What Do Eye Gaze Metrics Tell Us about Motor Imagery?

Many of the brain structures involved in performing real movements also have increased activity during imagined movements or during motor observation, and this could be the neural substrate underlying the effects of motor imagery in motor learning or motor rehabilitation. In the absence of any objective physiological method of measurement, it is currently impossible to be sure that the patient is indeed performing the task as instructed. Eye gaze recording during a motor imagery task could be a possible way to "spy" on the activity an individual is really engaged in. The aim of the present study was to compare the pattern of eye movement metrics during motor observation, visual and kinesthetic motor imagery (VI, KI), target fixation, and mental calculation. Twenty-two healthy subjects (16 females and 6 males), were required to perform tests in five conditions using imagery in the Box and Block Test tasks following the procedure described by Liepert et al. Eye movements were analysed by a non-invasive oculometric measure (SMI RED250 system). Two parameters describing gaze pattern were calculated: the index of ocular mobility (saccade duration over saccade + fixation duration) and the number of midline crossings (i.e. the number of times the subjects gaze crossed the midline of the screen when performing the different tasks). Both parameters were significantly different between visual imagery and kinesthesic imagery, visual imagery and mental calculation, and visual imagery and target fixation. For the first time we were able to show that eye movement patterns are different during VI and KI tasks. Our results suggest gaze metric parameters could be used as an objective unobtrusive approach to assess engagement in a motor imagery task. Further studies should define how oculomotor parameters could be used as an indicator of the rehabilitation task a patient is engaged in.

A semi-automated method for measuring the evolution of both lumen area and blood flow in carotid from Phase Contrast MRI.

Phase-Contrast (PC) velocimetry Magnetic Resonance Imaging (MRI) is a useful modality to explore cardiovascular pathologies, but requires the automatic segmentation of vessels and the measurement of both lumen area and blood flow evolutions. In this paper, we propose a semi-automated method for extracting lumen boundaries of the carotid artery and compute both lumen area and blood flow evolutions over the cardiac cycle. This method uses narrow band region-based active contours in order to correctly capture the lumen boundary without being corrupted by surrounding structures. This approach is compared to traditional edge-based active contours, considered in related works, which significantly underestimate lumen area and blood flow. Experiments are performed using both a sequence of a homemade phantom and sequences of 20 real carotids, including a comparison with manual segmentation performed by a radiologist expert. Results obtained on the phantom sequence show that the edge-based approach leads to an underestimate of carotid lumen area and related flows of respectively 18.68% and 4.95%. This appears significantly larger than weak errors obtained using the region-based approach (respectively 2.73% and 1.23%). Benefits appear even better on the real sequences. The edge-based approach leads to underestimates of 40.88% for areas and 13.39% for blood flows, compared to limited errors of 7.41% and 4.6% with our method. Experiments also illustrate the high variability and therefore the lack of reliability of manual segmentation.

Automated morphometry provides accurate and reproducible virtual staging of liver fibrosis in chronic hepatitis C.

