Virtual Unenhanced Dual-Energy CT Images Obtained with a Multimaterial Decomposition Algorithm: Diagnostic Value for Renal Mass and Urinary Stone Evaluation.

Background Virtual unenhanced (VUE) images obtained by using a dual-energy CT (DECT) multimaterial decomposition algorithm hold promise for diagnostic use in the abdomen in lieu of true unenhanced (TUE) images. Purpose To assess VUE images obtained from a DECT multimaterial decomposition algorithm in patients undergoing renal mass and urinary stone evaluation. Materials and Methods In this retrospective Health Insurance Portability and Accountability Act-compliant study, DECT was performed in patients undergoing evaluation for renal mass or urinary stone. VUE images were compared quantitatively to TUE images and qualitatively assessed by four independent radiologists. Differences in attenuation between VUE and TUE images were summarized by using 95% limits of agreement. Diagnostic performance in urinary stone detection was summarized by using area under the receiver operating characteristic curve, sensitivity, and specificity. Results A total of 221 patients (mean age ± standard deviation, 61 years ± 14; 129 men) with 273 renal masses were evaluated. Differences in renal mass attenuation between VUE and TUE images were within 3 HU for both enhancing masses (95% limits of agreement, -3.1 HU to 2.7 HU) and nonenhancing cysts (95% limits of agreement, -2.9 HU to 2.5 HU). Renal mass classification as enhancing mass versus nonenhancing cyst did not change (reclassification rate of enhancing masses, 0% [0 of 78]; 95% CI: 0, 5; reclassification rate of nonenhancing cysts, 0% [0 of 193]; 95% CI: 0, 2) with use of VUE in lieu of TUE images. Among 166 urinary stones evaluated, diagnostic performance of VUE images for stone detection was lower compared with that of TUE images (area under the receiver operating characteristic curve, 0.79 [95% CI: 0.73, 0.84] vs 0.93 [95% CI: 0.91, 0.95]; P < .001) due to reduced sensitivity of VUE for detection of stones 3 mm in diameter or less compared with those greater than 3 mm (sensitivity, 23% [25 of 108; 95% CI: 12, 40] vs 88% [126 of 144; 95% CI: 77, 94]; P < .001). Conclusion Compared with true unenhanced images, virtual unenhanced (VUE) images were unlikely to change renal mass classification as enhancing mass versus nonenhancing cyst. Diagnostic performance of VUE images remained suboptimal for urinary stone detection due to subtraction of stones 3 mm or less in diameter. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Sosna in this issue.

Image Quality Assessment of Abdominal CT by Use of New Deep Learning Image Reconstruction: Initial Experience.

OBJECTIVE. The purpose of this study was to perform quantitative and qualitative evaluation of a deep learning image reconstruction (DLIR) algorithm in contrast-enhanced oncologic CT of the abdomen. MATERIALS AND METHODS. Retrospective review (April-May 2019) of the cases of adults undergoing oncologic staging with portal venous phase abdominal CT was conducted for evaluation of standard 30% adaptive statistical iterative reconstruction V (30% ASIR-V) reconstruction compared with DLIR at low, medium, and high strengths. Attenuation and noise measurements were performed. Two radiologists, blinded to examination details, scored six categories while comparing reconstructions for overall image quality, lesion diagnostic confidence, artifacts, image noise and texture, lesion conspicuity, and resolution. RESULTS. DLIR had a better contrast-to-noise ratio than 30% ASIR-V did; high-strength DLIR performed the best. High-strength DLIR was associated with 47% reduction in noise, resulting in a 92-94% increase in contrast-to-noise ratio compared with that of 30% ASIR-V. For overall image quality and image noise and texture, DLIR scored significantly higher than 30% ASIR-V with significantly higher scores as DLIR strength increased. A total of 193 lesions were identified. The lesion diagnostic confidence, conspicuity, and artifact scores were significantly higher for all DLIR levels than for 30% ASIR-V. There was no significant difference in perceived resolution between the reconstruction methods. CONCLUSION. Compared with 30% ASIR-V, DLIR improved CT evaluation of the abdomen in the portal venous phase. DLIR strength should be chosen to balance the degree of desired denoising for a clinical task relative to mild blurring, which increases with progressively higher DLIR strengths.

