DWI Data Research Articles

Chemical shift-encoded multishot EPI for navigator-free prostate DWI.

DWI is an important contrast for prostate MRI to enable early and accurate detection of cancer. This study introduces a Dixon 3-shot-EPI protocol with structured low-rank reconstruction for navigator-free DWI. The aim is to overcome the limitations of single-shot EPI (ssh-EPI), such as geometric distortions and fat signal interference, while addressing the motion-induced phase variations of multishot EPI and simultaneously allowing water/fat separation. DWI data were acquired from 7 healthy volunteers using both Dixon 3-shot EPI and standard fat-suppressed ssh-EPI with similar scan times for comparison. Two readers evaluated image quality using a 5-point Likert scale regarding different aspects. The ADC values were quantitatively compared between protocols. To show feasibility in a clinical setting, the protocol was applied to two patients. From the reader scores, Dixon 3-shot EPI significantly reduced geometric distortion compared with ssh-EPI (p < 0.01), with no significant differences in edge definition, SNR, or overall image quality. There was no significant difference in ADC values between the two protocols. However, the Dixon multishot-EPI protocol offered advantages such as self-referenced B0 map-driven distortion correction, greater flexibility in imaging parameters, and superior fat suppression. In the patient data, the lesion could be clearly identified in both protocols and on the associated ADC maps. The proposed Dixon 3-shot-EPI protocol shows promise as an alternative to ssh-EPI for prostate DWI, providing reduced geometric distortions and improved fat suppression. It addresses common DWI issues based on EPI and enhances scanning flexibility, indicating potential for optimized imaging.

Linking functional and structural brain organisation with behaviour in healthy adults.

Multimodal data integration approaches, such as Linked Independent Component Analysis (LICA), increase sensitivity to brain-behaviour relationships and allow us to probe the relationship between modalities. Here we focus on inter-regional functional and structural organisation to determine if organisational patterns persist across modalities and if investigating multi-modality organisations provides increased sensitivity to brain-behaviour associations. We utilised multimodal magnetic resonance imaging (MRI; T1w, resting-state functional [fMRI] and diffusion weighted [DWI]) and behavioural data from the Human Connectome Project (HCP, n=676; 51% female). Unimodal features were extracted to produce individual grey matter density maps, probabilistic tractography connectivity matrices and connectopic maps from the T1w, DWI and fMRI data, respectively. DWI and fMRI analyses were restricted to subcortical regions for computational reasons. LICA was then used to integrate features, generating 100 novel independent components. Associations between these components and demographic/behavioural (n=308) variables were examined. 15 components were significantly associated with various demographic/behavioural measures. 2 components were strongly related to various measures of intoxication, driven by DWI and resemble components previously identified. Another component was driven by striatal functional data and related to working memory. A small number of components showed shared variance between structure and function but none of these displayed any significant behavioural associations. Our working memory findings provide support for the use of fMRI connectopic mapping in future research of working memory. Given the lack of behaviourally relevant shared variance between functional and structural organisation, as indexed here, we question the utility of integrating connectopic maps and tractography data.

Presentation of microstructural diffusion components by color schemes in abdominal organs.

Development of a color scheme representation to facilitate the interpretation of tri-exponential DWI data from abdominal organs, where multi-exponential behavior is more pronounced. Multi-exponential analysis of DWI data provides information about the microstructure of the tissue under study. The tri-exponential signal analysis generates numerous parameter images that are difficult to analyze individually. Summarized color images can simplify at-a-glance analysis. A color scheme was developed in which the slow, intermediate, and fast diffusion components were each assigned to a different red, green, and blue color channel. To improve the appearance of the image, histogram equalization, gamma correction, and white balance were used, and the processing parameters were adjusted. Examples of the resulting color maps of the diffusion fractions of healthy and pathological kidney and prostate are shown. The color maps obtained by the presented method show the merged information of the slow, intermediate, and fast diffusion components in a single view. A differentiation of the different fractions becomes clearly visible. Fast diffusion regimes, such as in the renal hilus, can be clearly distinguished from slow fractions, such as in dense tumor tissue. Combining the diffusion information from tri-exponential DWI analysis into a single color image allows for simplified interpretation of the diffusion fractions. In the future, such color images may provide additional information about the microstructural nature of the tissue under study.

Spatiotemporal encoding MRI in a portable low-field system.

