Attenuation-corrected PET Images Research Articles

Deep learning-based whole-body PSMA PET/CT attenuation correction utilizing Pix-2-Pix GAN.

Sequential PET/CT studies oncology patients can undergo during their treatment follow-up course is limited by radiation dosage. We propose an artificial intelligence (AI) tool to produce attenuation-corrected PET (AC-PET) images from non-attenuation-corrected PET (NAC-PET) images to reduce need for low-dose CT scans. A deep learning algorithm based on 2D Pix-2-Pix generative adversarial network (GAN) architecture was developed from paired AC-PET and NAC-PET images. 18F-DCFPyL PSMA PET-CT studies from 302 prostate cancer patients, split into training, validation, and testing cohorts (n = 183, 60, 59, respectively). Models were trained with two normalization strategies: Standard Uptake Value (SUV)-based and SUV-Nyul-based. Scan-level performance was evaluated by normalized mean square error (NMSE), mean absolute error (MAE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Lesion-level analysis was performed in regions-of-interest prospectively from nuclear medicine physicians. SUV metrics were evaluated using intraclass correlation coefficient (ICC), repeatability coefficient (RC), and linear mixed-effects modeling. Median NMSE, MAE, SSIM, and PSNR were 13.26%, 3.59%, 0.891, and 26.82, respectively, in the independent test cohort. ICC for SUVmax and SUVmean were 0.88 and 0.89, which indicated a high correlation between original and AI-generated quantitative imaging markers. Lesion location, density (Hounsfield units), and lesion uptake were all shown to impact relative error in generated SUV metrics (all p < 0.05). The Pix-2-Pix GAN model for generating AC-PET demonstrates SUV metrics that highly correlate with original images. AI-generated PET images show clinical potential for reducing the need for CT scans for attenuation correction while preserving quantitative markers and image quality.

Learning CT-free attenuation-corrected total-body PET images through deep learning.

Total-body PET/CT scanners with long axial fields of view have enabled unprecedented image quality and quantitative accuracy. However, the ionizing radiation from CT is a major issue in PET imaging, which is more evident with reduced radiopharmaceutical doses in total-body PET/CT. Therefore, we attempted to generate CT-free attenuation-corrected (CTF-AC) total-body PET images through deep learning. Based on total-body PET data from 122 subjects (29 females and 93 males), a well-established cycle-consistent generative adversarial network (Cycle-GAN) was employed to generate CTF-AC total-body PET images directly while introducing site structures as prior information. Statistical analyses, including Pearson correlation coefficient (PCC) and t-tests, were utilized for the correlation measurements. The generated CTF-AC total-body PET images closely resembled real AC PET images, showing reduced noise and good contrast in different tissue structures. The obtained peak signal-to-noise ratio and structural similarity index measure values were 36.92 ± 5.49 dB (p < 0.01) and 0.980 ± 0.041 (p < 0.01), respectively. Furthermore, the standardized uptake value (SUV) distribution was consistent with that of real AC PET images. Our approach could directly generate CTF-AC total-body PET images, greatly reducing the radiation risk to patients from redundant anatomical examinations. Moreover, the model was validated based on a multidose-level NAC-AC PET dataset, demonstrating the potential of our method for low-dose PET attenuation correction. In future work, we will attempt to validate the proposed method with total-body PET/CT systems in more clinical practices. The ionizing radiation from CT is a major issue in PET imaging, which is more evident with reduced radiopharmaceutical doses in total-body PET/CT. Our CT-free PET attenuation correction method would be beneficial for a wide range of patient populations, especially for pediatric examinations and patients who need multiple scans or who require long-term follow-up. • CT is the main source of radiation in PET/CT imaging, especially for total-body PET/CT devices, and reduced radiopharmaceutical doses make the radiation burden from CT more obvious. • The CT-free PET attenuation correction method would be beneficial for patients who need multiple scans or long-term follow-up by reducing additional radiation from redundant anatomical examinations. • The proposed method could directly generate CT-free attenuation-corrected (CTF-AC) total-body PET images, which is beneficial for PET/MRI or PET-only devices lacking CT image poses.

Transfer learning-based attenuation correction for static and dynamic cardiac PET using a generative adversarial network.

