A comparison study of inhomogeneous magnetization transfer (ihMT) and magnetization transfer (MT) in multiple sclerosis based on whole brain acquisition at 3.0 T

IntroductionMultiple sclerosis (MS) is a central nervous system disorder that may eventually affect its function. The clinical standard for MS severity is based on a clinical scale, which lacks lesion specific information. Magnetic resonance imaging of MS faces the challenge of myelin specificity, and in this work a new method inhomogeneous magnetization transfer (ihMT) is investigated as new biomarker of demyelination in MS.MethodsLocal ethics committee approved this study and written informed consents were obtained. Between Oct 2017 to May 2018, eighteen patients with relapsing-remitting MS (RRMS) (6 males, 12 females, mean age 31.2) and sixteen healthy volunteers (6 males, 10 females, mean age 30.4 years) were enrolled in this prospective study. All subjects underwent MRI exams including MT and ihMT imaging as well as the Expanded Disability Status Scale (EDSS) assessments. Independent sample t-test were used to compare the difference of ihMT parameters between healthy white matter (HWM) and normal appearing white matter (NAWM) and between HWM and MS lesions, respectively. Spearman correlation were used to analyze the correlation between ihMT parameters of MS lesions and EDSS score.ResultsThe ihMTR and qihMT demonstrate significant differences between WHM and NAWM groups, while no significant differences are observed for MTR and qMT. All parameters show significant differences between HWM and MS groups (p < 0.05). There was moderate negative correlation between MTR, qMT and EDSS score (−0.440 and −0.572), while there was a strong negative correlation between ihMTR and qihMT and EDSS score (−0.704 and −0.739).ConclusionBased on whole brain analysis at 3.0 T, ihMT showed better correlation with EDSS compared to magnetization transfer imaging, and may be a potentially valuable biomarker for demyelination in MS.     

Quantitative MRI texture analysis in chronic active multiple sclerosis lesions

ObjectiveRecently, there has been an increasing interest in “chronic enlarging” or “chronic active” multiple sclerosis (MS) lesions that are associated with clinical disability. However, investigation of dynamic lesion volume changes requires longitudinal MRI data from two or more time points. The aim of this study was to investigate the application of texture analysis (TA) on baseline T1-weighted 3D magnetization-prepared rapid acquisition gradient-echo (MPRAGE) images to differentiate chronic active from chronic stable MS lesions.Material and methodsTo identify chronic active lesions as compared to non-enhancing stable lesions, two MPRAGE datasets acquired on a 3 T MRI at baseline and after 12 months follow-up were applied to the Voxel-Guided Morphometry (VGM) algorithm. TA was performed on the baseline MPRAGE images, 36 texture features were extracted for each lesion.ResultsOverall, 374 chronic MS lesions (155 chronic active and 219 chronic stable lesions) from 60 MS patients were included in the final analysis. Multiple texture features including “DISCRETIZED_HISTO_Energy”, “GLCM_Energy”, “GLCM_Contrast” and “GLCM_Dissimilarity” were significantly higher in chronic active as compared to chronic stable lesions. Partial least squares regression yielded an area under the curve of 0.7 to differentiate both lesion types.ConclusionOur results suggest that multiple texture features extracted from MPRAGE images indicate higher intralesional heterogeneity, however they demonstrate only a fair accuracy to differentiate chronic active from chronic stable MS lesions.     