Demonstrate the potential of spatiotemporal encoding (SPEN) MRI to deliver largely undistorted 2D, 3D, and diffusion weighted images on a 110 mT portable system. SPEN's quadratic phase modulation was used to subsample the low-bandwidth dimension of echo planar acquisitions, delivering alias-free images with an enhanced immunity to image distortions in a laboratory-built, low-field, portable MRI system lacking multiple receivers. Healthy brain images with different SPEN time-bandwidth products and subsampling factors were collected. These compared favorably to EPI acquisitions including topup corrections. Robust 3D and diffusion weighted SPEN images of diagnostic value were demonstrated, with 2.5 mm isotropic resolutions achieved in 3 min scans. This performance took advantage of the low specific absorption rate and relative long TEs associated with low-field MRI. SPEN MRI provides a robust and advantageous fast acquisition approach to obtain faithful 3D images and DWI data in low-cost, portable, low-field systems without parallel acceleration.

MIITRA atlas: high resolution FOD template of the older adult brain

AbstractBackgroundHigh quality fiber orientation distribution (FOD) template of the older adult brain facilitates white matter studies in older adults. This work compared several methods to construct an FOD template of the older adult brain in the space of the MIITRA atlas(Wu et al.,2022).MethodT1w and DWI data from 400 non‐demented older adults (50% male;64.9‐98.9 yrs;54% white,43% black)(Bennett et al.,2018;Barnes et al.,2012) participating in the construction of the MIITRA atlas were used. Method 1: FOD‐based single modality registration(cost function:mean squared difference)(MRtrix3). Method 2: The nonlinear transformations generated from MIITRA T1w and DTI template construction were applied to the FODs(Wu et al.,2021). Method 3: Multichannel registration based on T1w, T2w, FA and MD information with equal weights(cost function:cross‐correlation)(ANTs). Method 4: Multichannel registration based on T1w and FOD information with equal weights(cost function:cross‐correlation)(MRtrix3). Method 5: Multichannel registration based on T1w, FA and FOD information with weights of 4,3,3 respectively(cost function:cross‐correlation)(MRtrix3). Method 6: Same as Method 5 followed by FOD registration performed on the FOD data that were already transformed by Method 5(cost function:cross‐correlation)(MRtrix3). The FOD templates generated with different methods were compared by visual inspection, image sharpness of the total apparent fiber density (AFD) map, precision of spatial normalization of FOD data used in template construction, and spatial matching with the existing MIITRA T1w and DTI templates.ResultThe FOD templates generated with Methods 2, 3 and 6 exhibited higher image sharpness(Fig.1A). Fiber crossings were more clearly defined in the FOD templates generated with Methods 1,4,5,6(Fig.1B). Method 6 allowed higher ACC and lower MSD of spatially normalized FOD data, indicating higher precision in spatial normalization(Fig.2A,B). Method 2 provided the highest spatial matching with the existing MIITRA templates, followed closely by Methods 4,5,6(Fig.2C,D). Overall, the FOD template generated with Method 6 achieved high image quality, was constructed based on precisely matched FOD data, and exhibited high spatial matching to the existing MIITRA templates(Fig.3).ConclusionAn FOD template of the older adult brain was constructed for the MIITRA atlas. The new template exhibits high image quality, is constructed based on precisely matched FOD data, and exhibits high spatial matching to other existing MIITRA templates.

Learning ADC maps from accelerated radial k-space diffusion-weighted MRI in mice using a deep CNN-transformer model.

To accelerate radially sampled diffusion weighted spin-echo (Rad-DW-SE) acquisition method for generating high quality ADC maps. A deep learning method was developed to generate accurate ADC maps from accelerated DWI data acquired with the Rad-DW-SE method. The deep learning method integrates convolutional neural networks (CNNs) with vision transformers to generate high quality ADC maps from accelerated DWI data, regularized by a monoexponential ADC model fitting term. A model was trained on DWI data of 147 mice and evaluated on DWI data of 36 mice, with acceleration factors of 4× and 8× compared to the original acquisition parameters. Ablation studies and experimental results have demonstrated that the proposed deep learning model generates higher quality ADC maps from accelerated DWI data than alternative deep learning methods under comparison when their performance is quantified in whole images as well as in regions of interest, including tumors, kidneys, and muscles. The deep learning method with integrated CNNs and transformers provides an effective means to accurately compute ADC maps from accelerated DWI data acquired with the Rad-DW-SE method.

Predicting TERT promoter mutation status using 1H-MR spectroscopy and stretched-exponential model of diffusion-weighted imaging in IDH-wildtype diffuse astrocytic glioma without intense enhancement.