The goal of this work is to demonstrate the feasibility of directly generating attenuation-corrected PET images from non-attenuation-corrected (NAC) PET images for both rest and stress-state static or dynamic [13N]ammonia MP PET based on a generative adversarial network. We recruited 60 subjects for rest-only scans and 14 subjects for rest-stress scans, all of whom underwent [13N]ammonia cardiac PET/CT examinations to acquire static and dynamic frames with both 3D NAC and CT-based AC (CTAC) PET images. We developed a 3D pix2pix deep learning AC (DLAC) framework via a U-net + ResNet-based generator and a convolutional neural network-based discriminator. Paired static or dynamic NAC and CTAC PET images from 60 rest-only subjects were used as network inputs and labels for static (S-DLAC) and dynamic (D-DLAC) training, respectively. The pre-trained S-DLAC network was then fine-tuned by paired dynamic NAC and CTAC PET frames of 60 rest-only subjects to derive an improved D-DLAC-FT for dynamic PET images. The 14 rest-stress subjects were used as an internal testing dataset and separately tested on different network models without training. The proposed methods were evaluated using visual quality and quantitative metrics. The proposed S-DLAC, D-DLAC, and D-DLAC-FT methods were consistent with clinical CTAC in terms of various images and quantitative metrics. The S-DLAC (slope = 0.9423, R2 = 0.947) showed a higher correlation with the reference static CTAC as compared to static NAC (slope = 0.0992, R2 = 0.654). D-DLAC-FT yielded lower myocardial blood flow (MBF) errors in the whole left ventricular myocardium than D-DLAC, but with no significant difference, both for the 60 rest-state subjects (6.63 ± 5.05% vs. 7.00 ± 6.84%, p = 0.7593) and the 14 stress-state subjects (1.97 ± 2.28% vs. 3.21 ± 3.89%, p = 0.8595). The proposed S-DLAC, D-DLAC, and D-DLAC-FT methods achieve comparable performance with clinical CTAC. Transfer learning shows promising potential for dynamic MP PET.

Eliminating CT radiation for clinical PET examination using deep learning

Clinical PET/CT examinations rely on CT modality for anatomical localization and attenuation correction of the PET data. However, the use of CT significantly increases the risk of ionizing radiation exposure for patients. We propose a deep learning framework to learn the relationship mapping between attenuation corrected (AC) PET and non-attenuation corrected (NAC) PET images to estimate PET attenuation maps and generate pseudo-CT images for medical observation. In this study, 5760, 1608 and 1351 pairs of transverse PET-CT slices were used as the training, validation, and testing sets, respectively, to implement the proposed framework. A pix2pix model was adopted to predict AC PET images from NAC PET images, which allowed the calculation of PET attenuation maps (µ-maps). The same model was then applied to generate realistic CT images from the calculated µ-maps. The quality of predicted AC PET and CT was assessed using normalized root mean square error (NRMSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and Pearson correlation coefficient (PCC). Relative to true AC PET, the synthetic AC PET achieved superior quantitative performances with 2.20 ± 1.17% NRMSE, 34.03 ± 4.73 dB PSNR, 97.90 ± 1.22% SSIM and 98.45 ± 1.31% PCC. The synthetic CT and synthetic AC PET images were deemed acceptable by radiologists who rated the images, as they provided sufficient anatomical and functional information, respectively. This work demonstrates that the proposed deep learning framework is a promising method in clinical applications, such as radiotherapy and low-dose imaging.

3D printed anthropomorphic left ventricular myocardial phantom for nuclear medicine imaging applications

BackgroundAnthropomorphic torso phantoms, including a cardiac insert, are frequently used to investigate the imaging performance of SPECT and PET systems. These phantom solutions are generally featuring a simple anatomical representation of the heart. 3D printing technology paves the way to create cardiac phantoms with more complex volume definition. This study aimed to describe how a fillable left ventricular myocardium (LVm) phantom can be manufactured using geometry extracted from a patient image.MethodsThe LVm of a healthy subject was segmented from 18F-FDG attenuation corrected PET image set. Two types of phantoms were created and 3D printed using polyethylene terephthalate glycol (PETG) material: one representing the original healthy LVm, and the other mimicking myocardium with a perfusion defect. The accuracy of the LVm phantom production was investigated by high-resolution CT scanning of 3 identical replicas. 99mTc SPECT acquisitions using local cardiac protocol were performed, without additional scattering media (“in air” measurements) for both phantom types. Furthermore, the healthy LVm phantom was inserted in the commercially available DataSpectrum Anthropomorphic Torso Phantom (“in torso” measurement) and measured with hot background and hot liver insert.ResultsPhantoms were easy to fill without any air-bubbles or leakage, were found to be reproducible and fully compatible with the torso phantom. Seventeen segments polar map analysis of the "in air” measurements revealed that a significant deficit in the distribution appeared where it was expected. 59% of polar map segments had less than 5% deviation for the "in torso” and "in air” measurement comparison. Excluding the deficit area, neither comparison had more than a 12.4% deviation. All the three polar maps showed similar apex and apical region values for all configurations.ConclusionsFillable anthropomorphic 3D printed phantom of LVm can be produced with high precision and reproducibility. The 3D printed LVm phantoms were found to be suitable for SPECT image quality tests during different imaging scenarios. The flexibility of the 3D printing process presented in this study provides scalable and anthropomorphic image quality phantoms in nuclear cardiology imaging.

