Non-attenuation Corrected Images Research Articles

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.

Comparison of the Diagnostic Performance of Myocardial Perfusion Scintigraphy with and Without Attenuation Correction.

Objectives:Myocardial perfusion scintigraphy (MPS) is an important diagnostic test for detecting of coronary artery stenosis (CAS); however, tissue attenuation can lead to a difference in accuracy. We evaluated the diagnostic accuracy of attenuation-corrected (AC) and non-attenuation-corrected (NC) MPS for the detection of CAS.Methods:We retrospectively recruited patients who underwent invasive coronary angiography within 10 months after Tc-99m sestamibi MPS. The AC and NC perfusion images were analyzed separately, and each myocardial segment was scored based on relative uptake from 0 to 4. The summed stress score (SSS), summed rest score (SRS), and summed difference score (SDS) were calculated. The diagnostic performances were analyzed using the area under the curve (AUC) of the receiver operating characteristic curve.Results:From 117 patients, significant coronary stenosis was present in 66 patients (56%). The SSS and SRS obtained from NC-images were higher than those from AC, supporting the presence of attenuation artifacts in NC images. The AUC of SSS and SDS were significantly higher than those of SRS in both AC- and NC-images, but no significant difference was found between the AUC of SSS, and those of SDS. The optimal cut-offs were >12 for AC-SSS, >15 for NC-SSS, >4 for AC-SDS and >3 for NC-SDS. There was no statistically significant difference in the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy among AC-SSS, NC-SSS, AC-SDS, and NC-SDS.Conclusion:NC-based Tc-99m-sestamibi MPS promised comparable accuracy to AC images by using different cut-off values for diagnosis.

"Virtual" attenuation correction: improving stress myocardial perfusion SPECT imaging using deep learning.

Myocardial perfusion imaging (MPI) using single-photon emission computed tomography (SPECT) is widely used for coronary artery disease (CAD) evaluation. Although attenuation correction is recommended to diminish image artifacts and improve diagnostic accuracy, approximately 3/4ths of clinical MPI worldwide remains non-attenuation-corrected (NAC). In this work, we propose a novel deep learning (DL) algorithm to provide "virtual" DL attenuation-corrected (DLAC) perfusion polar maps solely from NAC data without concurrent computed tomography (CT) imaging or additional scans. SPECT MPI studies (N = 11,532) with paired NAC and CTAC images were retrospectively identified. A convolutional neural network-based DL algorithm was developed and trained on half of the population to predict DLAC polar maps from NAC polar maps. Total perfusion deficit (TPD) was evaluated for all polar maps. TPDs from NAC and DLAC polar maps were compared to CTAC TPDs in linear regression analysis. Moreover, receiver-operating characteristic analysis was performed on NAC, CTAC, and DLAC TPDs to predict obstructive CAD as diagnosed from invasive coronary angiography. DLAC TPDs exhibited significantly improved linear correlation (p < 0.001) with CTAC (R2 = 0.85) compared to NAC vs. CTAC (R2 = 0.68). The diagnostic performance of TPD was also improved with DLAC compared to NAC with an area under the curve (AUC) of 0.827 vs. 0.780 (p = 0.012) with no statistically significant difference between AUC for CTAC and DLAC. At 88% sensitivity, specificity was improved by 18.9% for DLAC and 25.6% for CTAC. The proposed DL algorithm provided attenuation correction comparable to CTAC without the need for additional scans. Compared to conventional NAC perfusion imaging, DLAC significantly improved diagnostic accuracy.

Direct and indirect strategies of deep-learning-based attenuation correction for general purpose and dedicated cardiac SPECT.

