Improved liver R2* mapping by pixel-wise curve fitting with adaptive neighborhood regularization.

PURPOSE: To improve liver R2* mapping by incorporating adaptive neighborhood regularization into pixel-wise curve fitting. METHODS: Magnetic resonance imaging R2* mapping remains challenging because of the serial images with low signal-to-noise ratio. In this study, we proposed to exploit the neighboring pixels as regularization terms and adaptively determine the regularization parameters according to the interpixel signal similarity. The proposed algorithm, called the pixel-wise curve fitting with adaptive neighborhood regularization (PCANR), was compared with the conventional nonlinear least squares (NLS) and nonlocal means filter-based NLS algorithms on simulated, phantom, and in vivo data. RESULTS: Visually, the PCANR algorithm generates R2* maps with significantly reduced noise and well-preserved tiny structures. Quantitatively, the PCANR algorithm produces R2* maps with lower root mean square errors at varying R2* values and signal-to-noise-ratio levels compared with the NLS and nonlocal means filter-based NLS algorithms. For the high R2* values under low signal-to-noise-ratio levels, the PCANR algorithm outperforms the NLS and nonlocal means filter-based NLS algorithms in the accuracy and precision, in terms of mean and standard deviation of R2* measurements in selected region of interests, respectively. CONCLUSIONS: The PCANR algorithm can reduce the effect of noise on liver R2* mapping, and the improved measurement precision will benefit the assessment of hepatic iron in clinical practice. Magn Reson Med 80:792-801, 2018. © 2018 International Society for Magnetic Resonance in Medicine.

Correction to: Clinical recommendations for cardiovascular magnetic resonance mapping of T1, T2, T2* and extracellular volume: A consensus statement by the Society for Cardiovascular Magnetic Resonance (SCMR) endorsed by the European Association for Cardiovascular Imaging (EACVI).

CORRECTION TO: J CARDIOVASC MAGN RESON (2017) 19: 75. DOI: 10.1186/S12968-017-0389-8: In the original publication of this article [1] the "Competing interests" section was incorrect. The original publication stated the following competing interests.

Post-mortem study of the association between cardiac iron and fibrosis in transfusion dependent anaemia.

BACKGROUND: Heart failure related to cardiac siderosis remains a major cause of death in transfusion dependent anaemias. Replacement fibrosis has been reported as causative of heart failure in siderotic cardiomyopathy in historical reports, but these findings do not accord with the reversible nature of siderotic heart failure achievable with intensive iron chelation. METHODS: Ten whole human hearts (9 beta-thalassemia major, 1 sideroblastic anaemia) were examined for iron loading and fibrosis (replacement and interstitial). Five had died from heart failure, 4 had cardiac transplantation for heart failure, and 1 had no heart failure (death from a stroke). Heart samples iron content was measured using atomic emission spectroscopy. Interstitial fibrosis was quantified by computer using picrosirius red (PSR) staining and expressed as collagen volume fraction (CVF) with normal value for left ventricle <3%. RESULTS: The 9 hearts affected by heart failure had severe iron loading with very low T2* of 5.0 ± 2.0 ms (iron concentration 8.5 ± 7.0 mg/g dw) and diffuse granular myocardial iron deposition. In none of the 10 hearts was significant macroscopic replacement fibrosis present. In only 2 hearts was interstitial fibrosis present, but with low CVF: in one patient with no cardiac siderosis (death by stroke, CVF 5.9%) and in a heart failure patient (CVF 2%). In the remaining 8 patients, no interstitial fibrosis was seen despite all having severe cardiac siderosis and heart failure (CVF 1.86% ±0.87%). CONCLUSION: Replacement cardiac fibrosis was not seen in the 9 post-mortem hearts from patients with severe cardiac siderosis and heart failure leading to death or transplantation, which contrasts markedly to historical reports. Minor interstitial fibrosis was also unusual and very limited in extent. These findings accord with the potential for reversibility of heart failure seen in iron overload cardiomyopathy. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT00520559.

Clinical recommendations for cardiovascular magnetic resonance mapping of T1, T2, T2* and extracellular volume: A consensus statement by the Society for Cardiovascular Magnetic Resonance (SCMR) endorsed by the European Association for Cardiovascular Imaging (EACVI).