BACKGROUND: Liver fibrosis staging provides prognostic value, although hampered by observer variability. We used digital analysis to develop diagnostic morphometric scores for significant fibrosis, cirrhosis and fibrosis staging in chronic hepatitis C. MATERIALS AND METHODS: We automated the measurement of 44 classical and new morphometric descriptors. The reference was histological METAVIR fibrosis (F) staging (F0 to F4) on liver biopsies. The derivation population included 416 patients and liver biopsies ≥20 mm-length. Two validation population included 438 patients. RESULTS: In the derivation population, the area under the receiver operating characteristic (AUROC) for clinically significant fibrosis (F stage ≥2) of a logistic score combining 5 new descriptors (stellar fibrosis area, edge linearity, bridge thickness, bridge number, nodularity) was 0.957. The AUROC for cirrhosis of 6 new descriptors (edge linearity, nodularity, portal stellar fibrosis area, portal distance, granularity, fragmentation) was 0.994. Predicted METAVIR F staging combining 8 morphometric descriptors agreed well with METAVIR F staging by pathologists: κ = 0.868. Morphometric score of clinically significant fibrosis had a higher correlation with porto-septal fibrosis area (r s = 0.835) than METAVIR F staging (r s = 0.756, P < 0.001) and the same correlations with fibrosis biomarkers, e.g., serum hyaluronate: r s = 0.484 versus r s = 0.476 for METAVIR F (P = 0.862). In the validation population, the AUROCs of clinically significant fibrosis and cirrhosis scores were, respectively: 0.893 and 0.993 in 153 patients (biopsy < 20 mm); 0.955 and 0.994 in 285 patients (biopsy ≥ 20 mm). The three morphometric diagnoses agreed with consensus expert reference as well as or better than diagnoses by first-line pathologists in 285 patients, respectively: significant fibrosis: 0.733 versus 0.733 (κ), cirrhosis: 0.900 versus 0.827, METAVIR F: 0.881 versus 0.865. CONCLUSION: The new automated morphometric scores provide reproducible and accurate diagnoses of fibrosis stages via "virtual expert pathologist."

Numerical stability issues on channelized Hotelling observer under different background assumptions.

This paper addresses the numerical stability issue on the channelized Hotelling observer (CHO). The CHO is a well-known approach in the medical image quality assessment domain. Many researchers have found that the detection performance of the CHO does not increase with the number of channels, contrary to expectation. And to our knowledge, nobody in this domain has found the reason. We illustrated that this is due to the ill-posed problem of the scatter matrix and proposed a solution based on Tikhonov regularization. Although Tikhonov regularization has been used in many other domains, we show in this paper another important application of Tikhonov regularization. This is very important for researchers to continue the CHO (and other channelized model observer) investigation with a reliable detection performance calculation.

Channelized model observer for the detection and estimation of signals with unknown amplitude, orientation, and size.

As a task-based approach for medical image quality assessment, model observers (MOs) have been proposed as surrogates for human observers. While most MOs treat only signal-known-exactly tasks, there are few studies on signal-known-statistically (SKS) MOs, which are clinically more relevant. In this paper, we present a new SKS MO named channelized joint detection and estimation observer (CJO), capable of detecting and estimating signals with unknown amplitude, orientation, and size. We evaluate its estimation and detection performance using both synthesized (correlated Gaussian) backgrounds and real clinical (magnetic resonance) backgrounds. The results suggest that the CJO has good performance in the SKS detection-estimation task.

A perceptually relevant channelized joint observer (PCJO) for the detection-localization of parametric signals.

Many numerical observers have been proposed in the framework of task-based approach for medical image quality assessment. However, the existing numerical observers are still limited in diagnostic tasks: the detection task has been largely studied, while the localization task concerning one signal has been little studied and the localization of multiple signals has not been studied yet. In addition, most existing numerical observers need a priori knowledge about all the parameters of the underdetection signals, while only a few of them need at least two signal parameters. In this paper, we propose a novel numerical observer called the perceptually relevant channelized joint observer (PCJO), which cannot only detect but also localize multiple signals with unknown amplitude, orientation, size and location. We validated the PCJO for predicting human observer task performance by conducting a clinically relevant free-response subjective experiment in which six radiologists (including two experts) had to detect and localize multiple sclerosis (MS) lesions on magnetic resonance (MR) images. By using the jackknife alternative free-response operating characteristic (JAFROC) as the figure of merit (FOM), the detection-localization task performance of the PCJO was evaluated and then compared to that of the radiologists and two other numerical observers--channelized hotelling observer (CHO) and Goossenss CHO for detecting asymmetrical signals with random orientations. Overall, the results show that the PCJO performance was closer to that of the experts than to that of the other radiologists. The JAFROC1 FOMs of the PCJO (around 0.75) are not significantly different from those of the two experts (0.7672 and 0.7110), while the JAFROC1 FOMs of the numerical observers mentioned above (always over 0.84) outperform those of the experts. This indicates that the PCJO is a promising method for predicting radiologists' performance in the joint detection-localization task.