Quantifying cortical bone free water using short echo time (STE-MRI) at 1.5 T

PurposeThe purpose of our study was to use Dual-TR STE-MR protocol as a clinical tool for cortical bone free water quantification at 1.5 T and validate it by comparing the obtained results (MR-derived results) with dehydration results.MethodsHuman studies were compliant with HIPPA and were approved by the institutional review board. Short Echo Time (STE) MR imaging with different Repetition Times (TRs) was used for quantification of cortical bone free water T₁ (T₁free) and concentration (ρ_free). The proposed strategy was compared with the dehydration technique in seven bovine cortical bone samples. The agreement between the two methods was quantified by using Bland and Altman analysis. Then we applied the technique on a cross-sectional population of thirty healthy volunteers (18F/12M) and examined the association of the biomarkers with age.ResultsThe mean values of ρ_free for bovine cortical bone specimens were quantified as 4.37% and 5.34% by using STE-MR and dehydration techniques, respectively. The Bland and Altman analysis showed good agreement between the two methods along with the suggestion of 0.99% bias between them. Strong correlations were also reported between ρ_free (r² = 0.62) and T_1free and age (r² = 0.8). The reproducibility of the method, evaluated in eight subjects, yielded an intra-class correlation of 0.95.ConclusionSTE-MR imaging with dual-TR strategy is a clinical solution for quantifying cortical bone ρ_free and T_1free.     

Texture analysis of multiple sclerosis: a comparative study

The difficulty of using magnetic resonance imaging (MRI) to support early diagnosis of multiple sclerosis (MS) stems from the subtle pathological changes in the central nervous system (CNS). In this study, texture analysis was performed on MR images of MS patients and normal controls and a combined set of texture features were explored in order to better discriminate tissues between MS lesions, normal appearing white matter (NAWM) and normal white matter (NWM). Features were extracted from gradient matrix, run-length (RL) matrix, gray level co-occurrence matrix (GLCM), autoregressive (AR) model and wavelet analysis, and were selected based on greatest difference between different tissue types. The results of the combined set of texture features were compared with our previous results of GLCM-based features alone. The results of this study demonstrated that (1) with the combined set of texture features, classification was perfect (100%) between MS lesions and NAWM (or NWM), less successful (88.89%) among the three tissue types and worst (58.33%) between NAWM and NWM; (2) compared with GLCM-based features, the combined set of texture features were better at discriminating MS lesions and NWM, equally good at discriminating MS lesions and NAWM and at all three tissue types, but less effective in classification between NAWM and NWM. This study suggested that texture analysis with the combined set of texture features may be equally good or more advantageous than the commonly used GLCM-based features alone in discriminating MS lesions and NWM/NAWM and in supporting early diagnosis of MS.     

Changes in structural and functional connectivity during two years of fingolimod therapy for multiple sclerosis

BackgroundFingolimod, an oral drug, has been reported to reduce relapse rate in multiple sclerosis (MS). However disease progression may still occur in spite of control of inflammation. Functional imbalances within and between cerebral networks associated with disruption of structural and functional network integrity, have been reported in MS. An effective therapy is expected to stabilize such functional network integrity.ObjectiveThe purpose of this study was to investigate changes in structural and resting-state functional connectivity of motor and cognitive networks, and associated changes in neurologic scores in MS, during 2 years of fingolimod therapy.MethodsTwenty five subjects with MS were recruited for this study. Subjects were scanned with diffusion tensor imaging (DTI) and resting-state functional connectivity MRI (fcMRI) scan protocol at 3 T with 6-month interval over a period of 2 years. Neurologic performance scores of motor and cognitive performances were also obtained.ResultsDTI measures worsened during the 1st year and then stabilized; any trend of stabilization of fcMRI was delayed until the 2nd year. While motor performance did not change, cognitive performance showed improvement. Several baseline DTI measures correlated with relevant neurologic scores.ConclusionInitial worsening of motor and cognitive network was reported after 1 year of treatment, but seems DTI and fcMRI measures seem to stabilize after around one year fingolimod therapy.     

Meningiomas: Preoperative predictive histopathological grading based on radiomics of MRI