Determining the minimal ultra-low dose CT for reliable attenuation correction of 18F-FDG PET-CT: a phantom study.

To investigate minimal required sub milli-Sievert (mSv) ultra-low dose CT and corresponding tube current and voltage for reliable attenuation correction and semi- quantitation in 18F-FDG PET-CT in an effort for radiation dose reduction. Methods: We performed a PET-CT investigational study using a NEMA torso phantom containing six spheres (diameter: 10, 13, 17, 22, 28, 37 mm) filled with a fixed concentration of 60 kBq/ml and a background of 15 kBq/ml of 18F-FDG. Two sets of PET images, separated by 2 hours, were acquired for 3 minutes in a single bed position using 3-D mode with and without time-of-flight in a GE D-690 scanner. Several sets of CT images were acquired for attenuation correction with different combinations of tube voltage (80, 100, 120 kVp) and effective mAs (tube current-time product divided by pitch), using the maximum beam collimation (64 x 0.625 mm). The lowest CT acquisition technique available on this scanner is 10 mA, 0.4 s and 1.375 for the tube current, tube rotation time and pitch, respectively. The CT radiation dose was estimated based on the computed tomography dose index volume (CTDIvol) measurements performed following the standard methodology and the Imaging Performance Assessment of CT Scanners (ImPACT) calculator. Each of the CT techniques was used for attenuation correction to the same PET acquisition, using ordered-subset expectation maximum (OSEM) algorithm with 24 subsets and 2 iterations. The maximal and average radioactivity (kBq/ml) and standardized uptake values (SUV) of the spheres were measured. The minimal ultra-low dose CT for attenuation correction was determined by reproducible SUV measurements (±10%) compared to our reference CT protocol of 100 kVp and 80 mA for 0.5 s rotation. Results: The minimal ultra-low dose of CT for reproducible quantification in all spheres (<10% relative difference) was determined to be 0.3 mSv for a combination of 100 kVp and 10 mA at 0.5 s rotation, 0.984 helical pitch (0.26 mGy measured CTDIvol) . Based on these results we could confidently determine the CT parameters for reliable attenuation correction of PET images while significantly reducing the associated radiation dose. Conclusion: Our phantom study provided guidance in using ultra-low dose CT for precise attenuation correction and semi-quantification of 18F-FDG PET imaging, which can further reduce CT dose and radiation exposure to patients in clinical PET-CT studies. Clinical application: Based on the data, we can further reduce the radiation dose to sub-mSv using an ultra-low dose CT protocol for reliable attenuation correction in clinical 18F-FDG PET-CT studies.

Lesion detection in 18F-sodium fluoride bone imaging: a comparison of attenuation-corrected versus nonattenuation-corrected PET reconstructions from modern PET-CT systems.

An earlier study demonstrated comparable lesion detection between attenuation-corrected (AC) and nonattenuation-corrected (NAC) 18F-sodium fluoride (NaF) PET images, which is relevant for computed tomography (CT) radiation dose-saving. However, this finding may not be applicable to newer systems. The aim was to compare lesion detection between AC and NAC NaF PET images on modern PET-CT systems. One expert and one nonexpert observer retrospectively surveyed NaF PET data in 25 breast cancer patients. At both lesion and patient level, each observer classified bone abnormalities as malignant, equivocal or benign, from NAC and AC PET images in the absence of CT. Expert interpretation of NaF PET-CT with the review of all diagnostic imaging/pathology reports for at least the subsequent 12 months provided reference standard metastases status at the patient level. Two-tailed Wilcoxon signed-rank tests measured statistically significant differences in total lesion detection between AC and NAC PET. Quadratic-weighted kappa score measured agreement in patient metastases status between observers. On a lesion-basis, AC PET images showed significantly more lesions than NAC for both the expert (122 versus 96; P = 0.002) and nonexpert (146 versus 132; P = 0.036) observers, with a large number of patients demonstrating disparity between AC and NAC images. For metastases status at the patient level without CT, NAC PET showed slightly better diagnostic accuracy than AC due to fewer false-positive results, as fewer lesions were identified. AC PET data provided superior lesion detection to NAC in NaF bone examinations and are thus required for clinical interpretation.

