Diffusion weighted imaging: Technique and applications

Advancements in Positron Emission Tomography/Magnetic Resonance Imaging and Applications to Diagnostic Challenges in Neuroradiology

Since the clinical adoption of magnetic resonance (MR) in medical imaging, MR has proven to be a workhorse in diagnostic neuroradiology, with the ability to provide superb anatomic detail as well as additional functional and physiologic data, depending on the techniques utilized. Positron emission tomography/computed tomography has also shown irreplaceable diagnostic value in certain disease processes of the central nervous system by providing molecular and metabolic information through the development of numerous disease-specific PET tracers, many of which can be utilized as a diagnostic technique in and of themselves or can provide a valuable adjunct to information derived from MR. Despite these advances, many challenges still remain in neuroradiology, particularly in malignancy, neurodegenerative disease, epilepsy, and cerebrovascular disease. Through improvements in attenuation correction, motion correction, and PET detectors, combining the 2 modalities of PET and MR through simultaneous imaging has proven feasible and allows for improved spatial and temporal resolution without compromising either of the 2 individual modalities. The complementary information offered by both technologies has provided increased diagnostic accuracy in both research and many clinical applications in neuroradiology.

Evaluation of peripheral nerve injury by magnetic resonance neurography: A systematic review

In view of the limitations of current methods for assessing peripheral nerve injury, there is a need for technical innovations to improve diagnosis, surgical approach and postoperative monitoring. The objective of this study was to conduct a systematic review to analyze the applicability of magnetic resonance neurography in peripheral nerve injuries. The present systematic review focused on the use of magnetic resonance neurography. The literature was searched in the PUBMED, Cochrane Library and Virtual Health Library databases using the PICO method. One hundred sixty-two articles were retrieved with the terms "magnetic resonance imaging" and "peripheral nerve injury", with a filter for the last 10 years (2010-2020). Nineteen were eligible for the review. Most were reviews, with few systematic reviews of randomized controlled trials. Although not included in the recommended protocol, MRI is increasingly used due to its numerous advantages: it is non-invasive, providing objective visualization of neural and perineural tissues, fascicular representation as a result of high resolution, and objective visualization of serial interval images of successful treatment. This is one of the first systematic reviews of the literature regarding the use of magnetic resonance imaging neurography to assess peripheral nerve injury, highlighting the need to implement new imaging techniques in this field of medical practice.

Investigating diffusion patterns of brain metastases pre- and post-stereotactic radiosurgery: a feasibility study

Purpose.Metastatic complications are responsible for 90% of cancer-associated mortality. Magnetic resonance imaging (MRI) can be used to observe the brain's microstructure and potentially correlate changes with metastasis occurrence. Diffusion weighted imaging (DWI) is an MRI technique that utilizes the kinetics of water molecules within the body. The aim of this study is to use DWI to characterize diffusion changes within brain metastases in cancer patients pre- and post-stereotactic radiosurgery (SRS).Methods.We retrospectively analyzed 113 metastases from 13 patients who underwent SRS for brain metastasis recurrence. Longitudinal apparent diffusion coefficient (ADC) maps were registered to Gd-T1 images and CT, and clinical metastasis ROIs from all SRS treatments were retrospectively transferred onto these ADC maps for analysis. Metastases were characterized based on pre-SRS diffusion pattern, primary cancer site, and post-SRS outcome. ADC values were calculated pre- and post-SRS.Results.ADC values were significantly elevated (980.2 × 10^-6mm²s^-1and 1040.3 × 10^-6mm²s^-1pre- and post-SRS, respectively) when compared to healthy brain tissue (826.8 × 10^-6mm²s^-1) for all metastases. Three identified pre-SRS patterns were significantly different before SRS and within 6 months post-SRS. No significant differences were observed between different primaries pre-SRS. Post-SRS, Lung metastases ADC decreased by 86.2 × 10^-6mm²s^-1, breast metastases increased by 116.7 × 10^-6mm²s^-1, and genitourinary metastases showed no significant ADC change. SRS outcomes showed ADC variability pre-treatment but no significant differences pre- and post-SRS, except at 6-9 months post-SRS where progressing metastases were elevated when compared to other response groups.Conclusion. This study provided a unique opportunity to characterize diffusion changes in brain metastases before their manifestation on standard Gd-T1 images and post-SRS. Identified patterns may improve early detection of brain metastases as well as predict their response to treatment.