Parametric mapping techniques provide a non-invasive tool for quantifying tissue alterations in myocardial disease in those eligible for cardiovascular magnetic resonance (CMR). Parametric mapping with CMR now permits the routine spatial visualization and quantification of changes in myocardial composition based on changes in T1, T2, and T2*(star) relaxation times and extracellular volume (ECV). These changes include specific disease pathways related to mainly intracellular disturbances of the cardiomyocyte (e.g., iron overload, or glycosphingolipid accumulation in Anderson-Fabry disease); extracellular disturbances in the myocardial interstitium (e.g., myocardial fibrosis or cardiac amyloidosis from accumulation of collagen or amyloid proteins, respectively); or both (myocardial edema with increased intracellular and/or extracellular water). Parametric mapping promises improvements in patient care through advances in quantitative diagnostics, inter- and intra-patient comparability, and relatedly improvements in treatment. There is a multitude of technical approaches and potential applications. This document provides a summary of the existing evidence for the clinical value of parametric mapping in the heart as of mid 2017, and gives recommendations for practical use in different clinical scenarios for scientists, clinicians, and CMR manufacturers.

Validation of T2* in-line analysis for tissue iron quantification at 1.5 T.

BACKGROUND: There is a need for improved worldwide access to tissue iron quantification using T2* cardiovascular magnetic resonance (CMR). One route to facilitate this would be simple in-line T2* analysis widely available on MR scanners. We therefore compared our clinically validated and established T2* method at Royal Brompton Hospital (RBH T2*) against a novel work-in-progress (WIP) sequence with in-line T2* measurement from Siemens (WIP T2*). METHODS: Healthy volunteers (n = 22) and patients with iron overload (n = 78) were recruited (53 males, median age 34 years). A 1.5 T study (Magnetom Avanto, Siemens) was performed on all subjects. The same mid-ventricular short axis cardiac slice and transaxial slice through the liver were used to acquire both RBH T2* images and WIP T2* maps for each participant. Cardiac white blood (WB) and black blood (BB) sequences were acquired. Intraobserver, interobserver and interstudy reproducibility were measured on the same data from a subset of 20 participants. RESULTS: Liver T2* values ranged from 0.8 to 35.7 ms (median 5.1 ms) and cardiac T2* values from 6.0 to 52.3 ms (median 31 ms). The coefficient of variance (CoV) values for direct comparison of T2* values by RBH and WIP were 6.1-7.8 % across techniques. Accurate delineation of the septum was difficult on some WIP T2* maps due to artefacts. The inability to manually correct for noise by truncation of erroneous later echo times led to some overestimation of T2* using WIP T2* compared with the RBH T2*. Reproducibility CoV results for RBH T2* ranged from 1.5 to 5.7 % which were better than the reproducibility of WIP T2* values of 4.1-16.6 %. CONCLUSIONS: Iron estimation using the T2* CMR sequence in combination with Siemens' in-line data processing is generally satisfactory and may help facilitate global access to tissue iron assessment. The current automated T2* map technique is less good for tissue iron assessment with noisy data at low T2* values.

Comparison of 3 T and 1.5 T for T2* magnetic resonance of tissue iron.

BACKGROUND: T2* magnetic resonance of tissue iron concentration has improved the outcome of transfusion dependant anaemia patients. Clinical evaluation is performed at 1.5 T but scanners operating at 3 T are increasing in numbers. There is a paucity of data on the relative merits of iron quantification at 3 T vs 1.5 T. METHODS: A total of 104 transfusion dependent anaemia patients and 20 normal volunteers were prospectively recruited to undergo cardiac and liver T2* assessment at both 1.5 T and 3 T. Intra-observer, inter-observer and inter-study reproducibility analysis were performed on 20 randomly selected patients for cardiac and liver T2*. RESULTS: Association between heart and liver T2* at 1.5 T and 3 T was non-linear with good fit (R (2) = 0.954, p < 0.001 for heart white-blood (WB) imaging; R (2) = 0.931, p < 0.001 for heart black-blood (BB) imaging; R (2) = 0.993, p < 0.001 for liver imaging). R2* approximately doubled between 1.5 T and 3 T with linear fits for both heart and liver (94, 94 and 105 % respectively). Coefficients of variation for intra- and inter-observer reproducibility, as well as inter-study reproducibility trended to be less good at 3 T (3.5 to 6.5 %) than at 1.5 T (1.4 to 5.7 %) for both heart and liver T2*. Artefact scores for the heart were significantly worse with the 3 T BB sequence (median 4, IQR 2-5) compared with the 1.5 T BB sequence (4 [3-5], p = 0.007). CONCLUSION: Heart and liver T2* and R2* at 3 T show close association with 1.5 T values, but there were more artefacts at 3 T and trends to lower reproducibility causing difficulty in quantifying low T2* values with high tissue iron. Therefore T2* imaging at 1.5 T remains the gold standard for clinical practice. However, in centres where only 3 T is available, equivalent values at 1.5 T may be approximated by halving the 3 T tissue R2* with subsequent conversion to T2*.

