Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection

Accurate pulmonary nodule detection is a crucial step in lung cancer screening. Computer-aided detection (CAD) systems are not routinely used by radiologists for pulmonary nodule detection in clinical practice despite their potential benefits. Maximum intensity projection (MIP) images improve the detection of pulmonary nodules in radiological evaluation with computed tomography (CT) scans. Inspired by the clinical methodology of radiologists, we aim to explore the feasibility of applying MIP images to improve the effectiveness of automatic lung nodule detection using convolutional neural networks (CNNs). We propose a CNN-based approach that takes MIP images of different slab thicknesses (5 mm, 10 mm, 15 mm) and 1 mm axial section slices as input. Such an approach augments the two-dimensional (2-D) CT slice images with more representative spatial information that helps discriminate nodules from vessels through their morphologies. Our proposed method achieves sensitivity of 92.67% with 1 false positive per scan and sensitivity of 94.19% with 2 false positives per scan for lung nodule detection on 888 scans in the LIDC-IDRI dataset. The use of thick MIP images helps the detection of small pulmonary nodules (3 mm-10 mm) and results in fewer false positives. Experimental results show that utilizing MIP images can increase the sensitivity and lower the number of false positives, which demonstrates the effectiveness and significance of the proposed MIP-based CNNs framework for automatic pulmonary nodule detection in CT scans. The proposed method also shows the potential that CNNs could gain benefits for nodule detection by combining the clinical procedure.Comment: Submitted to IEEE TM

Correlation between choline level and Gd-DTPA enhancement in patients with brain metastases of mammary carcinoma

Single voxel 1HH double spin-echo MR spectroscopy was used to examine 15 cases of brain metastasis of mammary carcinoma (18 lesions) in relation to Gd-DTPA enhanced MR imaging. For lesions larger than 50% of MRS voxel size, there was significant correlation between Gd-DTPA-enhanced MRI signal and MRS-detected signal of choline (Cho) containing compounds (r = 0.86, P < 0.01; n = 8). The observed loss of correlation when including the smaller lesions was overcome by correcting for partial volume effects (r = 0.69, P < 0.002; n = 18). Metastasis spectra showed increased Cho compared with control spectra, except for those lesions showing detectable lactate (Lact) signal. The detection of Lact in four of the larger lesions coincided with comparatively low levels of creatine (Cr) and Cho and heterogeneous Gd-DTPA enhancement (ring-enhancement). It was concluded that in brain metastases of mammary carcinoma Lact represents a product of ischemia preceding/during tissue decay resulting in central necrosis, rather than tumor specific metabolism resulting in increas

Free DICOM de-identification tools in clinical research:functioning and safety of patient privacy

To compare non-commercial DICOM toolkits for their de-identification ability in removing a patient's personal health information (PHI) from a DICOM header. Ten DICOM toolkits were selected for de-identification tests. Tests were performed by using the system's default de-identification profile and, subsequently, the tools' best adjusted settings. We aimed to eliminate fifty elements considered to contain identifying patient information. The tools were also examined for their respective methods of customization. Only one tool was able to de-identify all required elements with the default setting. Not all of the toolkits provide a customizable de-identification profile. Six tools allowed changes by selecting the provided profiles, giving input through a graphical user interface (GUI) or configuration text file, or providing the appropriate command-line arguments. Using adjusted settings, four of those six toolkits were able to perform full de-identification. Only five tools could properly de-identify the defined DICOM elements, and in four cases, only after careful customization. Therefore, free DICOM toolkits should be used with extreme care to prevent the risk of disclosing PHI, especially when using the default configuration. In case optimal security is required, one of the five toolkits is proposed. aEuro cent Free DICOM toolkits should be carefully used to prevent patient identity disclosure. aEuro cent Each DICOM tool produces its own specific outcomes from the de-identification process. aEuro cent In case optimal security is required, using one DICOM toolkit is proposed

Use of a Thin-Section Archive and Enterprise 3-Dimensional Software for Long-Term Storage of Thin-Slice CT Data Sets—A Reviewers’ Response

Current developments in storage solutions, PACS, and client-server systems allow for 3D imaging at the desktop. This can be achieved together with full storage into PACS of all slices, including the very large thin-section CT datasets. This paper describes a possible setup, which has been in operation for several years now, in response to an article by Meenan et al. previously published in this journal (1)

Breath-hold MR cholangiopancreatography with three-dimensional, segmented, echo-planar imaging and volume rendering

End-expiration, 21-second breath-hold, three-dimensional magnetic resonance (MR) cholangiopancreatography (MRCP) was developed with segmented echo-planar imaging. In 15 healthy subjects and 14 randomly selected patients undergoing liver studies, three-dimensional MRCP images were obtained and volume rendered. In 15 (100%) healthy subjects and 13 (93%) patients, clear depiction of biliary, hepatic, and pancreatic ducts (with lumen diameter of at least 2 mm) was possible with good signal-to-noise ratio

Screening for lung cancer by imaging: the NELSON study

The NELSON trial is the first randomised lung cancer screening trial in which pulmonary nodule management is based on volumetry. This led to considerably less false-positive referrals compared to other lung cancer screening trials, with very high negative predictive values found in the first and second screening rounds. Mortality results are still pending, but the knowledge already gained in the NELSON trial and its side-studies provide valuable information in the field of screening for lung cancer