Magnetic resonance imaging in the management of Crohn's disease: a systematic review and meta-analysis

Objectives

Crohn’s disease (CD) is a condition that can occur in any part of the gastrointestinal tract, although usually forms in the colon and terminal ileum. Magnetic resonance imaging (MRI) has become a beneficial modality in the evaluation of small bowel activity. This study reports on a systematic review and meta-analysis of magnetic resonance enterography for the prediction of CD activity and evaluation of outcomes and possible complications.

Methods

Following the PRISMA guidelines, a total of 25 low-risk studies on established CD were selected, based on a QUADAS-II score of ≥ 9.

Results

A sensitivity of 90% was revealed in a pooled analysis of the 19 studies, with heterogeneity of χ² = 81.83 and I² of 80.3%. Also, a specificity of 89% was calculated, with heterogeneity of χ² = 65.12 and I² of 70.0%.

Conclusion

It was concluded that MRI provides an effective alternative to CT enterography in the detection of small bowel activity in CD patients under supervision of radiologist for assessment of disease activity and its complications. Its advantages include the avoidance of radiation exposure and good diagnostic accuracy.

Fusion of Higher Order Spectra and Texture Extraction Methods for Automated Stroke Severity Classification with MRI Images

This paper presents a scientific foundation for automated stroke severity classification. We have constructed and assessed a system which extracts diagnostically relevant information from Magnetic Resonance Imaging (MRI) images. The design was based on 267 images that show the brain from individual subjects after stroke. They were labeled as either Lacunar Syndrome (LACS), Partial Anterior Circulation Syndrome (PACS), or Total Anterior Circulation Stroke (TACS). The labels indicate different physiological processes which manifest themselves in distinct image texture. The processing system was tasked with extracting texture information that could be used to classify a brain MRI image from a stroke survivor into either LACS, PACS, or TACS. We analyzed 6475 features that were obtained with Gray-Level Run Length Matrix (GLRLM), Higher Order Spectra (HOS), as well as a combination of Discrete Wavelet Transform (DWT) and Gray-Level Co-occurrence Matrix (GLCM) methods. The resulting features were ranked based on the p-value extracted with the Analysis Of Variance (ANOVA) algorithm. The ranked features were used to train and test four types of Support Vector Machine (SVM) classification algorithms according to the rules of 10-fold cross-validation. We found that SVM with Radial Basis Function (RBF) kernel achieves: Accuracy (ACC) = 93.62%, Specificity (SPE) = 95.91%, Sensitivity (SEN) = 92.44%, and Dice-score = 0.95. These results indicate that computer aided stroke severity diagnosis support is possible. Such systems might lead to progress in stroke diagnosis by enabling healthcare professionals to improve diagnosis and management of stroke patients with the same resources.

Speckled brain lesions in Incontinentia Pigmenti patients with acquired brain syndromes

Incontinentia Pigmenti (IP) is a neurocutaneous syndrome, with malformations of cortical development and neurodevelopmental delay in some patients. Neonates with IP may develop acute encephalopathy with multifocal ischemic brain lesions with a speckled pattern on diffusion-weighted magnetic resonance imaging (MRI). We observed a similar MRI pattern in 4 female patients with IP who presented with childhood acute encephalopathy syndromes. These patients, aged 9 days to 13 years old, had acute neonatal encephalitis, Influenza A virus related acute necrotizing encephalopathy (ANE) of childhood, Influenza B virus related acute encephalopathy with biphasic seizures and late restricted diffusion (AESD) and acute disseminated encephalitis (ADEM) with transverse myelitis (TM). These lesions could possibly reflect the white matter changes in IP patients with encephalopathy.

Magnetic resonance biomarkers in radiation oncology: The report of AAPM Task Group 294

A magnetic resonance (MR) biologic marker (biomarker) is a measurable quantitative characteristic that is an indicator of normal biological and pathogenetic processes or a response to therapeutic intervention derived from the MR imaging process. There is significant potential for MR biomarkers to facilitate personalized approaches to cancer care through more precise disease targeting by quantifying normal versus pathologic tissue function as well as toxicity to both radiation and chemotherapy. Both of which have the potential to increase the therapeutic ratio and provide earlier, more accurate monitoring of treatment response. The ongoing integration of MR into routine clinical radiation therapy (RT) planning and the development of MR guided radiation therapy systems is providing new opportunities for MR biomarkers to personalize and improve clinical outcomes. Their appropriate use, however, must be based on knowledge of the physical origin of the biomarker signal, the relationship to the underlying biological processes, and their strengths and limitations. The purpose of this report is to provide an educational resource describing MR biomarkers, the techniques used to quantify them, their strengths and weakness within the context of their application to radiation oncology so as to ensure their appropriate use and application within this field.