Isocitrate dehydrogenase (IDH)-wildtype diffuse astrocytic glioma with telomerase reverse transcriptase (TERT) promoter mutation is defined as glioblastoma by the WHO 2021 criteria, revealing that TERT promotor mutation is highly associated with tumor aggressiveness. The aim of this study was to identify features from MR spectroscopy (MRS) and multi-exponential models of DWI distinguishing wild-type TERT (TERTw) from TERT promoter mutation (TERTm) in IDH-wildtype diffuse astrocytic glioma. Participants comprised 25 adult patients with IDH-wildtype diffuse astrocytic glioma. Participants were classified into TERTw and TERTm groups. Point-resolved spectroscopy sequences were used for MRS data acquisition. DWI was performed with 13 different b-factors. Peak height ratios of NAA/Cr and Cho/Cr were calculated from MRS data. Mean apparent diffusion coefficient (ADC), perfusion fraction (f), diffusion coefficient (D), pseudo-diffusion coefficient (D*), distributed diffusion coefficient (DDC), and heterogeneity index (α) were obtained using multi-exponential models from DWI data. Each parameter was compared between TERTw and TERTm using the Mann-Whitney U test. Correlations between parameters derived from MRS and DWI were also evaluated. NAA/Cr and Cho/Cr were both higher for TERTw than for TERTm. The α of TERTw was smaller than that of TERTm, while the f of TERTw was higher than that of TERTm. NAA/Cr correlated negatively with α, but not with other DWI parameters. Cho/Cr did not show significant correlations with any DWI parameters. The combination of NAA/Cr and α may have merit in clinical situation to predict the TERT mutation status of IDH-wildtype diffuse astrocytic glioma without intense enhancement.

Simultaneous superresolution reconstruction and distortion correction for single-shot EPI DWI using deep learning.

Single-shot (SS) EPI is widely used for clinical DWI. This study aims to develop an end-to-end deep learning-based method with a novel loss function in an improved network structure to simultaneously increase the resolution and correct distortions for SS-EPI DWI. Point-spread-function (PSF)-encoded EPI can provide high-resolution, distortion-free DWI images. A distorted image from SS-EPI can be described as the convolution between a PSF function with a distortion-free image. The deconvolution process to recover the distortion-free image can be achieved with a convolution neural network, which also learns the mapping function between low-resolution SS-EPI and high-resolution reference PSF-EPI to achieve superresolution. To suppress the oversmoothing effect, we proposed a modified generative adversarial network structure, in which a dense net with gradient map guidance and a multilevel fusion block was used as the generator. A fractional anisotropy loss was proposed to utilize the diffusion anisotropy information among diffusion directions. In vivo brain DWI data were used to test the proposed method. The results show that distortion-corrected high-resolution DWI images with restored structural details can be obtained from low-resolution SS-EPI images by taking advantage of the high-resolution anatomical images. Additionally, the proposed network can improve the quantitative accuracy of diffusion metrics compared with previously reported networks. Using high-resolution, distortion-free EPI-DWI images as references, a deep learning-based method to simultaneously increase the perceived resolution and correct distortions for low-resolution SS-EPI was proposed. The results show that DWI image quality and diffusion metrics can be improved.

Comparison of Diffusion Kurtosis Imaging and Standard Mono-Exponential Apparent Diffusion Coefficient in Diagnosis of Significant Prostate Cancer-A Correlation with Gleason Score Assessed on Whole-Mount Histopathology Specimens.

The study was undertaken to compare the diagnostic performance of diffusion kurtosis imaging (DKI) with the standard monoexponential (ME) apparent diffusion coefficient (ADC) model in the detection of significant prostate cancer (PCa), using whole-mount histopathology of radical prostatectomy specimens as a reference standard. 155 patients with prostate cancer had undergone multiparametric magnetic resonance imaging (mpMRI) at 3T before prostatectomy. Quantitative diffusion parameters-the apparent diffusion coefficient corrected for non-Gaussian behavior (Dapp), kurtosis (K), ADC1200, and ADC2000 were correlated with Gleason score and compared between cancerous and benign tissue and between GS ≤ 3 + 3 and GS ≥ 3 + 4 tumors. The mean values of all diffusion parameters (Dapp, K, ADC1200, ADC2000) were significantly different both between malignant and benign tissue and between GS ≤ 3 + 3 and GS ≥ 3 + 4 tumors. Although the kurtosis model was better fitted to DWI data, the diagnostic performance in receiver operating characteristic (ROC) analysis of DKI and the standard ADC model in the detection of significant PCa was similar in the peripheral zone (PZ) and in peripheral and transitional zones (TZ) together. In conclusion, our study was not able to demonstrate a clear superiority of the kurtosis model over standard ADC in the diagnosis of significant PCa in PZ and in both zones combined.

