Heart rate characteristic based modelling of atrial fibrillatory rate using implanted cardiac monitor data.

Objective. The objective of the present study is to investigate the feasibility of using heart rate characteristics to estimate atrial fibrillatory rate (AFR) in a cohort of atrial fibrillation (AF) patients continuously monitored with an implantable cardiac monitor. We will use a mixed model approach to investigate population effect and patient specific effects of heart rate characteristics on AFR, and will correct for the effect of previous ablations, episode duration, and onset date and time.Approach. The f-wave signals, from which AFR is estimated, were extracted using a QRST cancellation process of the AF episodes in a cohort of 99 patients (67% male; 57 ± 12 years) monitored for 9.2(0.2-24.3) months as median(min-max). The AFR from 2453 f-wave signals included in the analysis was estimated using a model-based approach. The association between AFR and heart rate characteristics, prior ablations, and episode-related features were modelled using fixed-effect and mixed-effect modelling approaches.Main results. The mixed-effect models had a better fit to the data than fixed-effect models showing h.c. of determination (R2 = 0.49 versusR2 = 0.04) when relating the variations of AFR to the heart rate features. However, when correcting for the other factors, the mixed-effect model showed the best fit (R2 = 0.04). AFR was found to be significantly affected by previous catheter ablations (p< 0.05), episode duration (p< 0.05), and irregularity of theRRinterval series (p< 0.05).Significance. Mixed-effect models are more suitable for AFR modelling. AFR was shown to be faster in episodes with longer duration, less organizedRRintervals and after several ablation procedures.

Atrial fibrillation episode patterns as predictor of clinical outcome of catheter ablation.

Methods for characterization of atrial fibrillation (AF) episode patterns have been introduced without establishing clinical significance. This study investigates, for the first time, whether post-ablation recurrence of AF can be predicted by evaluating episode patterns. The dataset comprises of 54 patients (age 56 ± 11 years; 67% men), with an implantable cardiac monitor, before undergoing the first AF catheter ablation. Two parameters of the alternating bivariate Hawkes model were used to characterize the pattern: AF dominance during the monitoring period (log(mu)) and temporal aggregation of episodes (beta1). Moreover, AF burden and AF density, a parameter characterizing aggregation of AF burden, were studied. The four parameters were computed from an average of 29 AF episodes before ablation. The risk of AF recurrence after catheter ablation using the Hawkes parameters log(mu) and beta1, AF burden, and AF density was evaluated. While the combination of AF burden and AF density is related to a non-significant hazard ratio, the combination of log(mu) and beta1 is related to a hazard ratio of 1.95 (1.03-3.70; p < 0.05). The Hawkes parameters showed increased risk of AF recurrence within 1 year after the procedure for patients with high AF dominance and high episode aggregation and may be used for pre-ablation risk assessment.

Repeatability and reproducibility of MRI-radiomic features: A phantom experiment on a 1.5 T scanner.

PURPOSE: Aim of this study is to assess the repeatability of radiomic features on magnetic resonance images (MRI) and their stability to variations in time of repetition (TR), time of echo (TE), slice thickness (ST), and pixel spacing (PS) using vegetable phantoms. METHODS: The organic phantom was realized using two cucumbers placed inside a cylindrical container, and the analysis was performed using T1-weighted (T1w), T2-weighted (T2w), and diffusion-weighted images. One dataset was used to test the repeatability of the radiomic features, whereas other four datasets were used to test the sensitivity of the different MRI sequences to image acquisition parameters (TR, TE, ST, and PS). Four regions of interest (ROIs) were segmented: two for the central part of each cucumber and two for the external parts. Radiomic features were extracted from each ROI using Pyradiomics. To assess the effect of preprocessing on the reduction of variability, features were extracted both before and after the preprocessing. The coefficient of variation (CV) and intra-class correlation coefficient (ICC) were used to evaluate variability. RESULTS: The use of intensity standardization increased the stability for the first-order statistics features. Shape and size features were always stable for all the analyses. Textural features were particularly sensitive to changes in ST and PS, although some increase in stability could be obtained by voxel size resampling. When images underwent image preprocessing, the number of stable features (ICC > 0.75 and mean absolute CV < 0.3) was 33 for apparent diffusion coefficient (ADC), 52 for T1w, and 73 for T2w. CONCLUSIONS: The most critical source of variability is related to changes in voxel size (either caused by changes in ST or PS). Preprocessing increases features stability to both test-retest and variation of the image acquisition parameters for all the types of analyzed MRI (T1w, T2w, and ADC), except for ST.