Differentiating Glioblastomas from Solitary Brain Metastases: An Update on the Current Literature of Advanced Imaging Modalities

Differentiating between glioblastomas and solitary brain metastases proves to be a challenging diagnosis for neuroradiologists, as both present with imaging patterns consisting of peritumoral hyperintensities with similar intratumoral texture on traditional magnetic resonance imaging sequences. Early diagnosis is paramount, as each pathology has completely different methods of clinical assessment. In the past decade, recent developments in advanced imaging modalities enabled providers to acquire a more accurate diagnosis earlier in the patient's clinical assessment, thus optimizing clinical outcome. Dynamic susceptibility contrast has been optimized for detecting relative cerebral blood flow and relative cerebral blood volume. Diffusion tensor imaging can be used to detect changes in mean diffusivity. Neurite orientation dispersion and density imaging is an innovative modality detecting changes in intracellular volume fraction, isotropic volume fraction, and extracellular volume fraction. Magnetic resonance spectroscopy is able to assist by providing a metabolic descriptor while detecting variable ratios of choline/N-acetylaspartate, choline/creatine, and N-acetylaspartate/creatine. Finally, radiomics and machine learning algorithms have been devised to assist in improving diagnostic accuracy while often utilizing more than one advanced imaging protocol per patient. In this review, we provide an update on all the current evidence regarding the identification and differentiation of glioblastomas from solitary brain metastases.

Advances in magnetic resonance imaging of orbital disease

Magnetic resonance imaging (MRI) is increasingly used by the orbital surgeon to aid in the diagnosis, surgical planning, and monitoring of orbital disease. MRI provides superior soft tissue detail compared with computed tomography or ultrasound, and advancing techniques enhance its ability to highlight abnormal orbital pathology. Diffusion-weighted imaging is a specialized technique that uses water molecule diffusion patterns in tissue to generate contrast signals and can help distinguish malignant from benign lesions. Steady-state free precession sequences such as Constructive Interference in Steady-State (CISS) and Fast Imaging Employing Steady-state Acquisition (FIESTA) generate highly detailed, 3-dimensional reconstructed images and are particularly useful in distinguishing structures adjacent to cerebral spinal fluid. Magnetic resonance angiography can be used to characterize vascular lesions within the orbit. New developments in magnetic field strength as well as the use of orbital surface coils achieve increasingly improved imaging resolution.

Novel Thoracic MRI Approaches for the Assessment of Pulmonary Physiology and Inflammation

Excessive pulmonary inflammation can lead to damage of lung tissue, airway remodelling and established structural lung disease. Novel therapeutics that specifically target inflammatory pathways are becoming increasingly common in clinical practice, but there is yet to be a similar stepwise change in pulmonary diagnostic tools. A variety of thoracic magnetic resonance imaging (MRI) tools are currently in development, which may soon fulfil this emerging clinical need for highly sensitive assessments of lung structure and function. Given conventional MRI techniques are poorly suited to lung imaging, alternate strategies have been developed, including the use of inhaled contrast agents, intravenous contrast and specialized lung MR sequences. In this chapter, we discuss technical challenges of performing MRI of the lungs and how they may be overcome. Key thoracic MRI modalities are reviewed, namely, hyperpolarized noble gas MRI, oxygen-enhanced MRI (OE-MRI), ultrashort echo time (UTE) MRI and dynamic contrast-enhanced (DCE) MRI. Finally, we consider potential clinical applications of these techniques including phenotyping of lung disease, evaluation of novel pulmonary therapeutic efficacy and longitudinal assessment of specific patient groups.

White matter deficits in cocaine use disorder: convergent evidence from in vivo diffusion tensor imaging and ex vivo proteomic analysis

