A comparison of high b-value vs standard b-value diffusion-weighted magnetic resonance imaging at 3.0 T for medulloblastomas

Deep learning with diffusion MRI as in vivo microscope reveals sex-related differences in human white matter microstructure

Biological sex is a crucial variable in neuroscience studies where sex differences have been documented across cognitive functions and neuropsychiatric disorders. While gross statistical differences have been previously documented in macroscopic brain structure such as cortical thickness or region size, less is understood about sex-related cellular-level microstructural differences which could provide insight into brain health and disease. Studying these microstructural differences between men and women paves the way for understanding brain disorders and diseases that manifest differently in different sexes. Diffusion MRI is an important in vivo, non-invasive methodology that provides a window into brain tissue microstructure. Our study develops multiple end-to-end classification models that accurately estimates the sex of a subject using volumetric diffusion MRI data and uses these models to identify white matter regions that differ the most between men and women. 471 male and 560 female healthy subjects (age range, 22-37 years) from the Human Connectome Project are included. Fractional anisotropy, mean diffusivity and mean kurtosis are used to capture brain tissue microstructure characteristics. Diffusion parametric maps are registered to a standard template to reduce bias that can arise from macroscopic anatomical differences like brain size and contour. This study employ three major model architectures: 2D convolutional neural networks, 3D convolutional neural networks and Vision Transformer (with self-supervised pretraining). Our results show that all 3 models achieve high sex classification performance (test AUC 0.92-0.98) across all diffusion metrics indicating definitive differences in white matter tissue microstructure between males and females. We further use complementary model architectures to inform about the pattern of detected microstructural differences and the influence of short-range versus long-range interactions. Occlusion analysis together with Wilcoxon signed-rank test is used to determine which white matter regions contribute most to sex classification. The results indicate that sex-related differences manifest in both local features as well as global features / longer-distance interactions of tissue microstructure. Our highly consistent findings across models provides new insight supporting differences between male and female brain cellular-level tissue organization particularly in the central white matter.

A practical evaluation of measures derived from compressed sensing diffusion spectrum imaging

Diffusion Spectrum Imaging (DSI) using dense Cartesian sampling of q-space has been shown to provide important advantages for modeling complex white matter architecture. However, its adoption has been limited by the lengthy acquisition time required. Sparser sampling of q-space combined with compressed sensing (CS) reconstruction techniques has been proposed as a way to reduce the scan time of DSI acquisitions. However prior studies have mainly evaluated CS-DSI in post-mortem or non-human data. At present, the capacity for CS-DSI to provide accurate and reliable measures of white matter anatomy and microstructure in the living human brain remains unclear. We evaluated the accuracy and inter-scan reliability of 6 different CS-DSI schemes that provided up to 80% reductions in scan time compared to a full DSI scheme. We capitalized on a dataset of 26 participants who were scanned over eight independent sessions using a full DSI scheme. From this full DSI scheme, we subsampled images to create a range of CS-DSI images. This allowed us to compare the accuracy and inter-scan reliability of derived measures of white matter structure (bundle segmentation, voxel-wise scalar maps) produced by the CS-DSI and the full DSI schemes. We found that CS-DSI estimates of both bundle segmentations and voxel-wise scalars were nearly as accurate and reliable as those generated by the full DSI scheme. Moreover, we found that the accuracy and reliability of CS-DSI was higher in white matter bundles that were more reliably segmented by the full DSI scheme. As a final step, we replicated the accuracy of CS-DSI in a prospectively acquired dataset (n = 20, scanned once). Together, these results illustrate the utility of CS-DSI for reliably delineating in vivo white matter architecture in a fraction of the scan time, underscoring its promise for both clinical and research applications.

Towards a Simplified and Cost-Effective Diagnostic Algorithm for the Surveillance of Intraductal Papillary Mucinous Neoplasms (IPMNs): Can We Save Contrast for Later?