PurposeWe aimed to develop a radiomics model to predict the histopathological grading of meningiomas by magnetic resonance imaging (MRI) before surgery.MethodsWe recruited 131 patients with pathological diagnosis of meningiomas. All the patients had undergone MRI before surgery on a 3.0 T MRI scanner to obtain T1 fluid- attenuated inversion recovery (T1 FLAIR) images, T2-weighted images (T2WI) and T1 FLAIR with contrast enhancement (CE-T1 FLAIR) images covering the whole brain. The removing features with low variance, univariate feature selection, and least absolute shrinkage and selection operator (LASSO) were used to select radiomics features. Six classifiers were used to train the models (logistic regression (LR), k-nearest neighbor (KNN), decision tree (DT), support vector machine (SVM), random forests (RF), and XGBoost), and then 24 models were established using a random verification method to differentiate low-grade from high-grade meningiomas. The performance was assessed by receiver-operating characteristic (ROC) analysis, the f1-score, sensitivity, and specificity.ResultsThe radiomics features were significantly associated with the histopathological grading. Quantitative imaging features (n = 1409) were extracted, and nine features were selected to predict the grades of meningiomas. The best performance of the radiomics model for the degree of differentiation was obtained by SVM (area under the curve (AUC), 0.956; 95% confidence interval (CI), 0.83–1.00; sensitivity, 0.87; specificity, 0.92; f1-score, 0.90).ConclusionThe radiomics models are of great value in predicting the histopathological grades of meningiomas, and have broad prospects in radiology and clinics.     

Discrimination between malignant and benign thyroid tumors by diffusion-weighted imaging – A systematic review and meta analysis

PurposeMagnetic resonance imaging is used to stage thyroid tumors. Diffusion weighted imaging (DWI) and apparent diffusion coefficient (ADC) can be used to reflect tumor microstructure. Our aim was to compare ADC values of malignant and benign thyroid lesions based on a large sample.MethodsMEDLINE library, EMBASE and SCOPUS databases were screened for the associations between ADC values and thyroid lesions up to August 2021. The primary endpoint of the systematic review were ADC values of benign and malignant thyroid lesions. In total, 29 studies were suitable for the analysis and were included into the present study.ResultsThe included studies comprised a total of 2137 lesions, 1118 (52.3%) benign and 1019 (47.7%) malignant lesions. The pooled mean ADC value of the benign thyroid lesions was 1.88 × 10⁻³ mm²/s [95% CI 1.77–2.0] and the pooled mean ADC value of malignant thyroid lesions was 1.15 × 10⁻³ mm²/s [95% CI 1.04–1.25].ConclusionsADC can well discriminate benign and malignant thyroid tumors. Therefore, DWI should be implemented into the presurgical diagnostic work-up in clinical routine.     

Normal appearing brain white matter changes in relapsing multiple sclerosis: Texture image and classification analysis in serial MRI scans

ObjectiveThere is a clinical interest in identifying normal appearing white matter (NAWM) areas in brain T2-weighted (T₂W) MRI scans in multiple sclerosis (MS) subjects. These areas are susceptible to disease development and areas need to be studied in order to find potential associations between texture feature changes and disease progression.MethodsThe subjects investigated had a first demyelinating event (Clinically Isolated Syndrome-CIS) at baseline (Time₀), and the NAWM₀ (i.e. NAWM at Time₀) of the brain tissue was subsequently converted to demyelinating plaques (as evaluated in a follow up MRI at Time_6–12). 38 untreated subjects that had developed a CIS, had brain MRI scans within an interval of 6–12 months (Time_6–12 at follow-up). An experienced MS neurologist manually delineated the demyelinating lesions at Time₀ (L₀) and at Time_6–12 (L_6–12). Areas in the Time_6–12 MRI scans, where new lesions had been developed, were mapped back to their corresponding NAWM areas on the Time₀ MR scans (ROIS₀). In addition, contralateral ROIs of similar size and shape were segmented on the same images at Time₀ (ROIS_C0) to form an intra-subject control group. Following that, texture features were extracted from all prescribed areas and MS lesions.ResultsTexture features were used as input into Support Vector Machine (SVM) models to differentiate between the following: NAWM₀ vs ROIS_C0, NAWM₀ vs NAWM_6–12, NAWM₀ vs L₀, NAWM_6–12 vs L_6–12, ROIS₀ vs L₀, ROIS₀ vs L_6–12 and ROIS₀ vs ROIS_C0, where the corresponding % correct classifications scores were 89%, 95%, 98%, 92%, 85%, 90% and 65% respectively.ConclusionsTexture features may provide complementary information for following up the development and progression of MS disease. Future work will investigate the proposed method on more subjects.     