Rapid look-up table method for noise-corrected curve fitting in the R2* mapping of iron loaded liver.

PURPOSE: Fitting the measured decay signal to the first moment in the presence of noncentral chi noise (M(1) NCM) can correctly address the effect of noise on the effective transverse relaxation rate (R2*) relaxometry of iron loaded liver. However, this method requires intensive computation, which restricts its application to R2* mapping. This work aims to develop a rapid implementation of the M(1) NCM method for R2* mapping. METHODS: The computation of the confluent hypergeometric function in the M(1) NCM model was approximated using cubic spline interpolation with breakpoints and coefficients precalculated and stored in a look-up table (M(1) NCM-LUT). The performance of the proposed M(1) NCM-LUT method was evaluated through simulation and based on in vivo liver R2* relaxometry data. RESULTS: In both simulation and in vivo studies, the maximum absolute difference between R2* maps generated by the M(1) NCM and M(1) NCM-LUT methods was nearly 10(-3) s(-1) or less, and the M(1) NCM-LUT method obtained a R2* map in approximately 1 s and achieved an acceleration of approximately five orders of magnitude. CONCLUSION: The proposed M(1) NCM-LUT method can significantly increase the speed of the liver R2* mapping using the M(1) NCM model. This development is important in promoting application of this R2* mapping technique for tissue iron quantification.

Automated interventricular septum segmentation for black-blood myocardial T2* measurement in thalassemia.

PURPOSE: To develop and validate an automated segmentation method that extracts the interventricular septum (IS) from myocardial black-blood images for the T2* measurement in thalassemia patients. MATERIALS AND METHODS: A total of 144 thalassemia major patients (age range, 11-51 years; 73 males) were scanned with a black-blood multi-echo gradient-echo sequence using a 1.5 Tesla Siemens Sonata system (flip angle 20°, sampling bandwidth 810 Hz/pixel, voxel size 1.56 × 1.56 × 10 mm(3) and variable fields of view (20-30) × 40 cm(2) depending on patient size). The improved Chan-Vese model with an automated initialization by the circular Hough transformation was implemented to segment the endocardial and epicardial margins of the left ventricle (LV). Consequently, the IS was extracted by analyzing the anatomical relation between the LV and the blood pool of the right ventricle, identified by intensity thresholding. The proposed automated IS segmentation (AISS) method was compared with the conventional manual method by using the Bland-Altman analysis and the coefficient of variation (CoV). RESULTS: The T2* measurements using the AISS method were in good agreement with those manually measured by experienced observers with a mean difference of 1.71% and a CoV of 4.15% (P < 0.001). CONCLUSION: Black-blood myocardial T2* measurement can be fully automated with the proposed AISS method.

T1 at 1.5T and 3T compared with conventional T2* at 1.5T for cardiac siderosis.