Deep-learning-based attenuation correction (AC) for SPECT includes both indirect and direct approaches. Indirect approaches generate attenuation maps (μ-maps) from emission images, while direct approaches predict AC images directly from non-attenuation-corrected (NAC) images without μ-maps. For dedicated cardiac SPECT scanners with CZT detectors, indirect approaches are challenging due to the limited field-of-view (FOV). In this work, we aim to 1) first develop novel indirect approaches to improve the AC performance for dedicated SPECT; and 2) compare the AC performance between direct and indirect approaches for both general purpose and dedicated SPECT. For dedicated SPECT, we developed strategies to predict truncated μ-maps from NAC images reconstructed with a small matrix, or full μ-maps from NAC images reconstructed with a large matrix using 270 anonymized clinical studies scanned on a GE Discovery NM/CT 570c SPECT/CT. For general purpose SPECT, we implemented direct and indirect approaches using 400 anonymized clinical studies scanned on a GE NM/CT 850c SPECT/CT. NAC images in both photopeak and scatter windows were input to predict μ-maps or AC images. For dedicated SPECT, the averaged normalized mean square error (NMSE) using our proposed strategies with full μ-maps was 1.20 ± 0.72% as compared to 2.21 ± 1.17% using the previous direct approaches. The polar map absolute percent error (APE) using our approaches was 3.24 ± 2.79% (R2 = 0.9499) as compared to 4.77 ± 3.96% (R2 = 0.9213) using direct approaches. For general purpose SPECT, the averaged NMSE of the predicted AC images using the direct approaches was 2.57 ± 1.06% as compared to 1.37 ± 1.16% using the indirect approaches. We developed strategies of generating μ-maps for dedicated cardiac SPECT with small FOV. For both general purpose and dedicated SPECT, indirect approaches showed superior performance of AC than direct approaches.

Post-reconstruction attenuation correction for SPECT myocardium perfusion imaging facilitated by deep learning-based attenuation map generation

BackgroundAttenuation correction can improve the quantitative accuracy of single-photon emission computed tomography (SPECT) images. Existing SPECT-only systems normally can only provide non-attenuation corrected (NC) images which are susceptible to attenuation artifacts. In this work, we developed a post-reconstruction attenuation correction (PRAC) approach facilitated by a deep learning-based attenuation map for myocardial perfusion SPECT imaging. MethodsIn the PRAC method, new projection data were estimated via forwardly projecting the scanner-generated NC image. Then an attenuation map, generated from NC image using a pretrained deep learning (DL) convolutional neural network, was incorporated into an offline reconstruction algorithm to obtain the attenuation-corrected images from the forwardly projected projections. We evaluated the PRAC method using 30 subjects with a DL network trained with 40 subjects, using the vendor-generated AC images and CT-based attenuation maps as the ground truth. ResultsThe PRAC methods using DL-generated and CT-based attenuation maps were both highly consistent with the scanner-generated AC image. The globally normalized mean absolute errors were 1.1% ± .6% and .7% ± .4% and the localized absolute percentage errors were 8.9% ± 13.4% and 7.8% ± 11.4% in the left ventricular (LV) blood pool, respectively, and − 1.3% ± 8.0% and − 3.8% ± 4.5% in the LV myocardium for PRAC methods using DL-generated and CT-based attenuation maps, respectively. The summed stress scores after PRAC using both attenuation maps were more consistent with the ground truth than those of the NC images. ConclusionWe developed a PRAC approach facilitated by deep learning-based attenuation maps for SPECT myocardial perfusion imaging. It may be feasible for this approach to provide AC images for SPECT-only scanner data.

The Comparison of Quantitative Evaluation Results of the MPS SPECT/CT and Coronary Angiography: Determining the Most Valuable Quantitative Evaluation Score

Objectives:This study aimed to determine the most important perfusion score in patient selection for coronary angiography (CA) by quantitatively evaluating myocardial perfusion scintigraphy (MPS).Methods:Patients who underwent MPS single-photon emission computerized tomography/computed tomograph imaging in our clinic between December 2017 and January 2019, without coronary artery disease (CAD) history, followed by CA were included in the study. CA was considered positive when there is a stenosis of 70% or more in at least one coronary vessel. The summed stress score, rest score, and differential score; total perfusion deficit (TPD); and the defect’s extent obtained from non-attenuation-corrected (NC) and attenuation-corrected (AC) images of 80 patients were evaluated using the Mann-Whitney U test. A p value of <0.05 was considered significant. Receiver operating characteristic (ROC) analysis was performed.Results:The scores obtained from NC and AC images showed a significant difference between the two groups for all scores except for the extent and TPD scores at rest from AC images. The applied ROC curves’ highest diagnostic value was determined as the TPD score at stress (TPDS) obtained from NC images (area under the curve: 0.880, 95% confidence interval, 0.807-0.952, p<0.001). The cut-off value obtained for the TPDS from the ROC curve was found to be 5.5.Conclusion:The scores obtained from NC images have more power to detect CAD than those obtained from AC images. Patients with no prior CAD history with TPDS score higher than 5 in MPS should be referred for CA with priority.

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.

Development of attenuation correction methods using deep learning in brain-perfusion single-photon emission computed tomography.