White matter (WM) abnormalities in patients with cocaine use disorder (CUD) have been studied; however, the reported effects on the human brain are heterogenous and most results have been obtained from male participants. In addition, biological data supporting the imaging findings and revealing possible mechanisms underlying the neurotoxic effects of chronic cocaine use (CU) on WM are largely restricted to animal studies. To evaluate the neurotoxic effects of CU in the WM, we performed an in vivo diffusion tensor imaging assessment of male and female cocaine users (n = 75) and healthy controls (HC) (n = 58). Moreover, we performed an ex vivo large-scale proteomic analysis using liquid chromatography-tandem mass spectrometry in postmortem brains of patients with CUD (n = 8) and HC (n = 12). Compared with the HC, the CUD group showed significant reductions in global fractional anisotropy (FA) (p < 0.001), and an increase in global mean (MD) and radial diffusion (RD) (both p < 0.001). The results revealed that FA, RD, and MD alterations in the CUD group were widespread along the major WM tracts, after analysis using the tract-based special statistics approach. Global FA was negatively associated with years of CU (p = 0.0421) and female sex (p < 0.001), but not with years of alcohol or nicotine use. Concerning the fibers connecting the left to the right prefrontal cortex, Brodmann area 9 (BA9), the CUD group presented lower FA (p = 0.006) and higher RD (p < 0.001) values compared with the HC group. A negative association between the duration of CU in life and FA values in this tract was also observed (p = 0.019). Proteomics analyses in BA9 found 11 proteins differentially expressed between cocaine users and controls. Among these, were proteins related to myelination and neuroinflammation. In summary, we demonstrate convergent evidence from in vivo diffusion tensor imaging and ex vivo proteomics analysis of WM disruption in CUD.

The diagnostic utility of diffusion weighted MRI imaging and ADC ratio to distinguish benign from malignant renal masses: sorting the kittens from the tigers

Background

MRI is playing an increasing role in risk stratification and non-invasive diagnosis of the undifferentiated small renal mass. This study was designed to assess the reliability of MRI in diagnostic evaluation of renal masses, specifically characterising lesions with diffusion weighted imaging (DWI) and apparent diffusion coefficient (ADC) values.

Methods

This is a retrospective analysis of patients undergoing MRI as part of their clinical workup for a renal mass suspicious for renal cell carcinoma (RCC) on CT or ultrasound followed by biopsy and/or surgical excision. All cases were conducted on 3 Tesla MRI, with conventional breath-held sequences, DWI and dynamic contrast enhanced phases. Tumour regions of interest were evaluated on ADC maps and compared with T2 weighted and post-contrast images.

Results

Of the 66 renal tumours included, 33 (50.0%) were Clear Cell RCC, 11 (16.7%) were Oncocytoma, nine (13.6%) were Angiomyolipoma (AML), nine (13.6%) were Papillary RCC and four (6.1%) were Chromophobe RCC. Oncocytoma had the largest ADC values, significantly larger than AMLs and all RCC subtypes (p < 0.001). The average ADC value was also significantly larger in Clear Cell RCCs compared to AMLs, and other RCC subtypes (p < 0.001).

Conclusions

MRI with DWI/ADC imaging may aid the differentiation of oncocytomas from RCCs and stratify RCC subtypes, Further studies are required to validate these findings.

Trial registration: Not applicable/retrospective study.

Noninvasive evaluation of early diabetic nephropathy using diffusion kurtosis imaging: an experimental study

OBJECTIVES

To evaluate the value of renal diffusion kurtosis imaging (DKI) in the diagnosis of early diabetic nephropathy (DN) in a rat model.

MATERIALS AND METHODS

Forty male Zucker diabetic fatty rats that spontaneously developed type 2 diabetes mellitus (DM) and 20 age-matched nondiabetic lean Zucker rats were included. Renal DKI scans and histological examinations were performed on the rats in batches at the end of the 4th, 8th, 12th, 16th, and 20th week after DM model was built. Based on renal histopathological appearance, included animals were divided into three groups: a nondiabetic control group, a DM group without DN, and an early DN group. Mean kurtosis (MK) and mean diffusivity (MD) values of renal cortex and medulla were analyzed statistically.

RESULTS

MK values of renal cortex and medulla tended to increase from the control group to the early DN group, respectively, while MD values tended to decrease. The cutoff MD and MK values of renal cortex and medulla showed different values in discriminating early DN from controls. Among them, cutoff MK value of medulla of 0.62 was the best parameter (sensitivity, 93.9%; specificity, 96.4%; and area under the curve, 0.95). For discriminate early DN from DM without DN and DM without DN from controls, cutoff MK value of renal cortex or medulla achieved an area under the curve of 0.76-0.85.

CONCLUSIONS

MR DKI may be valuable for the noninvasive detection of early DN, and MK value might serve as a more sensitive biomarker of early DN than MD value.