BACKGROUND: Myocardial black blood (BB) T2* relaxometry at 1.5T provides robust, reproducible and calibrated non-invasive assessment of cardiac iron burden. In vitro data has shown that like T2*, novel native Modified Look-Locker Inversion recovery (MOLLI) T1 shortens with increasing tissue iron. The relative merits of T1 and T2* are largely unexplored. We compared the established 1.5T BB T2* technique against native T1 values at 1.5T and 3T in iron overload patients and in normal volunteers. METHODS: A total of 73 subjects (42 male) were recruited, comprising 20 healthy volunteers (controls) and 53 patients (thalassemia major 22, sickle cell disease 9, hereditary hemochromatosis 9, other iron overload conditions 13). Single mid-ventricular short axis slices were acquired for BB T2* at 1.5T and MOLLI T1 quantification at 1.5T and 3T. RESULTS: In healthy volunteers, median T1 was 1014 ms (full range 939-1059 ms) at 1.5T and modestly increased to 1165ms (full range 1056-1224 ms) at 3T. All patients with significant cardiac iron overload (1.5T T2* values <20 ms) had T1 values <939 ms at 1.5T, and <1056 ms at 3T. Associations between T2* and T1 were found to be moderate with y =377 · x(0.282) at 1.5T (R(2) = 0.717), and y =406 · x(0.294) at 3T (R(2) = 0.715). Measures of reproducibility of T1 appeared superior to T2*. CONCLUSIONS: T1 mapping at 1.5T and at 3T can identify individuals with significant iron loading as defined by the current gold standard T2* at 1.5T. However, there is significant scatter between results which may reflect measurement error, but it is also possible that T1 interacts with T2*, or is differentially sensitive to aspects of iron chemistry or other biology. Hurdles to clinical implementation of T1 include the lack of calibration against human myocardial iron concentration, no demonstrated relation to cardiac outcomes, and variation in absolute T1 values between scanners, which makes inter-centre comparisons difficult. The relative merits of T1 at 3T versus T2* at 3T require further consideration.

Improved pixel-by-pixel MRI R2* relaxometry by nonlocal means.

PURPOSE: To investigate the feasibility of improving MRI R2* mapping by filtering the images before curve-fitting. METHODS: Pixel-by-pixel curve-fitting for the quantification of MRI relaxometry remains a challenge for low signal-to-noise ratio images. By computing the weighted mean of spatially adjacent pixels, the low-pass Gaussian (LPG) filter can suppress the noise but at the expense of blurring. By assigning high weights to pixels with similar neighborhood patches, the nonlocal means (NLM) algorithm can reduce noise while retaining intrinsic signals, however, its potential has not been explored in pixel-by-pixel MRI relaxometry, and in this study, we aimed to investigate the impact of the LPG and the NLM filtering on decay signals and MRI R2* mapping. These two filtering methods were compared on both simulated and in vivo data. RESULTS: Both LPG and NLM algorithms produces R2* maps with decreased root-mean-square-errors. The LPG filter blurs edges of R2* maps while the NLM algorithm preserves details well. The NLM consistently yields R2* mapping with smaller errors than the LPG filtering in all cases. CONCLUSION: Pixel-by-pixel fitting can skew MRI relaxometry. The NLM outperforms the conventional LPG filter and has the potential to provide more accurate pixel-by-pixel MRI relaxometry for improved tissue characterization.

A novel semiautomatic parenchyma extraction method for improved MRI R2* relaxometry of iron loaded liver.

PURPOSE: To propose and evaluate an automatic method of extracting parenchyma from a manually delineated whole liver for the R2* measurement of iron load. MATERIALS AND METHODS: In all, 108 transfusion-dependent patients with a wide range of hepatic iron content were scanned with a multiecho gradient-echo sequence. The R2* was measured by fitting the average signal of liver parenchyma, extracted by the proposed semiautomatic parenchyma extraction (SAPE), traditional manually delineated multiple regions-of-interest (mROIs), and T2* thresholding methods to the noise-corrected monoexponential model. The R2* measurement accuracy of the SAPE method was evaluated through simulation; the intra- and interobserver reproducibility of SAPE, mROI, and T2* thresholding were assessed from the in vivo data using coefficient of variation (CoV). RESULTS: In the simulation, the mean absolute percentage error of R2* measurement using SAPE was 0.23% (range 0.01%-1.09%). In vivo study, the CoVs of intra- and interobserver reproducibility were 0.83%, 1.39% for SAPE, 3.63%, 6.28% for mROI, and 1.62%, 2.66% for T2* thresholding, respectively. CONCLUSION: The SAPE method provides an accurate and reliable approach to assessing the overall hepatic iron content. The improved magnetic resonance imaging (MRI) R2* reproducibility using the SAPE method may lead to more accurate tissue characterization and increased diagnostic confidence.

Biopsy-based calibration of T2* magnetic resonance for estimation of liver iron concentration and comparison with R2 Ferriscan.