Differentiation of Pilocytic Astrocytoma from Glioblastoma using a Machine-Learning framework based upon quantitative T1 perfusion MRI

Background and purposeDifferentiation of pilocytic astrocytoma (PA) from glioblastoma is difficult using conventional MRI parameters. The purpose of this study was to differentiate these two similar in appearance tumors using quantitative T1 perfusion MRI parameters combined under a machine learning framework. Materials and methodsThis retrospective study included age/sex and location matched 26 PA and 33 glioblastoma patients with tumor histopathological characterization performed using WHO 2016 classification. Multi-parametric MRI data were acquired at 3 T scanner and included T1 perfusion and DWI data along with conventional MRI images. Analysis of T1 perfusion data using a leaky-tracer-kinetic-model, first-pass-model and piecewise-linear-model resulted in multiple quantitative parameters. ADC maps were also computed from DWI data. Tumors were segmented into sub-components such as enhancing and non-enhancing regions, edema and necrotic/cystic regions using T1 perfusion parameters. Enhancing and non-enhancing regions were combined and used as an ROI. A support-vector-machine classifier was developed for the classification of PA versus glioblastoma using T1 perfusion MRI parameters/features. The feature set was optimized using a random-forest based algorithm. Classification was also performed between the two tumor types using the ADC parameter. ResultsT1 perfusion parameter values were significantly different between the two groups. The combination of T1 perfusion parameters classified tumors more accurately with a cross validated error of 9.80% against that of ADC's 17.65% error. ConclusionThe approach of using quantitative T1 perfusion parameters based upon a support-vector-machine classifier reliably differentiated PA from glioblastoma and performed better classification than ADC.

Higher fiber‐tract myelin levels predict lower longitudinal rates of fibrillar tau independent of amyloid

AbstractBackgroundFibrillar tau progresses from initial epicenters of high tau accumulation to closely connected cortical regions during the course of Alzheimer’s disease (AD). Myelin ensheaths the interregional axonal connections, where ontogenetically late and thinly myelinated fibers are more susceptible to damage and may propel the propagation of fibrillar tau, as suggested by post‐mortem data (https://doi.org/10.1093/brain/awv236). Here we tested for the first time, in two independent samples of AD patients whether 1) regions connected to epicenters by more thickly myelinated fiber tracts show lower rates of tau‐PET accumulation, and 2) differences in regional amyloid‐PET levels explain the association between myelin and tau‐PET accumulation.MethodsWe included two independently recruited samples with longitudinal AV1451‐tau‐PET of amyloid‐PET positive non‐demented and demented individuals (ADNI: n = 123, mean FU = 1.53[0.69‐3.95] years; BioFINDER: n = 39, mean FU = 1.87[1.21‐2.78] years). Adopting our previously established connectivity‐based prediction approach (https://doi.org/10.1126/sciadv.abd1327), we first determined tau epicenters by selecting the top 10% ROIs with highest tau‐PET uptake. Next, we computed for each tau epicenter the connectivity‐based distance to the ROIs in the remainder of the brain, based on resting‐state fMRI data from the Human Connectome Project (HCP). To determine the myelin levels in the underlying fiber tracts, we reconstructed fiber‐tracts for each epicenter‐to‐ROI pair based on DWI data from HCP and extracted regional myelin‐water‐fraction (MWF) values for each tract from an overlaid myelin‐water‐imaging atlas (https://doi.org/10.1111/jon.12657). In order to test whether fiber tract myelination attenuates the connectivity‐based tau‐spreading, we tested the interaction between epicenter connectivity‐based distance and MWF in the corresponding fiber‐tracts on the rate of tau‐PET accumulation in the connected regions. In a sensitivity analysis restricted to the sufficiently powered ADNI‐sample, we controlled the interaction analysis for regional amyloid‐PET levels within target ROIs.ResultsWe found a significant interaction of connectivity‐based distance by fiber‐tract MWF on the rate of tau‐PET accumulation (ADNI: β = 2.08, p = 0.009; BioFINDER: β = 1.876, p = 0.028; Figure 1), such that regions connected by highly myelinated fiber‐tracts showed lower rates of connectivity‐dependent tau accumulation. When controlling for regional amyloid‐PET levels, the interaction remained significant (ADNI: β = 2.23, p = 0.006).ConclusionHigher fiber‐tract myelination is associated with lower rates of tau progression in AD, independent of beta‐amyloid.