The increased detection of pancreatic cysts in recent years has triggered extensive diagnostic investigations to clarify their potential risk of malignancy, resulting in a large number of patients undergoing numerous imaging follow-up studies for many years. Therefore, there is a growing need for optimization of the current surveillance protocol to reduce both healthcare costs and waiting lists, while still maintaining appropriate sensibility and specificity. Imaging is an essential tool for evaluating patients with intraductal papillary mucinous neoplasms (IPMNs) since it can assess several predictors for malignancy and thus guide further management recommendations. Although contrast-enhanced magnetic resonance imaging (MRI) with magnetic resonance cholangiopancreatography (MRCP) has been widely recommended by most international guidelines, recent results support the use of unenhanced abbreviated-MRI (A-MRI) protocols as a surveillance tool in patients with IPMN. In fact, A-MRI has shown high diagnostic performance in malignant detection, with high sensitivity and specificity as well as excellent interobserver agreement. The aim of this paper is, therefore, to discuss the current available evidence on whether the implementation of an abbreviated-MRI (A-MRI) protocol for cystic pancreatic lesion surveillance could improve healthcare economics and reduce waiting lists in clinical practice without significantly reducing diagnostic accuracy.

Revealing the biology behind MRI signatures in high grade glioma

Magnetic resonance imaging (MRI) measurements are routinely collected during the treatment of high-grade gliomas (HGGs) to characterize tumor boundaries and guide surgical tumor resection. Using spatially matched MRI and transcriptomics we discovered HGG tumor biology captured by MRI measurements. We strategically overlaid the spatially matched omics characterizations onto a pre-existing transcriptional map of glioblastoma multiforme (GBM) to enhance the robustness of our analyses. We discovered that T1+C measurements, designed to capture vasculature and blood brain barrier (BBB) breakdown and subsequent contrast extravasation, also indirectly reveal immune cell infiltration. The disruption of the vasculature and BBB within the tumor creates a permissive infiltrative environment that enables the transmigration of anti-inflammatory macrophages into tumors. These relationships were validated through histology and enrichment of genes associated with immune cell transmigration and proliferation. Additionally, T2-weighted (T2W) and mean diffusivity (MD) measurements were associated with angiogenesis and validated using histology and enrichment of genes involved in neovascularization. Furthermore, we establish an unbiased approach for identifying additional linkages between MRI measurements and tumor biology in future studies, particularly with the integration of novel MRI techniques. Lastly, we illustrated how noninvasive MRI can be used to map HGG biology spatially across a tumor, and this provides a platform to develop diagnostics, prognostics, or treatment efficacy biomarkers to improve patient outcomes.

Differentiating between adult intracranial medulloblastoma and ependymoma using MRI

AIM

To investigate the value of routine magnetic resonance imaging (MRI) examination combined with diffusion-weighted imaging (DWI) in the differential diagnosis of adult intracranial medulloblastomas and ependymomas.

MATERIALS AND METHODS

MRI images of 18 medulloblastomas and 18 ependymomas in adult patients were analysed retrospectively, and the differences in MRI features of lesions and apparent diffusion coefficient (ADC) of solid lesions between the two groups were recorded. Independent sample t-tests and χ2 tests were used to analyse the differences in MRI signs and maximum ADC (ADC_max), minimum ADC (ADC_min), and mean ADC (ADC_mean) values between the two groups. The receiver operating characteristic (ROC) curve was used to determine the differential diagnostic efficacy and optimal threshold for each ADC value.

RESULTS

Age, tumour location, and tumour enhancement were significantly different between adult medulloblastoma and ependymoma (p<0.05). The ADC_max (0.69 ± 0.11 versus 1.04 ± 0.20 × 10^-3 mm²/s, p<0.001), ADC_min (0.57 ± 0.12 versus 0.96 ± 0.21 × 10^-3 mm²/s, p<0.001), and ADC_mean (0.62 ± 0.11 versus 1.00 ± 0.20 × 10^-3 mm²/s, p<0.001) values were significantly lower in adult medulloblastoma than in ependymoma. The areas under the ROC curves of ADC_max, ADC_min, and ADC_mean were 0.951, 0.957, and 0.966, respectively. The optimal ADC_mean threshold was 0.75 × 10^-3 mm²/s, with a sensitivity of 88.9% and a specificity of 88.9%.

CONCLUSION

Routine MRI examination combined with DWI helps differentiate between intracranial infratentorial medulloblastoma and ependymoma in adults.

