Diffusion-weighted MRI Data Sets Research Articles

An Approach to Automated Diagnosis and Texture Analysis of the Fetal Liver & Placenta in Fetal Growth Restriction

Fetal growth restriction (FGR) is a prevalent pregnancy condition characterised by failure of the fetus to reach its genetically predetermined growth potential. The multiple aetiologies, coupled with the risk of fetal complications - encompassing neurodevelopmental delay, neonatal morbidity, and stillbirth - motivate the need to improve holistic assessment of the FGR fetus using MRI. We hypothesised that the fetal liver and placenta would provide insights into FGR biomarkers, unattainable through conventional methods. Therefore, we explore the application of model fitting techniques, linear regression machine learning models, deep learning regression, and Haralick textured features from multi-contrast MRI for multi-fetal organ analysis of FGR. We employed T2 relaxometry and diffusion-weighted MRI datasets (using a combined T2-diffusion scan) for 12 normally grown and 12 FGR gestational age (GA) matched pregnancies (Estimated Fetal Weight below 3rd centile, Median 28+/-3wks). We applied the Intravoxel Incoherent Motion Model, which describes circulatory properties of the fetal organs, and analysed the resulting features distinguishing both cohorts. We additionally used novel multi-compartment models for MRI fetal analysis, which exhibit potential to provide a multi-organ FGR assessment, overcoming the limitations of empirical indicators - such as abnormal artery Doppler findings - to evaluate placental dysfunction. The placenta and fetal liver presented key differentiators between FGR and normal controls, with significant decreased perfusion, abnormal fetal blood motion and reduced fetal blood oxygenation. This may be associated with the preferential shunting of the fetal blood towards the fetal brain, affecting supply to the liver. These features were further explored to determine their role in assessing FGR severity, by employing simple machine learning models to predict FGR diagnosis (100% accuracy in test data, n=5), GA at delivery, time from MRI scan to delivery, and baby weight. We additionally explored the use of deep learning to regress the latter three variables, training a convolutional neural network with our liver and placenta voxel-level parameter maps, obtained from our multi-compartment model fitting. Image texture analysis of the fetal organs demonstrated prominent textural variations in the placental perfusion fractions maps between the groups (p<0.0009), and spatial differences in the incoherent fetal capillary blood motion in the liver (p<0.009). This research serves as a proof-of-concept, investigating the effect of FGR on fetal organs, measuring differences in perfusion and oxygenation within the placenta and fetal liver, and their prognostic importance in automated diagnosis using simple machine learning models.

Multi‐parametric Neuroimaging in a Non‐Human Primate Model of Sporadic Cerebral Amyloid Angiopathy (Saimiri boliviensis boliviensis)

AbstractBackgroundSquirrel monkeys (SQMs) are a unique non‐human primate model (NHP), which unlike other NHPs, develops extensive age‐associated cerebral amyloid angiopathy (CAA). The growing recognition of CAA contribution to clinical decline and CAA‐related complications in current clinical trials underscore the utility of the SQM model for studying CAA pathology development. Neuroimaging can play a key role in noninvasively detecting and monitoring disease progression in conjunction with histological methods. We have previously shown the utility of quantitative R2 * to map age‐related differences in SQM neuropathology cross‐sectionally and longitudinally. Here we aim to complement our protocol with advanced MRI approaches sensitive to brain microstructure to enable early and specific detection of alteration in tissue integrity. Our overarching goals aim to establish the in vivo and ex vivo MRI metrics profile of young and geriatric SQM cohorts.Method2D T2‐w (a) Turbo‐spin‐echo (TSE) and (b) FLAIR MRIs were acquired in vivo and ex vivo, while (c) multi‐shell diffusion‐weighted MRI (DWI) was limited to ex vivo imaging. Diffusion tensor imaging (DTI) and Diffusion kurtosis imaging (DKI) metrics were derived from comprehensive DWI datasets. Immunohistochemistry was subsequently performed to characterize the microstructural underpinnings of MRI abnormalities.ResultBothin vivo and ex vivo T2‐w TSE and FLAIR helped identify spontaneous asymmetric hyperintensities (ARIA‐E) in geriatric SQMs (21‐23yrs), predominantly in the parieto‐occipital WM, not seen in the young cohort (5‐yrs). Importantly, DKI‐derived mean kurtosis proved to be the most sensitive of DWI metrics for detecting lesions in the parieto‐occipital region. Widely adopted DTI metrics were far less sensitive in identifying microstructural changes. Histological assessment of region‐matched sections revealed Iba1 microgliosis, as well as immunoreactivity for both extravascular fibrinogen and decreased MBP when compared to the contralateral side. Aß immunohistochemistry (6E10/4G8) confirmed presence of CAA in the affected regions.ConclusionAdvanced DWI models are sensitive and specific neuroimaging tools for identifying abnormalities associated with tissue integrity that are potentially linked to age‐associated cerebrovascular dysfunction. Ex vivo conventional MRI detected similar abnormalities observed in vivo and proved to be a useful step to validate the performance of advanced imaging strategies, which can help bridge the gap between in vivo MRI and histology.