Quantification of the Effect of Shuttling on Computed Tomography Perfusion Parameters by Investigation of Aortic Inputs on Different Table Positions From Shuttle-Mode Scans of Lung and Liver Tumors.

OBJECTIVES: The aim of this study was to quantify the effect of shuttling on computed tomography perfusion (CTp) parameters derived from shuttle-mode body CT images using aortic inputs from different table positions. METHODS: Axial shuttle-mode CT scans were acquired from 6 patients (10 phases, 2 nonoverlapping table positions 1.4 seconds apart) after contrast agent administration. Artifacts resulting from the shuttling motion were corrected with nonrigid registration before computing CTp maps from 4 aortic levels chosen from the most superior and inferior slices of each table position scan. The effect of shuttling on CTp parameters was estimated by mean differences in mappings obtained from aortic inputs in different table positions. Shuttling effect was also quantified using 95% limits of agreement of CTp parameter differences within-table and between-table aortic positions from the interaortic mean CTp values. RESULTS: Blood flow, permeability surface, and hepatic arterial fraction differences were insignificant (P > 0.05) for both within-table and between-table comparisons. The 95% limits of agreement for within-table blood volume (BV) value deviations obtained from lung tumor regions were less than 4.7% (P = 0.18) compared with less than 12.2% (P = 0.003) for between-table BV value deviations. The 95% limits of agreement of within-table deviations for liver tumor regions were less than 1.9% (P = 0.55) for BV and less than 3.2% (P = 0.23) for mean transit time, whereas between-table BV and mean transit time deviations were less than 11.7% (P < 0.01) and less than 14.6% (P < 0.01), respectively. Values for normal liver tissue regions were concordant. CONCLUSIONS: Computed tomography perfusion parameters acquired from aortic levels within-table positions generally yielded higher agreement than mappings obtained from aortic levels between-table positions indicating differences due to shuttling effect.

Evaluation of Abdominal Computed Tomography Image Quality Using a New Version of Vendor-Specific Model-Based Iterative Reconstruction.

PURPOSE: To qualitatively and quantitatively compare abdominal computed tomography (CT) images reconstructed with a new version of model-based iterative reconstruction (Veo 3.0; GE Healthcare) to those created with Veo 2.0. MATERIALS AND METHODS: This retrospective study was approved by our institutional review board and was Health Insurance Portability and Accountability Act compliant. The raw data from 29 consecutive patients who had undergone CT abdomen scanning was used to reconstruct 4 sets of 3.75-mm axial images: Veo 2.0, Veo 3.0 standard, Veo 3.0 5% resolution preference (RP), and Veo 3.0 20% RP. A slice thickness optimization of 3.75 mm and texture feature was selected for Veo 3.0 reconstructions.The images were reviewed by 3 independent readers in a blinded, randomized fashion using a 5-point Likert scale and 5-point comparative scale.Multiple 2-dimensional circular regions of interest were defined for noise and contrast-to-noise ratio measurements. Line profiles were drawn across the 7 lp/cm bar pattern of the CatPhan 600 phantom for spatial resolution evaluation. RESULTS: The Veo 3.0 standard image set was scored better than Veo 2.0 in terms of artifacts (mean difference, 0.43; 95% confidence interval [95% CI], 0.25-0.6; P < 0.0001), overall image quality (mean difference, 0.87; 95% CI, 0.62-1.13; P < 0.0001) and qualitative resolution (mean difference, 0.9; 95% CI, 0.69-1.1; P < 0.0001). Although the Veo 3.0 standard and RP05 presets were preferred across most categories, the Veo 3.0 RP20 series ranked best for bone detail. Image noise and spatial resolution increased along a spectrum with Veo 2.0 the lowest and RP20 the highest. CONCLUSION: Veo 3.0 enhances imaging evaluation relative to Veo 2.0; readers preferred Veo 3.0 image appearance despite the associated mild increases in image noise. These results provide suggested parameters to be used clinically and as a basis for future evaluations, such as focal lesion detection, in the oncology setting.

