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Background Dynamic myocardial computed tomography perfusion (CTP) is a novel technique able to depict cardiac ischemia. Purpose To evaluate the impact of a four-dimensional noise reduction filter (similarity filter [4D-SF]) on image quality in dynamic CTP imaging, allowing for substantial radiation dose reduction. Material and Methods Dynamic CTP datasets of 30 patients (16 women) with suspected coronary artery disease, acquired with a 320-slice CT system, were retrieved, reconstructed with the deep learning-based algorithm of the system (DLR), and filtered with the 4D-SF. For each case, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in six regions of interest (33–38mm2) were calculated before and after filtering, in four-chamber and short-axis views, and t-tested. Furthermore, six radiologists of different expertise evaluated subjective image preference by answering five visual grading analysis-type questions (regarding acceptable level of noise, absence of artifacts, natural appearance, cardiac contour sharpness, diagnostic acceptability) using a 5-point scale. The results were analyzed using visual grade characteristics (VGC) and intraclass correlation coefficient (ICC). Results Mean SNR in four-chamber view (unfiltered vs. filtered) were: septum=4.1 ± 2.1 versus 7.6 ± 5.6; lateral wall=4.5 ± 2.0 versus 8.0 ± 4.9; CNRseptum=16.6 ± 8.9 versus 31.7 ± 28; lateral wall=16.2 ± 8.9 versus 31.3 ± 28.9. Similar results were obtained in short-axis view. The perceived filtered image quality indicated decreased noise (VGCAUC=0.96) and artifacts (0.65), improved natural appearance (0.59), cardiac contour sharpness (0.74), and diagnostic acceptability (0.78). The inter-observer variability was excellent (ICC=0.79). All results were statistically significant ( P Conclusion Similarity filtering after DLR improves image quality, possibly enabling dose reduction in dynamic CTP imaging in patient with suspected chronic coronary syndrome.

TO THE EDITOR: In The Netherlands, the residency training programs for nuclear medicine and radiology were merged in 2015. This integrated residency training program was born from the vision that clinicians should not have to deal with multiple imaging specialists who are modality-based and

Contains fulltext : 122870.pdf (Publisher’s version ) (Open Access) To assess the effect of bone suppression imaging on observer performance in detecting lung nodules in chest radiographs.Posteroanterior (PA) and lateral digital chest radiographs of 111 (average age 65) patients with a CT proven solitary nodule (median diameter 15mm), and 189 (average age 63) controls were read by 5 radiologists and 3 residents. Conspicuity of nodules on the radiographs was classified in obvious (n=32), moderate (n=32), subtle (n=29) and very subtle (n=18). Observers read the PA and lateral chest radiographs without and with an additional PA bone suppressed image (BSI) (ClearRead Bone Suppression 2.4, Riverain Technologies, Ohio) within one reading session. Multi reader multi case (MRMC) receiver operating characteristics (ROC) were used for statistical analysis.ROC analysis showed improved detection with use of BSI compared to chest radiographs alone (AUC=0.883 versus 0.855; p=0.004). Performance also increased at high specificities exceeding 80\% (pAUC=0.136 versus 0.124; p=0.0007). Operating at a specificity of 90\%, sensitivity increased with BSI from 66\% to 71\% (p=0.0004). Increase of detection performance was highest for nodules with moderate and subtle conspicuity (p=0.02; p=0.03).Bone suppressed images improve radiologists' detection performance for pulmonary nodules, especially for those of moderate and subtle conspicuity.

Background: The diagnostic evaluation of patients presenting with possible lung cancer is often complex and time consuming. A rapid outpatient diagnostic program (RODP) including 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) and contrast-enhanced computed tomography (CT) as a routine diagnostic tool may improve timeliness, however the diagnostic performance of such a combined approach of RODP remains unclear. Objectives: We evaluated timeliness of care and diagnostic performance of FDG-PET and contrast-enhanced CT (FDG-PET/CT) in an RODP for all patients referred with a chest X-ray suspicious of lung cancer. Methods: Charts of patients referred to the 2-day RODP of our tertiary care university clinic after an abnormal chest X-ray between 1999 and 2009 were reviewed. Between 1999 and 2005 co-registered FDG-PET and CT imaging took place; from September 2005 onwards, a hybrid system was used. We analyzed timeliness of care and diagnostic performance of FDG-PET/CT to differentiate malignant from benign lesions. Results: In 386 patients available for analysis, 260 were diagnosed with lung cancer and 23 had another type of malignancy; in 78 patients benign disease was confirmed, and in another 45 the diagnosis was not pathologically confirmed but a median 24.5-month follow-up confirmed a benign outcome. Sensitivity, specificity, negative and positive predictive values and accuracy of FDG-PET/CT to differentiate lung cancer from benign disease were 97.7, 60.2, 92.5, 84.0 and 85.8%, respectively. Lung cancer patients had a median referral, diagnostic and therapeutic delay of 7, 2 and 19 days, respectively. Conclusions: FDG-PET/CT in an RODP setting for suspected lung cancer has high performance in detecting cancer and facilitates timely care.