BACKGROUND: There is a need to standardise non-invasive measurements of liver iron concentrations (LIC) so clear inferences can be drawn about body iron levels that are associated with hepatic and extra-hepatic complications of iron overload. Since the first demonstration of an inverse relationship between biopsy LIC and liver magnetic resonance (MR) using a proof-of-concept T2* sequence, MR technology has advanced dramatically with a shorter minimum echo-time, closer inter-echo spacing and constant repetition time. These important advances allow more accurate calculation of liver T2* especially in patients with high LIC. METHODS: Here, we used an optimised liver T2* sequence calibrated against 50 liver biopsy samples on 25 patients with transfusional haemosiderosis using ordinary least squares linear regression, and assessed the method reproducibility in 96 scans over an LIC range up to 42 mg/g dry weight (dw) using Bland-Altman plots. Using mixed model linear regression we compared the new T2*-LIC with R2-LIC (Ferriscan) on 92 scans in 54 patients with transfusional haemosiderosis and examined method agreement using Bland-Altman approach. RESULTS: Strong linear correlation between ln(T2*) and ln(LIC) led to the calibration equation LIC = 31.94(T2*)-1.014. This yielded LIC values approximately 2.2 times higher than the proof-of-concept T2* method. Comparing this new T2*-LIC with the R2-LIC (Ferriscan) technique in 92 scans, we observed a close relationship between the two methods for values up to 10 mg/g dw, however the method agreement was poor. CONCLUSIONS: New calibration of T2* against liver biopsy estimates LIC in a reproducible way, correcting the proof-of-concept calibration by 2.2 times. Due to poor agreement, both methods should be used separately to diagnose or rule out liver iron overload in patients with increased ferritin.

Calibration of myocardial T2 and T1 against iron concentration.

BACKGROUND: The assessment of myocardial iron using T2* cardiovascular magnetic resonance (CMR) has been validated and calibrated, and is in clinical use. However, there is very limited data assessing the relaxation parameters T1 and T2 for measurement of human myocardial iron. METHODS: Twelve hearts were examined from transfusion-dependent patients: 11 with end-stage heart failure, either following death (n=7) or cardiac transplantation (n=4), and 1 heart from a patient who died from a stroke with no cardiac iron loading. Ex-vivo R1 and R2 measurements (R1=1/T1 and R2=1/T2) at 1.5 Tesla were compared with myocardial iron concentration measured using inductively coupled plasma atomic emission spectroscopy. RESULTS: From a single myocardial slice in formalin which was repeatedly examined, a modest decrease in T2 was observed with time, from mean (± SD) 23.7 ± 0.93 ms at baseline (13 days after death and formalin fixation) to 18.5 ± 1.41 ms at day 566 (p<0.001). Raw T2 values were therefore adjusted to correct for this fall over time. Myocardial R2 was correlated with iron concentration [Fe] (R2 0.566, p<0.001), but the correlation was stronger between LnR2 and Ln[Fe] (R2 0.790, p<0.001). The relation was [Fe] = 5081•(T2)-2.22 between T2 (ms) and myocardial iron (mg/g dry weight). Analysis of T1 proved challenging with a dichotomous distribution of T1, with very short T1 (mean 72.3 ± 25.8 ms) that was independent of iron concentration in all hearts stored in formalin for greater than 12 months. In the remaining hearts stored for <10 weeks prior to scanning, LnR1 and iron concentration were correlated but with marked scatter (R2 0.517, p<0.001). A linear relationship was present between T1 and T2 in the hearts stored for a short period (R2 0.657, p<0.001). CONCLUSION: Myocardial T2 correlates well with myocardial iron concentration, which raises the possibility that T2 may provide additive information to T2* for patients with myocardial siderosis. However, ex-vivo T1 measurements are less reliable due to the severe chemical effects of formalin on T1 shortening, and therefore T1 calibration may only be practical from in-vivo human studies.

Labelling with dynamics: A data-efficient learning paradigm for medical image segmentation.

The success of deep learning on image classification and recognition tasks has led to new applications in diverse contexts, including the field of medical imaging. However, two properties of deep neural networks (DNNs) may limit their future use in medical applications. The first is that DNNs require a large amount of labeled training data, and the second is that the deep learning-based models lack interpretability. In this paper, we propose and investigate a data-efficient framework for the task of general medical image segmentation. We address the two aforementioned challenges by introducing domain knowledge in the form of a strong prior into a deep learning framework. This prior is expressed by a customized dynamical system. We performed experiments on two different datasets, namely JSRT and ISIC2016 (heart and lungs segmentation on chest X-ray images and skin lesion segmentation on dermoscopy images). We have achieved competitive results using the same amount of training data compared to the state-of-the-art methods. More importantly, we demonstrate that our framework is extremely data-efficient, and it can achieve reliable results using extremely limited training data. Furthermore, the proposed method is rotationally invariant and insensitive to initialization.