KEY POINTS

• In this article, diffusion kurtosis imaging (DKI) was used to detect the changes in the kidneys due to early diabetic nephropathy (DN). • MR DKI may be valuable for the noninvasive detection of early DN. • The mean kurtosis values of renal cortex and medulla might serve as a more sensitive biomarker of early DN than the mean diffusivity values.

Molecular and Diffusion Tensor Imaging Biomarkers of Traumatic Brain Injury: Principles for Investigation and Integration

The last 20 years have seen the advent of new technologies that enhance the diagnosis and prognosis of traumatic brain injury (TBI). There is recognition that TBI affects the brain beyond initial injury, in some cases inciting a progressive neuropathology that leads to chronic impairments. Medical researchers are now searching for biomarkers to detect and monitor this condition. Perhaps the most promising developments are in the biomolecular and neuroimaging domains. Molecular assays can identify proteins indicative of neuronal injury and/or degeneration. Diffusion imaging now allows sensitive evaluations of the brain's cellular microstructure. As the pace of discovery accelerates, it is important to survey the research landscape and identify promising avenues of investigation. In this review, we discuss the potential of molecular and diffusion tensor imaging (DTI) biomarkers in TBI research. Integration of these technologies could advance models of disease prognosis, ultimately improving care. To date, however, few studies have explored relationships between molecular and DTI variables in patients with TBI. Here, we provide a short primer on each technology, review the latest research, and discuss how these biomarkers may be incorporated in future studies.

White matter microstructural abnormalities in primary insomnia: A systematic review of diffusion tensor imaging studies

Primary insomnia (PI), the most common sleep disorder, is primarily characterized by difficulties in initiating and maintaining sleep and deficits in daytime functioning. Study of white matter (WM) connections of the brain might provide valuable information regarding the underlying neural mechanism of PI. Diffusion tensor imaging (DTI) provides non-invasive access to the microstructural and network properties of brain WM, and thus, a great opportunity to quantitatively and sensitively study the human brain. The current literature of PI does not provide a consistent explanation of the etiology and pathology of the disorder; thus, we searched PubMed, EMBASE, and SCOPUS for DTI studies that compared WM integrity or network organization between PI patients and healthy controls to integrate all existing literature as an insight for further studies, and, hopefully, effective prevention and management of the disorder. English peer-reviewed full-text publications that investigated the diffusion indices of PI patients or those with insomnia symptoms compared with a group of healthy controls were included. We included 11 studies and extracted the data for qualitative synthesis. Except for one study, all studies were rated as high-quality, based on quality assessment. In aggregation, a total of 541 patients with PI and 679 healthy controls were included in this study. Our review of DTI studies suggests that WM disruptions in PI are better characterized in the context of neural networks. Frontostriatal, frontothalamic, and corticocortiscal networks, as well as the limbic system, seem to be the main neural networks with microstructural and network alterations in patients with PI. To illustrate, different parts of corona radiate and internal capsule within the corticosubcortical networks and superior longitudinal fasciculus within the corticocortical networks showed altered microstructural properties in PI patients. In view of the fact that the findings from individual studies are heterogeneous, it is difficult to derive consistent findings from the results, and the divergence of the findings must not be disregarded. Thus, this study is a starting point for future studies, and its implications for etiology and pathogenesis of insomnia should be regarded cautiously.

Routes of the thalamus through the history of neuroanatomy

The most distant roots of neuroanatomy trace back to antiquity, with the first human dissections, but no document which would identify the thalamus as a brain structure has reached us. Claudius Galenus (Galen) gave to the thalamus the name 'thalamus nervorum opticorum', but later on, other names were used (e.g., anchae, or buttocks-like). In 1543, Andreas Vesalius provided the first quality illustrations of the thalamus. During the 19th century, tissue staining techniques and ablative studies contributed to the breakdown of the thalamus into subregions and nuclei. The next step was taken using radiomarkers to identify connections in the absence of lesions. Anterograde and retrograde tracing methods arose in the late 1960s, supporting extension, revision, or confirmation of previously established knowledge. The use of the first viral tracers introduced a new methodological breakthrough in the mid-1970s. Another important step was supported by advances in neuroimaging of the thalamus in the 21th century. The current review follows the history of the thalamus through these technical revolutions from Antiquity to the present day.