Correction of Motion Artifacts From Shuttle Mode Computed Tomography Acquisitions for Body Perfusion Imaging Applications.

OBJECTIVE: The aim of this study was to investigate the feasibility of shuttle-mode computed tomography (CT) technology for body perfusion applications by quantitatively assessing and correcting motion artifacts. METHODS: Noncontrast shuttle-mode CT scans (10 phases, 2 nonoverlapping bed locations) were acquired from 4 patients on a GE 750HD CT scanner. Shuttling effects were quantified using Euclidean distances (between-phase and between-bed locations) of corresponding fiducial points on the shuttle and reference phase scans (prior to shuttle mode). Motion correction with nonrigid registration was evaluated using sum-of-squares differences and distances between centers of segmented volumes of interest on shuttle and references images. RESULTS: Fiducial point analysis showed an average shuttling motion of 0.85 ± 1.05 mm (between-bed) and 1.18 ± 1.46 mm (between-phase), respectively. The volume-of-interest analysis of the nonrigid registration results showed improved sum-of-squares differences from 2950 to 597, between-bed distance from 1.64 to 1.20 mm, and between-phase distance from 2.64 to 1.33 mm, respectively, averaged over all cases. CONCLUSIONS: Shuttling effects introduced during shuttle-mode CT acquisitions can be computationally corrected for body perfusion applications.

Effect on perfusion values of sampling interval of computed tomographic perfusion acquisitions in neuroendocrine liver metastases and normal liver.

OBJECTIVE: This study aimed to assess the effects of sampling interval (SI) of computed tomographic (CT) perfusion acquisitions on CT perfusion values in normal liver and liver metastases from neuroendocrine tumors. METHODS: Computed tomographic perfusion in 16 patients with neuroendocrine liver metastases was analyzed using distributed-parameter modeling to yield tissue blood flow, blood volume, mean transit time, permeability, and hepatic arterial fraction for tumor and normal liver. Computed tomographic perfusion values for the reference SI of 0.5 s (SI0.5) were compared with those of SI data sets of 1 second, 2 seconds, 3 seconds, and 4 seconds using mixed-effects model analyses. RESULTS: Increases in SI beyond 1 second were associated with significant and increasing departures of CT perfusion parameters from the reference values at SI0.5 (P ≤ 0.0009). Computed tomographic perfusion values deviated from the reference with increasing uncertainty with increasing SIs. Findings for normal liver were concordant. CONCLUSIONS: Increasing SIs beyond 1 second yield significantly different CT perfusion parameter values compared with the reference values at SI0.5.

Effect of pre-enhancement set point on computed tomographic perfusion values in normal liver and metastases to the liver from neuroendocrine tumors.

OBJECTIVE: The objective of this study was to assess the effects of pre-enhancement set point (T1) positioning on computed tomographic perfusion (CTp) parameter values. METHODS: The CTp data from 16 patients with neuroendocrine liver metastases were analyzed with distributed parameter modeling to yield tissue blood flow (BF), blood volume, mean transit time, permeability, and hepatic arterial fraction for tumor and normal liver, with displacements in T1 of ±0.5, ±1.0, ±2.0 seconds, relative to the reference standard. A linear mixed-effects model was used to assess the displacement effects. RESULTS: Effects on the CTp parameter values were variable: BF was not significantly affected, but T1 positions of ≥+1.0 second and -2.0 seconds or longer significantly affected the other CTp parameters (P ≤ 0.004). Mean differences in the CTp parameter values versus the reference standard for BF, blood volume, mean transit time, permeability, and hepatic arterial fraction ranged from -5.0% to 5.2%, -12.7% to 8.9%, -12.5% to 8.1%, -5.3% to 5.7%, and -12.9% to 26.0%, respectively. CONCLUSIONS: CTp parameter values can be significantly affected by T1 positioning.

Defining internal target volume using positron emission tomography for radiation therapy planning of moving lung tumors.