MRI-guided attenuation correction in torso PET/MRI: Assessment of segmentation-, atlas-, and deep learning-based approaches in the presence of outliers.

PurposeWe compare the performance of three commonly used MRI‐guided attenuation correction approaches in torso PET/MRI, namely segmentation‐, atlas‐, and deep learning‐based algorithms.MethodsTwenty‐five co‐registered torso 18F‐FDG PET/CT and PET/MR images were enrolled. PET attenuation maps were generated from in‐phase Dixon MRI using a three‐tissue class segmentation‐based approach (soft‐tissue, lung, and background air), voxel‐wise weighting atlas‐based approach, and a residual convolutional neural network. The bias in standardized uptake value (SUV) was calculated for each approach considering CT‐based attenuation corrected PET images as reference. In addition to the overall performance assessment of these approaches, the primary focus of this work was on recognizing the origins of potential outliers, notably body truncation, metal‐artifacts, abnormal anatomy, and small malignant lesions in the lungs.ResultsThe deep learning approach outperformed both atlas‐ and segmentation‐based methods resulting in less than 4% SUV bias across 25 patients compared to the segmentation‐based method with up to 20% SUV bias in bony structures and the atlas‐based method with 9% bias in the lung. The deep learning‐based method exhibited superior performance. Yet, in case of sever truncation and metallic‐artifacts in the input MRI, this approach was outperformed by the atlas‐based method, exhibiting suboptimal performance in the affected regions. Conversely, for abnormal anatomies, such as a patient presenting with one lung or small malignant lesion in the lung, the deep learning algorithm exhibited promising performance compared to other methods.ConclusionThe deep learning‐based method provides promising outcome for synthetic CT generation from MRI. However, metal‐artifact and body truncation should be specifically addressed.

Feasibility of Deep Learning-Guided Attenuation and Scatter Correction of Whole-Body 68Ga-PSMA PET Studies in the Image Domain.

This study evaluates the feasibility of direct scatter and attenuation correction of whole-body 68Ga-PSMA PET images in the image domain using deep learning. Whole-body 68Ga-PSMA PET images of 399 subjects were used to train a residual deep learning model, taking PET non-attenuation-corrected images (PET-nonAC) as input and CT-based attenuation-corrected PET images (PET-CTAC) as target (reference). Forty-six whole-body 68Ga-PSMA PET images were used as an independent validation dataset. For validation, synthetic deep learning-based attenuation-corrected PET images were assessed considering the corresponding PET-CTAC images as reference. The evaluation metrics included the mean absolute error (MAE) of the SUV, peak signal-to-noise ratio, and structural similarity index (SSIM) in the whole body, as well as in different regions of the body, namely, head and neck, chest, and abdomen and pelvis. The deep learning-guided direct attenuation and scatter correction produced images of comparable visual quality to PET-CTAC images. It achieved an MAE, relative error (RE%), SSIM, and peak signal-to-noise ratio of 0.91 ± 0.29 (SUV), -2.46% ± 10.10%, 0.973 ± 0.034, and 48.171 ± 2.964, respectively, within whole-body images of the independent external validation dataset. The largest RE% was observed in the head and neck region (-5.62% ± 11.73%), although this region exhibited the highest value of SSIM metric (0.982 ± 0.024). The MAE (SUV) and RE% within the different regions of the body were less than 2.0% and 6%, respectively, indicating acceptable performance of the deep learning model. This work demonstrated the feasibility of direct attenuation and scatter correction of whole-body 68Ga-PSMA PET images in the image domain using deep learning with clinically tolerable errors. The technique has the potential of performing attenuation correction on stand-alone PET or PET/MRI systems.