Fiber orientation distribution function from non-negative sparse recovery with quantitative analysis of local fiber orientations and tractography using DW-MRI datasets

Diffusion weighted MRI (DW-MRI) is the unique non-invasive imaging modality capable of estimating in vivo the structure of the white matter. In this paper, we propose, evaluate and validate a new DW-MRI method to model and recover high quality tractogram even with multiple fiber populations in a voxel and from a limited number of acquisitions.Our method relies on the estimation of the Fiber Orientation Distribution (FOD) function, parameterized as a non-negative sum of rank-1 tensors and the use of a non-negative sparse recovery scheme to efficiently recover the tensors, and their number. Each fiber population of a voxel is characterized by the orientation and the weight of a rank-1 tensor.Using both deterministic and probabilistic tractography algorithms, we show that our method is able to accurately reconstruct narrow crossing fibers and obtain a high quality connectivity reconstruction even from a limited number of acquisitions. To this end, a validation scheme based on the connectivity recovered from tractography is developed to quantitatively evaluate and analyze the performance of our method. The tractometer tool is used to quantify the tractography obtained from a simulated DW-MRI dataset including a high angular resolution dataset of 60 gradient directions and a dataset of 30 gradient directions, each of them corrupted with Rician noise of SNR 10 and 20. The performance of our FOD model and its impact on the tractography results are also demonstrated and illustrated on in vivo DW-MRI datasets with high and low angular resolutions.

MP20-18 SEMI-AUTOMATIC SEGMENTATION ANALYSIS OF DIFFUSION-WEIGHTED MRI AS AN IMAGING BIOMARKER FOR HISTOLOGICAL GRADE OF BLADDER CANCER