Non-contrast enhanced diagnosis of acute myocarditis based on the 17-segment heart model using 2D-feature tracking magnetic resonance imaging

PurposeThe aim of this study was to investigate the diagnostic value of myocardial deformation analysis based on the 17-segment heart model using non-contrast enhanced (CE) 2D tissue feature tracking (2D-FT) technique.Material and methodsSeventy patients with suspected myocarditis underwent a cardiovascular magnetic resonance (CMR) examination at 1.5 Tesla. A contrast-agent-free part of this CMR protocol was additionally performed in forty healthy volunteers (HV). Besides standard CMR data sets, 2D-FT derived segmental and global longitudinal, radial, and circumferential deformation parameters were analyzed. The 2D-FT results were compared to the combined findings from CMR imaging and endomyocardial biopsy (EMB).ResultsPatients were assigned to three groups depending on their ejection fraction (EF) (<40%, 40–55%, ≥55%). Compared to HV, impaired EF (<55%) was significantly correlated to reduced segmental and global strain and strain rate values. The circumferential deformation analysis was more sensitive to myocardial changes than longitudinal and radial analysis. The segmental strain/strain rate had an accuracy of 84.3%/70.0% for the diagnosis of an acute myocarditis, stated by EMB and CMR in 42 of 70 patients. In patients with preserved EF, acute myocarditis could be ruled out using only segmental strain analysis with a negative predictive value of 87.5%.ConclusionIn patients with suspected myocarditis, the deformation analysis based on the 17-segment heart model provides valuable information about functional myocardial inhomogeneity. This quantitative approach could be used in addition to the clinical standard CMR protocol and represents a promising tool in the framework of a prospective automatized multiparametric CMR imaging analysis.     

The effect of morphological and microstructural integrity of the corpus callosum on cognition,fatigue and depression in mildly disabled MS patients

AimTo assess the value of callosal morphological and microstructural integrity in assessing different cognitive domains, fatigue and depression in mildly disabled multiple sclerosis (MS) patients.Materials and methodsWe assessed 29 mildly disabled MS patients and 15 healthy controls using 3T magnetic resonance images (T1-weighted, FLAIR and DTI) and neuropsychological tests assessing different cognitive functions, depression and fatigue. We compared the added value of morphological measures (corpus callosum area corrected for total intracranial volume, index, circularity and the more detailed thickness profile) and diffusion features (fractional anisotropy and mean diffusivity) in multilinear models including standard clinical and whole-brain parameters in assessing neuropsychological scores.ResultsEven in mildly disabled MS patients, a significant reduction of the corpus callosum (p < 0.001) was observed in comparison to healthy controls. Callosal area, index and circularity were significantly (p < 0.002) related to whole-brain white matter volume, T2 lesion load and deep grey matter volume, but not with cortical grey matter.The combination of commonly used imaging and clinical parameters explained between 7% (Fatigue) and 50% (processing speed, verbal memory) of the adjusted variance. Inclusion of the mean diffusivity increased the adjusted R² significantly to 69% (p = 0.004) and 71% (p = 0.002) for visuospatial and verbal memory respectively.ConclusionOur results show that callosal features may be used as an alternative to measuring whole-brain volumes. Furthermore, the microstructural integrity of the corpus callosum can help to predict an MS patient's memory performance.     

Spatial normalization of multiple sclerosis brain MRI data depends on analysis method and software package