Image downsampling expedited adaptive least-squares (IDEAL) fitting improves intravoxel incoherent motion (IVIM) analysis in the human kidney.

To improve the reliability of intravoxel incoherent motion (IVIM) model parameter estimation for the DWI in the kidney using a novel image downsampling expedited adaptive least-squares (IDEAL) approach. The robustness of IDEAL was investigated using simulated DW-MRI data corrupted with different levels of Rician noise. Subsequently, the performance of the proposed method was tested by fitting bi- and triexponential IVIM model to in vivo renal DWI data acquired on a clinical 3 Tesla MRI scanner and compared to conventional approaches (fixed D* and segmented fitting). The numerical simulations demonstrated that the IDEAL algorithm provides robust estimates of the IVIM parameters in the presence of noise (SNR of 20) as indicated by relatively low absolute percentage bias (maximal sMdPB <20%) and normalized RMSE (maximal RMSE <28%). The analysis of the in vivo data showed that the IDEAL-based IVIM parameter maps were less noisy and more visually appealing than those obtained using the fixed D* and segmented methods. Further, coefficients of variation for nearly all IVIM parameters were significantly reduced in cortex and medulla for IDEAL-based biexponential (coefficients of variation: 4%-50%) and triexponential (coefficients of variation: 7.5%-75%) IVIM modelling compared to the segmented (coefficients of variation: 4%-120%) and fixed D* (coefficients of variation: 17%-174%) methods, reflecting greater accuracy of this method. The proposed fitting algorithm yields more robust IVIM parameter estimates and is less susceptible to poor SNR than the conventional fitting approaches. Thus, the IDEAL approach has the potential to improve the reliability of renal DW-MRI analysis for clinical applications.

Improved geometric accuracy of whole body diffusion-weighted imaging at 1.5T and 3T using reverse polarity gradients

Whole body diffusion-weighted imaging (WB-DWI) is increasingly used in oncological applications, but suffers from misalignments due to susceptibility-induced geometric distortion. As such, DWI and structural images acquired in the same scan session are not geometrically aligned, leading to difficulties in e.g. lesion detection and segmentation. In this work we assess the performance of the reverse polarity gradient (RPG) method for correction of WB-DWI geometric distortion. Multi-station DWI and structural magnetic resonance imaging (MRI) data of healthy controls were acquired at 1.5T (n = 20) and 3T (n = 20). DWI data was distortion corrected using the RPG method based on b = 0 s/mm2 (b0) and b = 50 s/mm2 (b50) DWI acquisitions. Mutual information (MI) between low b-value DWI and structural data increased with distortion correction (P < 0.05), while improvements in region of interest (ROI) based similarity metrics, comparing the position of incidental findings on DWI and structural data, were location dependent. Small numerical differences between non-corrected and distortion corrected apparent diffusion coefficient (ADC) values were measured. Visually, the distortion correction improved spine alignment at station borders, but introduced registration-based artefacts mainly for the spleen and kidneys. Overall, the RPG distortion correction gave an improved geometric accuracy for WB-DWI data acquired at 1.5T and 3T. The b0- and b50-based distortion corrections had a very similar performance.

Computed DWI MRI Results in Superior Capability for N-Stage Assessment of Non-Small Cell Lung Cancer Than That of Actual DWI, STIR Imaging, and FDG-PET/CT.

Computed diffusion-weighted imaging (cDWI) is a mathematical computation technique that generates DWIs for any b-value by using actual DWI (aDWI) data with at least two different b-values and may improve differentiation of metastatic from nonmetastatic lymph nodes. To determine the appropriate b-value for cDWI to achieve a better diagnostic capability for lymph node staging (N-staging) in non-small cell lung cancer (NSCLC) patients compared to aDWI, short inversion time (TI) inversion recovery (STIR) imaging, or positron emission tomography with 2-[fluorine-18] fluoro-2-deoxy-d-glucose combined with computed tomography (FDG-PET/CT). Prospective. A total of 245 (127 males and 118 females; mean age 72 years) consecutive histopathologically confirmed NSCLC patients. A 3 T, half-Fourier single-shot turbo spin-echo sequence, electrocardiogram (ECG)-triggered STIR fast advanced spin-echo (FASE) sequence with black blood and STIR acquisition and DWI obtained by FASE with b-values of 0 and 1000 sec/mm2 . From aDWIs with b-values of 0 and 1000 (aDWI1000 ) sec/mm2 , cDWI using 400 (cDWI400 ), 600 (cDWI600 ), 800 (cDWI800 ), and 2000 (cDWI2000 ) sec/mm2 were generated. Then, 114 metastatic and 114 nonmetastatic nodes (mediastinal and hilar lymph nodes) were selected and evaluated with a contrast ratio (CR) for each cDWI and aDWI, apparent diffusion coefficient (ADC), lymph node-to-muscle ratio (LMR) on STIR, and maximum standard uptake value (SUVmax ). Receiver operating characteristic curve (ROC) analysis, Youden index, and McNemar's test. Area under the curve (AUC) of CR600 was significantlylargerthan the CR400 , CR800 , CR2000 , aCR1000 , and SUVmax . Comparison of N-staging accuracy showed that CR600 was significantly higher than CR400 , CR2000 , ADC, aCR1000 , and SUVmax , although there were no significant differences with CR800 (P=0.99) and LMR (P=0.99). cDWI with b-value at 600 sec/mm2 may have potential to improve N-staging accuracy as compared with aDWI, STIR, and PET/CT. 2 TECHNICAL EFFICACY: Stage 2.