Ensemble classification combining ResNet and handcrafted features with three-steps training.

Objective.This work presents an ECG classifier for variable leads as a contribution to the Computing in Cardiology Challenge/CinC Challenge 2021. It aims to integrate deep and classic machine learning features into a single model, exploring the proper structure and training procedure.Approach.From the initial 88 253 signals, only 84 210 were included. Low quality and unscored recordings were excluded. Three different database subsets of 40 365 recording each were created by dividing in three normal sinus rhythm and sinus bradycardia recordings. Each subset was used to train a different model with shared architecture integrated as an ensemble to provide the final classification through major voting. Models contained a deep branch composed of a modified ResNet with dilation convolutional layers and squeeze and excitation Block that took as input windowed ECG signals. This was concatenated with a wide branch that integrated 20 cardiac rhythm features into a fully connected 3-layered network. Three different training steps were studied: just the deep branch (D), wide integration and training (D+W), and a final fine tuning of the deep branch posterior to wide training (D+W+D).Main Results.Results obtained in a local test set formed by a stratified 12.5% split of the given full dataset were presented for 2-lead and 12-lead models. The best training method was the 3-step D + W + D procedure obtaining a challenge metric of 0.709 and 0.677 for 12 and 2-lead models respectively.Significance.Integration of handcrafted features and deep learning model not only may increase the generalization capacity of the network but also provide a path to add explicit information into the classification decision process. To the best of our knowledge this is the first work studying the training procedure to properly integrate both types of information for ECG signals classification.

Association between ventricular repolarization parameters and cardiovascular death in patients of the SWISS-AF cohort.

BACKGROUND: The effect of the ventricular repolarization heterogeneity has not been systematically assessed in patients with atrial fibrillation (AF). Aim of this study is to assess ventricular repolarization heterogeneity as predictor of cardiovascular (CV) death and/or other CV events in patients with AF. METHODS: From the multicenter prospective Swiss-AF (Swiss Atrial Fibrillation) Cohort Study, we enrolled 1711 patients who were in sinus rhythm (995) or AF (716). Resting ECG recordings of 5-min duration were obtained at baseline. Parameters assessing ventricular repolarization were computed (QTc, Tpeak-Tend, J-Tpeak and V-index). RESULTS: During AF, the V-index was found repeatable (no differences when computed over the whole recording, on the first 2.5-min and on the last 2.5-min segments). During a mean follow-up time of 2.6 ± 1.0 years, 90 patients died for CV reasons. In bivariate Cox regression analysis (adjusted for age only), the V-index was associated with an increased risk of CV death, both in the subgroup of patients in sinus rhythm (SR) as well as those in AF. In multivariate analysis adjusted for clinical risk factors and medications, both prolonged QTc and V-index were independently associated with an increased risk of CV death (QTc: hazard ratio [HR] 2.78, 95% CI 1.79-4.32, p < 0.001; V-index: HR 1.73, 95% CI 1.12-2.69, p = 0.014). CONCLUSIONS: QTc and V-index, measured in a single 5-min ECG recording, were independent predictors of CV death in a cohort of patients with AF and might be a valuable tool for further risk stratification to guide patient management. Clinical Trial Identifier Swiss-AF study: NCT02105844.