Improved MRI R2 * relaxometry of iron-loaded liver with noise correction.

Accurate and reproducible MRI R2 * relaxometry for tissue iron quantification is important in managing transfusion-dependent patients. MRI data are often acquired using array coils and reconstructed by the root-sum-square algorithm, and as such, measured signals follow the noncentral chi distribution. In this study, two noise-corrected models were proposed for the liver R2 * quantification: fitting the signal to the first moment and fitting the squared signal to the second moment in the presence of the noncentral chi noise. These two models were compared with the widely implemented offset and truncation models on both simulation and in vivo data. The results demonstrated that the "slow decay component" of the liver R2 * was mainly caused by the noise. The offset model considerably overestimated R2 * values by incorrectly adding a constant to account for the slow decay component. The truncation model generally produced accurate R2 * measurements by only fitting the initial data well above the noise level to remove the major source of errors, but underestimated very high R2 * values due to the sequence limit of obtaining very short echo time images. Both the first and second-moment noise-corrected models constantly produced accurate and precise R2 * measurements by correctly addressing the noise problem.

Liver iron quantification by 3 tesla MRI: calibration on a rabbit model.

PURPOSE: To determine the feasibility of liver iron quantification by 3 Tesla (T) MRI using a novel iron overload rabbit model. MATERIALS AND METHODS: Forty-two rabbits underwent iron dextran loading from 1 to 15 weeks. MRI signal intensity ratio (SIR) was measured using a gradient-echo sequence, and R2(1/T2) measured using an eight-echo spin-echo sequence at 3T. Ex vivo hepatic pathology was obtained for all rabbits studied. Postmortem assessments of liver iron concentration (LIC) were conducted in an atomic absorption spectrophotometer. MRI measures were fitted against LIC using linear regression for 30 of the iron-loaded rabbits. The remaining 12 iron-loaded rabbits were used to test the prediction accuracy of the derived models. RESULTS: LIC was linearly correlated to both liver-to-muscle SIR (r = -0.845) and R2 (r = 0.965) in a range achieved in this study (LIC < 10 mg/g dry tissue) at 3T. By regression, the linear equations were determined as: Y1 = 10.581-5.924X1 (Y1 : LIC, X1 :SIR); Y2 = -1.273+0.103X2 (Y2 :LIC, X2 :R2). In the 12 test rabbits, the predicted LICs using the derived equations agreed well with the results obtained using the spectrophotometer. CONCLUSION: Both SIR and R2 are highly correlated with LIC in a novel rabbit model. MRI quantification of liver iron overload is feasible at 3T.

Automated truncation method for myocardial T2* measurement in thalassemia.

PURPOSE: To propose an automated truncation method for myocardial T2* measurement and evaluate this method on a large population of patients with iron loading in the heart and scanned at multiple magnetic resonance imaging (MRI) centers. MATERIALS AND METHODS: A total of 550 thalassemia patients were scanned at 20 international centers using a variety of MR scanners (Siemens, Philips, or GE). A single mid-ventricular short axis slice was imaged. All patient data were anonymized before the T2* were measured by expert observers using standard techniques. These same datasets were then retrospectively processed using the proposed automated truncation method by another independent observer and the resulting T2* measurements were compared with those of expert readings. RESULTS: The T2* measurements using the automated method showed good agreement with those measured by expert observers using standard techniques (P = 0.95) with a low coefficient of variation (1.6%). CONCLUSION: This study demonstrates feasibility and good reproducibility of a new automated truncation method for myocardial T2* measurement. This approach simplifies the overall analysis and can be easily incorporated into T2* analysis software to facilitate further development of a fully automated myocardial tissue iron quantification.

In vivo comparison of myocardial T1 with T2 and T2* in thalassaemia major.