Steatosis degree, measured by morphometry, is linked to other liver lesions and metabolic syndrome components in patients with NAFLD.

BACKGROUND AND AIM: We carried out morphometric measurements of steatosis to evaluate relationships between steatosis degree and other liver lesions or metabolic syndrome components in nonalcoholic fatty liver disease (NAFLD). PATIENTS AND METHODS: We developed an algorithm to measure steatosis area. Two hundred and fourteen patients with NAFLD were included in derivation (10) and validation (204) groups. Controls consisted of patients who were steatosis-free (12), patients with chronic hepatitis C (188), and patients with alcoholic chronic liver disease (94). RESULTS: Accuracy of steatosis area was considered as good or very good in at least 72% of cases by three pathologists. Steatosis areas were as follows: NAFLD = 10.3 ± 9.7%, virus = 2.4 ± 3.1%, alcohol = 7.8 ± 8.2% (P<0.0001). Steatosis area was closely related to steatosis grades in NAFLD (P<0.0001 for linear trend). Steatosis area increased from the fibrosis stage F0 to the fibrosis state F2, then decreased in the stages F3 and F4 (cirrhosis) (P<0.0001 for quadratic trend). Fibrosis was present in an average steatosis area of approximately 4% (defining significant steatosis) and in nonalcoholic steatohepatitis by approximately 8% (defining severe steatosis). Steatosis and fibrosis area increased symmetrically until approximately 10%, then steatosis area decreased to null as average fibrosis area reached 32%. Average fasting glycemia (approximately 92 mg/dl) or triglycerides and BMI plateaued before a steatosis area of approximately 4%, then increased thereafter. Significant steatosis was present in 61.3% of NAFLD versus 20.2% of viral hepatitis (P<0.0001) and in 58.7% of alcoholic liver diseases (P=0.674). CONCLUSIONS: The average threshold of steatosis area is 4% for the development of fibrosis or metabolic syndrome components and 8% for nonalcoholic steatohepatitis. Steatosis area may contribute to defining the normal range and clinical course of metabolic components.

Noninvasive liver steatosis quantification using MRI techniques combined with blood markers.

AIMS: To evaluate the accuracy of different techniques of MRI steatosis quantification, based on histological grading and quantification of liver steatosis. PATIENTS AND METHODS: Twenty-three patients (21 with nonalcoholic fatty liver disease and two controls) were included. Steatosis was evaluated in liver specimens using histological grading (five grades) and steatosis area (% of liver surface) was computed using an inhouse automated image analysis. The following five MRI quantification techniques were performed: two-point Dixon, three-point Dixon, DUAL, spin echo method and a new technique called multi-echo gradient-echo (MFGRE). Interobserver (two observers) and intersite (three different liver sites) agreements were evaluated for the two best-performing methods. RESULTS: Steatosis area was correlated with steatosis grade: Rs (Spearman coefficient) = 0.82, P value of less than 0.001. The steatosis area was significantly different between S0-S2 and S3-S4 grades: 4.2 + or - 2.4 versus 16.4 + or - 8.9% (P< 0.001). Correlations between the MRI techniques and steatosis area (or grading) were: MFGRE, Rs = 0.72 (0.78); spin echo method, Rs = 0.72 (0.76); DUAL, Rs =0.71 (0.76); two-point Dixon, Rs = 0.71 (0.75); three-point Dixon, Rs = 0.67 (0.77). Interobserver (Ric = 0.99) and intersite (Ric = 0.97) agreements were excellent for the liver steatosis measurement by MFGRE. The noninvasive diagnosis of the steatosis area was improved by adding blood markers like ALT and triglycerides to MFGRE (aR2: 0.805). CONCLUSION: MRI, and in particular the MFGRE method, provides accurate and automatic quantification for the noninvasive evaluation of liver steatosis, either as a single measurement or in combination with blood variables.