Heart Rate Variability and Clinical Features as Predictors of Atrial Fibrillation Recurrence After Catheter Ablation: A Pilot Study.

Single-procedure catheter ablation success rate is as low as 52% in atrial fibrillation (AF) patients. This study evaluated the feasibility of using clinical data and heart rate variability (HRV) features extracted from an implantable cardiac monitor (ICM) to predict recurrences in patients prior to undergoing catheter ablation for AF. HRV-derived features were extracted from the 500 beats preceding the AF onset and from the first 2 min of the last AF episode recorded by an ICM of 74 patients (67% male; 57 ± 12 years; 26% non-paroxysmal AF; 57% AF recurrence) before undergoing their first AF catheter ablation. Two types of classification algorithm were studied to predict AF recurrence: single classifiers including support vector machines, classification and regression trees, and K-nearest neighbor classifiers as well as ensemble classifiers. The sequential forward floating search algorithm was used to select the optimum feature set for each classification method. The optimum weighted voting method, which used an optimum combination of the single classifiers, was the best overall classifier (accuracy = 0.82, sensitivity = 0.76, and specificity = 0.87). Clinical and HRV features can be used to predict rhythm outcome using an ensemble classifier which would enable a more effective pre-ablation patient triage that could reduce the economic and personal burden of the procedure by increasing the success rate of first catheter ablation.

Unsupervised Classification of Atrial Fibrillation Triggers Using Heart Rate Variability Features Extracted from Implantable Cardiac Monitor Data.

Catheter ablation is a common treatment of atrial fibrillation (AF), but its success rate is around 60%. It is believed that the success rate can be improved if the procedure were to be guided by the specific AF triggers found in the "Flashback", i.e. the trend of around 500 ventricular beats preceding the AF onset stored in an implantable cardiac monitor (ICM). The need to automatically classify these different triggers: atrial tachycardia (AT), atrial flutter, premature atrial contractions (PAC) or Spontaneous AF has motivated the design in this paper of an unsupervised classification method evaluating statistical and geometrical Heart Rate Variability (HRV) features extracted from the Flashback. From a cohort of 132 patients (57± 12 years, male 67%), 528 Flashbacks were extracted and classified into 5 different clusters after the Principal Component Analysis (PCA) was computed on the HRV features. 2 principal components explained more than 95% of the variance and were a combination of the mean R-R interval, Square root of the mean squared differences of successive R-R intervals (RMSSD), Standard deviation of the R-R intervals (SDNN) and Poincare descriptors, SD1 and SD2. RMSSD and SD1 were significantly different among all clusters (p-value < 0.05, with Holm's correction) showing that distinct patterns can be found using this method.Clinical Relevance-Preliminary step towards ablation strategy guidance using the AF trigger patterns to improve catheter ablation success rates.

Atrial fibrillation detection using photoplethysmographic signal: the effect of the observation window.

A correct and early diagnosis of cardiac arrhythmias could improve patients' quality of life. The aim of this study is to classify the cardiac rhythm (atrial fibrillation, AF, or normal sinus rhythm NSR) from the photoplethysmographic (PPG) signal and assess the effect of the observation window length. Simulated signals are generated with a PPG simulator previously proposed. The different window lengths taken into account are 20, 30, 40, 50, 100, 150, 200, 250 and 300 beats. After systolic peak detection algorithm, 10 features are computed on the inter-systolic interval series, assessing variability and irregularity of the series. Then, feature selection was performed (using the sequential forward floating search algorithm) which identified two variability parameters (Mean and rMSSD) as the best selection. Finally, the classification by linear support vector machine was performed. Using only two features, accuracy was very high for all the analyzed observation window lengths, going from 0.913±0.055 for length equal to 20 to 0.995±0.011 for length equal to 300 beats.Clinical relevance These preliminary results show that short PPG signals (20 beats) can be used to correctly detect AF.

A simple model to detect atrial fibrillation via visual imaging.