A survey by the European Society of Breast Imaging on the implementation of breast diffusion-weighted imaging in clinical practice.

ObjectivesTo perform a survey among all European Society of Breast Imaging (EUSOBI) radiologist members to gather representative data regarding the clinical use of breast DWI.MethodsAn online questionnaire was developed by two board-certified radiologists, reviewed by the EUSOBI board and committees, and finally distributed among EUSOBI active and associated (not based in Europe) radiologist members. The questionnaire included 20 questions pertaining to technical preferences (acquisition time, magnet strength, breast coils, number of b values), clinical indications, imaging evaluation, and reporting. Data were analyzed using descriptive statistics, the Chi-square test of independence, and Fisher’s exact test.ResultsOf 1411 EUSOBI radiologist members, 275/1411 (19.5%) responded. Most (222/275, 81%) reported using DWI as part of their routine protocol. Common indications for DWI include lesion characterization (using an ADC threshold of 1.2–1.3 × 10−3 mm2/s) and prediction of response to chemotherapy. Members most commonly acquire two separate b values (114/217, 53%), with b value = 800 s/mm2 being the preferred value for appraisal among those acquiring more than two b values (71/171, 42%). Most did not use synthetic b values (169/217, 78%). While most mention hindered diffusion in the MRI report (161/213, 76%), only 142/217 (57%) report ADC values.ConclusionThe utilization of DWI in clinical practice among EUSOBI radiologists who responded to the survey is generally in line with international recommendations, with the main application being the differentiation of benign and malignant enhancing lesions, treatment response assessment, and prediction of response to chemotherapy. Report integration of qualitative and quantitative DWI data is not uniform.Key Points • Clinical performance of breast DWI is in good agreement with the current recommendations of the EUSOBI International Breast DWI working group. • Breast DWI applications in clinical practice include the differentiation of benign and malignant enhancing, treatment response assessment, and prediction of response to chemotherapy. • Report integration of DWI results is not uniform. Supplementary InformationThe online version contains supplementary material available at 10.1007/s00330-022-08833-0.

Effects of echo time on IVIM quantifications of locally advanced breast cancer in clinical diffusion-weighted MRI at 3 T.

The purpose of this study was to investigate the effects of echo time dependence in IVIM quantification of the pseudo-diffusion fraction in breast cancer and whether correcting for the echo time dependence offers added clinical value. Fifteen patients with biopsy-proven breast cancer underwent a 3 T MRI examination with an extended DWI protocol at two different echo times (TE = 53 ms, b = 0, 50 s/mm2 ; TE = 77 ms, b = 0, 50, 120, 200, 400, 700 s/mm2 ). Volumes of interest were delineated around the tumors. In addition, simulated MRI data were generated for different levels of signal-to-noise ratio and two values for the blood T2 relaxation time (T2p = 100 ms and 150 ms). The pseudo-diffusion signal fraction was estimated from the simulated and in vivo tumor data using both the standard IVIM model and an extended IVIM model that accounts for the echo time dependence arising from distinct transverse relaxation times. Simulations showed that the standard IVIM model overestimated the pseudo-diffusion fraction by 25% (T2p = 100 ms) and 60 % (T2p = 150 ms) (p < 0.0001 at SNR = 50). In vivo, the estimated apparent T2 value at b = 50 s/mm2 was around 8% lower than at b = 0 s/mm2 (p = 0.01) demonstrating a removal of the signal contribution from blood with long T2 associated with pseudo-diffusion. Using two different fixed values for T2p = 100, 150 ms, the pseudo-diffusion fraction was 15% and 46% higher in the standard model compared with the echo-time-corrected model (p < 0.01). The standard IVIM model was found to overestimate the pseudo-diffusion fraction by 15% to 46% compared with the echo-time-corrected model in breast tumor DWI data acquired at 3 T. Our results suggest that a corrected model may give more accurate results in terms of signal fractions, but may not justify the added time needed to acquire the additional data in terms of clinical value.