Improved PET/MRI attenuation correction in the pelvic region using a statistical decomposition method on T2-weighted images

BackgroundAttenuation correction of PET/MRI is a remaining problem for whole-body PET/MRI. The statistical decomposition algorithm (SDA) is a probabilistic atlas-based method that calculates synthetic CTs from T2-weighted MRI scans. In this study, we evaluated the application of SDA for attenuation correction of PET images in the pelvic region.Materials and methodTwelve patients were retrospectively selected from an ongoing prostate cancer research study. The patients had same-day scans of [11C]acetate PET/MRI and CT. The CT images were non-rigidly registered to the PET/MRI geometry, and PET images were reconstructed with attenuation correction employing CT, SDA-generated CT, and the built-in Dixon sequence-based method of the scanner. The PET images reconstructed using CT-based attenuation correction were used as ground truth.ResultsThe mean whole-image PET uptake error was reduced from − 5.4% for Dixon-PET to − 0.9% for SDA-PET. The prostate standardized uptake value (SUV) quantification error was significantly reduced from − 5.6% for Dixon-PET to − 2.3% for SDA-PET.ConclusionAttenuation correction with SDA improves quantification of PET/MR images in the pelvic region compared to the Dixon-based method.

Truncation compensation and metallic dental implant artefact reduction in PET/MRI attenuation correction using deep learning-based object completion

The susceptibility of MRI to metallic objects leads to void MR signal and missing information around metallic implants. In addition, body truncation occurs in MR imaging for large patients who exceed the transaxial field-of-view of the scanner. Body truncation and metal artefacts translate to incomplete MRI-derived attenuation correction (AC) maps, consequently resulting in large quantification errors in PET imaging. In this work, we propose a deep learning-based approach to predict the missing information/regions in MR images affected by metallic artefacts and/or body truncation aiming at reducing quantification errors in PET/MRI. Twenty-five whole-body (WB) co-registered PET, CT, and MR images were used for training and evaluation of the object completion approach. CT-based attenuation corrected PET images were considered as reference for the quantitative evaluation of the proposed approach. Its performance was compared to the 3-class segmentation-based AC approach (containing background air, soft-tissue and lung) obtained from MR images. The metal-induced artefacts affected 8.1 ± 1.8% of the volume of the head region when using the 3-class AC maps. This error reduced to 0.9 ± 0.5% after application of object completion on MR images. Consequently, quantification errors in PET images reduced from −57.5 ± 11% to −18.5 ± 5% in the head region after metal artefact correction. The percentage of the torso volume affected by body truncation in the 3-class AC maps reduced from 9.8 ± 1.9% to 0.6 ± 0.3% after truncation compensation. PET quantification errors in the affected regions were also reduced from −45.5 ± 10% to −9.5 ± 3% after truncation compensation. The quantitative results demonstrated promising performance of the proposed approach towards the completion of MR images corrupted by metal artefacts and/or body truncation in the context of WB PET/MR imaging.

Renal Cortical 68Ga-PSMA-11 PET and 99mTc-DMSA Images.

68Ga-prostate-specific membrane antigen (PSMA) ligands are novel PET radiotracers for prostate cancer. These radiotracers also localize in the normal renal cortex to a high degree and can demonstrate parenchymal defects. We recently started a prospective research study to compare 68Ga-PSMA-11 PET/CT with 99mTc-dimercaptosuccinic acid (DMSA) scan in adults with pyelonephritis. Here, we present a side-by-side comparison of renal cortical PSMA PET/CT and DMSA images of an adult patient with chronic recurring pyelonephritis. Methods: Our prospective study was approved by the Ethical Committees. DMSA images (multiple planar and SPECT) were obtained 3 h after intravenous injection of 111 MBq (3 mCi) of 99mTc-DMSA. On a separate day, we obtained PET/CT images of the kidneys 60 min after intravenous injection of 74 MBq (2 mCi) of 68Ga-PSMA-11 after the patient provided written informed consent. Results: The patient was a 46-y-old woman with history of chronic recurring pyelonephritis. Both DMSA scan and PSMA PET/CT demonstrated slight cortical thinning with mildly reduced uptake in the upper pole of the right kidney, with no significant cortical defects. There was an excellent distribution of activity in the renal cortex and better image resolution with PSMA PET than with DMSA scan. Non-attenuation-corrected PSMA PET images also showed the same findings, with reasonable image quality. Conclusion: In our first case, 68Ga-PSMA-11 PET imaging provided promising results in an adult patient with pyelonephritis. The results of our prospective study on a larger number of adult patients will provide a more accurate comparison of 68Ga-PSMA-11 PET to 99mTc-DMSA scanning in pyelonephritis.