Heterogeneity analysis of MRI T2 maps for measurement of early tumor response to radiotherapy

External beam radiotherapy (XRT) is a widely used cancer treatment, yet responses vary dramatically among patients. These differences are not accounted for in clinical practice, partly due to a lack of sensitive early response biomarkers. We hypothesize that quantitative magnetic resonance imaging (MRI) measures reflecting tumor heterogeneity can provide a sensitive and robust biomarker of early XRT response. MRI T2 mapping was performed every 72 hours following 10 Gy dose XRT in two models of pancreatic cancer propagated in the hind limb of mice. Interquartile range (IQR) of tumor T2 was presented as a potential biomarker of radiotherapy response compared with tumor growth kinetics, and biological validation was performed through quantitative histology analysis. Quantification of tumor T2 IQR showed sensitivity for detection of XRT-induced tumor changes 72 hours after treatment, outperforming T2-weighted and diffusion-weighted MRI, with very good robustness. Histological comparison revealed that T2 IQR provides a measure of spatial heterogeneity in tumor cell density, related to radiation-induced necrosis. Early IQR changes were found to correlate to subsequent tumor volume changes, indicating promise for treatment response prediction. Our preclinical findings indicate that spatial heterogeneity analysis of T2 MRI can provide a translatable method for early radiotherapy response assessment. We propose that the method may in future be applied for personalization of radiotherapy through adaptive treatment paradigms.

Diagnostic value of diffusion-weighted imaging/magnetic resonance imaging for peritoneal metastasis from malignant tumor: A systematic review and meta-analysis

BACKGROUND

Previous meta-analyses examined either multiple tools for the diagnosis of peritoneal metastases (PMs), but not diffusion-weighted imaging (DWI), or included only 1 tumor type. This study aimed to determine the summary diagnostic value of DWI/magnetic resonance imaging in determining PMs originating from various tumors.

METHODS

PubMed, Embase, and Cochrane library were searched for available papers up to 2019/12. Pooled estimates for sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and accuracy were calculated using random-effects models.

RESULTS

Ten studies were included and could be used to calculate the pooled sensitivity and specificity. The pooled sensitivity of DWI for PMs was 89% (95% confidence interval [CI]: 83%-93%). The pooled specificity was 86% (95% CI: 79%-91%). When considering only the retrospective studies, the pooled sensitivity of DWI for PMs was 85% (95% CI: 81%-89%). The pooled specificity was 84% (95% CI: 72%-92%). When considering only the studies about gastrointestinal tumors, the pooled sensitivity of DWI for PMs was 97% (95% CI: 68%-100%). The pooled specificity was 86% (95% CI: 69%-95%). No publication bias was observed (P = dd.27).

CONCLUSION

DWI magnetic resonance imaging is highly sensitive and specific for the detection of PMs from various abdominal cancers.

Diffusion-weighted magnetic resonance imaging (MRI) without susceptibility artifacts: single-shot stimulated echo acquisition mode (STEAM) MRI with iterative reconstruction and spatial regularization

This work describes a new method for diffusion-weighted (DW) magnetic resonance imaging (MRI) without susceptibility artifacts. The technique combines a DW spin-echo module and a single-shot stimulated echo acquisition mode (STEAM) MRI readout with undersampled radial trajectories and covers a volume by a gapless series of cross-sectional slices. In a first step, optimal coil sensitivities for all slices are obtained from a series of non-DW acquisitions by nonlinear inverse reconstruction with regularization to the image and coil sensitivities of a directly neighboring slice. In a second step, these coil sensitivities are used to compute all series of non-DW and DW images by linear inverse reconstruction with spatial regularization to a neighboring image. Proof-of-principle applications to the brain (51 sections) and prostate (31 sections) of healthy subjects were realized for a protocol with two b-values and 6 gradient directions at 3 T. Including averaging the measuring times for studies of the brain at 1.0×1.0×3.0 mm3 resolution (b =1,000 s mm-2) and prostate at 1.4×1.4×3.0 mm3 resolution (b =600 s mm-2) were 2.5 min and 4.5 min, respectively. All reconstructions were accomplished online with use of a multi-GPU computer integrated into the MRI system. The resulting non-DW images, mean DW images averaged across directions and maps of the apparent diffusion coefficient confirm the absence of geometric distortions or false signal alterations and demonstrate diagnostic image quality. The novel method for DW STEAM MRI of a volume without susceptibility artifacts warrants extended clinical trials.

Single shot echo planar imaging (ssEPI) vs single shot turbo spin echo (ssTSE) DWI of the orbit in patients with ocular melanoma

OBJECTIVES

Diffusion weighted imaging (DWI) has become important for orbital imaging. However, the echoplanar imaging (EPI) DWI has inherent obstacles due to susceptibility to magnetic field inhomogeneities. We conducted a comparative study assessing the image quality of orbits in a patient cohort with uveal melanoma (UM). We hypothesized that single shot turbo spin echo (ssTSE) DWI would have better image quality in terms of less distortion and artifacts and yield better tissue evaluation compared to ssEPI-DWI.