Automatic detection of atrial fibrillation (AF) is a challenging issue. In this study we proposed and validated a model to identify AF by using facial video recordings. We analyzed photoplethysmographic imaging (PPGi) signals, extracted from video of a subject's face. Sixty-eight patients were included: 30 in sinus rhythm (SR), 25 in AF and 13 presenting with atrial flutter or frequent ectopic beats (ARR). Twenty-six indexes were computed. The dataset was divided in three subsets: the training, validation, and test set, containing, respectively, 58, 29, and 13% of the data. Mean of inter-systolic interval series (M), Local Maxima Similarity (LMS), and pulse harmonic strength (PHS) indexes were significantly different among all groups. Variability and irregularity parameters had the lowest values in SR, the highest in AF, with intermediate values in ARR. The PHS was higher in SR than in ARR, and higher in ARR than in AF. The LMS index was the highest in SR, intermediate in ARR and the lowest in AF. Similarity indexes were higher in SR than in AF and ARR. A model with three features, namely M, Similarity1 and LMS was chosen. With this model, the accuracy for the validation set was 0.947±0.007 for SR, 0.954±0.004 for AF and 0.919±0.006 for ARR; for the test set (never-seen data), accuracy was 0.876±0.021 for SR, 0.870±0.030 for AF and 0.863±0.029 for ARR. A contactless video-based monitoring can be used to detect AF, differentiating it from SR and from frequent ectopies.

Assessment of spatial heterogeneity of ventricular repolarization after multi-channel blocker drugs in healthy subjects.

BACKGROUND AND OBJECTIVES: In contrast to potassium channel blockers, drugs affecting multiple channels seem to reduce torsadogenic risks. However, their effect on spatial heterogeneity of ventricular repolarization (SHVR) is still matter of investigation. Aim of this work is to assess the effect of four drugs blocking the human ether-à-go-go-related gene (hERG) potassium channel, alone or in combination with other ionic channel blocks, on SHVR, as estimated by the V-index on short triplicate 10 s ECG. METHODS: The V-index is an estimate of the standard deviation of the repolarization times of the myocytes across the entire myocardium, obtained from multi-lead surface electrocardiograms. Twenty-two healthy subjects received a pure hERG potassium channel blocker (dofetilide) and 3 other drugs with additional varying degrees of sodium and calcium (L-type) channel block (quinidine, ranolazine, and verapamil), as well as placebo. A one-way repeated-measures Friedman test was performed to compare the V-index over time. RESULTS: Computer simulations and Bland-Altman analysis supported the reliability of the estimates of V-index on triplicate 10 s ECG. Ranolazine, verapamil and placebo did not affect the V-index. On the contrary, after quinidine and dofetilide administration, an increase of V-index from predose to its peak value was observed (ΔΔV-index values were 19 ms and 27 ms, respectively, p < 0.05). CONCLUSIONS: High torsadogenic drugs (dofetilide and quinidine) affected significantly the SHVR, as quantified by the V-index. The metric has therefore a potential in assessing drug arrhythmogenicity.

Sex-related electrocardiographic differences in patients with different types of atrial fibrillation: Results from the SWISS-AF study.