Computed tomography (CT)-based attenuation correction (CTAC) in single-photon emission computed tomography (SPECT) is highly accurate, but it requires hybrid SPECT/CT instruments and additional radiation exposure. To obtain attenuation correction (AC) without the need for additional CT images, a deep learning method was used to generate pseudo-CT images has previously been reported, but it is limited because of cross-modality transformation, resulting in misalignment and modality-specific artifacts. This study aimed to develop a deep learning-based approach using non-attenuation-corrected (NAC) images and CTAC-based images for training to yield AC images in brain-perfusion SPECT. This study also investigated whether the proposed approach is superior to conventional Chang's AC (ChangAC). In total, 236 patients who underwent brain-perfusion SPECT were randomly divided into two groups: the training group (189 patients; 80%) and the test group (47 patients; 20%). Two models were constructed using Autoencoder (AutoencoderAC) and U-Net (U-NetAC), respectively. ChangAC, AutoencoderAC, and U-NetAC approaches were compared with CTAC using qualitative analysis (visual evaluation) and quantitative analysis (normalized mean squared error [NMSE] and the percentage error in each brain region). Statistical analyses were performed using the Wilcoxon signed-rank sum test and Bland-Altman analysis. U-NetAC had the highest visual evaluation score. The NMSE results for the U-NetAC were the lowest, followed by AutoencoderAC and ChangAC (P<0.001). Bland-Altman analysis showed a fixed bias for ChangAC and AutoencoderAC and a proportional bias for ChangAC. ChangAC underestimated counts by 30-40% in all brain regions. AutoencoderAC and U-NetAC produced mean errors of <1% and maximum errors of 3%, respectively. New deep learning-based AC methods for AutoencoderAC and U-NetAC were developed. Their accuracy was higher than that obtained by ChangAC. U-NetAC exhibited higher qualitative and quantitative accuracy than AutoencoderAC. We generated highly accurate AC images directly from NAC images without the need for intermediate pseudo-CT images. To verify our models' generalizability, external validation is required.

The performance of quantitation methods in the evaluation of cardiac implantable electronic device (CIED) infection: A technical review

BackgroundQuantitative assessment of [18F]-FDG PET/CT images has been shown to be useful in the diagnosis of cardiac implantable electronic device (CIED) infection. This study aimed to compare the accuracy of various quantitative methods, using the same patient cohort and to assess the utility of dual time point imaging. MethodsThe study comprised a retrospective review of 80 [18F]-FDG PET/CT studies. Of these, 41 were oncological patients with an asymptomatic CIED in situ (Group 1), and 39 were studies performed in patients with symptomatic devices. Of these, 14 were subsequently deemed on follow-up to be non-infected (Group 2), and 25 confirmed as infected post-device extraction (Group 3). Ratios of maximal uptake around the CIED in both the attenuation corrected and non-attenuation corrected images were calculated to regions of normal physiological uptake, along with the maximal standardized uptake value (SUVmax) alone. Receiver operating characteristic analysis was performed for all methods at both time points. Measurement reliability was assessed using the intraclass correlation coefficient (ICC). ResultsUsing Group 1 as a reference, all methods gave an area under the curve (AUC) greater than 0.93. Using Group 2 as reference, the accuracy varied greatly, with AUC values ranging from 0.71 to 0.97. The hepatic blood pool (HBP) ratio gave the highest AUC values. The calculated ICC values for each method showed the SUVmax and HBP measurement to have the greatest reliability, with values of 1.0 and 0.97, respectively. ConclusionsQuantitation of [18F] FDG uptake was found to have a high degree of accuracy in confirming the diagnosis of CIED infection. Normalization to HBP uptake was found to give the greatest AUC and demonstrated excellent reliability. Inconsistencies from published data indicate that individual imaging centers should only use published data for guidance.

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.

Impact of relative head sensitivity differences in multi-head gamma cameras.

Cerebral perfusion single photon emission computer tomography (SPECT) can be used to identify epileptogenic foci. A (99m)Tc ethyl cysteinate dimer SPECT of the brain showed clinically evident differences in uptake between the CT attenuation corrected image and the Chang attenuation corrected image. The upper right hemisphere of the brain showed apparent diffuse hyperperfusion in the CT attenuation corrected image while the Chang attenuation corrected image, after reconstruction that appears to average projections, showed symmetrical cerebral perfusion. On review of archived patient data, this artefact was also observed in multiple previous cerebral SPECT studies undertaken on the same camera. Phantom investigation was used to identify the cause of the artefact as a difference in relative head sensitivity. The investigation also characterised the extent and nature of this artefact for CT attenuation corrected images, Chang attenuation corrected images and non-attenuation corrected images.

