Semi-quantitative and quantitative dynamic contrast-enhanced (DCE) MRI parameters as prostate cancer imaging biomarkers for biologically targeted radiation therapy.

BACKGROUND: Biologically targeted radiation therapy treatment planning requires voxel-wise characterisation of tumours. Dynamic contrast enhanced (DCE) DCE MRI has shown promise in defining voxel-level biological characteristics. In this study we consider the relative value of qualitative, semi-quantitative and quantitative assessment of DCE MRI compared with diffusion weighted imaging (DWI) and T2-weighted (T2w) imaging to detect prostate cancer at the voxel level. METHODS: Seventy prostate cancer patients had multiparametric MRI prior to radical prostatectomy, including T2w, DWI and DCE MRI. Apparent Diffusion Coefficient (ADC) maps were computed from DWI, and semi-quantitative and quantitative parameters computed from DCE MRI. Tumour location and grade were validated with co-registered whole mount histology. Kolmogorov-Smirnov tests were applied to determine whether MRI parameters in tumour and benign voxels were significantly different. Cohen's d was computed to quantify the most promising biomarkers. The Parker and Weinmann Arterial Input Functions (AIF) were compared for their ability to best discriminate between tumour and benign tissue. Classifier models were used to determine whether DCE MRI parameters improved tumour detection versus ADC and T2w alone. RESULTS: All MRI parameters had significantly different data distributions in tumour and benign voxels. For low grade tumours, semi-quantitative DCE MRI parameter time-to-peak (TTP) was the most discriminating and outperformed ADC. For high grade tumours, ADC was the most discriminating followed by DCE MRI parameters Ktrans, the initial rate of enhancement (IRE), then TTP. Quantitative parameters utilising the Parker AIF better distinguished tumour and benign voxel values than the Weinmann AIF. Classifier models including DCE parameters versus T2w and ADC alone, gave detection accuracies of 78% versus 58% for low grade tumours and 85% versus 72% for high grade tumours. CONCLUSIONS: Incorporating DCE MRI parameters with DWI and T2w gives improved accuracy for tumour detection at a voxel level. DCE MRI parameters should be used to spatially characterise tumour biology for biologically targeted radiation therapy treatment planning.

Temperature dependence, accuracy, and repeatability of T1 and T2 relaxation times for the ISMRM/NIST system phantom measured using MR fingerprinting.

PURPOSE: Before MR fingerprinting (MRF) can be adopted clinically, the derived quantitative values must be proven accurate and repeatable over a range of T1 and T2 values and temperatures. Correct assessment of accuracy and precision as well as comparison between measurements can only be performed when temperature is either controlled or corrected for. The purpose of this study was to investigate the temperature dependence of T1 and T2 MRF values and evaluate the accuracy and repeatability of temperature-corrected relaxation values derived from a B1 -corrected MRF-fast imaging with steady-state precession implementation using 2 different dictionary sizes. METHODS: The International Society of MR in Medicine/National Institute of Standards and Technology phantom was scanned using an MRF sequence of 2 different lengths, a variable flip angle T1 , and a multi-echo spin echo T2 at 14 temperatures ranging from 15°C to 28°C and investigated with a linear regression model. Temperature-corrected accuracy was evaluated by correlating T1 and T2 times from each MRF dictionary with reference values. Repeatability was assessed using the coefficient of variation, with measurements taken over 30 separate sessions. RESULTS: There was a statistically significant fit of the model for MRF-derived T1 and T2 and temperature (p < 0.05) for all the spheres with a T1 > 500 ms. Both MRF methods showed a strong linear correlation with reference values for T1 (R2 = 0.996) and T2 (R2 = 0.982). MRF repeatability for T1 values was ≤1.4% and for T2 values was ≤3.4%. CONCLUSION: MRF demonstrated relaxation times with a temperature dependence similar to that of conventional mapping methods. Temperature-corrected T1 and T2 values from both dictionaries showed adequate accuracy and excellent repeatability in this phantom study.

Understanding lower limb muscle volume adaptations to amputation.