Substantial disagreement exists over appropriate PET segmentation techniques for non-small cell lung cancer. Currently, no segmentation algorithm explicitly considers tumor motion in determining tumor borders. We developed an automatic PET segmentation model as a function of target volume, motion extent, and source-to-background ratio (the VMSBR model). The purpose of this work was to apply the VMSBR model and six other segmentation algorithms to a sample of lung tumors. PET and 4D CT were performed in the same imaging session for 23 patients (24 tumors) for radiation therapy planning. Internal target volumes (ITVs) were autosegmented on maximum intensity projection (MIP) of cine CT. ITVs were delineated on PET using the following methods: 15%, 35%, and 42% of maximum activity concentration, standardized uptake value (SUV) of 2.5 g/mL, 15% of mean activity concentration plus background, a linear function of mean SUV, and the VMSBR model. Predicted threshold values from each method were compared to measured optimal threshold values, and resulting volume magnitudes were compared to cine-CT-derived ITV. Correlation between predicted and measured threshold values ranged from slopes of 0.29 for the simplest single-threshold techniques to 0.90 for the VMSBR technique. R2 values ranged from 0.07 for the simplest single-threshold techniques to 0.86 for the VMSBR technique. The VMSBR segmentation technique that included volume, motion, and source-to-background ratio, produced accurate ITVs in patients when compared with cine-CT-derived ITV.

Metastases to the liver from neuroendocrine tumors: effect of duration of scan acquisition on CT perfusion values.

PURPOSE: To assess the effects of acquisition duration on computed tomographic (CT) perfusion parameter values in neuroendocrine liver metastases and normal liver tissue. MATERIALS AND METHODS: This retrospective study was institutional review board approved, with waiver of informed consent. CT perfusion studies in 16 patients (median age, 57.5 years; range, 42.0-69.7 years), including six men (median, 54.1 years; range, 42.0-69.7), and 10 women (median, 59.3 years; range 43.6-66.3), with neuroendocrine liver metastases were analyzed by means of distributed parametric modeling to determine tissue blood flow, blood volume, mean transit time, permeability, and hepatic arterial fraction for tumors and normal liver tissue. Analyses were undertaken with acquisition time of 12-590 seconds. Nonparameteric regression analyses were used to evaluate the functional relationships between CT perfusion parameters and acquisition duration. Evidence for time invariance was evaluated for each parameter at multiple time points by inferring the fitted derivative to assess its proximity to zero as a function of acquisition time by using equivalence tests with three levels of confidence (20%, 70%, and 90%). RESULTS: CT perfusion parameter values varied, approaching stable values with increasing acquisition duration. Acquisition duration greater than 160 seconds was required to obtain at least low confidence stability in any of the CT perfusion parameters. At 160 seconds of acquisition, all five CT perfusion parameters stabilized with low confidence in tumor and normal tissues, with the exception of hepatic arterial fraction in tumors. After 220 seconds of acquisition, there was stabilization with moderate confidence for blood flow, blood volume, and hepatic arterial fraction in tumors and normal tissue, and for mean transit time in tumors; however, permeability values did not satisfy the moderate stabilization criteria in both tumors and normal tissue until 360 seconds of acquisition. Blood flow, mean transit time, permeability, and hepatic arterial fraction were significantly different between tumor and normal tissue at 360 seconds (P < .001). CONCLUSION: CT perfusion parameter values are affected by acquisition duration and approach progressively stable values with increasing acquisition times. Online supplemental material is available for this article.

Effect of sampling frequency on perfusion values in perfusion CT of lung tumors.