Diagnosis of different liver fibrosis characteristics by blood tests in non-alcoholic fatty liver disease.

AIMS: Our aim was to develop an accurate, non-invasive, blood-test-based method for identifying the main characteristics of liver fibrosis in non-alcoholic fatty liver disease (NAFLD). METHODS: Fibrosis was staged according to NASH-CRN and Metavir systems in 226 patients with NAFLD. A fully automated algorithm measured the fractal dimension (FD) and the area of fibrosis (AOF). Independent predictors of diagnostic targets were determined using bootstrap methods. RESULTS: (i) Development. Significant fibrosis defined by NASH-CRN F ≥2 was diagnosed by weight, glycaemia, aspartate aminotransferase (AST), alanine aminotransferase (ALT) and prothrombin index [area under the receiver operating characteristic (AUROC)=0.867]; significant fibrosis defined by Metavir F ≥2 was diagnosed by weight, age, glycaemia, AST, ALT, ferritin and platelets (FibroMeter AUROC=0.941, P<0.005). AOF was estimated by the combination of hyaluronic acid, glycaemia, AST, ALT, platelets and prothrombin index ((a) R(2) =0.530), while FD was estimated by hyaluronic acid, glycaemia, AST/ALT, weight and platelets ((a) R(2) =0.529). (ii) Evaluation. Although NASH-CRN was a better system for fibrosis staging, Metavir staging was a better reference for blood test. Thus, the patient rate with predictive values ≥90% by tests was 97.3% with Metavir reference vs. 66.5% with NASH-CRN reference (P<10(-3)). FibroMeter showed a significantly higher AUROC than the NAFLD fibrosis score for significant fibrosis, but not for severe fibrosis or cirrhosis, with both staging systems. Relationships between fibrosis lesions were well reflected by blood tests, e.g., the correlation between histological area and FD of fibrosis (r(s) =0.971, P<10(-3)) was well reflected by the relationship between respective blood tests (r(s) =0.852, P<10(-3)). CONCLUSIONS: Different characteristics of fibrosis in NAFLD can be diagnosed and quantified by blood tests with excellent accuracy.

Fuzzy algorithms to extract vacuoles of steatosis on liver histological color images.

In this paper, we present an automatic, robust and reliable process to quantify liver steatosis. The degree of steatosis is a useful marker of steatohepatitis. This degree is routinely assessed visually by an expert and then lacks of accuracy and robustness. The process that we have developed is divided in two steps. A fuzzy classification first merges into classes pixels according to their intensity. We use a generalized objective function that allows to detect micro and blurredness vacuoles of steatosis. Then, regions with inhomogeneous texture and irregular shape were eliminated with compactness and standard deviation parameters. The obtained results are good correlated with expert graduation (in five levels). A better correlation is obtained with a more precise grading.

Model based and iconic multimodal registration to merge gated cardiac PET, CT and MR sequences.

In this paper, we present a multimodal registration method applied to gated positron emission tomography (PET), X-ray Computed Tomography (CT) and Magnetic Resonance (MR) cardiac images. CT images acquired on the same device as the PET ones are used as link to merge anatomical MR images and functional PET images. The registration process is divided in two steps: a 3D structure registration and a grey-levels registration. This approach enables global to local transformations. The structure registration uses a 3D biventricular heart model initialized on CT and MR data to define a rigid transform. This global registration is then refined with a grey-levels step based on mutual information and free form deformations. To improve endocardium registration, we propose a composite PET-CT image to find the optimal transformation on MR image. We also take into account the temporal problematic of heart motion by initializing the searched transformation, at a current frame, with the composition of a monomodal transformation (representing the heart motion between the previous and current frames) and a multimodal one (representing the spatial transformation between the two images at the previous frame).