Efficacy of tuberculosis (TB) treatment is often monitored using chest radiography. Monitoring size of cavities in pulmonary tuberculosis is important as the size predicts severity of the disease and its persistence under therapy predicts relapse. The authors present a method for automatic cavity segmentation in chest radiographs.A two stage method is proposed to segment the cavity borders, given a user defined seed point close to the center of the cavity. First, a supervised learning approach is employed to train a pixel classifier using texture and radial features to identify the border pixels of the cavity. A likelihood value of belonging to the cavity border is assigned to each pixel by the classifier. The authors experimented with four different classifiers:k-nearest neighbor (kNN), linear discriminant analysis (LDA), GentleBoost (GB), and random forest (RF). Next, the constructed likelihood map was used as an input cost image in the polar transformed image space for dynamic programming to trace the optimal maximum cost path. This constructed path corresponds to the segmented cavity contour in image space.The method was evaluated on 100 chest radiographs (CXRs) containing 126 cavities. The reference segmentation was manually delineated by an experienced chest radiologist. An independent observer (a chest radiologist) also delineated all cavities to estimate interobserver variability. Jaccard overlap measure Ω was computed between the reference segmentation and the automatic segmentation; and between the reference segmentation and the independent observer's segmentation for all cavities. A median overlap Ω of 0.81 (0.76 ± 0.16), and 0.85 (0.82 ± 0.11) was achieved between the reference segmentation and the automatic segmentation, and between the segmentations by the two radiologists, respectively. The best reported mean contour distance and Hausdorff distance between the reference and the automatic segmentation were, respectively, 2.48 ± 2.19 and 8.32 ± 5.66 mm, whereas these distances were 1.66 ± 1.29 and 5.75 ± 4.88 mm between the segmentations by the reference reader and the independent observer, respectively. The automatic segmentations were also visually assessed by two trained CXR readers as "excellent," "adequate," or "insufficient." The readers had good agreement in assessing the cavity outlines and 84% of the segmentations were rated as "excellent" or "adequate" by both readers.The proposed cavity segmentation technique produced results with a good degree of overlap with manual expert segmentations. The evaluation measures demonstrated that the results approached the results of the experienced chest radiologists, in terms of overlap measure and contour distance measures. Automatic cavity segmentation can be employed in TB clinics for treatment monitoring, especially in resource limited settings where radiologists are not available.

Item does not contain fulltext The potential of (18)F-FDG PET changes was evaluated for prediction of response to concomitant chemoradiotherapy in patients with locally advanced non-small cell lung cancer (NSCLC). METHODS: For 28 patients, (18)F-FDG PET was performed before treatment, at the end of the second week of treatment, and at 2 wk and 3 mo after the completion of treatment. Standardized uptake value (SUV), maximum SUV, metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were obtained. Early metabolic changes were defined as fractional change (DeltaTLG) when (18)F-FDG PET at the end of the second week was compared with pretreatment (18)F-FDG PET. In-treatment metabolic changes, as measured by serial (18)F-FDG PET, were correlated with standard criteria of response evaluation of solid tumors by means of CT imaging (Response Evaluation Criteria In Solid Tumors 1.1). Parameters were analyzed for stratification in progression-free survival (PFS). RESULTS: When compared with early metabolic nonresponders, a DeltaTLG decrease of 38% or more was associated with a significantly longer PFS (1-y PFS 80% vs. 36%, P = 0.02). Pretreatment TLG was found to be a prognostic factor for PFS. CONCLUSION: The degree of change in TLG was predictive for response to concomitant chemoradiotherapy as early as the end of the second week into treatment for patients with locally advanced NSCLC. Pretreatment TLG was prognostic for PFS.