OBJECTIVE: The purpose of this study was to assess as a potential means of limiting radiation exposure the effect on perfusion CT values of increasing sampling intervals in lung perfusion CT acquisition. SUBJECTS AND METHODS: Lung perfusion CT datasets in patients with lung tumors (> 2.5 cm diameter) were analyzed by distributed parameter modeling to yield tumor blood flow, blood volume, mean transit time, and permeability values. Scans were obtained 2-7 days apart with a 16-MDCT scanner without intervening therapy. Linear mixed-model analyses were used to compare perfusion CT values for the reference standard sampling interval of 0.5 second with those of datasets obtained at sampling intervals of 1, 2, and 3 seconds, which included relative shifts to account for uncertainty in preenhancement set points. Scan-rescan reproducibility was assessed by between-visit coefficient of variation. RESULTS: Twenty-four lung perfusion CT datasets in 12 patients were analyzed. With increasing sampling interval, mean and 95% CI blood flow and blood volume values were increasingly overestimated by up to 14% (95% CI, 11-18%) and 8% (95% CI, 5-11%) at the 3-second sampling interval, and mean transit time and permeability values were underestimated by up to 11% (95% CI, 9-13%) and 3% (95% CI, 1-6%) compared with the results in the standard sampling interval of 0.5 second. The differences were significant for blood flow, blood volume, and mean transit time for sampling intervals of 2 and 3 seconds (p ≤ 0.0002) but not for the 1-second sampling interval. The between-visit coefficient of variation increased with subsampling for blood flow (32.9-34.2%), blood volume (27.1-33.5%), and permeability (39.0-42.4%) compared with the values in the 0.5-second sampling interval (21.3%, 23.6%, and 32.2%). CONCLUSION: Increasing sampling intervals beyond 1 second yields significantly different perfusion CT parameter values compared with the reference standard (up to 18% for 3 seconds of sampling). Scan-rescan reproducibility is also adversely affected.

Effect of scan duration on CT perfusion values in metastases from renal cell carcinoma.

Objective: CT perfusion (CTp) values are affected by CT scan acquisition duration (tacq); their reproducibility is adversely affected by uncertainty in their measurement. The objectives were to assess the effects of tacq on CTp parameter values in metastases from renal cell carcinoma (mRCC) in thoracic and abdominal locations. Materials and Methods: 131 CTp evaluations in 53 patients with mRCC were retrospectively analyzed by distributed parameter modeling to yield tissue blood flow (BF), blood volume (BV), mean transit time (MTT), permeability (PS), and also hepatic arterial perfusion (HAP) and hepatic arterial fraction (HAF) for liver metastases and normal liver, with tacq from 25 to 590 s. Penalized piecewise polynomial regression (SPLINE) characterized functional relationships between CTp parameters and acquisition duration, tacq. Evidence for time-invariance was evaluated for each parameter at multiple time points by conducting inference on the fitted derivative to assess its proximity to zero as a function of acquisition time. Equivalence testing was implemented with three levels of confidence (low (20%), moderate (70%), high (95%)). Results: Systematic and non-systematic variability was observed for CTp parameter values with limited tacq. All parameters in all locations approached increasing stability with increasing tacq. PS, HAP and HAF required longer acquisition times than BF, BV and MTT to attain comparable levels of stability. Stabilization tended to require longer acquisition in liver than other tissues. tacq=380 s was required to obtain at least moderate level of confidence for all parameters and organs. Conclusion: Increasing tacq yields increasingly more stable CT perfusion parameters, and thereby better reproducibility.

Use of weekly 4DCT-based ventilation maps to quantify changes in lung function for patients undergoing radiation therapy.

PURPOSE: A method has been proposed to calculate ventilation maps from four-dimensional computed tomography (4DCT) images. Weekly 4DCT data were acquired throughout the course of radiation therapy for patients with lung cancer. The purpose of our work was to use ventilation maps calculated from weekly 4DCT data to study how ventilation changed throughout radiation therapy. METHODS: Quantitative maps representing ventilation were generated for six patients. Deformable registration was used to link corresponding lung volume elements between the inhale and exhale phases of the 4DCT dataset. Following spatial registration, corresponding Hounsfield units were input into a density-change-based model for quantifying the local ventilation. The ventilation data for all weeks were registered to the pretreatment ventilation image set. We quantitatively analyzed the data by defining regions of interest (ROIs) according to dose (V(20)) and lung lobe and by tracking the weekly ventilation of each ROI throughout treatment. The slope of the linear fit to the weekly ventilation data was used to evaluate the change in ventilation throughout treatment. A positive slope indicated an increase in ventilation, a negative slope indicated a decrease in ventilation, and a slope of 0 indicated no change. The dose ROI ventilation and slope data were used to study how ventilation changed throughout treatment as a function of dose. The lung lobe ROI ventilation data were used to study the impact of the presence of tumor on pretreatment ventilation. In addition, the lobe ROI data were used to study the impact of tumor reduction on ventilation change throughout treatment. RESULTS: Using the dose ROI data, we found that three patients had an increase in weekly ventilation as a function of dose (slopes of 1.1, 1.4, and 1.5) and three patients had no change or a slight decrease in ventilation as a function of dose (slopes of 0.3, -0.6, -0.5). Visually, pretreatment ventilation appeared to be lower in the lobes that contained tumor. Pretreatment ventilation was 39% for lobes that contained tumor and 54% for lobes that did not contain tumor. The difference in ventilation between the two groups was statistically significant (p = 0.017). When the weekly lobe ventilation data were qualitatively observed, two distinct patterns emerged. When the tumor volume in a lobe was reduced, ventilation increased in the lobe. When the tumor volume was not reduced, the ventilation distribution did not change. The average slope of the group of lobes that contained tumors that shrank was 1.18, while the average slope of the group that did not contain tumors (or contained tumors that did not shrink) was -0.32. The slopes for the two groups were significantly different (p = 0.014). CONCLUSIONS: We did not find a consistent pattern of ventilation change as a function of radiation dose. Pretreatment ventilation was significantly lower for lobes that contained tumor, due to occlusion of the central airway. The weekly lobe ventilation data indicated that when tumor volume shrinks, ventilation increases, and when the thoracic anatomy is not visibly changed, ventilation is likely to remain unchanged.

