Computed tomography calcium score scan for attenuation correction of N-13 ammonia cardiac positron emission tomography: effect of respiratory phase and registration method

The use of coronary calcium scoring (CaScCT) for attenuation correction (AC) of 13N-ammonia PET/CT studies (NH3) is still being debated. We compare standard ACCT to CaScCT using various respiratory phases and co-registration methods for AC. Forty-one patients underwent a stress/rest NH3. Standard ACCT scans and CaScCT acquired during inspiration (CaScCTinsp, 26 patients) or expiration (CaScCTexp, 15 patients) were used to correct PET data for photon attenuation. Resulting images were compared using Pearson's correlation and Bland-Altman (BA) limits of agreement (LA) on segmental relative and absolute coronary blood flow (CBF) using both manual and automatic co-registration methods (rigid-body and deformable). For relative perfusion, CaScCTexp correlates better than CaScCTinsp with ACCT when using manual co-registration (r=0.870; P<0.001 and r=0.732; P<0.001, respectively). Automatic co-registration provides the best correlation between CaScCTexp and ACCT for relative perfusion (r=0.956; P<0.001). Both CaScCTinsp and CaScCTexp yielded excellent correlations with ACCT for CBF when using manual co-registration (r=0.918; P<0.001; BA mean bias 0.05ml/min/g; LA: −0.42 to +0.3ml/min/g and r=0.97; P<0.001; BA mean bias 0.1ml/min/g; LA: −0.65 to +0.5ml/min/g, respectively). The use of CaScCTexp and deformable co-registration is best suited for AC to quantify relative perfusion and CBF enabling substantial radiation dose reductio

Evaluation of a novel deep learning-based classifier for perifissural nodules

OBJECTIVES: To evaluate the performance of a novel convolutional neural network (CNN) for the classification of typical perifissural nodules (PFN). METHODS: Chest CT data from two centers in the UK and The Netherlands (1668 unique nodules, 1260 individuals) were collected. Pulmonary nodules were classified into subtypes, including "typical PFNs" on-site, and were reviewed by a central clinician. The dataset was divided into a training/cross-validation set of 1557 nodules (1103 individuals) and a test set of 196 nodules (158 individuals). For the test set, three radiologically trained readers classified the nodules into three nodule categories: typical PFN, atypical PFN, and non-PFN. The consensus of the three readers was used as reference to evaluate the performance of the PFN-CNN. Typical PFNs were considered as positive results, and atypical PFNs and non-PFNs were grouped as negative results. PFN-CNN performance was evaluated using the ROC curve, confusion matrix, and Cohen's kappa. RESULTS: Internal validation yielded a mean AUC of 91.9% (95% CI 90.6-92.9) with 78.7% sensitivity and 90.4% specificity. For the test set, the reader consensus rated 45/196 (23%) of nodules as typical PFN. The classifier-reader agreement (k = 0.62-0.75) was similar to the inter-reader agreement (k = 0.64-0.79). Area under the ROC curve was 95.8% (95% CI 93.3-98.4), with a sensitivity of 95.6% (95% CI 84.9-99.5), and specificity of 88.1% (95% CI 81.8-92.8). CONCLUSION: The PFN-CNN showed excellent performance in classifying typical PFNs. Its agreement with radiologically trained readers is within the range of inter-reader agreement. Thus, the CNN-based system has potential in clinical and screening settings to rule out perifissural nodules and increase reader efficiency. KEY POINTS: • Agreement between the PFN-CNN and radiologically trained readers is within the range of inter-reader agreement. • The CNN model for the classification of typical PFNs achieved an AUC of 95.8% (95% CI 93.3-98.4) with 95.6% (95% CI 84.9-99.5) sensitivity and 88.1% (95% CI 81.8-92.8) specificity compared to the consensus of three readers

Assessing Reliability of Myocardial Blood Flow After Motion Correction With Dynamic PET Using a Bayesian Framework

The estimation of myocardial blood flow (MBF) in dynamic PET can be biased by many different processes. A major source of error, particularly in clinical applications, is patient motion. Patient motion, or gross motion, creates displacements between different PET frames as well as between the PET frames and the CT-derived attenuation map, leading to errors in MBF calculation from voxel time series. Motion correction techniques are challenging to evaluate quantitatively and the impact on MBF reliability is not fully understood. Most metrics, such as signal-to-noise ratio (SNR), are characteristic of static images, and are not specific to motion correction in dynamic data. This study presents a new approach of estimating motion correction quality in dynamic cardiac PET imaging. It relies on calculating a MBF surrogate, K 1 , along with the uncertainty on the parameter. This technique exploits a Bayesian framework, representing the kinetic parameters as a probability distribution, from which the uncertainty measures can be extracted. If the uncertainty extracted is high, the parameter studied is considered to have high variability - or low confidence - and vice versa. The robustness of the framework is evaluated on simulated time activity curves to ensure that the uncertainties are consistently estimated at the multiple levels of noise. Our framework is applied on 40 patient datasets, divided in 4 motion magnitude categories. Experienced observers manually realigned clinical datasets with 3D translations to correct for motion. K 1 uncertainties were compared before and after correction. A reduction of uncertainty after motion correction of up to 60% demonstrates the benefit of motion correction in dynamic PET and as well as provides evidence of the usefulness of the new method presented