You have accessJournal of UrologyImaging/Radiology: Uroradiology II1 Apr 2018MP20-18 SEMI-AUTOMATIC SEGMENTATION ANALYSIS OF DIFFUSION-WEIGHTED MRI AS AN IMAGING BIOMARKER FOR HISTOLOGICAL GRADE OF BLADDER CANCER Kasumi Kaneko, Soichiro Yoshida, Taro Takahara, Shugo Yajima, Tsuyoshi Sakamoto, Thomas Kwee, Yuki Arita, Minato Yokoyama, Junichiro Ishioka, Yoh Matsuoka, Kazutaka Saito, Kazunori Kihara, and Yasuhisa Fujii Kasumi KanekoKasumi Kaneko More articles by this author , Soichiro YoshidaSoichiro Yoshida More articles by this author , Taro TakaharaTaro Takahara More articles by this author , Shugo YajimaShugo Yajima More articles by this author , Tsuyoshi SakamotoTsuyoshi Sakamoto More articles by this author , Thomas KweeThomas Kwee More articles by this author , Yuki AritaYuki Arita More articles by this author , Minato YokoyamaMinato Yokoyama More articles by this author , Junichiro IshiokaJunichiro Ishioka More articles by this author , Yoh MatsuokaYoh Matsuoka More articles by this author , Kazutaka SaitoKazutaka Saito More articles by this author , Kazunori KiharaKazunori Kihara More articles by this author , and Yasuhisa FujiiYasuhisa Fujii More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2018.02.688AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES The apparent diffusion coefficient (ADC) value is a quantitative parameter for the degree of water diffusion within of tissue, and is calculated from diffusion-weighted MRI (DWI). Previous studies have reported that the ADC value reflects histological characteristics in several malignancies. So far, the ADC value was usually calculated manually, and operator-related errors was induced. To utilize the ADC value as an imaging biomarker, standardization of the objective assessment of the ADC value is needed. Here, we newly developed an objective method for volumetric ADC value analysis using semi-automatic segmentation. The calculated ADC value represents all pixels of the tumor: the global ADC value (gADC). We evaluated the usefulness of gADC as an imaging biomarker for the histological grade of bladder cancer (BC). METHODS A total of 55 consecutive pT1 urothelial cell BC patients who underwent MRI including DWI (b values of 0 and 1000 sec/mm2) before transurethral resection between 2010 and 2015, were retrospectively evaluated. 2 independent readers analyzed the DWI datasets using newly developed medical imaging software, BD score (PixSpace, Japan). The pixels of the tumor in each patient, indicating an ADC from 0.3 x 10-3 to 1.5 x 10-3 mm2/s, were identified from all image slices, and the volume (tVol) and the gADC of the identified pixels were calculated. We compared tVol and gADC with the histological grade of the tumor. RESULTS 11 tumors whose gADC was out-of-measuring range and 2 tumors greater than 5 cm in size were excluded, and the remaining 42 (median tumor size: 2.0 cm (range: 0.5-4.0 cm), dominant grade 3/1-2: n=22/20) were eligible for the analysis. The gADC1 measured by reader 1 and gADC2 measured by reader 2 were significantly lower for grade 3 tumors than for grade 1-2 tumors (median gADC1: 1.05 x 10-3 vs 1.18 x 10-3 mm2/sec, p = 0.023; gADC2 value: 1.05 x10-3 vs 1.18 x 10-3 mm2/sec, p = 0.030). Inter-observer analysis revealed an intraclass correlation coefficient for the gADC and tVol of ρ = 0.96 and 0.98 respectively (p<0.001 for both) CONCLUSIONS The semi-automatic segmentation analysis enables objective evaluations of DWI signals with high concordance between the readers. The gADC reflects the histological grades of BC. The gADC can be an imaging biomarker of BC. © 2018FiguresReferencesRelatedDetails Volume 199Issue 4SApril 2018Page: e260 Advertisement Copyright & Permissions© 2018MetricsAuthor Information Kasumi Kaneko More articles by this author Soichiro Yoshida More articles by this author Taro Takahara More articles by this author Shugo Yajima More articles by this author Tsuyoshi Sakamoto More articles by this author Thomas Kwee More articles by this author Yuki Arita More articles by this author Minato Yokoyama More articles by this author Junichiro Ishioka More articles by this author Yoh Matsuoka More articles by this author Kazutaka Saito More articles by this author Kazunori Kihara More articles by this author Yasuhisa Fujii More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

Simulated field maps for susceptibility artefact correction in interventional MRI.

Intraoperative MRI (iMRI) is a powerful modality for acquiring images of the brain to facilitate precise image-guided neurosurgery. Diffusion-weighted MRI (DW-MRI) provides critical information about location, orientation and structure of nerve fibre tracts, but suffers from the "susceptibility artefact" stemming from magnetic field perturbations due to the step change in magnetic susceptibility at air-tissue boundaries in the head. An existing approach to correcting the artefact is to acquire a field map by means of an additional MRI scan. However, to recover true field maps from the acquired field maps near air-tissue boundaries is challenging, and acquired field maps are unavailable in historical MRI data sets. This paper reports a detailed account of our method to simulate field maps from structural MRI scans that was first presented at IPCAI 2014 and provides a thorough experimental and analysis section to quantitatively validate our technique. We perform automatic air-tissue segmentation of intraoperative MRI scans, feed the segmentation into a field map simulation step and apply the acquired and the simulated field maps to correct DW-MRI data sets. We report results for 12 patient data sets acquired during anterior temporal lobe resection surgery for the surgical management of focal epilepsy. We find a close agreement between acquired and simulated field maps and observe a statistically significant reduction in the susceptibility artefact in DW-MRI data sets corrected using simulated field maps in the vicinity of the resection. The artefact reduction obtained using acquired field maps remains better than that using the simulated field maps in all evaluated regions of the brain. The proposed simulated field maps facilitate susceptibility artefact reduction near the resection. Accurate air-tissue segmentation is key to achieving accurate simulation. The proposed simulation approach is adaptable to different iMRI and neurosurgical applications.