DIAGNOSTIC ROLE OF DIFFUSION-WEIGHTED IMAGE OF THE LIVER WITH MAGNETIC RESONANCE IMAGING IN PREDICTING ABSTINENCE DISORDERS IN PATIENTS WITH ALCOHOLIC LIVER DISEASE

Objective. To assess the diagnostic role of diffusion-weighted images of the liver with magnetic resonance imaging in predicting abstinence disorders in patients with alcoholic liver disease.&#x0D; Methods. A total of 122 patients with ALD aged 48±5.4 years were examined. The survey algorithm we used included: performing liver DWI with MRI (n=122) with b-value values of 100/600/1000, ultrasound of abdominal organs with clinical elastography – 97 (80%) patients. Trepan liver biopsy was chosen as a reference method (n=64).&#x0D; Results. The patients were monitored for 2.5 years. The terms of follow-up were selected individually, depending on the results of clinical and laboratory research methods. A high correlation was established (r=0.879), when comparing clinical elastography and quantitative indicators of DWI of the liver, at admission and during dynamic observation of patients, also at the middle level, the data obtained correlated with the results of trephine biopsy of the liver (r=0.721). After 3 months, 6 (15%) of 40 patients showed normalization of biochemical blood test parameters with no diffusion restriction according to the results of DWI of the liver. Based on the results obtained, a high correlation was noted between changes in the biochemical blood test and MRI data in the DWI mode. After 9 months of follow-up, according to DWI data, 34 patients showed persistence of cytolysis syndrome and limited diffusion on DWI of the liver. After collecting an additional history and clarifying the details of the lifestyle of the patients' relatives, it was found that these patients continued to consume alcoholic beverages against the background of the received treatment, which was manifested by the presence of diffusion restriction on MRI in the DWI mode, which was a magnetic resonance sign of the presence of inflammatory processes in the structure of the parenchyma liver. After 12 months, positive dynamics – the absence of diffusion restriction according to the results of DWI of the liver was noted in 34 patients, which indicates the effectiveness of using the qualitative characteristics of DWI of the liver to assess the violation of the abstinence regimen (AUROC=0.906 (95% CI 0.872-0.916)). But in 16 (13%) patients from this group, changes in the biochemical blood test were noted, but no diffusion limitation was noted according to the DWI of the liver. Patients (n=16) underwent a correction of the received treatment – after 1 month there was a positive trend. There was a correlation of quantitative parameters of DWI of the liver with clinical forms of ALD, regardless of the presence or absence of diffusion restriction (r=0.936). Next, we assessed the prognostic and diagnostic significance of the developed criteria for DWI of the liver for patients with ALD on admission. The results of the study indicated the effectiveness of using the diagnostic and prognostic model of MRI in the DWI mode for patients with ALD on admission and in dynamic observation.&#x0D; Conclusions. 1. A high correlation was found between the quantitative parameters of DWI of the liver and clinical elastography (r=0.879) at admission and follow-up. Average correlation relationship of DWI of the liver with the results of trephine biopsy of the liver in patients with ALD on admission and follow-up (r=0.721).&#x0D; &#x0D; There was a high correlation between the results of DWI of the liver on MRI with the data of clinical and laboratory parameters in dynamic observation of patients with ALD: no diffusion limitation – positive (r=0.887); yes – negative (r=0.887).&#x0D; The high prognostic and diagnostic value of DWI of the liver in assessing the violation of the abstinence regimen in patients with ALD was established (AUROC=0.906 (95% CI 0.872-0.916)).&#x0D; Prognostic and diagnostic criteria for liver DWI on MRI in patients with ALD at admission: qualitative characteristic – AUROC=0.846 (95% CI 0.811-0.862), quantitative characteristic – AUROC=0.909 (95% CI 0.879-0.912); with dynamic observation: qualitative characteristic – AUROC=0.949 (95% CI 0.907-0.965), quantitative characteristic – AUROC=0.917 (95% CI 0.876-0.932).&#x0D;

NeuroMix-A single-scan brain exam.