PET/CT Image Artifacts Caused by the Arms.

PET/CT images are usually obtained in the arms-up position in patients with no head and neck pathology and in the arms-down position to image the head and neck area. The arms usually cause artifacts regardless of up or down positioning. These artifacts include beam hardening, scatter, truncation, and cold areas (cold artifacts) in obese or large patients; motion artifacts; implanted-metal-object artifacts; and artifacts related to radiotracer extravasation at the injection site. In this review article, we will discuss the mechanisms of these artifacts and suggest solutions to reduce or eliminate them, such as reviewing the non-attenuation-corrected PET images, performing extended-field-of-view reconstruction, not applying scatter correction, and using software to correct beam-hardening, scatter, and truncation artifacts. We will present various PET/CT images before and after corrections for such artifacts.

Direct attenuation correction of brain PET images using only emission data via a deep convolutional encoder-decoder (Deep-DAC).

To obtain attenuation-corrected PET images directly from non-attenuation-corrected images using a convolutional encoder-decoder network. Brain PET images from 129 patients were evaluated. The network was designed to map non-attenuation-corrected (NAC) images to pixel-wise continuously valued measured attenuation-corrected (MAC) PET images via an encoder-decoder architecture. Image quality was evaluated using various evaluation metrics. Image quantification was assessed for 19 radiomic features in 83 brain regions as delineated using the Hammersmith atlas (n30r83). Reliability of measurements was determined using pixel-wise relative errors (RE; %) for radiomic feature values in reference MAC PET images. Peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM) values were 39.2 ± 3.65 and 0.989 ± 0.006 for the external validation set, respectively. RE (%) of SUVmean was - 0.10 ± 2.14 for all regions, and only 3 of 83 regions depicted significant differences. However, the mean RE (%) of this region was 0.02 (range, - 0.83 to 1.18). SUVmax had mean RE (%) of - 3.87 ± 2.84 for all brain regions, and 17 regions in the brain depicted significant differences with respect to MAC images with a mean RE of - 3.99 ± 2.11 (range, - 8.46 to 0.76). Homogeneity amongst Haralick-based radiomic features had the highest number (20) of regions with significant differences with a mean RE (%) of 7.22 ± 2.99. Direct AC of PET images using deep convolutional encoder-decoder networks is a promising technique for brain PET images. The proposed deep learning method shows significant potential for emission-based AC in PET images with applications in PET/MRI and dedicated brain PET scanners. • We demonstrate direct emission-based attenuation correction of PET images without using anatomical information. • We performed radiomics analysis of 83 brain regions to show robustness of direct attenuation correction of PET images. • Deep learning methods have significant promise for emission-based attenuation correction in PET images with potential applications in PET/MRI and dedicated brain PET scanners.

Contribution of nonattenuation-corrected images on FDG-PET/CT in the assessment of solitary pulmonary nodules.

In this study, we aim to determine the diagnostic performance of nonattenuation-corrected (NAC) and attenuation-corrected (AC) FDG-PET/CT images in the assessment of solitary pulmonary nodule (SPN). We reviewed the images of 41 patients who underwent FDG-PET/CT to diagnose SPNs. The visual analysis of FDG uptake intensity in SPN on AC and NAC PET images was made using a four-point score from 1 to 4 on both AC and NAC PET images. The cutoff value of SUVmax and visual uptake scores for malignancy were defined as≥2.5 and≥3, respectively. The significant visual uptake (≥2 visual point score) on AC and NAC PET images was considered to be positive 18F-FDG PET findings for lesion detectability. The sensitivity, specificity and diagnostic accuracy were calculated for AC and NAC PET images. Based on the histopathology and imaging data, 22 of the SPNs (54%) were malignant and 19 of them (46%) were benign. The sensitivity and NPV were found to be 100% in the detection of SPNs for AC and NAC PET images. For all SPNs and SPNs≤2cm, NAC PET image had a higher diagnostic performance for the SPN characterization as malignant or benign, when compared with AC PET image. The success rates of AC and NAC PET images were found to be similar for the detection of SPNs. NAC PET image had a higher diagnostic performance for the SPN characterization. It is thought that NAC PET image may provide additional contributions for characterization of SPNs.