Effect of dual vascular input functions on CT perfusion parameter values and reproducibility in liver tumors and normal liver.

OBJECTIVE: To assess the impact on absolute values and reproducibility of adding portal venous (PV) to arterial input functions in computed tomographic perfusion (CTp) evaluations of liver tumors and normal liver. METHODS: Institutional review board approval and written informed consent were obtained; the study complied with Health Insurance Portability and Accountability Act regulations. Computed tomographic perfusion source data sets, obtained from 7 patients (containing 9 liver tumors) on 2 occasions, 2 to 7 days apart, were analyzed by deconvolution modeling using dual ("Liver" protocol: PV and aorta) and single ("Body" protocol: aorta only) vascular inputs. Identical tumor, normal liver, aortic and, where applicable, PV regions of interest were used in corresponding analyses to generate tissue blood flow (BF), blood volume (BV), mean transit time (MTT), and permeability (PS) values. Test-retest variability was assessed by within-patient coefficients of variation. RESULTS: For liver tumor and normal liver, median BF, BV, and PS were significantly higher for the Liver protocol than for the Body protocol: 171.3 to 177.8 vs 39.4 to 42.0 mL/min per 100 g, 17.2 to 18.7 vs 3.1 to 4.2 mL/100 g, and 65.1 to 78.9 vs 50.4 to 66.1 mL/min per 100 g, respectively (P < 0.01 for all). There were no differences in MTT between protocols. Within-patient coefficients of variation were lower for all parameters with the Liver protocol than with the Body protocol: BF, 7.5% to 11.2% vs 11.7% to 20.8%; BV, 10.1% to 14.4% vs 16.6% to 30.1%; MTT, 4.2% to 5.5% vs 10.4% to 12.9%; and PS, 7.3% to 12.1% vs 12.6% to 20.3%, respectively. CONCLUSION: Utilization of dual vascular input CTp liver analyses has substantial impact on absolute CTp parameter values and test-retest variability. Incorporation of the PV inputs may yield more precise results; however, it imposes substantial practical constraints on acquiring the necessary data.

Reproducibility of CT perfusion parameters in liver tumors and normal liver.