Positron‐emission tomography pitfalls related to oral prosthesis

This case report describes false-positive positron-emission tomography/computed tomography (PET/CT) findings related to oral prostheses and its implications in cancer surveillance. In head and neck cancer management, F18-flurodeoxyglucose (FDG) PET/CT is widely accepted for evaluating treatment response and detecting recurrence. Interpretation of FDG PET/CT images in this setting is often challenging due to various prostheses and reconstruction methods. Following surgery for squamous cell carcinoma of the maxillary alveolus, a 61-year-old female had a FDG PET/CT scan on a 7-month follow-up that showed high FDG uptake along the resection site. Clinical examination showed no signs of inflammation or recurrence. Repeat FDG PET/CT without the prosthesis was normal. The PET/CT attenuation-corrected images demonstrated high FDG uptake (standardized uptake value: 11.6) along the resection site corresponding to contrast-enhanced CT images of the lesion. PET/CT nonattenuation-corrected images also confirmed increased activity. Repeat PET/CT without the prosthesis was normal. FDG is not tumor specific; it can accumulate in inflammation, infection, and post-therapy settings. Metallic and high-density prostheses show radial artifacts on CT and falsely elevated FDG uptake on PET/ CT in adjacent areas. Salivary pooling may concentrate FDG. The presence of oral prostheses has not been described as a cause of this high level of activity. PET/CT images that demonstrate intense activity corresponding to dense structures should be viewed with caution. A detailed history and physical exam as well as knowledge of artifacts are pertinent for the managing physician. Laryngoscope, 2012.

Comparative Accuracy of CT Attenuation-Corrected and Non–Attenuation-Corrected SPECT Myocardial Perfusion Imaging

The aim of the present study was to evaluate whether computed tomography based-attenuation correction (CT-AC) provides any advantage over non-attenuation-corrected (NAC) images for qualitative and quantitative analysis of single photon emission tomography (SPECT) myocardial perfusion imaging (MPI). We retrospectively evaluated data of 171 patients who underwent stress rest MPI SPECT/CT as per standard protocol. Angiography done within ±3 months of MPI was taken as reference standard. Two readers independently evaluated CT-AC and NAC images. Receiver operating characteristic curve analysis was done using ≥50% and ≥70% stenosis as cutoff. The size and severity of perfusion defects were also compared on CT-AC and NAC images. For both readers, the area under the receiver operating characteristic curve was larger for CT-AC images than for NAC images at both ≥50% and ≥70% cutoff, but the difference was not significant. CT-AC images had significantly lower sensitivity for detecting right coronary artery disease compared with NAC (29% vs. 50% for reader 1 and 25.8% vs. 43.2% for reader 2). However, the specificity improved with CT-AC. Inferior defects were significantly smaller in CT-AC than NAC (P = 0.0002), with no significant difference for anterior defects (P = 0.544). There was significant variation in severity between CT-AC and NAC images for both overall (P = 0.001) as well as for inferior defects (P = 0.0007), but not for anterior defects (P = 0.279). In our study, the CT-based AC improved the specificity but decreased the sensitivity leading to nonsignificant improvement in overall diagnostic accuracy of Tc-99m sestamibi/tetrofosmin MPI.

18F-fluoride PET/CT for bone scanning

Summary Aim: 18F-fluoride PET/CT is a promising tool for bone scanning. Recently, guidelines concerning the conduct of 18F-fluoride PET/CT have been published. One open question of the German guideline was the necessity of attenuation correction for 18F-fluoride PET/CT. We evaluated the need for a CT-based attenuation correction in 18F-fluoride PET/CT scans for the detection of bone lesions. Patients and methods: We retrospectively analyzed wholebody 18F-Fluoride PET/CT scans of 59 cancer patients. The lesions were categorized as malignant, benign or inconclusive. This assignment was performed for every lesion in both: attenuation corrected (AC) and non-attenuation- corrected (NAC) images. The maximum standardized uptake values (SUVmax) of the lesion in the AC images were also determined. Results: All bone lesions were detected in both image modalities. The AC images revealed 201 lesions categorized as malignant, 114 as benign and 35 as inconclusive. Without an AC, the results were 209, 116 and 25, respectively (p &gt; 0.05). 10/35 lesions categorized as inconclusive in the AC images were categorized as malignant in the NAC images, whereas 8 lesions were confirmed after comparison with other imaging modalities and follow-up data and 2 lesions were categorized as benign. The SUVmax for lesions identified as malignant showed a broad overlap with the SUV max of benign lesions and can consequently not be used for differentiation. Conclusion: An AC is not necessary for detecting bone lesions on 18F-fluoride PET/CT scans as the detection capability is identical for NAC imaging and lesion assignment was even better than with AC imaging. SUVmax seems not to improve the differentiation between malignant and benign bone lesions.