BackgroundSpatially normalizing brain MRI data to a template is commonly performed to facilitate comparisons between individuals or groups. However, the presence of multiple sclerosis (MS) lesions and other MS-related brain pathologies may compromise the performance of automated spatial normalization procedures. We therefore aimed to systematically compare five commonly used spatial normalization methods for brain MRI – including linear (affine), and nonlinear MRIStudio (LDDMM), FSL (FNIRT), ANTs (SyN), and SPM (CAT12) algorithms – to evaluate their performance in the presence of MS-related pathologies.Methods3 Tesla MRI images (T1-weighted and T2-FLAIR) were obtained for 20 participants with MS from an ongoing cohort study (used to assess a real dataset) and 1 healthy control participant (used to create a simulated lesion dataset). Both raw and lesion-filled versions of each participant's T1-weighted brain images were warped to the Montreal Neurological Institute (MNI) template using all five normalization approaches for the real dataset, and the same procedure was then repeated using the simulated lesion dataset (i.e., total of 400 spatial normalizations). As an additional quality-assurance check, the resulting deformations were also applied to the corresponding lesion masks to evaluate how each processing pipeline handled focal white matter lesions. For each normalization approach, inter-subject variability (across normalized T1-weighted images) was quantified using both mutual information (MI) and coefficient of variation (COV), and the corresponding normalized lesion volumes were evaluated using paired-sample t-tests.ResultsAll four nonlinear warping methods outperformed conventional linear normalization, with SPM (CAT12) yielding the highest MI values, lowest COV values, and proportionately-scaled lesion volumes. Although lesion-filling improved spatial normalization accuracy for each of the methods tested, these effects were small compared to differences between normalization algorithms.ConclusionsSPM (CAT12) warping, ideally combined with lesion-filling, is recommended for use in future MS brain imaging studies requiring spatial normalization.     

Magnetic resonance imaging measurements of vascular permeability and extracellular volume fraction of breast tumors by dynamic Gd-DTPA-enhanced relaxometry

Vascular permeability (k(ep), min(-1)) and extracellular volume fraction (v(e)) are tissue parameters of great interest to characterize malignant tumor lesions. Indeed, it is well known that tumors with high blood supply better respond to therapy than poorly vascularized tumors, and tumors with large extracellular volume tend to be more malignant than tumors showing lower extracellular volume. Furthermore, the transport of therapeutic agents depends on both extracellular volume fraction and vessel permeability. Thus, before treatment, these tissue parameters may prove useful to evaluate tumor aggressiveness and to predict responsiveness to therapy and variations during cytotoxic therapies could allow to assess treatment efficacy and early modified therapy schedules in case of poor responsiveness. As a consequence, there is a need to develop methods that could be routinely used to determine these tissue parameters. In this work, blood-tissue permeability and extracellular volume fraction information were derived from magnetic resonance imaging dynamic longitudinal relaxation rate (R(1)) mapping obtained after an intravenous bolus injection of Gd-DTPA in a group of 92 female patients with breast lesions, 68 of these being histologically proven to be with carcinoma. For the sake of comparison, 24 benign lesions were studied. The measurement protocol based on two-dimensional gradient echo sequences and a monoexponential plasma kinetic model was that validated in the occasion of previous animal experiments. As a consequence of neoangiogenesis, results showed a higher permeability in malignant than in benign lesions, whereas the extracellular volume fraction value did not allow any discrimination between benign and malignant lesions. The method, which can be easily implemented whatever the imaging system used, could advantageously be used to quantify lesion parameters (k(ep) and v(e)) in routine clinical imaging. Because of its large reproducibility, the method could be useful for intersite comparisons and follow-up studies.     

Differentiation of bone metastases from prostate cancer and benign red marrow depositions of the pelvic bone with multiparametric MRI

PurposeTo investigate the diagnostic utilities of imaging parameters derived from T1-weighted imaging (T1WI), diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to differentiate bone metastases from prostate cancer and benign red marrow depositions of the pelvic bone.Materials and methodsThirty-six lesions from 36 patients with prostate cancer were analyzed with T1WI, DWI, and DCE-MRI. The lesions were classified in the bone metastases (n = 22) and benign red marrow depositions (n = 14). Lesion-muscle ratio (LMR), apparent diffusion coefficient (ADC), volume transfer constant (K^trans), reflux rate (Kep), and volume fraction of the extravascular extracellular matrix (Ve) values were obtained from the lesions. The imaging parameters of the both groups were compared using the Mann-Whitney U test, receiver operating characteristics (ROC) curves were analyzed. For the ROC curves, area under the curves (AUCs) were compared.ResultsThe ADC, K^trans, K_ep, and V_e values of bone metastases were significantly higher than those of benign red marrow depositions (Mann-Whitney U test, p < 0.05). However, there was no significant difference in LMR between the two groups (Mann-Whitney U test, p = 0.360). The AUCs of K^trans_, K_ep, ADC, V_e, and LMR were 0.896, 0.844, 0.812, 0.724, and 0.448, respectively. In the pairwise comparison of ROC curves, the AUCs of K^trans and K_ep was significantly higher than LMR.ConclusionsK^trans, K_ep, V_e, and ADC values can be used as imaging tools to differentiate bone metastases from prostate cancer and benign red marrow depositions of the pelvic bone.     