PURPOSE: To assess the reproducibility of computed tomographic (CT) perfusion measurements in liver tumors and normal liver and effects of motion and data acquisition time on parameters. MATERIALS AND METHODS: Institutional review board approval and written informed consent were obtained for this prospective study. The study complied with HIPAA regulations. Two CT perfusion scans were obtained 2-7 days apart in seven patients with liver tumors with two scanning phases (phase 1: 30-second breath-hold cine; phase 2: six intermittent free-breathing cines) spanning 135 seconds. Blood flow (BF), blood volume (BV), mean transit time (MTT), and permeability-surface area product (PS) for tumors and normal liver were calculated from phase 1 with and without rigid registration and, for combined phases 1 and 2, with manually and rigid-registered phase 2 images, by using deconvolution modeling. Variability was assessed with within-patient coefficients of variation (CVs) and Bland-Altman analyses. RESULTS: For tumors, BF, BV, MTT, and PS values and reproducibility varied by analytical method, the former by up to 11%, 23%, 21%, and 138%, respectively. Median PS values doubled with the addition of phase 2 data to phase 1 data. The best overall reproducibility was obtained with rigidly registered phase 1 and phase 2 images, with within-patient CVs for BF, BV, MTT, and PS of 11.2%, 14.4%, 5.5% and 12.1%, respectively. Normal liver evaluations were similar, except with marginally lower variability. CONCLUSION: Absolute values and reproducibility of CT perfusion parameters were markedly influenced by motion and data acquisition time. PS, in particular, probably requires data acquisition beyond a single breath hold, for which motion-correction techniques are likely necessary.

Reproducibility of perfusion parameters obtained from perfusion CT in lung tumors.

OBJECTIVE: The purpose of this article is to assess the variability of perfusion CT measurements in lung tumors and the effects of motion and duration of data acquisition on perfusion CT parameter values. SUBJECTS AND METHODS: Two perfusion CT scans were obtained in 11 patients with lung tumors, 2-7 days apart, using phase 1 scans (30-second breath-hold cine) followed by phase 2 scans (six intermittent helical breath-holds), spanning 125 seconds. Tumor blood flow (BF), blood volume (BV), mean transit time (MTT), and permeability were calculated for phase 1 using all-cine and motion-corrected (rigidly registered) images, both with and without matching phase 2 images (manually or rigidly registered). Variability was assessed by the within-patient coefficient of variation (CV) and Bland-Altman analyses. RESULTS: BF, BV, MTT, and permeability values varied widely by method of analysis (median BF, 45.3-65.1 mL/min/100 g; median BV, 2.6-3.8 mL/100 g; median MTT, 3.6-4.1 seconds, and median permeability, 13.7-39.3 mL/min/100 g), as did within-patient CVs (10.9-114.4%, 25.3-117.6%, 22.3-51.5%, and 29.6-134.9%, respectively). Parameter values and variability were affected by motion and duration of data analyzed: permeability values doubled when phase 2 images were added to phase 1 data. Overall, the best reproducibility was obtained with registered phase 1 and 2 data, with within-patient CVs of 11.6%, 26.5%, 45.4%, and 30.2%, respectively. CONCLUSION: The absolute values and reproducibility of perfusion parameters in lung tumors are markedly influenced by motion and duration of data acquisition. Permeability, in particular, probably requires data acquisition beyond a single breath-hold. The smallest variability in parameter values was obtained with motion correction and extended acquisition durations.

Artificial Intelligence in PET: An Industry Perspective.

Artificial intelligence (AI) has significant potential to positively impact and advance medical imaging, including positron emission tomography (PET) imaging applications. AI has the ability to enhance and optimize all aspects of the PET imaging chain from patient scheduling, patient setup, protocoling, data acquisition, detector signal processing, reconstruction, image processing, and interpretation. AI poses industry-specific challenges which will need to be addressed and overcome to maximize the future potentials of AI in PET. This article provides an overview of these industry-specific challenges for the development, standardization, commercialization, and clinical adoption of AI and explores the potential enhancements to PET imaging brought on by AI in the near future. In particular, the combination of on-demand image reconstruction, AI, and custom-designed data-processing workflows may open new possibilities for innovation which would positively impact the industry and ultimately patients.

Design and system evaluation of a dual-panel portable PET (DP-PET).