Small average differences in attenuation corrected images between men and women in myocardial perfusion scintigraphy: a novel normal stress database

BackgroundThe American Society of Nuclear Cardiology and the Society of Nuclear Medicine state that incorporation of attenuation-corrected (AC) images in myocardial perfusion scintigraphy (MPS) will improve image quality, interpretive certainty, and diagnostic accuracy. However, commonly used software packages for MPS usually include normal stress databases for non-attenuation corrected (NC) images but not for attenuation-corrected (AC) images. The aim of the study was to develop and compare different normal stress databases for MPS in relation to NC vs. AC images, male vs. female gender, and presence vs. absence of obesity. The principal hypothesis was that differences in mean count values between men and women would be smaller with AC than NC images, thereby allowing for construction and use of gender-independent AC stress database.MethodsNormal stress perfusion databases were developed with data from 126 male and 205 female patients with normal MPS. The following comparisons were performed for all patients and separately for normal weight vs. obese patients: men vs. women for AC; men vs. women for NC; AC vs. NC for men; and AC vs. NC for women.ResultsWhen comparing AC for men vs. women, only minor differences in mean count values were observed, and there were no differences for normal weight vs. obese patients. For all other analyses major differences were found, particularly for the inferior wall.ConclusionsThe results support the hypothesis that it is possible to use not only gender independent but also weight independent AC stress databases.

The utility of the nonattenuation corrected 18F-FDG PET images in the characterization of solitary pulmonary lesions

To determine the accuracy of nonattenuation corrected (NAC) F-fluorodeoxyglucose positron emission tomography (F-FDG PET) images in the evaluation of solitary pulmonary lesion as compared with more established methods. Fifty-six patients received F-FDG PET/CT for diagnosing solitary pulmonary nodules or mass lesions based on histopathology (n=39) and clinical follow-up (n=17). Visual pulmonary lesion FDG uptake was graded by consensus of two nuclear medicine physicians on both attenuation corrected (AC) [absent, less than mediastinal blood pool (MBP), equal to MBP, greater than MBP] and NAC (absent, less than skin, equal to skin, greater than skin) images. Standardized uptake values (SUV) were also measured from AC images. SUV, visual AC, and visual NAC methods' diagnostic performances were compared, distinguishing benign from malignant pulmonary nodules. There were 34 malignant and 22 benign lesions. Lesion diameter varied from 5 to 100 mm (mean ± SD, 24.0 ± 17.9 mm). The NAC, AC, and SUV method sensitivities and specificities were 100/64%, 91/59%, and 79/77%, respectively. For lesions less than 3 cm, NAC, AC, and SUV methods yielded accuracies of 85%, 78%, and 73%, respectively. The NAC method was the most sensitive and accurate especially for small nodules. Visual assessment of NAC F-FDG PET images alone may provide a more accurate characterization of solitary pulmonary lesions.

Attenuation Artifact From Sclerotic Bone Can Mimic Active Bone Metastasis on PET-CT

We report the PET-CT appearance of sclerotic bone mimicking active bone metastasis in a 57-year-old woman with right breast cancer and bone and hepatic metastases. Hybrid PET-CT was performed 1 month after completion of surgery and chemoradiation treatment. PET-CT demonstrated hypermetabolic foci in the right pulmonary hilum and liver. Increased hypermetabolic activity in L1 and L3 vertebral bodies was also seen corresponding to sclerotic vertebral bodies on CT study. The activity in L1 and L3 vertebral bodies was not visualized on nonattenuation-corrected images, consistent with an attenuation correction artifact resulting from extensive sclerosis in these regions.

Is non-attenuation-corrected PET inferior to body attenuation-corrected PET or PET/CT in lung cancer?