METHODS

ssEPI-DWI and ssTSE-DWI of orbits were obtained from 50 patients with uveal melanoma who were prospectively enrolled in the study. Distortion ratio (DR), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), diffusion signal properties, and apparent diffusion coefficient (ADC) values were collected and compared between ssEPI-DWI and ssTSE-DWI. Two reviewers evaluated and compared the geometric distortion, susceptibility and ghosting artifacts, resolution, demarcation of ocular mass, and overall quality.

RESULTS

A higher DR was found in ssEPI-DWI compared to ssTSE-DWI (p < 0.001). SNR and CNR were lower for the temporal lobe cortex (p ≤ 0.004), but higher for melanoma in ssEPI-DWI than ssTSE-DWI (p ≤ 0.037). Geometric distortion and artifacts were more common in ssEPI-DWI (p < 0.001). Resolution (p ≤ 0.013) and overall quality (p < 0.001) were better in ssTSE-DWI. Ocular masses were demarcated better on ssEPI-DWI (p ≤ 0.002). Significant negative correlations between T1 and T2 signal intensities (r = -0.369, p ≤ 0.008) and positive correlations between T2 and both DWI signal intensities (r = 0.686 and p < 0.001 for ssEPI-DWI, r = 0.747 and p < 0.001 for ssTSE-DWI) were revealed.

CONCLUSION

With less geometric distortion and susceptibility artifacts, better resolution, and overall quality, ssTSE-DWI can serve as an alternative to ssEPI-DWI for orbital DWI.

ADVANCES IN KNOWLEDGE

ssTSE-DWI can be a better alternative of diffusion imaging of orbits with less susceptibility artifact and geometric distortion compared to ssEPI-DWI.

A Confidence Habitats Methodology in MR Quantitative Diffusion for the Classification of Neuroblastic Tumors

Simple Summary

There is growing interest in applying quantitative diffusion techniques to magnetic resonance imaging for cancer diagnosis and treatment. These measurements are used as a surrogate marker of tumor cellularity and aggressiveness, although there may be factors that introduce some bias to these approaches. Thus, we explored a novel methodology based on confidence habitats and voxel uncertainty to improve the power of the apparent diffusion coefficient to discriminate between benign and malignant neuroblastic tumor profiles in children. We were able to show this offered an improved sensitivity and negative predictive value relative to standard voxel-based methodologies.

Abstract

Background/Aim: In recent years, the apparent diffusion coefficient (ADC) has been used in many oncology applications as a surrogate marker of tumor cellularity and aggressiveness, although several factors may introduce bias when calculating this coefficient. The goal of this study was to develop a novel methodology (Fit-Cluster-Fit) based on confidence habitats that could be applied to quantitative diffusion-weighted magnetic resonance images (DWIs) to enhance the power of ADC values to discriminate between benign and malignant neuroblastic tumor profiles in children. Methods: Histogram analysis and clustering-based algorithms were applied to DWIs from 33 patients to perform tumor voxel discrimination into two classes. Voxel uncertainties were quantified and incorporated to obtain a more reproducible and meaningful estimate of ADC values within a tumor habitat. Computational experiments were performed by smearing the ADC values in order to obtain confidence maps that help identify and remove noise from low-quality voxels within high-signal clustered regions. The proposed Fit-Cluster-Fit methodology was compared with two other methods: conventional voxel-based and a cluster-based strategy. Results: The cluster-based and Fit-Cluster-Fit models successfully differentiated benign and malignant neuroblastic tumor profiles when using values from the lower ADC habitat. In particular, the best sensitivity (91%) and specificity (89%) of all the combinations and methods explored was achieved by removing uncertainties at a 70% confidence threshold, improving standard voxel-based sensitivity and negative predictive values by 4% and 10%, respectively. Conclusions: The Fit-Cluster-Fit method improves the performance of imaging biomarkers in classifying pediatric solid tumor cancers and it can probably be adapted to dynamic signal evaluation for any tumor.