Respiration-Averaged CT for Attenuation Correction of PET Images - Impact on PET Texture Features in Non-Small Cell Lung Cancer Patients.

PurposeWe compared attenuation correction of PET images with helical CT (PET/HCT) and respiration-averaged CT (PET/ACT) in patients with non-small-cell lung cancer (NSCLC) with the goal of investigating the impact of respiration-averaged CT on 18F FDG PET texture parameters.Materials and MethodsA total of 56 patients were enrolled. Tumors were segmented on pretreatment PET images using the adaptive threshold. Twelve different texture parameters were computed: standard uptake value (SUV) entropy, uniformity, entropy, dissimilarity, homogeneity, coarseness, busyness, contrast, complexity, grey-level nonuniformity, zone-size nonuniformity, and high grey-level large zone emphasis. Comparisons of PET/HCT and PET/ACT were performed using Wilcoxon signed-rank tests, intraclass correlation coefficients, and Bland-Altman analysis. Receiver operating characteristic (ROC) curves as well as univariate and multivariate Cox regression analyses were used to identify the parameters significantly associated with disease-specific survival (DSS). A fixed threshold at 45% of the maximum SUV (T45) was used for validation.ResultsSUV maximum and total lesion glycolysis (TLG) were significantly higher in PET/ACT. However, texture parameters obtained with PET/ACT and PET/HCT showed a high degree of agreement. The lowest levels of variation between the two modalities were observed for SUV entropy (9.7%) and entropy (9.8%). SUV entropy, entropy, and coarseness from both PET/ACT and PET/HCT were significantly associated with DSS. Validation analyses using T45 confirmed the usefulness of SUV entropy and entropy in both PET/HCT and PET/ACT for the prediction of DSS, but only coarseness from PET/ACT achieved the statistical significance threshold.ConclusionsOur results indicate that 1) texture parameters from PET/ACT are clinically useful in the prediction of survival in NSCLC patients and 2) SUV entropy and entropy are robust to attenuation correction methods.

Abstract P5-01-10: Correlation of apparent diffusion coefficient with standardized uptake value in invasive lobular carcinoma of breast using in hybrid 18F-FDG PET/MR

Abstract Purpose: To compare the apparent diffusion coefficient (ADC) with maximum standardized uptake values (SUV) derived from combined 18F-FDG PET/MRI in invasive lobular carcinoma (ILC) patients. Methods: From 2012 Aug to 2015 Feb, 53 women with histologically proven ILC (mean age, 50.3 ± 7.7 y-o) underwent hybrid 18F-FDG PET/MR (Siemens Biograph mMR) scan for preoperative assessment. During PET acquisition, simultaneous diffusion-weighted imaging (DWI, b values: 0, 400, 800 s/mm2) was performed using breast coil. ILC over than 2 Cm in size were analyzed. Regions of interest (ROI) were drawn covering the entire ILC on the attenuation-corrected PET-image and the monoexponential ADC-map on PET/MR workstation. All the patients were received surgical treatment within 3 weeks after scan. Spearman rank correlation coefficient (ρ) was calculated to examine the correlation between SUVmax and ADC value. Results: Total 25 ILC (5.4±1.3 cm; 2.1-6.3 cm) of 22 women were enrolled. 24 ILC showed ER (+)/PR (+) and all ILC showed HER2 (-). 15 patients were confirmed metastatic axillary lymph nodes. The mean of SUVmax was 2.14 ± 2.2 (0.91-11.9) and mean of ADC was 1.02 ×10-3 (0.82 -1.31 ×10-3) mm2/s. Higher histologic grade was correlated with higher SUV (ρ = -0.6, p=0.001). And SUV showed inverse correlation with ADC (ρ = -0.48, p=0.016). Conclusion: The present data show inverse correlation between increased glucose-metabolism and cellularity in ILC patients. 18F-FDG PET and DWI thus may offer complementary information for the evaluation of treatment response in ILC. Citation Format: Choi JE, Kang SH, Lee SJ, Kong EJ. Correlation of apparent diffusion coefficient with standardized uptake value in invasive lobular carcinoma of breast using in hybrid 18F-FDG PET/MR. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P5-01-10.

Tissue probability-based AC for neurological PET/MR using SPM8.