Contains fulltext : 125314.pdf (Publisher’s version ) (Open Access) SETTING: A busy urban health centre in Lusaka, Zambia. OBJECTIVE: To compare the accuracy of automated reading (CAD4TB) with the interpretation of digital chest radiograph (CXR) by clinical officers for the detection of tuberculosis (TB). DESIGN: A retrospective analysis was performed on 161 subjects enrolled in a TB specimen bank study. CXRs were analysed using CAD4TB, which computed an image abnormality score (0-100). Four clinical officers scored the CXRs for abnormalities consistent with TB. We compared the automated readings and the readings by clinical officers against the bacteriological and radiological results used as reference. We report here the area under the receiver operating characteristic curve (AUC) and kappa () statistics. RESULTS: Of 161 enrolled subjects, 97 had bacteriologically confirmed TB and 120 had abnormal CXR. The AUCs for CAD4TB and the clinical officers were respectively 0.73 and 0.65-0.75 in comparison with the bacteriological reference, and 0.91 and 0.89-0.94 in comparison with the radiological reference. P values indicated no significant differences, except for one clinical officer who performed significantly worse than CAD4TB (P < 0.05) using the bacteriological reference. values for CAD4TB and clinical officers with radiological reference were respectively 0.61 and 0.49-0.67. CONCLUSION: CXR assessment using CAD4TB and by clinical officers is comparable. CAD4TB has potential as a point-of-care test and for the automated identification of subjects who require further examinations.

The diagnostic evaluation of patients presenting with possible lung cancer is often complex and time consuming. A rapid outpatient diagnostic program (RODP) including (18)F-fluorodeoxyglucose positron emission tomography (FDG-PET) and contrast-enhanced computed tomography (CT) as a routine diagnostic tool may improve timeliness, however the diagnostic performance of such a combined approach of RODP remains unclear.We evaluated timeliness of care and diagnostic performance of FDG-PET and contrast-enhanced CT (FDG-PET/CT) in an RODP for all patients referred with a chest X-ray suspicious of lung cancer.Charts of patients referred to the 2-day RODP of our tertiary care university clinic after an abnormal chest X-ray between 1999 and 2009 were reviewed. Between 1999 and 2005 co-registered FDG-PET and CT imaging took place; from September 2005 onwards, a hybrid system was used. We analyzed timeliness of care and diagnostic performance of FDG-PET/CT to differentiate malignant from benign lesions.In 386 patients available for analysis, 260 were diagnosed with lung cancer and 23 had another type of malignancy; in 78 patients benign disease was confirmed, and in another 45 the diagnosis was not pathologically confirmed but a median 24.5-month follow-up confirmed a benign outcome. Sensitivity, specificity, negative and positive predictive values and accuracy of FDG-PET/CT to differentiate lung cancer from benign disease were 97.7, 60.2, 92.5, 84.0 and 85.8%, respectively. Lung cancer patients had a median referral, diagnostic and therapeutic delay of 7, 2 and 19 days, respectively.FDG-PET/CT in an RODP setting for suspected lung cancer has high performance in detecting cancer and facilitates timely care.

Contains fulltext : 109994.pdf (Publisher’s version ) (Closed access) INTRODUCTION: Delays in the diagnosis of lung cancer are under debate and may affect outcome. The objectives of this study were to compare various delays in a rapid outpatient diagnostic program (RODP) for suspected lung cancer patients with those described in literature and with guideline recommendations, to investigate the effects of referral route and symptoms on delays, and to establish whether delays were related to disease stage and outcome. METHODS: A retrospective chart study was conducted of all patients with suspected lung cancer, referred to the RODP of our tertiary care university clinic between 1999 and 2009. Patient characteristics, tumor stage and different delays were analyzed. RESULTS: Medical charts of 565 patients were retrieved. 290 patients (51.3%) were diagnosed with lung cancer, 48 (8.5%) with another type of malignancy, and in 111 patients (19.6%) the radiological anomaly was diagnosed as non-malignant. In 112 (19.8%) no immediate definite diagnosis was obtained, however in 82 of these cases (73.2%) the proposed follow-up strategy confirmed a benign outcome. The median first line delay was 54 days, IQR (interquartile range) 20-104 days, median patient delay 19 days (IQR 4-52 days), median referral delay was 7 days (IQR 5-9 days), median diagnostic delay 2 days (IQR 1-19 days). In 87% a diagnosis was obtained within 3 weeks after visiting a chest physician and 52.5% started curative therapy within 2 weeks after diagnosis. Patients presenting with hemoptysis had shorter first line delays. The RODP care was generally far more timely compared to literature and published guidelines, except for both referral and palliative therapeutic delay. No specific delay was significantly related to disease stage or survival. CONCLUSIONS: An RODP results in a timely diagnosis well within guideline recommendations. Patient and first line delay account for most of total patient delay. Within the limitations of this retrospective study, we found no association with disease stage or survival. 01 maart 2012