Implement a fast, motion-robust pulse sequence that acquires T1 -weighted, T2 -weighted, T2* -weighted, T2 fluid-attenuated inversion recovery, and DWI data in one run with only one prescription and one prescan. A software framework was developed that configures and runs several sequences in one main sequence. Based on that framework, the NeuroMix sequence was implemented, containing motion robust single-shot sequences using EPI and fast spin echo (FSE) readouts (without EPI distortions). Optional multi-shot sequences that provide better contrast, higher resolution, or isotropic resolution could also be run within the NeuroMix sequence. An optimized acquisition order was implemented that minimizes times where no data is acquired. NeuroMix is customizable and takes between 1:20 and 4 min for a full brain scan. A comparison with the predecessor EPIMix revealed significant improvements for T2 -weighted and T2 fluid-attenuated inversion recovery, while taking only 8 s longer for a similar configuration. The optional contrasts were less motion robust but offered a significant increase in quality, detail, and contrast. Initial clinical scans on 1 pediatric and 1 adult patient showed encouraging image quality. The single-shot FSE readouts for T2 -weighted and T2 fluid-attenuated inversion recovery and the optional multishot FSE and 3D-EPI contrasts significantly increased diagnostic value compared with EPIMix, allowing NeuroMix to be considered as a standalone brain MRI application.

Differentiating atypical meningioma from anaplastic meningioma using diffusion weighted imaging

PurposeTo explore the value of MRI conventional features and apparent diffusion coefficient (ADC) on the differential diagnosis of atypical meningioma (AtM) and anaplastic meningioma (AnM). Materials and methodsThis retrospective study analyzed the preoperative clinical data, MRI conventional features, and DWI data of 55 AtM and 25 AnM confirmed by pathology in our hospital. The clinical features, MRI conventional features, ADCmean, ADCmin, and relative ADC (rADC) values were compared between the two tumors by Chi-square test or an independent sample t-test. Receiver operating characteristic curve (ROC) and binary logistic regression analysis were used to evaluate the diagnostic efficacy of each parameter to differentiate between these tumors. ResultsThe MRI conventional features had a certain ability to distinguish AnM and AtM, with an area under the curve value (AUC) of 0.824 (95% CI, 0.723–0.900). The ADCmean, ADCmin, and rADC values were significantly higher in AtM compared to AnM (all P < 0.05). ADCmean had the best identification effect with an AUC of 0.867 (95% CI, 0.772–0.933) among them, at an cut-off of 0.817 × 10−3 mm2/s, the sensitivity and specificity of distinguishing AtM from AnM were 78.18% and 88.00%, respectively. A combination of ADCmean and MRI conventional features showed the optimum discrimination ability for the two tumors, the AUC, sensitivity, specificity, and accuracy were 0.918 (95% CI, 0.835–0.967), 80.00%, 94.55%, and 90.00%, respectively. ConclusionMRI conventional features combined with ADCmean, as a non-invasive method, has potential clinical value in the preoperative diagnosis of AtM and AnM.

Comparison of different ROI analysis methods for liver lesion characterization with simplified intravoxel incoherent motion (IVIM)

This study investigated the impact of different ROI placement and analysis methods on the diagnostic performance of simplified IVIM-DWI for differentiating liver lesions. 1.5/3.0-T DWI data from a respiratory-gated MRI sequence (b = 0, 50, 250, 800 s/mm2) were analyzed in patients with malignant (n = 74/54) and benign (n = 35/19) lesions. Apparent diffusion coefficient ADC = ADC(0,800) and IVIM parameters D1′ = ADC(50,800), D2′ = ADC(250,800), f1′ = f(0,50,800), f2′ = f(0,250,800), and D*' = D*(0,50,250,800) were calculated voxel-wise. For each lesion, a representative 2D-ROI, a 3D-ROI whole lesion, and a 3D-ROI from “good” slices were placed, including and excluding centrally deviating areas (CDA) if present, and analyzed with various histogram metrics. The diagnostic performance of 2D- and 3D-ROIs was not significantly different; e.g. AUC (ADC/D1′/f1′) were 0.958/0.902/0.622 for 2D- and 0.942/0.892/0.712 for whole lesion 3D-ROIs excluding CDA at 1.5 T (p > 0.05). For 2D- and 3D-ROIs, AUC (ADC/D1′/D2′) were significantly higher, when CDA were excluded. With CDA included, AUC (ADC/D1′/D2′/f1′/D*') improved when low percentiles were used instead of averages, and was then comparable to the results of average ROI analysis excluding CDA. For lesion differentiation the use of a representative 2D-ROI is sufficient. CDA should be excluded from ROIs by hand or automatically using low percentiles of diffusion coefficients.