The quantitative accuracy of brain PET/MR has been reported to be reduced compared to PET/CT due to difficulties related to bone in MR-based attenuation correction (MRAC), especially in regions near the skull. To reduce these effects, we designed an offline, tissue probability-based (TPB-AC) using SPM8, which includes soft tissue, air and bone. Thus, both the increased attenuation due to skull and the reduced attenuation due to sinuses are taken into account. The method was developed to use the data collected with the standard anatomical MRAC consisting of 3D T1 FFE, with isotropic voxel size of 2 mm. Thus, collection of additional sequences or modifications to the standard attenuation correction sequence are not required. Data from F18-FDG brain studies performed both with PET/CT and PET/MR was used in AC validation. Seven patients were reconstructed with the standard attenuation map, TPB-AC and CTAC in PET/MR. Regional analysis between attenuation corrected PET images was conducted using CTAC as reference. Using TPB-AC, the relative difference in seven subjects when compared to CTAC in all regions was -3 %, -2 %, -5 %, 4 %, -3 %, 1 %, 0 %. In clinical AC the difference was -9 %, -8 %, -9 %, -3 %, -10 %, -5 %, -6 %. Region-wise, the benefit from TPB-AC was most pronounced in precentral, superior frontal and middle frontal gyrus where underestimation from clinical AC of: -11 %, -12 % and -12 % was reduced to: -1%, -2 % and -1 % when using TPB-AC. Tissue probability-based AC was deemed a promising interim method for PET/MR AC until standardized, commercialized solutions become widely available. To test the robustness of the method, patients with dental implants or deformed anatomy would have to be included in the study group.

Metal artefact reduction algorithms prevent false positive results when assessing patients for cardiac implantable electronic device infection

Software-based metal artefact reduction (MAR) techniques are available to reduce artefacts from cardiac implantable electronic devices (CIED) in the CT data. The impact of disabling MAR techniques on quantification of 18F-FDG uptake around the CIED has not been examined. We consider the importance of enabling MAR in patients with suspected CIED infection to prevent inaccuracies in quantification of tissue tracer uptake on the attenuation-corrected PET images.

Correlation of the apparent diffusion coefficient (ADC) with the standardized uptake value (SUV) in lymph node metastases of non-small cell lung cancer (NSCLC) patients using hybrid 18F-FDG PET/MRI.

ObjectiveTo compare the apparent diffusion coefficient (ADC) in lymph node metastases of non-small cell lung cancer (NSCLC) patients with standardized uptake values (SUV) derived from combined 18F-fluoro-deoxy-glucose-positron emission tomography/magnetic resonance imaging (18F-FDG PET/MRI).Material and Methods38 patients with histopathologically proven NSCLC (mean age 60.1 ± 9.5y) received whole-body PET/CT (Siemens mCT™) 60min after injection of a mean dose of 280 ± 50 MBq 18F-FDG and subsequent PET/MRI (mean time after tracer injection: 139 ± 26 min, Siemens Biograph mMR). During PET acquisition, simultaneous diffusion-weighted imaging (DWI, b values: 0, 500, 1000 s/mm²) was performed. A maximum of 10 lymph nodes per patient suspicious for malignancy were analyzed. Regions of interest (ROI) were drawn covering the entire lymph node on the attenuation-corrected PET-image and the monoexponential ADC-map. According to histopathology or radiological follow-up, lymph nodes were classified as benign or malignant. Pearson’s correlation coefficients were calculated for all lymph node metastases correlating SUVmax and SUVmean with ADCmean.ResultsA total of 146 suspicious lymph nodes were found in 25 patients. One hundred lymph nodes were eligible for final analysis. Ninety-one lymph nodes were classified as malignant and 9 as benign according to the reference standard. In malignant lesions, mean SUVmax was 9.1 ± 3.8 and mean SUVmean was 6.0 ± 2.5 while mean ADCmean was 877.0 ± 128.6 x10-5 mm²/s in PET/MRI. For all malignant lymph nodes, a weak, inverse correlation between SUVmax and ADCmean as well as SUVmean and ADCmean (r = -0.30, p<0.05 and r = -0.36, p<0.05) existed.ConclusionThe present data show a weak inverse correlation between increased glucose-metabolism and cellularity in lymph node metastases of NSCLC patients. 18F-FDG-PET and DWI thus may offer complementary information for the evaluation of treatment response in lymph node metastases of NSCLC.