The effect of spatial resolution on the reproducibility of diffusion imaging when controlled signal to noise ratio

Diffusion Tensor Imaging of Rat Rotator Cuff Muscle with Histopathological Correlation: An Exploratory Study

Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique that can be used to assess microstructural features of skeletal muscle that are related to tissue function. Although widely used, direct correlations between DTI derived metrics such as fractional anisotropy and spatially matched tissue microstructure assessed with histology have not been performed. This study investigated the relationship between scalar-based DTI measurements and histologically derived muscle microstructural measurements in rat rotator cuff muscles. Despite meticulous co-localization of MRI and histology data, negligible correlations were found between DTI metrics and histological measurements including muscle fiber diameter, cross-sectional area, and surface-to-volume ratio. These findings highlight the challenges in validating DTI with histology due to requirements in anatomical co-localization, necessity of high-quality histology, and consideration of diffusion measurement scales. Our findings underscore the need for further research with optimized imaging parameters to enhance our knowledge regarding the sensitivity of DTI to important features of muscle microstructure.

Deep learning based diagnosis of Parkinson's Disease using diffusion magnetic resonance imaging

The diagnostic performance of a combined architecture on Parkinson's disease using diffusion tensor imaging was evaluated. A convolutional neural network was trained from multiple parcellated brain regions. A greedy algorithm was proposed to combine the models from individual regions into a complex one. Total 305 Parkinson's disease patients (aged 59.9±9.7 years old) and 227 healthy control subjects (aged 61.0±7.4 years old) were enrolled from 3 retrospective studies. The participants were divided into training with ten-fold cross-validation (N = 432) and an independent blind dataset (N = 100). Diffusion-weighted images were acquired from a 3T scanner. Fractional anisotropy and mean diffusivity were calculated and was subsequently parcellated into 90 cerebral regions of interest based on the Automatic Anatomic Labeling template. A convolutional neural network was implemented which contained three convolutional blocks and a fully connected layer. Each convolutional block consisted of a convolutional layer, activation layer, and pooling layer. This model was trained for each individual region. A greedy algorithm was implemented to combine multiple regions as the final prediction. The greedy algorithm predicted the area under curve of 94.1±3.2% from the combination of fractional anisotropy from 22 regions. The model performance analysis showed that the combination of 9 regions is equivalent. The best area under curve was 74.7±5.4% from the right postcentral gyrus. The current study proposed an architecture of convolutional neural network and a greedy algorithm to combine from multiple regions. With diffusion tensor imaging, the algorithm showed the potential to distinguish patients with Parkinson's disease from normal control with satisfactory performance.

The clinical guiding value of a radiomics model for androgen deprivation therapy for prostate cancer

OBJECTIVE

Three models were used to evaluate prostate cancer after androgen deprivation therapy (ADT) and to determine the value of detecting residual lesions after treatment.

METHODS

We retrospectively analysed patients with prostate cancer who received ADT from January 2018 to June 2019. Patients were divided into ADT responder and ADT non-responder groups, and clinical risk factors were determined. Regions of interest were manually contoured on each slice on fat-saturated-T2-weighted imaging, and radiomic features were extracted. Uni- and multivariate logistic regression were used to establish radiomics, clinical and combined models.

RESULTS

There were 23 ADT non-responders and 20 ADT responders. In the clinical model, total prostate-specific antigen concentration and T stage were independent predictors of efficacy (area under the curve (AUC) = 0.774). The characteristics, MinIntensity and Correlation_ angle135_offset4 indicated an effective clinical model (AUC = 0.807). GLCMEntropy_ AllDirection_offset1_SD was the best feature to differentiate residual lesions from the central gland (CG) (Lesion-CG model, AUC = 0.955). Correlation_angle135_offset4, GLCMEntropy_ AllDirection_offset4_SD and GLCMEntropy_AllDirection_offset7_SD differentiated residual lesions from the peripheral zone (PZ) (Lesion-PZ model, AUC = 0.855). The AUC for the combined model was 0.904.