Single-shell to multi-shell dMRI transformation using spatial and volumetric multilevel hierarchical reconstruction framework

Single or Multi-shell high angular resolution diffusion imaging (HARDI) has become an important dMRI acquisition technique for studying brain white matter fibers. Existing single-shell HARDI makes it challenging to estimate the intravoxel structure up to the desired resolution. However, multi-shell acquisition (with multiple b-values) can provide higher resolution for the intravoxel structure, which further helps in getting accurate fiber tracts; But, this comes at the cost of larger acquisition time and larger setup. Hence, we propose a novel deep learning architecture for the reconstruction of diffusion MRI volumes for different b-values (degree of diffusion weighting) using acquisitions at a fixed b-value (termed as single-shell) acquisition. This reconstruction has been performed in the spherical harmonics space to better manage varying gradient directions. In this work, we have demonstrated such a reconstruction for b = 3000 s/mm² and b = 2000 s/mm² from b = 1000 s/mm². The proposed Multilevel Hierarchical Spherical Harmonics Coefficients Reconstruction (MHSH) framework takes advantage of contextual information within each slice as well as across the slices by involving Slice Level ReconNet (SLRNet) network and a Volumetric ROI Level ReconNet (VPLRNet) network, respectively. Three-loss functions have been used to optimize network learning, i.e., L₁, Adversarial, and Total Variation Loss. Finally, the network is trained and validated on the publicly available HCP data-set with standard qualitative and quantitative performance measures and achieves promising results.

Advantage of high b value diffusion-weighted imaging for differentiation of common pediatric brain tumors in posterior fossa

PURPOSE

The pediatric posterior fossa (PF) brain tumors with higher frequencies are embryonal tumors (ET), ependymal tumors (EPN) and pilocytic astrocytomas (PA), however, it is often difficult to make a differential diagnosis among them with conventional MRI. The ADC calculated from DWI could be beneficial for diagnostic work up.

METHOD

We acquired DWI at b = 1000 and 4000(s/mm²). The relationship between ADC and the three types of brain tumors was evaluated with Mann-Whitney U test. We also performed simple linear regression analysis to evaluate the relationship between ADC and cellularity, and implemented receiver operating characteristic curve (ROC curve) to test the diagnostic performance among tumors.

RESULTS

The highest ADC (b1000/b4000 × 10^-3 mm²/s) was observed in PA (1.02-1.91/0.73-1.28), followed by PF-EPN (0.83-1.28/0.60-0.79) and the lowest was ET (0.41-0.75/0.29-0.47). There was significant difference among the groups in both ADC value (b-1000/b-4000: ET vs. PF-EPN p < 0.0001/0.0001, ET vs. PA p < 0.0001/0.0001, PF-EPN vs. PA p < 0.0001/0.0001). ROC analysis revealed that ADC in both b-values showed complete separation between ET and PF-EPN. And it also revealed that ADC at b-4000 could differentiate PF-EPN and PA (96.0%) better than ADC at b-1000 (90.1%). The stronger negative correlation was observed between the ADC and cellularity at b-4000 than at b-1000 (R²  = 0.7415 vs.0.7070) CONCLUSIONS: ADC of ET was significantly lower than the other two groups, and ADC of PA was significantly higher than the other two groups in both b-1000 and b-4000. Our results showed that ADC at b-4000 was more useful than ADC at b-1000 especially for differentiation between PF-EPN and PA.

Gaussian mixture model for texture characterization with application to brain DTI images

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A Gaussian mixture model to classify the pixel distribution of main brain tissues is introduced.

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A hemisphere approach is proposed.

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Mixing probabilities at the sub-class and class levels are estimated.

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The k-means algorithm optimizes the parameters of the mixture distributions.

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A difference in the mixing probabilities between hemispheres is determined.

