance guided radiation therapy.

PURPOSE: Daily online adaptive plan quality in magnetic resonance imaging guided radiation therapy (MRgRT) is difficult to assess in relation to the fully optimized, high quality plans traditionally established offline. Machine learning prediction models developed in this work are capable of predicting 3D dose distributions, enabling the evaluation of online adaptive plan quality to better inform adaptive decision-making in MRgRT. METHODS: Artificial neural networks predicted 3D dose distributions from input variables related to patient anatomy, geometry, and target/organ-at-risk relationships in over 300 treatment plans from 53 patients receiving adaptive, linac-based MRgRT for abdominal cancers. The models do not include any beam related variables such as beam angles or fluence and were optimized to balance errors related to raw dose and specific plan quality metrics used to guide daily online adaptive decisions. RESULTS: Averaged over all plans, the dose prediction error and the absolute error were 0.1 ± 3.4 Gy (0.1 ± 6.2%) and 3.5 ± 2.4 Gy (6.4 ± 4.3%) respectively. Plan metric prediction errors were -0.1 ± 1.5%, -0.5 ± 2.1%, -0.9 ± 2.2 Gy, and 0.1 ± 2.7 Gy for V95, V100, D95, and Dmean respectively. Plan metric prediction absolute errors were 1.1 ± 1.1%, 1.5 ± 1.5%, 1.9 ± 1.4 Gy, and 2.2 ± 1.6 Gy. Approximately 10% (25) of the plans studied were clearly identified by the prediction models as inferior quality plans needing further optimization and refinement. CONCLUSION: Machine learning prediction models for treatment plan 3D dose distributions in online adaptive MRgRT were developed and tested. Clinical integration of the models requires minimal effort, producing 3D dose predictions for a new patient's plan using only target and OAR structures as inputs. These models can enable improved workflows for MRgRT through more informed plan optimization and plan quality assessment in real time.

Using prediction models to evaluate magnetic resonance image guided radiation therapy plans.

Comprehensive analysis of daily, online adaptive plan quality and safety in magnetic resonance imaging (MRI) guided radiation therapy is critical to its widespread use. Artificial neural network models developed with offline plans created after simulation were used to analyze and compare online plans that were adapted and reoptimized in real time prior to treatment. Roughly one third of 60Co adapted plans were of inferior quality relative to fully optimized, offline plans, but MRI-linac adapted plans were essentially equivalent to offline plans. The models also enabled clear justification that MRI-linac plans are superior to 60Co in an overwhelming majority of cases.