It is considered that one of the great strengths of PET imaging is the ability to correct for body attenuation. This enables better lesion uptake quantification and quality of PET images. The aim of this work is to compare the sensitivity of non-attenuation-corrected (NAC) PET images, the gamma photons (GPAC) and CT attenuation-corrected (CTAC) images in detecting and staging of lung cancer. We have studied 66 patients undergoing PET/CT examinations for detecting and staging NSC lung cancer. The patients were injected with 18-FDG; 5 MBq/kg under fasting conditions and examination was started 60 min later. Transmission data were acquired by a spiral CT X-ray tube and by gamma photons emitting Cs-137l source and were used for the patient body attenuation correction without correction for respiratory motion. In 55 of 66 patients we performed both attenuation correction procedures and in 11 patients only CT attenuation correction. In seven patients with solitary nodules PET was negative and in 59 patients with lung cancer PET/CT was positive for pulmonary or other localization. In the group of 55 patients we found 165 areas of focal increased 18-FDG uptake in NAC, 165 in CTAC and 164 in GPAC PET images.In the patients with only CTAC we found 58 areas of increased 18-FDG uptake on NAC and 58 areas lesions on CTAC. In the patients with positive PET we found 223 areas of focal increased uptake in NAC and 223 areas in CTAC images. The sensitivity of NAC was equal to the sensitivity of CTAC and GPAC images. The visualization of peripheral lesions was better in NAC images and the lesions were better localized in attenuation-corrected images. In three lesions of the thorax the localization was better in GPAC and fused images than in CTAC images.

PET recognition of pulmonary metastases on PET/CT imaging: impact of attenuation-corrected and non-attenuation-corrected PET images

The aims of this study were to assess the performance of FDG PET at PET/CT imaging for the detection of pulmonary metastases and to evaluate differences in lesion detectability on attenuation-corrected (AC) and non-attenuation corrected (NAC) PET images. The institutional PET/CT database was searched for patients with pulmonary metastases of 3-60 mm in diameter. Ninety-two patients with 438 metastases to the lungs were included in the study. The primary tumours were 33 malignant melanomas, 12 carcinomas of unknown primary, 11 colorectal carcinomas, eight differentiated thyroid carcinomas, seven aggressive non-Hodgkin's lymphomas, six head and neck cancers, three breast cancers, two prostate cancers and ten others. Lesion detectability was visually compared between PET and CT and between AC and NAC PET images using a five-point scale. Of the 438 pulmonary metastases, 174 were detected with FDG PET (39.7%), six of them on NAC images only (not significant). Visual scores were higher on NAC images in 41.4% and equal in 54.6% of lesions. The sensitivity of FDG PET increased significantly from 0.405 for metastases of 5-7 mm in diameter to 0.784 for lesions of 8-10 mm and to 0.935 for lesions measuring 11-29 mm in diameter. No metastases smaller than 5 mm in diameter were seen on PET images. FDG PET/CT is useful for the assessment of pulmonary metastases. The frequency of lesion detection is similar for AC and NAC PET images. A reduced sensitivity of FDG PET has to be considered for lesions smaller than 11 mm in diameter.

Differentiation Between Malignant and Benign Pleural Effusion in Patients With Extra-Pleural Primary Malignancies

We sought to define an accurate diagnostic approach for differentiating benign from malignant pleural effusion on positron emission tomography-computed tomography (PET-CT). PET-CT studies of 31 patients with primary extrapleural malignancy and pleural effusion were reviewed retrospectively. CT parameters assessed were size and density (Hounsfield units, or HU) of the effusion and density (HU) and morphology of any solid pleural abnormality. Interpretation of PET data included review of the attenuation-corrected and nonattenuation-corrected images. PET-CT parameters that were found to be significant in identifying malignant pleural effusion included focal increased uptake of 18-fluorodeoxyglucose in the pleura (P<0.0001) and the presence of solid pleural abnormalities on CT (P<0.002): the sensitivity was 86% and 71%, respectively, and the specificity was 90% for each of the 2 parameters. A PET-CT pattern composed of pleural uptake and increased effusion activity on nonattenuation-corrected images was associated with sensitivity of 95%, specificity of 80%, positive predictive value of 91%, negative predictive value of 89%, and accuracy of 90%. On PET-CT, the presence of concomitant pleural abnormalities is the most accurate criterion in determining the malignant nature of pleural effusion.