Amputation of a major limb, and the subsequent return to movement with a prosthesis, requires the development of compensatory strategies to account for the loss. Such strategies, over time, lead to regional muscle atrophy and hypertrophy through chronic under or overuse of muscles compared to uninjured individuals. The aim of this study was to quantify the lower limb muscle parameters of persons with transtibial and transfemoral amputations using high resolution MRI to ascertain muscle volume and to determine regression equations for predicting muscle volume using femur- and tibia-length, pelvic-width, height, and mass. Twelve persons with limb loss participated in this study and their data were compared to six matched control subjects. Subjects with unilateral transtibial amputation showed whole-limb muscle volume loss in the residual-limb, whereas minor volume changes in the intact limb were found, providing evidence for a compensation strategy that is dominated by the intact-limb. Subjects with bilateral-transfemoral amputations showed significant muscle volume increases in the short adductor muscles with an insertion not affected by the amputation, the hip flexors, and the gluteus medius, and significant volume decreases in the longer adductor muscles, rectus femoris, and hamstrings. This study presents a benchmark measure of muscle volume discrepancies in persons with limb-loss, and can be used to understand the compensation strategies of persons with limb-loss and the impact on muscle volume, thus enabling the development of optimised intervention protocols, conditioning therapies, surgical techniques, and prosthetic devices that promote and enhance functional capability within the population of persons with limb loss.

Automatic stratification of prostate tumour aggressiveness using multiparametric MRI: a horizontal comparison of texture features.

Background: Previous studies have identified apparent diffusion coefficient (ADC) from diffusion-weighted imaging (DWI) can stratify prostate cancer into high- and low-grade disease (HG and LG, respectively). In this study, we consider the improvement of incorporating texture features (TFs) from T2-weighted (T2w) multiparametric magnetic resonance imaging (mpMRI) relative to mpMRI alone to predict HG and LG disease. Material and methods: In vivo mpMRI was acquired from 30 patients prior to radical prostatectomy. Sequences included T2w imaging, DWI and dynamic contrast enhanced (DCE) MRI. In vivo mpMRI data were co-registered with 'ground truth' histology. Tumours were delineated on the histology with Gleason scores (GSs) and classed as HG if GS ≥ 4 + 3, or LG if GS ≤ 3 + 4. Texture features based on three statistical families, namely the grey-level co-occurrence matrix (GLCM), grey-level run length matrix (GLRLM) and the grey-level size zone matrix (GLSZM), were computed from T2w images. Logistic regression models were trained using different feature subsets to classify each lesion as either HG or LG. To avoid overfitting, fivefold cross validation was applied on feature selection, model training and performance evaluation. Performance of all models generated was evaluated using the area under the curve (AUC) method. Results: Consistent with previous studies, ADC was found to discriminate between HG and LG with an AUC of 0.76. Of the three statistical TF families, GLCM (plus select mpMRI features including ADC) scored the highest AUC (0.84) with GLRLM plus mpMRI similarly performing well (AUC = 0.82). When all TFs were considered in combination, an AUC of 0.91 (95% confidence interval 0.87-0.95) was achieved. Conclusions: Incorporating T2w TFs significantly improved model performance for classifying prostate tumour aggressiveness. This result, however, requires further validation in a larger patient cohort.

Synchrotron XRF imaging of Alzheimer's disease basal ganglia reveals linear dependence of high-field magnetic resonance microscopy on tissue iron concentration.

BACKGROUND: Chemical imaging of the human brain has great potential for diagnostic and monitoring purposes. The heterogeneity of human brain iron distribution, and alterations to this distribution in Alzheimer's disease, indicate iron as a potential endogenous marker. The influence of iron on certain magnetic resonance imaging (MRI) parameters increases with magnetic field, but is under-explored in human brain tissues above 7 T. NEW METHOD: Magnetic resonance microscopy at 9.4 T is used to calculate parametric images of chemically-unfixed post-mortem tissue from Alzheimer's cases (n = 3) and healthy controls (n = 2). Iron-rich regions including caudate nucleus, putamen, globus pallidus and substantia nigra are analysed prior to imaging of total iron distribution with synchrotron X-ray fluorescence mapping. Iron fluorescence calibration is achieved with adjacent tissue blocks, analysed by inductively coupled plasma mass spectrometry or graphite furnace atomic absorption spectroscopy. RESULTS: Correlated MR images and fluorescence maps indicate linear dependence of R2, R2* and R2' on iron at 9.4 T, for both disease and control, as follows: [R2(s-1) = 0.072[Fe] + 20]; [R2*(s-1) = 0.34[Fe] + 37]; [R2'(s-1) = 0.26[Fe] + 16] for Fe in µg/g tissue (wet weight). COMPARISON WITH EXISTING METHODS: This method permits simultaneous non-destructive imaging of most bioavailable elements. Iron is the focus of the present study as it offers strong scope for clinical evaluation; the approach may be used more widely to evaluate the impact of chemical elements on clinical imaging parameters. CONCLUSION: The results at 9.4 T are in excellent quantitative agreement with predictions from experiments performed at lower magnetic fields.