Associations between IVIM histogram parameters and histopathology in rectal cancer

PurposeHistogram analysis can better reflect tumor heterogeneity than conventional imaging analysis. The present study analyzed possible correlations between histogram analysis parameters derived from Intravoxel-incoherent imaging (IVIM) and histopathological features in rectal cancer (RC).MethodsSeventeen patients with histopathologically proven rectal adenocarcinomas were retrospectively acquired. In all cases, pelvic MRI was performed. Diffusion weighted imaging was obtained using a multi-slice single-shot echo-planar imaging sequence with b values of 0, 50, 200, 500 and 1000 s/mm². Simplified IVIM analysis was performed using the IntelliSpace portal, version 10 and the following images were generated: f (perfusion fraction) map, D (true diffusion coefficient) map, and ADC map utilizing all b-values. Histogram based analysis of signal intensities was performed for every IVIM map using an in-house matlab tool. Histopathology was investigated using Ki 67 specimens with calculation of Ki 67-index and cellularity. CD31 stained specimens were used for calculation of microvessel density (MVD).ResultsThere were statistically significant correlations between Ki 67 index and mode derived from ADC as well as entropy from f, r=−0.50, p=.04 and r=−0.55, p=.02, respectively. MVD correlated well with parameters derived from f.ConclusionIVIM histogram analysis parameters can reflect histopathology in RC. ADC and D values are associated with proliferation potential. Perfusion fraction f is associated with MVD.     

Gadolinium- and superparamagnetic-iron-oxide-enhanced MR findings of intrapancreatic accessory spleen in five patients

Purpose

The purposes of this study were to describe dynamic gadolinium-enhanced magnetic resonance imaging (MRI) findings of intrapancreatic accessory spleen(s) (IPAS) in five patients and to show how superparamagnetic iron oxide (SPIO) enhancement can be used for definite characterization in two cases.

Materials and Methods

An MRI database was searched for patients who had pancreatic tail lesions with imaging features compatible with IPAS between June 2005 and July 2007. Five (four male, one female) patients (age: mean±S.D., 58±9.8 years; range, 50–75 years) were identified. All patients were examined with standard gadolinium-enhanced MRI protocol. Additionally, two patients were examined with SPIO-enhanced MRI protocol. All MRI examinations were retrospectively and blindly evaluated by two radiologists for the predetermined findings, and their final diagnoses were noted.

Results

One pancreatic tail lesion was detected in each patient. All of these lesions were single, focal, well-marginated and located within 3 cm of the distal tail of the pancreas. The mean size (mean±S.D.) of the lesions was (2.02±0.64)×(1.72±0.42) cm², and all lesions had a rounded morphology. The signal intensity of all lesions was similar to that of the spleen on all sequences, including precontrast, postgadolinium and post-SPIO sequences. The reviewers confidently diagnosed IPAS in two patients who had SPIO-enhanced MRI. In the remaining three patients, the reviewers favored the diagnosis of IPAS based on the findings of standard gadolinium-enhanced MRI; however, they could not definitively exclude the other differential diagnoses.

Conclusion

The discovery of a well-marginated, rounded mass in the distal aspect of the tail of the pancreas with signal intensity features of the spleen on all precontrast and postgadolinium sequences suggests the diagnosis of IPAS. However, SPIO-enhanced MRI can be used to characterize the lesion and to establish the definite diagnosis of IPAS in case of clinical doubt.     

