Psychophysical Evaluation of Visual vs. Computer-Aided Detection of Brain Lesions on Magnetic Resonance Images.

BACKGROUND: Magnetic resonance imaging (MRI) diagnosis is usually performed by analyzing contrast-weighted images, where pathology is detected once it reached a certain visual threshold. Computer-aided diagnosis (CAD) has been proposed as a way for achieving higher sensitivity to early pathology. PURPOSE: To compare conventional (i.e., visual) MRI assessment of artificially generated multiple sclerosis (MS) lesions in the brain's white matter to CAD based on a deep neural network. STUDY TYPE: Prospective. POPULATION: A total of 25 neuroradiologists (15 males, age 39 ± 9, 9 ± 9.8 years of experience) independently assessed all synthetic lesions. FIELD STRENGTH/SEQUENCE: A 3.0 T, T2 -weighted multi-echo spin-echo (MESE) sequence. ASSESSMENT: MS lesions of varying severity levels were artificially generated in healthy volunteer MRI scans by manipulating T2 values. Radiologists and a neural network were tasked with detecting these lesions in a series of 48 MR images. Sixteen images presented healthy anatomy and the rest contained a single lesion at eight increasing severity levels (6%, 9%, 12%, 15%, 18%, 21%, 25%, and 30% elevation in T2 ). True positive (TP) rates, false positive (FP) rates, and odds ratios (ORs) were compared between radiological diagnosis and CAD across the range lesion severity levels. STATISTICAL TESTS: Diagnostic performance of the two approaches was compared using z-tests on TP rates, FP rates, and the logarithm of ORs across severity levels. A P-value <0.05 was considered statistically significant. RESULTS: ORs of identifying pathology were significantly higher for CAD vis-à-vis visual inspection for all lesions' severity levels. For a 6% change in T2 value (lowest severity), radiologists' TP and FP rates were not significantly different (P = 0.12), while the corresponding CAD results remained statistically significant. DATA CONCLUSION: CAD is capable of detecting the presence or absence of more subtle lesions with greater precision than the representative group of 25 radiologists chosen in this study. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 3.

Correcting for imaging gradients-related bias of T2 relaxation times at high-resolution MRI.

PURPOSE: High-resolution animal imaging is an integral part of preclinical drug development and the investigation of diseases' pathophysiology. Quantitative mapping of T2 relaxation times (qT2 ) is a valuable tool for both preclinical and research applications, providing high sensitivity to subtle tissue pathologies. High-resolution T2 mapping, however, suffers from severe underestimation of T2 values due to molecular diffusion. This affects both single-echo and multi-echo spin echo (SSE and MESE), on top of the well-known contamination of MESE signals by stimulated echoes, and especially on high-field and preclinical scanners in which high imaging gradients are used in comparison to clinical scanners. METHODS: Diffusion bias due to imaging gradients was analyzed by quantifying the effective b-value for each coherence pathway in SSE and MESE protocols, and incorporating this information in a joint T2 -diffusion reconstruction algorithm. Validation was done on phantoms and in vivo mouse brain using a 9.4T and a 7T MRI scanner. RESULTS: Underestimation of T2 values due to strong imaging gradients can reach up to 70%, depending on scan parameters and on the sample's diffusion coefficient. The algorithm presented here produced T2 values that agreed with reference spectroscopic measurements, were reproducible across scan settings, and reduced the average bias of T2 values from -33.5 ± 20.5% to -0.1 ± 3.6%. CONCLUSIONS: A new joint T2 -diffusion reconstruction algorithm is able to negate imaging gradient-related underestimation of T2 values, leading to reliable mapping of T2 values at high resolutions.

Data-driven algorithm for myelin water imaging: Probing subvoxel compartmentation based on identification of spatially global tissue features.

PURPOSE: Multicomponent analysis of MRI T2 relaxation time (mcT2 ) is commonly used for estimating myelin content by separating the signal at each voxel into its underlying distribution of T2 values. This voxel-based approach is challenging due to the large ambiguity in the multi-T2 space and the low SNR of MRI signals. Herein, we present a data-driven mcT2 analysis, which utilizes the statistical strength of identifying spatially global mcT2 motifs in white matter segments before deconvolving the local signal at each voxel. METHODS: Deconvolution is done using a tailored optimization scheme, which incorporates the global mcT2 motifs without additional prior assumptions regarding the number of microscopic components. The end results of this process are voxel-wise myelin water fraction maps. RESULTS: Validations are shown for computer-generated signals, uniquely designed subvoxel mcT2 phantoms, and in vivo human brain. Results demonstrated excellent fitting accuracy, both for the numerical and the physical mcT2 phantoms, exhibiting excellent agreement between calculated myelin water fraction and ground truth. Proof-of-concept in vivo validation is done by calculating myelin water fraction maps for white matter segments of the human brain. Interscan stability of myelin water fraction values was also estimated, showing good correlation between scans. CONCLUSION: We conclude that studying global tissue motifs prior to performing voxel-wise mcT2 analysis stabilizes the optimization scheme and efficiently overcomes the ambiguity in the T2 space. This new approach can improve myelin water imaging and the investigation of microstructural compartmentation in general.

Changes in High-Frequency Intracardiac Electrogram Indicate Cardiac Ischemia.

High-frequency QRS (HFQRS) analysis of surface ECG is a reliable marker of cardiac ischemia (CI). This study aimed to assess the response of HFQRS signals from standard intracardiac electrodes (iHFQRS) to CI in swine and compare them with conventional ST-segment deviations. Devices with three intracardiac leads were implanted in three swine in a controlled environment. CI was induced by inflating a balloon in epicardial coronary arteries. A designated signal-processing algorithm was applied to quantify the iHFQRS content before, during, and after each occlusion. iHFQRS time responses were compared to conventional ST-segment deviations. Thirty-three over thirty-nine (85%) of the occlusions presented significant reduction in the iHFQRS signal, preceding ST-segment change, being the only indicator of CI in brief occlusions. iHFQRS was found to be an early indicator for the onset of CI and demonstrated superior sensitivity to conventional ST-segment deviations during brief ischemic episodes.

Diagnosing and monitoring pleural effusion using parametric electrical impedance tomography - a computational 3D model and preliminary experimental results.

PURPOSE: Diagnosing and monitoring pleural effusion (PE) is challenging due unsuitability of existing modalities. In the present study, a novel parametric electrical impedance tomography (pEIT) technique, tailored to a clinically feasible system to diagnose PE is presented. METHODS: An electrical impedance tomography (EIT) numeric solver was applied to a 3D realistic normal model and five PE models to simulate sets of surface measurements. Simulations were triggered by a series of eight independent projections using five electrodes positioned around the thorax. The relative changes in the potential between the PE models and the normal model were assessed and the error in the estimated PE volume was examined at varying signal to noise ratio (SNR) levels. For experimental feasibility, measurements were performed in four healthy subjects and were correlated with the potentials that were calculated from the normal model. RESULTS: Relative potential changes were notable (reached until ~55%) and increased with the increasing PE volumes. Maximal error of ± 20 [mL] was obtained for SNR levels >50 [dB]. The feasibility real measurements in healthy subjects showed a strong linear correlation (R2 > 0.85) and a successful diagnosis for all subjects. CONCLUSION: The proposed technique can estimate PE volumes from a redundant set of measurements in a realistic 3D human model and may be utilized for monitoring PE patients.