Lung cancer prediction by Deep Learning to identify benign lung nodules

INTRODUCTION: Deep Learning has been proposed as promising tool to classify malignant nodules. Our aim was to retrospectively validate our Lung Cancer Prediction Convolutional Neural Network (LCP-CNN), which was trained on US screening data, on an independent dataset of indeterminate nodules in an European multicentre trial, to rule out benign nodules maintaining a high lung cancer sensitivity. METHODS: The LCP-CNN has been trained to generate a malignancy score for each nodule using CT data from the U.S. National Lung Screening Trial (NLST), and validated on CT scans containing 2106 nodules (205 lung cancers) detected in patients from from the Early Lung Cancer Diagnosis Using Artificial Intelligence and Big Data (LUCINDA) study, recruited from three tertiary referral centers in the UK, Germany and Netherlands. We pre-defined a benign nodule rule-out test, to identify benign nodules whilst maintaining a high sensitivity, by calculating thresholds on the malignancy score that achieve at least 99 % sensitivity on the NLST data. Overall performance per validation site was evaluated using Area-Under-the-ROC-Curve analysis (AUC). RESULTS: The overall AUC across the European centers was 94.5 % (95 %CI 92.6-96.1). With a high sensitivity of 99.0 %, malignancy could be ruled out in 22.1 % of the nodules, enabling 18.5 % of the patients to avoid follow-up scans. The two false-negative results both represented small typical carcinoids. CONCLUSION: The LCP-CNN, trained on participants with lung nodules from the US NLST dataset, showed excellent performance on identification of benign lung nodules in a multi-center external dataset, ruling out malignancy with high accuracy in about one fifth of the patients with 5-15 mm nodules

Automatic registration and alignment on a template of cardiac stress rest reoriented SPECT images

Single photon emission computed tomography #SPECT# imaging with 201 Tl or 99m Tc agent is used to assess the location or the extentofmyocardial infarction or ischemia. A method is proposed to decrease the e#ect of operator variability in the visual or quantitativeinterpretation of scintigraphic myocardial perfusion studies. To e#ect this, the patient&apos;s myocardial images #target cases# are registered automatically over a template image, utilizing a non-rigid transformation. The intermediate steps are: 1. Extraction of feature points in both stress and rest 3D images. The images are resampled in a polar geometry to detect edge points, which in turn are #ltered by the use of a priori constraints. The remaining feature points are assumed to be points on the edges of the left ventricular myocardium. 2. Registration of stress and rest images with a global a#ne transformation. The matching method is an adaptation of the Iterative Closest Point algorithm. 3. Registration and morphologi..

Development and application of image analysis techniques to study structural and metabolic neurodegeneration in the human hippocampus using MRI and PET

Despite the association between hippocampal atrophy and a vast array of highly debilitating neurological diseases, such as Alzheimer’s disease and frontotemporal lobar degeneration, tools to accurately and robustly quantify the degeneration of this structure still largely elude us. In this thesis, we firstly evaluate previously-developed hippocampal segmentation methods (FMRIB’s Integrated Registration and Segmentation Tool (FIRST), Freesurfer (FS), and three versions of a Classifier Fusion (CF) technique) on two clinical MR datasets, to gain a better understanding of the modes of success and failure of these techniques, and to use this acquired knowledge for subsequent method improvement (e.g., FIRSTv3). Secondly, a fully automated, novel hippocampal segmentation method is developed, termed Fast Marching for Automated Segmentation of the Hippocampus (FMASH). This combined region-growing and atlas-based approach uses a 3D Sethian Fast Marching (FM) technique to propagate a hippocampal region from an automatically-defined seed point in the MR image. Region growth is dictated by both subject-specific intensity features and a probabilistic shape prior (or atlas). Following method development, FMASH is thoroughly validated on an independent clinical dataset from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), with an investigation of the dependency of such atlas-based approaches on their prior information. In response to our findings, we subsequently present a novel label-warping approach to effectively account for the detrimental effects of using cross-dataset priors in atlas-based segmentation. Finally, a clinical application of MR hippocampal segmentation is presented, with a combined MR-PET analysis of wholefield and subfield hippocampal changes in Alzheimer’s disease and frontotemporal lobar degeneration. This thesis therefore contributes both novel computational tools and valuable knowledge for further neurological investigations in both the academic and the clinical field.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Computed tomography calcium score scan for attenuation correction of N-13 ammonia cardiac positron emission tomography:effect of respiratory phase and registration method

<p>The use of coronary calcium scoring (CaScCT) for attenuation correction (AC) of N-13-ammonia PET/CT studies (NH3) is still being debated. We compare standard ACCT to CaScCT using various respiratory phases and co-registration methods for AC. Forty-one patients underwent a stress/rest NH3. Standard ACCT scans and CaScCT acquired during inspiration (CaScCTinsp, 26 patients) or expiration (CaScCTexp, 15 patients) were used to correct PET data for photon attenuation. Resulting images were compared using Pearson's correlation and Bland-Altman (BA) limits of agreement (LA) on segmental relative and absolute coronary blood flow (CBF) using both manual and automatic co-registration methods (rigid-body and deformable). For relative perfusion, CaScCTexp correlates better than CaScCTinsp with ACCT when using manual co-registration (r = 0.870; P <0.001 and r = 0.732; P <0.001, respectively). Automatic co-registration provides the best correlation between CaScCTexp and ACCT for relative perfusion (r = 0.956; P <0.001). Both CaScCTinsp and CaScCTexp yielded excellent correlations with ACCT for CBF when using manual co-registration (r = 0.918; P <0.001; BA mean bias 0.05 ml/min/g; LA: -0.42 to +0.3 ml/min/g and r = 0.97; P <0.001; BA mean bias 0.1 ml/min/g; LA: -0.65 to +0.5 ml/min/g, respectively). The use of CaScCTexp and deformable co-registration is best suited for AC to quantify relative perfusion and CBF enabling substantial radiation dose reduction.</p>