A Gaussian mixture model (GMM)-based classification technique is employed for a quantitative global assessment of brain tissue changes by using pixel intensities and contrast generated by b-values in diffusion tensor imaging (DTI). A hemisphere approach is also proposed. A GMM identifies the variability in the main brain tissues at a macroscopic scale rather than searching for tumours or affected areas. The asymmetries of the mixture distributions between the hemispheres could be used as a sensitive, faster tool for early diagnosis. The k-means algorithm optimizes the parameters of the mixture distributions and ensures that the global maxima of the likelihood functions are determined. This method has been illustrated using 18 sub-classes of DTI data grouped into six levels of diffusion weighting (b = 0; 250; 500; 750; 1000 and 1250 s/mm²) and three main brain tissues. These tissues belong to three subjects, i.e., healthy, multiple haemorrhage areas in the left temporal lobe and ischaemic stroke. The mixing probabilities or weights at the class level are estimated based on the sub-class-level mixing probability estimation. Furthermore, weighted Euclidean distance and multiple correlation analysis are applied to analyse the dissimilarity of mixing probabilities between hemispheres and subjects. The silhouette data evaluate the objective quality of the clustering. By using a GMM in the present study, we establish an important variability in the mixing probability associated with white matter and grey matter between the left and right hemispheres.

Embryonal Tumors of the Central Nervous System: From the Radiologic Pathology Archives

Embryonal tumors of the central nervous system (CNS) are highly malignant undifferentiated or poorly differentiated tumors of neuroepithelial origin and have been defined as a category in the World Health Organization (WHO) classification since the first edition of the "Blue Book" in 1979. This category has evolved over time to reflect our ever-improving understanding of tumor biology and behavior. With the most recent update in 2016, many previous histologic diagnoses incorporate molecular parameters for the first time (genetically defined entities). While medulloblastoma and atypical teratoid/rhabdoid tumor are familiar carryovers from the 2007 CNS WHO classification, there are major changes to the embryonal tumor category: for example, elimination of the term CNS primitive neuroectodermal tumor and addition of a new genetically defined entity, embryonal tumor with multilayered rosettes, C19MC-altered. The purpose of this article is to discuss both the radiologic-pathologic features of CNS embryonal tumors and the new molecularly defined types/subtypes that will become the standard classification/terminology for future diagnoses and tumor research.

Imaging features of medulloblastoma: Conventional imaging, diffusion-weighted imaging, perfusion-weighted imaging, and spectroscopy: From general features to subtypes and characteristics

Medulloblastoma is a frequent high-grade neoplasm among pediatric brain tumours. Its classical imaging features are a midline tumour growing into the fourth ventricle, hyperdense on CT-scan, displaying a hypersignal when using diffusion-weighted imaging, with a variable contrast enhancement. Nevertheless, atypical imaging features have been widely reported, varying according to the age of the patient, and histopathological subtype. In this study, we review the classical and atypical imaging features of medulloblastomas, with emphasis on advanced MRI techniques, histopathological and molecular subtypes and characteristics, and follow-up modalities.

Evaluation of different mathematical models and different b-value ranges of diffusion-weighted imaging in peripheral zone prostate cancer detection using b-value up to 4500 s/mm2

OBJECTIVES

To evaluate the diagnostic performance of different mathematical models and different b-value ranges of diffusion-weighted imaging (DWI) in peripheral zone prostate cancer (PZ PCa) detection.

METHODS

Fifty-six patients with histologically proven PZ PCa who underwent DWI-magnetic resonance imaging (MRI) using 21 b-values (0-4500 s/mm2) were included. The mean signal intensities of the regions of interest (ROIs) placed in benign PZs and cancerous tissues on DWI images were fitted using mono-exponential, bi-exponential, stretched-exponential, and kurtosis models. The b-values were divided into four ranges: 0-1000, 0-2000, 0-3200, and 0-4500 s/mm2, grouped as A, B, C, and D, respectively. ADC, <D>, D*, f, DDC, α, Dapp, and Kapp were estimated for each group. The adjusted coefficient of determination (R2) was calculated to measure goodness-of-fit. Receiver operating characteristic curve analysis was performed to evaluate the diagnostic performance of the parameters.

RESULTS

All parameters except D* showed significant differences between cancerous tissues and benign PZs in each group. The area under the curve values (AUCs) of ADC were comparable in groups C and D (p = 0.980) and were significantly higher than those in groups A and B (p< 0.05 for all). The AUCs of ADC and Kapp in groups B and C were similar (p = 0.07 and p = 0.954), and were significantly higher than the other parameters (p< 0.001 for all). The AUCs of ADC in group D was slightly higher than Kapp (p = 0.002), and both were significantly higher than the other parameters (p< 0.001 for all).