BACKGROUND: Integrated whole-body PET/MR technology continues to mature and is now extensively used in clinical settings. However, due to the special design architecture, integrated whole-body PET/MR comes with a few inherent limitations. Firstly, whole-body PET/MR lacks sensitivity and resolution for focused organs. Secondly, broader clinical access of integrated PET/MR has been significantly restricted due to its prohibitively high cost. The MR-compatible PET insert is an independent and removable PET scanner which can be placed within an MRI bore. However, the mobility and configurability of all existing MR-compatible PET insert prototypes remain limited. METHODS: An MR-compatible portable PET insert prototype, dual-panel portable PET (DP-PET), has been developed for simultaneous PET/MR imaging. Using SiPM, digital readout electronics, novel carbon fiber shielding, phase-change cooling, and MRI compatible battery power, DP-PET was designed to achieve high-sensitivity and high-resolution with compatibility with a clinical 3-T MRI scanner. A GPU-based reconstruction method with resolution modeling (RM) has been developed for the DP-PET reconstruction. We evaluated the system performance on PET resolution, sensitivity, image quality, and the PET/MR interference. RESULTS: The initial results reveal that the DP-PET prototype worked as expected in the MRI bore and caused minimal compromise to the MRI image quality. The PET performance was measured to show a spatial resolution ≤ 2.5 mm (parallel to the detector panels), maximum sensitivity = 3.6% at the center of FOV, and energy resolution = 12.43%. MR pulsing introduces less than 2% variation to the PET performance measurement results. CONCLUSIONS: We developed a MR-compatible PET insert prototype and performed several studies to begin to characterize the performance of the proposed DP-PET. The results showed that the proposed DP-PET performed well in the MRI bore and would cause little influence on the MRI images. The Derenzo phantom test showed that the proposed reconstruction method could obtain high-quality images using DP-PET.

Target definition of moving lung tumors in positron emission tomography: correlation of optimal activity concentration thresholds with object size, motion extent, and source-to-background ratio.

PURPOSE: Hardware integration of fluorodeoxyglucose positron emission tomography (PET) with computed tomography (CT) in combined PET/CT scanners has provided radiation oncologists and physicists with new possibilities for 3-D treatment simulation. The use of PET/CT simulation for target delineation of lung cancer is becoming popular and many studies concerning automatic segmentation of PET images have been performed. Several of these studies consider size and source-to-background (SBR) in their segmentation methods but neglect respiratory motion. The purpose of the current study was to develop a functional relationship between optimal activity concentration threshold, tumor volume, motion extent, and SBR using multiple regression techniques by performing an extensive series of phantom scans simulating tumors of varying sizes, SBR, and motion amplitudes. Segmented volumes on PET were compared with the "motion envelope" of the moving sphere defined on cine CT. METHODS: A NEMA IEC thorax phantom containing six spheres (inner diameters ranging from 10 to 37 mm) was placed on a motion platform and moved sinusoidally at 0-30 mm (at 5 mm intervals) and six different SBRs (ranging from 5:1 to 50:1), producing 252 combinations of experimental parameters. PET images were acquired for 18 min and split into three 6 min acquisitions for reproducibility. The spheres (blurred on PET images due to motion) were segmented at 1% of maximum activity concentration intervals. The optimal threshold was determined by comparing deviations between the threshold volume surfaces with a reference volume surface defined on cine CT. Optimal activity concentration thresholds were normalized to background and multiple regression was used to determine the relationship between optimal threshold, volume, motion, and SBR. Standardized regression coefficients were used to assess the relative influence of each variable. The segmentation model was applied to three lung cancer patients and segmented regions of interest were compared with those segmented on cine CT. RESULTS: The resulting model and coefficients provided a functional form that fit the phantom data with an adjusted R2 = 0.96. The most significant contributor to threshold level was SBR. Surfaces of PET-segmented volumes of three lung cancer patients were within 2 mm of the reference CT volumes on average. CONCLUSIONS: The authors successfully developed an expression for optimal activity concentration threshold as a function of object volume, motion, and SBR.

Correction of misaligned slices in multi-slice cardiovascular magnetic resonance using slice-to-volume registration.

A popular technique to reduce respiratory motion for cardiovascular magnetic resonance is to perform a multi-slice acquisition in which a patient holds their breath multiple times during the scan. The feasibility of rigid slice-to-volume registration to correct for misalignments of slice stacks in such images due to differing breath-hold positions is explored. Experimental results indicate that slice-to-volume registration can compensate for the typical misalignments expected. Correction of slice misalignment results in anatomically more correct images, as well as improved left ventricular volume measurements. The interstudy reproducibility has also been improved reducing the number of samples needed for cardiac MR studies.