Purpose To examine the factors that affect inter- and intraobserver agreement for pulmonary nodule type classification on low-radiation-dose computed tomographic (CT) images, and their potential effect on patient management. Materials and Methods Nodules (n = 160) were randomly selected from the Dutch-Belgian Lung Cancer Screening Trial cohort, with equal numbers of nodule types and similar sizes. Nodules were scored by eight radiologists by using morphologic categories proposed by the Fleischner Society guidelines for management of pulmonary nodules as solid, part solid with a solid component smaller than 5 mm, part solid with a solid component 5 mm or larger, or pure ground glass. Inter- and intraobserver agreement was analyzed by using Cohen κ statistics. Multivariate analysis of variance was performed to assess the effect of nodule characteristics and image quality on observer disagreement. Effect on nodule management was estimated by differentiating CT follow-up for ground-glass nodules, solid nodules 8 mm or smaller, and part-solid nodules smaller than 5 mm from immediate diagnostic work-up for solid nodules larger than 8 mm and part-solid nodules 5 mm or greater. Results Pair-wise inter- and intraobserver agreement was moderate (mean κ, 0.51 [95% confidence interval, 0.30, 0.68] and 0.57 [95% confidence interval, 0.47, 0.71]). Categorization as part-solid nodules and location in the upper lobe significantly reduced observer agreement (P = .012 and P < .001, respectively). By considering all possible reading pairs (28 possible combinations of observer pairs × 160 nodules = 4480 possible agreements or disagreements), a discordant nodule classification was found in 36.4% (1630 of 4480), related to presence or size of a solid component in 88.7% (1446 of 1630). Two-thirds of these discrepant readings (1061 of 1630) would have potentially resulted in different nodule management. Conclusion There is moderate inter- and intraobserver agreement for nodule classification by using current recommendations for low-radiation-dose CT examinations of the chest. Discrepancies in nodule categorization were mainly caused by disagreement on the size and presence of a solid component, which may lead to different management in the majority of cases with such discrepancies. (©) RSNA, 2015.

Purpose: Efficacy of tuberculosis (TB) treatment is often monitored using chest radiography. Monitoring size of cavities in pulmonary tuberculosis is important as the size predicts severity of the disease and its persistence under therapy predicts relapse. The authors present a method for automatic cavity segmentation in chest radiographs. Methods: A two stage method is proposed to segment the cavity borders, given a user defined seed point close to the center of the cavity. First, a supervised learning approach is employed to train a pixel classifier using texture and radial features to identify the border pixels of the cavity. A likelihood value of belonging to the cavity border is assigned to each pixel by the classifier. The authors experimented with four different classifiers:k-nearest neighbor (kNN), linear discriminant analysis (LDA), GentleBoost (GB), and random forest (RF). Next, the constructed likelihood map was used as an input cost image in the polar transformed image space for dynamic programming to trace the optimal maximum cost path. This constructed path corresponds to the segmented cavity contour in image space. Results: The method was evaluated on 100 chest radiographs (CXRs) containing 126 cavities. The reference segmentation was manually delineated by an experienced chest radiologist. An independent observer (a chest radiologist) also delineated all cavities to estimate interobserver variability. Jaccard overlap measure Ω was computed between the reference segmentation and the automatic segmentation; and between the reference segmentation and the independent observer's segmentation for all cavities. A median overlap Ω of 0.81 (0.76 ± 0.16), and 0.85 (0.82 ± 0.11) was achieved between the reference segmentation and the automatic segmentation, and between the segmentations by the two radiologists, respectively. The best reported mean contour distance and Hausdorff distance between the reference and the automatic segmentation were, respectively, 2.48 ± 2.19 and 8.32 ± 5.66 mm, whereas these distances were 1.66 ± 1.29 and 5.75 ± 4.88 mm between the segmentations by the reference reader and the independent observer, respectively. The automatic segmentations were also visually assessed by two trained CXR readers as “excellent,” “adequate,” or “insufficient.” The readers had good agreement in assessing the cavity outlines and 84% of the segmentations were rated as “excellent” or “adequate” by both readers. Conclusions: The proposed cavity segmentation technique produced results with a good degree of overlap with manual expert segmentations. The evaluation measures demonstrated that the results approached the results of the experienced chest radiologists, in terms of overlap measure and contour distance measures. Automatic cavity segmentation can be employed in TB clinics for treatment monitoring, especially in resource limited settings where radiologists are not available.