CONCLUSIONS

Our models can guide the clinical treatment of patients with different ADT responses. Furthermore, the radiomics model can detect prostate cancer that is non-responsive to ADT.

Fixel-Based Analysis of White Matter Degeneration in Patients With Progressive Supranuclear Palsy or Multiple System Atrophy, as Compared to Parkinson's Disease

Introduction: White matter degeneration may contribute to clinical symptoms of parkinsonism.

Objective: We used fixel-based analysis (FBA) to compare the extent and patterns of white matter degeneration in different parkinsonian syndromes—including idiopathic Parkinson's disease (PD), multiple system atrophy (MSA), and progressive supranuclear palsy (PSP).

Methods: This is a retrospective interpretation of prospectively acquired data of patients recruited in previous studies during 2008 and 2019. Diffusion-weighted images were acquired on a 3-Tesla scanner (diffusion weighting b = 1000 s/mm²–applied along either 64 or 30 non-collinear directions) from 53 patients with PD (men/women: 29/24; mean age: 65.06 ± 5.51 years), 47 with MSA (men/women: 20/27; mean age: 63.00 ± 7.19 years), and 50 with PSP men/women: 20/30; mean age: 65.96 ± 3.14 years). Non-parametric permutation tests were used to detect intergroup differences in fixel-related indices—including fiber density, fiber cross-section, and their combination.

Results: Patterns of white matter degeneration were significantly different between PD and atypical parkinsonisms (MSA and PSP). Compared with patients with PD, those with MSA and PSP showed a more extensive white matter involvement—noticeably descending tracts from primary motor cortex to corona radiata and cerebral peduncle. Lesions of corpus callosum were specific to PSP and absent in both MSA and PD.

Discussion: FBA identified specific patterns of white matter changes in MSA and PSP patients compared to PD. Our results proved the utility of FBA in evaluation of implied biological processes of white matter changes in parkinsonism. Our study set the stage for future applications of this technique in patients with parkinsonian syndromes.

T1 and T2∗ relaxation time in the parcellated myocardium of healthy Taiwanese participants: A single center study

Background

Quantitative maps from cardiac MRI provide objective information for myocardial tissue. The study aimed to report the T1 and T2∗ relaxation time and its relationship with clinical parameters in healthy Taiwanese participants.

Methods

Ninety-three participants were enrolled between 2014 and 2016 (Males/Females: 43/50; age: 49.7 ± 11.3/49.9 ± 10.3). T1 and T2∗ weighted images were obtained by MOLLI recovery and 3D fully flow compensated gradient echo sequences with a 3T MR scanner, respectively. The T1 map of the myocardium was parcellated into 16 partitions from the American Heart Association. The septal part of basal, mid-cavity, and apical view was selected for the T2∗ map. The difference of quantitative map by sex and age groups were evaluated by Student's TTEST and ANOVA, respectively. The relationship between T1, T2∗ map, and clinical parameters, such as ejection fraction, pulse rate, and blood pressures, were evaluated with partial correlation by controlling BMI and age.

Results

Male participants decreased T1 relaxation time in partitions which located in the mid-cavity and apical before 55 years old compared with females (Male/Female: 1143.1.4 ± 72.0–1191.1 ± 37.0/1180.1 ± 54.5–1326.1 ± 113.3 msec, p < 0.01). For female participants, T1 relaxation time was correlated negatively with systolic pressure (p < 0.01) and pulse rate (p < 0.01) before 45 years old. Besides, T1 and T2∗ relaxation time were positively and negatively correlated with ejection fraction and pulse rate after 45 years old in male participants, respectively. Decreased T2∗ relaxation time could be noticed in participants after 45 years old compared with youngers (26.0 ± 6.5/21.9 ± 8.0 msec; 25.2 ± 5.0/21.6 ± 7.2 msec, p < 0.05).