BACKGROUND: Sex-related electrocardiographic differences are a well-known phenomenon, but not their expression in patients with atrial fibrillation (AF). In this study we aim to assess the presence of significant sex-related differences in ECG features, with particular attention to P-wave parameters, of a large cohort of patients affected by different types of AF. METHODS: A 5-min resting 16-lead ECG was evaluated for 1119 AF patients in sinus rhythm. The durations of the main ECG waves and intervals were measured for both atrial and ventricular activity. Moreover, the beat-to-beat P-wave variability was computed for lead II and for the first principal component (PC1) computed across the 16 leads. The percentage of variance explained by PC1 was computed. RESULTS: Males compared to females showed significantly longer RR interval (1.02 ± 0.16 s vs 0.97 ± 0.15 s, p < .001), PQ interval (191 ± 34 ms vs 183 ± 35 ms, p = .008), QRS duration (105 ± 17 ms vs 98 ± 13 ms, p = .021), significantly lower percentage of variance explained by PC1 and P-wave variability. Males with paroxysmal AF compared to females with paroxysmal AF had significantly longer RR interval (1.01 ± 0.17 s vs 0.96 ± 0.14 s, p < .001), shorter QTc (388 ± 27 ms vs 402 ± 27 ms, p < .001), lower P-wave variability in PC1. Males with persistent AF compared to females with persistent AF had significantly shorter QTc interval (396 ± 30 ms vs 407 ± 26 ms, p = .019), longer PQ interval (194 ± 35 ms vs 182 ± 30 ms, p = .037), higher V1 terminal force (2.1 ± 1.2 mV*ms vs 1.8 ± 1 mV*ms, p = .007), lower percentage of variance explained by PC1. CONCLUSIONS: AF patients present with several sex-related ECG differences. Consequently, sex should be taken into account when developing ECG algorithms identifying patients at risk for AF progression.

Assessment of the effect of intensity standardization on the reliability of T1-weighted MRI radiomic features: experiment on a virtual phantom.

The effect of time of repetition (TR) and time of echo (TE) on radiomic features was evaluated using a virtual phantom. Forty-two T1-weighted MRI images of the same virtual phantom were simulated with TR and TE in a range used in clinical practice. Fifty-eight radiomic features were considered for this analysis. Features were extracted from 3 different regions of interest (ROIs) from the original images and from images that underwent intensity standardization (linear intensity standardization, Z-score standardization and histogram matching). Intraclass correlation coefficient (ICC) was used to assess the reliability of the radiomic features and a threshold of 0.75 was used to discriminate features with good or bad reliability. The coefficient of determination R2 was used to quantify correlation between features and image acquisition parameters. The majority of radiomic features (76%) had good reliability (ICC>0.75) and 66% of the features were uncorrelated with TR and TE (R2<; 0.5). Intensity standardization (in particular histogram matching) significantly reduced the correlation. Intensity standardization also increased the reliability of FOS features, but histogram matching significantly reduced the reliability of GLCM features.

Quantification of Spatial Heterogeneity of Ventricular Repolarization During Early-Stage Cardiac Ischemia Induced by Coronary Angioplasty.

Coronary angioplasty (CA) is a surgical procedure meant to break the plaque and restore the blood flow in obstructed coronary arteries. It is based on inserting an inflatable balloon with a catheter in the clogged artery. When the balloon inflation is prolonged, it also provides an excellent model to investigate the electrophysiological changes due to early ischemia. In this work, we tested whether early cardiac ischemia induced by prolonged balloon inflations might lead to changes in spatial heterogeneity of ventricular repolarization (SHVR), as measured by the V-index on the 12-lead ECG. The metric was recently shown to significantly improve the ECG sensitivity for the diagnosis of non-ST elevation myocardial infarction, in patients presenting to the emergency department. The analysis was retrospectively performed on the data of 104 patients who underwent prolonged CA (STAFF III dataset). The V-index was estimated before, during and post-occlusion (limiting the analysis to the first inflation). Successively, it was quantified on short 90 s overlapping windows, during occlusion, to assess the time evolution of SHVR. V-index values estimated during occlusion were significantly larger (median: 6.2 ms, p <; 0.05) than baseline room values. Also, pre- and post-occlusion values did not differ (p > 0.05), suggesting a complete recovery after CA. SHVR progressively increased during the occlusion with respect to baseline (median reaching 55.6 ms vs 34.2 ms). In conclusion, the V-index detected changes in SHVR due to early-stage cardiac ischemia.

A Stimulus-Response Processing Framework for Pupil Dynamics Assessment during Iso-Luminant Stimuli.