Multiparametric MRI and radiomics in prostate cancer: a review.

Multiparametric MRI (mpMRI) is an imaging modality that combines anatomical MR imaging with one or more functional MRI sequences. It has become a versatile tool for detecting and characterising prostate cancer (PCa). The traditional role of mpMRI was confined to PCa staging, but due to the advanced imaging techniques, its role has expanded to various stages in clinical practises including tumour detection, disease monitor during active surveillance and sequential imaging for patient follow-up. Meanwhile, with the growing speed of data generation and the increasing volume of imaging data, it is highly demanded to apply computerised methods to process mpMRI data and extract useful information. Hence quantitative analysis for imaging data using radiomics has become an emerging paradigm. The application of radiomics approaches in prostate cancer has not only enabled automatic localisation of the disease but also provided a non-invasive solution to assess tumour biology (e.g. aggressiveness and the presence of hypoxia). This article reviews mpMRI and its expanding role in PCa detection, staging and patient management. Following that, an overview of prostate radiomics will be provided, with a special focus on its current applications as well as its future directions.

Anthropometric Scaling of Anatomical Datasets for Subject-Specific Musculoskeletal Modelling of the Shoulder.

Linear scaling of generic shoulder models leads to substantial errors in model predictions. Customisation of shoulder modelling through magnetic resonance imaging (MRI) improves modelling outcomes, but model development is time and technology intensive. This study aims to validate 10 MRI-based shoulder models, identify the best combinations of anthropometric parameters for model scaling, and quantify the improvement in model predictions of glenohumeral loading through anthropometric scaling from this anatomical atlas. The shoulder anatomy was modelled using a validated musculoskeletal model (UKNSM). Ten subject-specific models were developed through manual digitisation of model parameters from high-resolution MRI. Kinematic data of 16 functional daily activities were collected using a 10-camera optical motion capture system. Subject-specific model predictions were validated with measured muscle activations. The MRI-based shoulder models show good agreement with measured muscle activations. A tenfold cross-validation using the validated personalised shoulder models demonstrates that linear scaling of anthropometric datasets with the most similar ratio of body height to shoulder width and from the same gender (p < 0.04) yields best modelling outcomes in glenohumeral loading. The improvement in model reliability is significant (p < 0.02) when compared to the linearly scaled-generic UKNSM. This study may facilitate the clinical application of musculoskeletal shoulder modelling to aid surgical decision-making.

Diffusion weighted and dynamic contrast enhanced MRI as an imaging biomarker for stereotactic ablative body radiotherapy (SABR) of primary renal cell carcinoma.

PURPOSE: To explore the utility of diffusion and perfusion changes in primary renal cell carcinoma (RCC) after stereotactic ablative body radiotherapy (SABR) as an early biomarker of treatment response, using diffusion weighted (DWI) and dynamic contrast enhanced (DCE) MRI. METHODS: Patients enrolled in a prospective pilot clinical trial received SABR for primary RCC, and had DWI and DCE MRI scheduled at baseline, 14 days and 70 days after SABR. Tumours <5cm diameter received a single fraction of 26 Gy and larger tumours received three fractions of 14 Gy. Apparent diffusion coefficient (ADC) maps were computed from DWI data and parametric and pharmacokinetic maps were fitted to the DCE data. Tumour volumes were contoured and statistics extracted. Spearman's rank correlation coefficients were computed between MRI parameter changes versus the percentage tumour volume change from CT at 6, 12 and 24 months and the last follow-up relative to baseline CT. RESULTS: Twelve patients were eligible for DWI analysis, and a subset of ten patients for DCE MRI analysis. DCE MRI from the second follow-up MRI scan showed correlations between the change in percentage voxels with washout contrast enhancement behaviour and the change in tumour volume (ρ = 0.84, p = 0.004 at 12 month CT, ρ = 0.81, p = 0.02 at 24 month CT, and ρ = 0.89, p = 0.001 at last follow-up CT). The change in mean initial rate of enhancement and mean Ktrans at the second follow-up MRI scan were positively correlated with percent tumour volume change at the 12 month CT onwards (ρ = 0.65, p = 0.05 and ρ = 0.66, p = 0.04 at 12 month CT respectively). Changes in ADC kurtosis from histogram analysis at the first follow-up MRI scan also showed positive correlations with the percentage tumour volume change (ρ = 0.66, p = 0.02 at 12 month CT, ρ = 0.69, p = 0.02 at last follow-up CT), but these results are possibly confounded by inflammation. CONCLUSION: DWI and DCE MRI parameters show potential as early response biomarkers after SABR for primary RCC. Further prospective validation using larger patient cohorts is warranted.