CONCLUSIONS

ADC derived from conventional mono-exponential high b-value (3200 s/mm2) models is an optimal parameter for PZ PCa detection.

Preoperative grading of supratentorial gliomas using high or standard b-value diffusion-weighted MR imaging at 3T

PURPOSE

The goal of this study was to compare diffusion-weighted magnetic resonance imaging (DW-MRI) using high b-value (b=3000s/mm²) to DW-MRI using standard b-value (b=1000s/mm²) in the preoperative grading of supratentorial gliomas.

MATERIALS AND METHODS

Fifty-three patients with glioma had brain DW-MRI at 3T using two different b-values (b=1000s/mm² and b=3000s/mm²). There were 35 men and 18 women with a mean age of 40.5±17.1 years (range: 18-79 years). Mean, minimum, maximum, and range of apparent diffusion coefficient (ADC) values for solid tumor ROIs (ADC_mean, ADC_min, ADC_max, and ADC_diff), and the normalized ADC (ADC_ratio) were calculated. A Kruskal-Wallis statistic with Bonferroni correction for multiple comparisons was applied to detect significant ADC parameter differences between tumor grades by including or excluding 19 patients with an oligodendroglioma. Receiver operating characteristic curve analysis was conducted to define appropriate cutoff values for grading gliomas.

RESULTS

No differences in ADC derived parameters were found between grade II and grade III gliomas. Mean ADC values using standard b-value were 1.17±0.27×10^-3mm²/s [range: 0.63-1.61], 1.05±0.22×10^-3mm²/s [range: 0.73-1.33], and 0.86±0.23×10^-3mm²/s [range: 0.52-1.46] for grades II, III and IV gliomas, respectively. Using high b-value, mean ADC values were 0.89±0.24×10^-3mm²/s [range: 0.42-1.25], 0.82±0.20×10^-3mm²/s [range: 0.56-1.10], and 0.59±0.17×10^-3mm²/s [range: 0.40-1.01] for grades II, III and IV gliomas, respectively. ADC_mean, ADC_ratio, ADC_max, and ADC_min were different between grade II and grade IV gliomas at both standard and high b-values. Differences in ADC_mean, ADC_max, and ADC_diff were found between grade III and grade IV only using high b-value.

CONCLUSION

ADC parameters derived from DW-MRI using a high b-value allows a better differential diagnosis of gliomas, especially for differentiating grades III and IV, than those derived from DW-MRI using a standard b-value.

Evaluation of Free Breathing Versus Breath Hold Diffusion Weighted Imaging in Terms Apparent Diffusion Coefficient (ADC) and Signal-to-Noise Ratio (SNR) Values for Solid Abdominal Organs

Background

Our aim was to compare the apparent diffusion coefficient (ADC) values of normal abdominal parenchymal organs and signal-to-noise ratio (SNR) measurements in the same patients with breath hold (BH) and free breathing (FB) diffusion weighted imaging (DWI).

Material/Methods

Forty-eight patients underwent both BH and FB DWI. Spherical region of interest (ROI) was placed on the right hepatic lobe, spleen, pancreas, and renal cortices. ADC values were calculated for each organ on each sequence using an automated software. Image noise, defined as the standard deviation (SD) of the signal intensities in the most artifact-free area of the image background was measured by placing the largest possible ROI on either the left or the right side of the body outside the object in the recorded field of view. SNR was calculated using the formula: SNR=signal intensity (SI)_(organ)/standard deviation (SD)_(noise).

Results

There were no statistically significant differences in ADC values of the abdominal organs between BH and FB DWI sequences (p>0.05). There were statistically significant differences between SNR values of organs on BH and FB DWIs. SNRs were found to be better on FB DWI than BH DWI (p<0.001).

Conclusions

Free breathing DWI technique reduces image noise and increases SNR for abdominal examinations. Free breathing technique is therefore preferable to BH DWI in the evaluation of abdominal organs by DWI.