Pupil size is governed by the synergic action of the Autonomic Nervous System. Pupil Diameter (PD) is primarily influenced by the light level and it is responsive to variations of global luminance level. However, recent studies have shown that there is also a high-level interpretation which could modulate this physiological response. In this paper, we develop an ad-hoc protocol based on iso-luminant stimuli and validate its effectiveness for the analysis of high-level modulation of pupil response. A visual illusion was reproduced from literature and adapted in two different colors. Prior to the response analysis, a reconstruction of the missing data due to blinks and other artifacts were reconstructed by using a recently developed signal reconstruction algorithm (Iterative - Single Spectrum Analysis: I-SSA); then both time and frequency domain parameters were extracted from the PD signal. Results indicate that there are peculiarly different responses to iso-luminant stimuli with different image structures and dominating colors, thus indicating a possible high-level processing mechanism. Our results pave the way for future evaluation of comatose or generic unconscious state based on non-contact pupil dynamics assessment.

Use of apparent diffusion coefficient images to predict response to induction chemotherapy in sinonasal cancer.

The purpose of this study is to identify a set of radiomic features extracted from apparent diffusion coefficient (ADC) maps, obtained using baseline diffusion weighted magnetic resonance imaging (DW-MRI), which are able to predict the outcome of induction chemotherapy (IC) in sinonasal cancers. Such prediction could help the clinician defining the better treatment for a particular patient. Eighty-eight radiomic features were extracted from the ADC maps of 15 patients that underwent IC. A preliminary filtering of the features was made by assessing their stability to geometrical transformations of the region of interest (ROI). Mann-Whitney tests corrected for control of false discoveries were performed to identify the features that could discriminate between responsive and nonresponsive patients (4 and 11 respectively). Twenty features were found to be able to discriminate the two groups and they can potentially be used for prediction of response to treatment.

Assessment of Stability and Discrimination Capacity of Radiomic Features on Apparent Diffusion Coefficient Images.

The objectives of the study are to develop a new way to assess stability and discrimination capacity of radiomic features without the need of test-retest or multiple delineations and to use information obtained to perform a preliminary feature selection. Apparent diffusion coefficient (ADC) maps were computed from diffusion-weighted magnetic resonance images (DW-MRI) of two groups of patients: 18 with soft tissue sarcomas (STS) and 18 with oropharyngeal cancers (OPC). Sixty-nine radiomic features were computed, using three different histogram discretizations (16, 32, and 64 bins). Geometrical transformations (translations) of increasing entity were applied to the regions of interest (ROIs), and the intra-class correlation coefficient (ICC) was used to compare the features computed on the original and modified ROIs. The distribution of ICC values for minimal and maximal entity translations (ICC10 and ICC100, respectively) was used to adjust thresholds of ICC (ICCmin and ICCmax) used to discriminate between good, unstable (ICC10 < ICCmin), and non-discriminative features (ICC100 > ICCmax). Fifty-four and 59 radiomic features passed the stability-based selection for all the three histogram discretizations for the OPC and STS datasets, respectively. The excluded features were similar across the different histogram discretizations (Jaccard's index 0.77 ± 0.13 and 0.9 ± 0.1 for OPC and STS, respectively) but different between datasets (Jaccard's index 0.19 ± 0.02). The results suggest that the observed radiomic features are mainly stable and discriminative, but the stability depends on the region of the body under observation. The method provides a way to assess stability without the need of test-retest or multiple delineations.

Radiomic analysis of soft tissues sarcomas can distinguish intermediate from high-grade lesions.