Voxel-wise prostate cell density prediction using multiparametric magnetic resonance imaging and machine learning.

BACKGROUND: There are currently no methods to estimate cell density in the prostate. This study aimed to develop predictive models to estimate prostate cell density from multiparametric magnetic resonance imaging (mpMRI) data at a voxel level using machine learning techniques. MATERIAL AND METHODS: In vivo mpMRI data were collected from 30 patients before radical prostatectomy. Sequences included T2-weighted imaging, diffusion-weighted imaging and dynamic contrast-enhanced imaging. Ground truth cell density maps were computed from histology and co-registered with mpMRI. Feature extraction and selection were performed on mpMRI data. Final models were fitted using three regression algorithms including multivariate adaptive regression spline (MARS), polynomial regression (PR) and generalised additive model (GAM). Model parameters were optimised using leave-one-out cross-validation on the training data and model performance was evaluated on test data using root mean square error (RMSE) measurements. RESULTS: Predictive models to estimate voxel-wise prostate cell density were successfully trained and tested using the three algorithms. The best model (GAM) achieved a RMSE of 1.06 (± 0.06) × 103 cells/mm2 and a relative deviation of 13.3 ± 0.8%. CONCLUSION: Prostate cell density can be quantitatively estimated non-invasively from mpMRI data using high-quality co-registered data at a voxel level. These cell density predictions could be used for tissue classification, treatment response evaluation and personalised radiotherapy.

Predicting prostate tumour location from multiparametric MRI using Gaussian kernel support vector machines: a preliminary study.

The performance of a support vector machine (SVM) algorithm was investigated to predict prostate tumour location using multi-parametric MRI (mpMRI) data. The purpose was to obtain information of prostate tumour location for the implementation of bio-focused radiotherapy. In vivo mpMRI data were collected from 16 patients prior to radical prostatectomy. Sequences included T2-weighted imaging, diffusion-weighted imaging and dynamic contrast enhanced imaging. In vivo mpMRI was registered with 'ground truth' histology, using ex vivo MRI as an intermediate registration step to improve accuracy. Prostate contours were delineated by a radiation oncologist and tumours were annotated on histology by a pathologist. Five patients with minimal imaging artefacts were selected for this study. A Gaussian kernel SVM was trained and tested on different patient data subsets. Parameters were optimised using leave-oneout cross validation. Signal intensities of mpMRI were used as features and histology annotations as true labels. Prediction accuracy, as well as area under the curve (AUC) of the receiver operating characteristics (ROC) curve, were used to assess performance. Results demonstrated the prediction accuracy ranged from 70.4 to 87.1% and AUC of ROC ranged from 0.81 to 0.94. Additional investigations showed the apparent diffusion coefficient map from diffusion weighted imaging was the most important imaging modality for predicting tumour location. Future work will incorporate additional patient data into the framework to increase the sensitivity and specificity of the model, and will be extended to incorporate predictions of biological characteristics of the tumour which will be used in bio-focused radiotherapy optimisation.

Iron deficiency in parkinsonism: region-specific iron dysregulation in Parkinson's disease and multiple system atrophy.

Alpha synuclein pathology is widespread and found in diverse cell types in multiple system atrophy (MSA) as compared to Parkinson's disease (PD). The reason for this differential distribution is unknown. Regional differences in the distribution of iron are associated with neurodegenerative diseases, and here we characterize the relationship between iron homeostasis proteins and regional concentration, distribution and form of iron in MSA and PD. In PD substantia nigra, tissue iron and expression of the iron export protein ferroportin increased, while the iron storage protein ferritin expression was unchanged. In the basis pontis of MSA cases, increased total iron concentration coupled with a disproportionate increase in ferritin in dysmorphic microglia and a reduction in ferroportin expression. This is supported by isothermal remanent magnetisation evidence consistent with elevated concentrations of ferritin-bound iron in MSA basis pontis. Conventional opinion holds that excess iron is involved in neurodegeneration. Our data support that this may be the case in PD. While region-specific changes in iron are evident in both PD and MSA, the mechanisms of iron dysregulation appear quite distinct, with a failure to export iron from the MSA basis pontis coupling with significant intracellular accumulation of ferritin iron. This pattern also occurs, to a lesser extent, in the MSA putamen. Despite the excess tissue iron, the manner of iron dysregulation in MSA is reminiscent of changes in anemia of chronic disease, and our preliminary data, coupled with the widespread pathology and involvement of multiple cell types, may evidence a deficit in bioavailabile iron.