Abnormal blood-brain barrier water exchange in chronic multiple sclerosis lesions: A preliminary study

Relapsing-remitting multiple sclerosis (RRMS) is associated with persistent blood-brain barrier (BBB) dysfunction. The impact of this persistent dysfunction in both active and chronic MS lesions has yet to be investigated due to technological challenges associated with invasive assessment of BBB water transportation (e.g. ¹⁵O-PET). The purpose of this study was to test if persistent BBB dysfunction in RRMS manifests as lower BBB water exchange in chronic lesions using a recently developed noninvasive MRI paradigm. Patients with relapsing-remitting MS and healthy subjects were recruited for this prospective study. The novel Intrinsic Diffusivity Encoding of Arterial Labeled Spins (IDEALS) MRI method was used to map BBB water extraction fraction (E_w) and water permeability surface area product (PS_w), as well as cerebral blood flow (CBF). Regional differences in BBB water exchange were evaluated between MS patients (normal appearing white matter [NAWM] and normal appearing gray matter [NAGM]) and healthy subjects (white matter [WM] and gray matter [GM]) and within MS subjects in non gadolinium-based contrast-agent (GBCA) enhancing chronic lesions, perilesional areas, and NAWM. Significantly lower PS_w and E_w were observed in NAWM compared to WM (ΔPS_w: −11.9 mL/100 g/min, p < .05; ΔE_w: −4.3%, p < .01). Significantly lower E_w was observed in NAGM compared to GM (ΔE_w: −12.1%, p < .01). Significantly lower PS_w and CBF were observed in non-GBCA contrast enhancing lesions compared to NAWM (ΔPS_w = −11.5 mL/100 g/min, p < .05; ΔCBF = −8.1 mL/100 g/min, p < .05). E_w was significantly higher in non-GBCA enhancing chronic MS lesions compared to NAWM (ΔE_w = 1.6%, p < .05). The lower BBB water exchange in chronic MS lesions is consistent with previously reported observations and may demonstrate metabolic changes associated with MS.     

Recovery of accurate T2 from historical 1.5 tesla proton density and T2-weighted images: Application to 7-year T2 changes in multiple sclerosis brain

ObjectiveTo determine accurate quantitative transverse relaxation times (T₂) using retrospective clinical images and apply it to examine 7-year changes in multiple sclerosis (MS) brain.MethodsA method for T₂ mapping from retrospective proton density (PD) and T₂-weighted fast spin echo images was recently introduced, but requires measurement of flip angles. We examined whether 1.5 T flip angle variation in brain can be predicted, thus enabling T₂ analysis of historical PD and T₂-weighted images without a concurrent flip angle map. After method validation in healthy volunteers, retrospective longitudinal T₂ analysis was performed in 14 MS subjects over seven years. Changes in patient T₂ values were compared with brain atrophy, T₂ lesion load and disability score in MS.ResultsSimilar flip angle maps across volunteers enabled retrospective T₂ from PD and T₂-weighted images even when different refocusing angles were used. Over seven years, significant T₂ changes of 2–4% were observed when using T₂ modelling and the 7-year effect size for globus pallidus T₂ was 0.56, which was more significant than brain atrophy. No significant T₂ results were found when using exponential fit, which cannot account for refocusing angle variation. Moreover, change is T₂ in globus pallidus and internal capsule correlated with MS disability score over time when using T₂ modelling.ConclusionsAccurate quantitative T₂ can be extracted from standard clinical 1.5 T MRI exams that include PD and T₂-weighted imaging even when no flip angle map is available. This method was applied retrospectively to examine seven year changes in MS.     

Comparison of multiple sclerosis clinical subgroups using navigated spin echo diffusion-weighted imaging.