PURPOSE: To compare myocardial T1 against T2 and T2* in patients with thalassemia major (TM) for myocardial iron characterization. MATERIALS AND METHODS: A total of 106 TM patients (29 ± 10 years; 58 males) were studied on a 1.5 Tesla scanner using dedicated T1, T2*, and T2 relaxometry sequences. A single mid-ventricular short axis slice was acquired within a breath-hold. RESULTS: In patients with myocardial iron overload (T2* < 20 ms; n = 52), there were linear correlations between T2 and T2* (r = 0.82; P = 0.0), and between T1 and T2* (r = 0.83; P = 0.0). In patients with no myocardial iron (n = 54), T2* values were scattered with no significant correlation against T2 or T1. For all patients (n = 106) there was a strong linear correlation (r = 0.93; P = 0.0) between myocardial T1 and T2. CONCLUSION: In patients with iron overload, myocardial T2 and T1 are correlated with T2*. In patients with low or normal myocardial iron concentration, other factors become dominant in affecting T2* values as shown by scattered T2* data. Myocardial T1 correlates linearly with T2 measurements in all patients suggesting that these two relaxation parameters avoid extrinsic magnetic field inhomogeneity effects and may potentially provide improved myocardial tissue characterization.

Functional differentiation of the dorsal striatum: a coordinate-based neuroimaging meta-analysis.

Background: The dorsal striatum, a nucleus in the basal ganglia, plays a key role in the execution of cognitive functions in the human brain. Recent studies have focused on how the dorsal striatum participates in a single cognitive function, whereas the specific roles of the caudate and putamen in performing multiple cognitive functions remain unclear. In this paper we conducted a meta-analysis of the relevant neuroimaging literature to understand the roles of subregions of the dorsal striatum in performing different functions. Methods: PubMed, Web of Science, and BrainMap Functional Database were searched to find original functional magnetic resonance imaging (fMRI) studies conducted on healthy adults under reward, memory, emotion, and decision-making tasks, and relevant screening criteria were formulated. Single task activation, contrast activation, and conjunction activation analyses were performed using the activation likelihood estimation (ALE) method for the coordinate-based meta-analysis to evaluate the differences and linkages. Results: In all, 112 studies were included in this meta-analysis. Analysis revealed that, of the 4 single activation tasks, reward, memory, and emotion tasks all activated the putamen more, whereas decision-making tasks activated the caudate body. Contrast analysis showed that the caudate body played an important role in the 2 cooperative activation tasks, but conjunction activation results found that more peaks appeared in the caudate head. Discussion: Different subregions of the caudate and putamen assume different roles in processing complex cognitive behaviors. Functional division of the dorsal striatum identified specific roles of 15 different subregions, reflecting differences and connections between the different subregions in performing different cognitive behaviors.

On T2* magnetic resonance and cardiac iron.

BACKGROUND: Measurement of myocardial iron is key to the clinical management of patients at risk of siderotic cardiomyopathy. The cardiovascular magnetic resonance relaxation parameter R2* (assessed clinically via its reciprocal, T2*) measured in the ventricular septum is used to assess cardiac iron, but iron calibration and distribution data in humans are limited. METHODS AND RESULTS: Twelve human hearts were studied from transfusion-dependent patients after either death (heart failure, n=7; stroke, n=1) or transplantation for end-stage heart failure (n=4). After cardiovascular magnetic resonance R2* measurement, tissue iron concentration was measured in multiple samples of each heart with inductively coupled plasma atomic emission spectroscopy. Iron distribution throughout the heart showed no systematic variation between segments, but epicardial iron concentration was higher than in the endocardium. The mean ± SD global myocardial iron causing severe heart failure in 10 patients was 5.98 ± 2.42 mg/g dry weight (range, 3.19 to 9.50 mg/g), but in 1 outlier case of heart failure was 25.9 mg/g dry weight. Myocardial ln[R2*] was strongly linearly correlated with ln[Fe] (R²=0.910, P<0.001), leading to [Fe]=45.0×(T2*)⁻¹·²² for the clinical calibration equation with [Fe] in milligrams per gram dry weight and T2* in milliseconds. Midventricular septal iron concentration and R2* were both highly representative of mean global myocardial iron. CONCLUSIONS: These data detail the iron distribution throughout the heart in iron overload and provide calibration in humans for cardiovascular magnetic resonance R2* against myocardial iron concentration. The iron values are of considerable interest in terms of the level of cardiac iron associated with iron-related death and indicate that the heart is more sensitive to iron loading than the liver. The results also validate the current clinical practice of monitoring cardiac iron in vivo by cardiovascular magnetic resonance of the midseptum.