Concentration of various trace elements in the rat retina and their distribution in different structures.

Inductively coupled plasma mass spectrometry (ICP-MS) was used to quantify the total amount of trace elements in retina from adult male Sprague-Dawley rats (n = 6). Concentration of trace elements within individual retinal areas in frozen sections of the fellow eye was established with the use of two methodologies: (1) particle-induced X-ray emission (PIXE) in combination with 3D depth profiling with Rutherford backscattering spectrometry (RBS) and (2) synchrotron X-ray fluorescence (SXRF) microscopy. The most abundant metal in the retina was zinc, followed by iron and copper. Nickel, manganese, chromium, cobalt, selenium and cadmium were present in very small amounts. The PIXE and SXRF analysis yielded a non-homogenous pattern distribution of metals in the retina. Relatively high levels of zinc were found in the inner part of the photoreceptor inner segments (RIS)/outer limiting membrane (OLM), inner nuclear layer and plexiform layers. Iron was found to accumulate in the retinal pigment epithelium/choroid layer and RIS/OLM. Copper in turn, was localised primarily in the RIS/OLM and plexiform layers. The trace elements iron, copper, and zinc exist in different amounts and locations in the rat retina.

Modest amyloid deposition is associated with iron dysregulation, microglial activation, and oxidative stress.

There is a well-established literature indicating a relationship between iron in brain tissue and Alzheimer's disease (AD). More recently, it has become clear that AD is associated with neuroinflammatory and oxidative changes which probably result from microglial activation. In this study, we investigated the correlative changes in microglial activation, oxidative stress, and iron dysregulation in a mouse model of AD which exhibits early-stage amyloid deposition. Microfocus X-ray absorption spectroscopy analysis of intact brain tissue sections prepared from AßPP/PS1 transgenic mice revealed the presence of magnetite, a mixed-valence iron oxide, and local elevations in iron levels in tissue associated with amyloid-ß-containing plaques. The evidence indicates that the expression of markers of microglial activation, CD11b and CD68, and astrocytic activation, GFAP, were increased, and were histochemically determined to be adjacent to amyloid-ß-containing plaques. These findings support the contention that, in addition to glial activation and oxidative stress, iron dysregulation is an early event in AD pathology.

High field magnetic resonance microscopy of the human hippocampus in Alzheimer's disease: quantitative imaging and correlation with iron.

We report R(2) and R(2) in human hippocampus from five unfixed post-mortem Alzheimer's disease (AD) and three age-matched control cases. Formalin-fixed tissues from opposing hemispheres in a matched AD and control were included for comparison. Imaging was performed in a 600MHz (14T) vertical bore magnet at MR microscopy resolution to obtain R(2) and R(2) (62 µm×62 µm in-plane, 80 µm slice thickness), and R(1) at 250 µm isotropic resolution. R(1), R(2) and R(2) maps were computed for individual slices in each case, and used to compare subfields between AD and controls. The magnitudes of R(2) and R(2) changed very little between AD and control, but their variances in the Cornu Ammonis and dentate gyrus were significantly higher in AD compared for controls (p<0.001). To investigate the relationship between tissue iron and MRI parameters, each tissue block was cryosectioned at 30 µm in the imaging plane, and iron distribution was mapped using synchrotron microfocus X-ray fluorescence spectroscopy. A positive correlation of R(2) and R(2)* with iron was demonstrated. While studies with fixed tissues are more straightforward to conduct, fixation can alter iron status in tissues, making measurement of unfixed tissue relevant. To our knowledge, these data represent an advance in quantitative imaging of hippocampal subfields in unfixed tissue, and the methods facilitate direct analysis of the relationship between MRI parameters and iron. The significantly increased variance in AD compared for controls warrants investigation at lower fields and in-vivo, to determine if this parameter is clinically relevant.