The apparent diffusion coefficient (ADC) of tissue provides an indication of the size, shape, and orientation of the water spaces in tissue. Thus, pathologic differences between lesions in multiple sclerosis (MS) patients with different clinical courses may be reflected by changes in ADC measurements in lesions and white matter. Twelve healthy subjects and 35 MS patients with a relapsing-remitting (n = 10), benign (n = 8), secondary progressive (n = 8) and primary progressive (n = 9) clinical course were studied. T2-weighted and post-gadolinium T1-weighted images were obtained using a 1.5 T Signa Echospeed magnetic resonance imaging (MRI) system. Diffusion-weighted imaging was implemented using a pulsed gradient spin echo (PGSE) sequence with diffusion gradients applied in turn along three orthogonal directions in order to obtain the average apparent diffusion coefficient (ADCav). Navigator echo correction and cardiac gating were used to reduce motion artifact. ADC maps were derived using a two point calculation based on the Stejskal-Tanner formula. Diffusion anisotropy was estimated using the van Gelderen formula to calculate an anisotropy index. MS lesions had a higher ADC and reduced anisotropy compared with normal appearing white matter. Highest ADC values were found in gadolinium enhancing lesions and non-enhancing hypointense lesions on T1-weighted imaging. MS white matter had a slightly higher ADC and lower anisotropy than white matter of healthy subjects. Lesion and white matter ADC values did not differ between patients with different clinical courses of MS. There was no correlation between lesion ADC and disability. Diffusion-weighted imaging with measurement of ADC using the PGSE method provides quantitative information on acute edematous MS lesions and chronic lesions associated with demyelination and axonal loss but does not distinguish between clinical subtypes of MS.     

Blood oxygenation level dependent magnetic resonance imaging and diffusion weighted MRI imaging for benign and malignant breast cancer discrimination

PurposeThe purpose of this study is to assess Blood oxygenation level dependent Magnetic Resonance Imaging (BOLD-MRI) and Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) in the differentiation of benign and malignant breast lesions.MethodsFifty-nine breast lesions (26 benign and 33 malignant lesions) pathologically proven in 59 patients were included in this retrospective study. As BOLD parameters were estimated basal signal S₀ and the relaxation rate R2*, diffusion and perfusion parameters were derived by DWI (pseudo-diffusion coefficient (Dp), perfusion fraction (fp) and tissue diffusivity (Dt)). Wilcoxon-Mann-Whitney U test and Receiver operating characteristic (ROC) analyses were calculated and area under ROC curve (AUC) was obtained. Moreover, pattern recognition approaches (linear discrimination analysis (LDA), support vector machine, k-nearest neighbours, decision tree) with least absolute shrinkage and selection operator (LASSO) method and leave one out cross validation approach were considered.ResultsA significant discrimination was obtained by the standard deviation value of S0, as BOLD parameter, that reached an AUC of 0.76 with a sensitivity of 65%, a specificity of 85% and an accuracy of 76%. No significant discrimination was obtained considering diffusion and perfusion parameters. Considering LASSO results, the features to use as predictors were all extracted parameters except that the mean value of R2* and the best result was obtained by a LDA that obtained an AUC = 0.83, with a sensitivity of 88%, a specificity of 77% and an accuracy of 83%.ConclusionsGood performance to discriminate benign and malignant lesions could be obtained using BOLD and DWI derived parameters with a LDA classification approach. However, these findings should be proven on larger and several dataset with different MR scanners.     

Triple-negative breast cancer: Pretreatment magnetic resonance imaging features and clinicopathological factors associated with recurrence

PurposeWe aimed to investigate the magnetic resonance imaging (MRI) features and clinicopathologic factors with recurrence of triple-negative breast cancer (TNBC).Patients and methodsWe identified 281 patients with 288 surgically confirmed TNBC lesions who underwent pretreatment MRI between 2009 and 2015. The presence of intratumoral high signal on T2-weighted images, high-signal rim on diffusion-weighted images (DWI), and rim enhancement on the dynamic contrast-enhanced MRI and clinicopathological data were collected. Cox proportional analysis was performed.ResultsOf the 288 lesions, 36 (12.5%) recurred after a median follow-up of 18 months (range, 3.6–68.3 months). Rim enhancement (hazard ratio [HR] = 3.15; 95% confidence interval [CI] = 1.01, 9.88; p = .048), and lymphovascular invasion (HR = 2.73, 95% CI = 1.20, 6.23; p = .016) were independently associated with disease recurrence. While fibroglandular volume, background parenchymal enhancement, intratumoral T2 high signal, and high-signal rim on DWI, were not found to be risk factors for recurrence.ConclusionPretreatment MRI features may help predict a high risk of recurrence in patients with TNBC.