PURPOSE: To assess the feasibility of grading soft tissue sarcomas (STSs) using MRI features (radiomics). MATERIALS AND METHODS: MRI (echo planar SE, 1.5T) from 19 patients with STSs and a known histological grading, were retrospectively analyzed. The apparent diffusion coefficient (ADC) maps, obtained by diffusion-weighted imaging acquisitions, were analyzed through 65 radiomic features, intensity-based (first order statistics, FOS) and texture (gray level co-occurrence matrix, GLCM; and gray level run length matrix, GLRLM) features. Feature selection (sequential forward floating search) and classification (k-nearest neighbor classifier) were performed to distinguish intermediate- from high-grade STSs. Classification was performed using the three different sub-groups of features separately as well as all the features together. The entire dataset was divided in three subsets: the training, validation and test set, containing, respectively, 60, 30, and 10% of the data. RESULTS: Intermediate-grade lesions had a higher and less disperse ADC values compared with high-grade ones: most of FOS related to intensity are higher for the intermediate-grade STSs, while FOS related to signal variability were higher in the high grade (e.g., the feature variance is 2.6*105 ± 0.9*105 versus 3.3*105 ± 1.6*105 , P = 0.3). The GLCM features related to entropy and dissimilarity were higher in the high-grade. When performing classification, the best accuracy is obtained with a maximum of three features for each subgroup, FOS features being those leading to the best classification (validation set: FOS accuracy 0.90 ± 0.11, area under the curve [AUC] 0.85 ± 0.16; test set: FOS accuracy 0.88 ± 0.25, AUC 0.87 ± 0.34). CONCLUSION: Good accuracy and AUC could be obtained using only few Radiomic features, belonging to the FOS class. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:829-840.

Stability assessment of first order statistics features computed on ADC maps in soft-tissue sarcoma.

Radiomics extracts a large number of features from medical images to perform a quantitative characterization. Aim of this study was to assess radiomic features stability and relevance. Apparent diffusion coefficient (ADC) maps were computed from diffusion-weighted magnetic resonance images (DW-MRI) of 18 patients diagnosed with soft-tissue sarcomas (STSs). Thirty-seven intensity-based features were computed on the regions of interest (ROIs). First, ROIs of the images were subjected to translations and rotations in specific ranges. The 37 features computed on the original and transformed ROIs were compared in terms of percentage of variations. The intra-class correlation coefficient (ICC) was computed. To be accepted, a feature should satisfy the following conditions: the ICC after a minimum entity transformation is > 0.6 and the ICC after a maximum entity translation is <; 0.4. In total, 31 features out of 37 were accepted by the algorithm. This stability analysis can be used as a first step in the features selection process.

Pool size ratio of the substantia nigra in Parkinson's disease derived from two different quantitative magnetization transfer approaches.

PURPOSE: We sought to measure quantitative magnetization transfer (qMT) properties of the substantia nigra pars compacta (SNc) in patients with Parkinson's disease (PD) and healthy controls (HCs) using a full qMT analysis and determine whether a rapid single-point measurement yields equivalent results for pool size ratio (PSR). METHODS: Sixteen different MT-prepared MRI scans were obtained at 3 T from 16 PD patients and eight HCs, along with B1, B0, and relaxation time maps. Maps of PSR, free and macromolecular pool transverse relaxation times ([Formula: see text], [Formula: see text]) and rate of MT exchange between pools (k mf ) were generated using a full qMT model. PSR maps were also generated using a single-point qMT model requiring just two MT-prepared images. qMT parameter values of the SNc, red nucleus, cerebral crus, and gray matter were compared between groups and methods. RESULTS: PSR of the SNc was the only qMT parameter to differ significantly between groups (p < 0.05). PSR measured via single-point analysis was less variable than with the full MT model, provided slightly better differentiation of PD patients from HCs (area under curve 0.77 vs. 0.75) with sensitivity of 0.75 and specificity of 0.87, and was better than transverse relaxation time in distinguishing PD patients from HCs (area under curve 0.71, sensitivity 0.87, and specificity 0.50). CONCLUSION: The increased PSR observed in the SNc of PD patients may provide a novel biomarker of PD, possibly associated with an increased macromolecular content. Single-point PSR mapping with reduced variability and shorter scan times relative to the full qMT model appears clinically feasible.