Conclusion

Reference T1 and T2∗ relaxation time from cardiac MRI in healthy Taiwanese participants were provided with sex and age-dependent manners. The relationship between clinical parameters and T1 or T2∗ relaxation time was also established and could be further investigated for its potential application in healthy/sub-healthy participants.

A Method for the Prediction of Clinical Outcome Using Diffusion Magnetic Resonance Imaging: Application on Parkinson's Disease

Robust early prediction of clinical outcomes in Parkinson's disease (PD) is paramount for implementing appropriate management interventions. We propose a method that uses the baseline MRI, measuring diffusion parameters from multiple parcellated brain regions, to predict the 2-year clinical outcome in Parkinson's disease. Diffusion tensor imaging was obtained from 82 patients (males/females = 45/37, mean age: 60.9 ± 7.3 years, baseline and after 23.7 ± 0.7 months) using a 3T MR scanner, which was normalized and parcellated according to the Automated Anatomical Labelling template. All patients were diagnosed with probable Parkinson's disease by the National Institute of Neurological Disorders and Stroke criteria. Clinical outcome was graded using disease severity (Unified Parkinson's Disease Rating Scale and Modified Hoehn and Yahr staging), drug administration (levodopa equivalent daily dose), and quality of life (39-item PD Questionnaire). Selection and regularization of diffusion parameters, the mean diffusivity and fractional anisotropy, were performed using least absolute shrinkage and selection operator (LASSO) between baseline diffusion index and clinical outcome over 2 years. Identified features were entered into a stepwise multivariate regression model, followed by a leave-one-out/5-fold cross validation and additional blind validation using an independent dataset. The predicted Unified Parkinson's Disease Rating Scale for each individual was consistent with the observed values at blind validation (adjusted R² 0.76) by using 13 features, such as mean diffusivity in lingual, nodule lobule of cerebellum vermis and fractional anisotropy in rolandic operculum, and quadrangular lobule of cerebellum. We conclude that baseline diffusion MRI is potentially capable of predicting 2-year clinical outcomes in patients with Parkinson's disease on an individual basis.

Prediction of the Clinical Severity of Progressive Supranuclear Palsy by Diffusion Tensor Imaging

Progressive supranuclear palsy (PSP) is characterized by a rapid and progressive clinical course. A timely and objective image-based evaluation of disease severity before standard clinical assessments might increase the diagnostic confidence of the neurologist. We sought to investigate whether features from diffusion tensor imaging of the entire brain with a machine learning algorithm, rather than a few pathogenically involved regions, may predict the clinical severity of PSP. Fifty-three patients who met the diagnostic criteria for probable PSP were subjected to diffusion tensor imaging. Of them, 15 underwent follow-up imaging. Clinical severity was assessed by the neurological examinations. Mean diffusivity and fractional anisotropy maps were spatially co-registered, normalized, and parcellated into 246 brain regions from the human Brainnetome atlas. The predictors of clinical severity from a stepwise linear regression model were determined after feature reduction by the least absolute shrinkage and selection operator. Performance estimates were obtained using bootstrapping, cross-validation, and through application of the model in the patients who underwent repeated imaging. The algorithm confidently predicts the clinical severity of PSP at the individual level (adjusted R²: 0.739 and 0.892, p < 0.001). The machine learning algorithm for selection of diffusion tensor imaging-based features is accurate in predicting motor subscale of unified Parkinson’s disease rating scale and postural instability and gait disturbance of PSP.

The many (sur)faces of B cells

This issue of the Biomedical Journal is dedicated to the latest findings concerning the complex development and functions of B lymphocytes, including their origins during embryogenesis, their meticulous control by the CD22 receptor and different types of T cells, as well as the immunosuppressive abilities of certain B cell subsets. Furthermore, we learn about the complicated genetic background of a rare cardiac disease, the surgical outcomes of pure conus medullaris syndrome and occurrences of tuberculous spondylitis after percutaneous vertebroplasty. Finally, we discover that brain waves could very well be used for biometric authentication and that diffusion imaging displays good reproducibility through a spectrum of spatial resolutions.