Motion Compensated Abdominal Diffusion Weighted MRI by Simultaneous Image Registration and Model Estimation (SIR-ME).

Non-invasive characterization of water molecule's mobility variations by quantitative analysis of diffusion-weighted MRI (DW-MRI) signal decay in the abdomen has the potential to serve as a biomarker in gastrointestinal and oncological applications. Accurate and reproducible estimation of the signal decay model parameters is challenging due to the presence of respiratory, cardiac, and peristalsis motion. Independent registration of each b-value image to the b-value=0 s/mm(2) image prior to parameter estimation might be sub-optimal because of the low SNR and contrast difference between images of varying b-value. In this work, we introduce a motion-compensated parameter estimation framework that simultaneously solves image registration and model estimation (SIR-ME) problems by utilizing the interdependence of acquired volumes along the diffusion weighting dimension. We evaluated the improvement in model parameters estimation accuracy using 16 in-vivo DW-MRI data sets of Crohn's disease patients by comparing parameter estimates obtained using the SIR-ME model to the parameter estimates obtained by fitting the signal decay model to the acquired DW-MRI images. The proposed SIR-ME model reduced the average root-mean-square error between the observed signal and the fitted model by more than 50%. Moreover, the SIR-ME model estimates discriminate between normal and abnormal bowel loops better than the standard parameter estimates.

Perpendicular fibre tracking for neural fibre bundle analysis using diffusion MRI

Information on the directionality and structure of axonal fibres in neural tissue can be obtained by analysing diffusion-weighted MRI data sets. Several fibre tracking algorithms have been presented in the literature that trace the underlying field of principal orientations of water diffusion, which correspond to the local primary eigenvectors of the diffusion tensor field. However, the majority of the existing techniques ignore the secondary and tertiary orientations of diffusion, which contain significant information on the local patterns of diffusion. In this paper, we introduce the idea of perpendicular fibre tracking and present a novel dynamic programming method that traces surfaces, which are locally perpendicular to the axonal fibres. This is achieved by using a cost function, with geometric and fibre orientation constraints, that is evaluated dynamically for every voxel in the image domain starting from a given seed point. The proposed method is tested using synthetic and real DW-MRI data sets. The results conclusively demonstrate the accuracy and effectiveness of our method.

Clinical single-shot diffusion-weighted MRI of the human brain on a short-bore medium-field imager.

Diffusion-weighted MRI (DWI) is becoming important for assessment of acute stroke. Until recently single-shot DWI required expensive technology such as echoplanar imaging (EPI) available only at some research sites. A new medium-field (1.0 T) short-bore MR imager has been developed with which DWI data sets can be acquired. We prospectively studied 169 patients on this 1.0 T commercial system. After conventional imaging, DWI was performed with a single-shot multi-slice sequence with b values 0 an 900 s/mm2, and with the gradients switched in three directions. The apparent diffusion coefficients were calculated with online calculation software. There were 50 patients with totally normal MRI, and 17 had strokes, these strokes were detected as areas of high signal on the images at a maximal b value. There was a drop in the ADC in ischaemic regions: in sub-acute infarcts, the values were between 0.41 and 0.531 x 10(-3) mm2/s. In old infarcts the ADC was 1.15 x 10(-3) mm2/s. Cerebrospinal fluid (CSF) gave low signal whereas areas in the brain had more intermediate intensities (CSF: 3.00; deep white matter: 0.75, cortical grey matter: 0.80, basal ganglia (thalamus): 0.70 and cerebellar white matter: 0.65 x 10(-3) mm2/s. Anisotropy was detected as areas of restricted diffusion along the tracts. These preliminary data show that DWI can be acquired successfully on a medium-field short-bore system. This should allow the technique to be implemented at more sites, therefore facilitating the diagnosis of acute stroke and rendering early intervention feasible.