Detection of White Matter Ultrastructural Changes for Amyotrophic Lateral Sclerosis Characterization: A Diagnostic Study from Dti-Derived Data

In amyotrophic lateral sclerosis (ALS), magnetic resonance imaging (MRI) allows investigation at the microstructural level, employing techniques able to reveal white matter changes. In the current study, a diffusion tensor imaging (DTI) analysis, with a collection of apparent diffusion coefficient (ADC) and fractional anisotropy (FA) indexes, was performed in ALS patients to correlate geno- and phenotype features with MRI data, to investigate an in-vivo correlation of different neuropathological patterns. All patients who underwent the MR-DTI analysis were retrospectively recruited. MRI scan was collected within three months from diagnosis. FA and ADC values were collected in corpus callosum (CC), corona radiata (CR), cerebral peduncle (CR), cerebellar peduncle (CbP) and corticospinal tract at posterior limb of internal capsule (CST). DTI analysis performed in the whole ALS cohort revealed significant FA reduction and ADC increase in all selected regions, as widespread changes. Moreover, we observed a higher value of FA in rCR in bulbar patients. A positive correlation between ALS Functional Rating Scale-Revised and FA in rCP was evident. In consideration of the non-invasiveness, the reliability and the easy reproducibility of the method, we believe that brain MRI with DTI analyses may represent a valid tool usable as a diagnostic marker in ALS.

Diffusion-weighted MRI versus FDG-PET/CT for diagnosing pancreatic cancer: an indirect comparison meta-analysis

BACKGROUND

Fluorodeoxyglucose positron emission tomography (FDG-PET)/computed tomography (CT) and diffusion-weighted magnetic resonance imaging (DWI or DW-MRI) are tools for the diagnosis of pancreatic cancer. However, comparison of their diagnostic performance remains unknown.

PURPOSE

To indirectly compare the diagnostic value of DWI and FDG-PET/CT in the detection of pancreatic cancer.

MATERIAL AND METHODS

A literature search of PubMed, Embase, and Cochrane Library electronic databases for articles published through May 2018 yielded 875 articles. For the meta-analysis, we included 26 studies evaluating the efficacy of DWI and FDG-PET/CT for determining pancreatic cancer with a total of 1377 patients. QUADAS (Quality Assessment of Diagnostic Accuracy Studies) was used to assess the study quality. Sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and the area under the receiver operating characteristic curves (AUC) with their 95% confidence intervals were calculated for each individual study.

RESULTS

There were no significant differences between DWI and FDG-PET/CT for sensitivity, specificity, PLR, NLR, or DOR, while DWI AUC was higher than that of FDG-PET/CT for the detection of pancreatic cancer.

CONCLUSION

The diagnostic value of both DWI and FDG-PET/CT were comparable and, hence, both techniques seem to be equally useful tools for the diagnosis of pancreatic cancer.

Image Denoising Using Non-Local Means (NLM) Approach in Magnetic Resonance (MR) Imaging: A Systematic Review

The non-local means (NLM) noise reduction algorithm is well known as an excellent technique for removing noise from a magnetic resonance (MR) image to improve the diagnostic accuracy. In this study, we undertook a systematic review to determine the effectiveness of the NLM noise reduction algorithm in MR imaging. A systematic literature search was conducted of three databases of publications dating from January 2000 to March 2020; of the 82 publications reviewed, 25 were included in this study. The subjects were categorized into four major frameworks and analyzed for each research result. Research in NLM noise reduction for MR images has been increasing worldwide; however, it was found to have slightly decreased since 2016. It was found that the NLM technique was most frequently used on brain images taken using the general MR imaging technique; these were most frequently performed during simultaneous real and simulated experimental studies. In particular, comparison parameters were frequently used to evaluate the effectiveness of the algorithm on MR images. The ultimate goal is to provide an accurate method for the diagnosis of disease, and our conclusion is that the NLM noise reduction algorithm is a promising method of achieving this goal.

Applications of Computational Methods in Biomedical Breast Cancer Imaging Diagnostics: A Review

With the exponential increase in new cases coupled with an increased mortality rate, cancer has ranked as the second most prevalent cause of death in the world. Early detection is paramount for suitable diagnosis and effective treatment of different kinds of cancers, but this is limited to the accuracy and sensitivity of available diagnostic imaging methods. Breast cancer is the most widely diagnosed cancer among women across the globe with a high percentage of total cancer deaths requiring an intensive, accurate, and sensitive imaging approach. Indeed, it is treatable when detected at an early stage. Hence, the use of state of the art computational approaches has been proposed as a potential alternative approach for the design and development of novel diagnostic imaging methods for breast cancer. Thus, this review provides a concise overview of past and present conventional diagnostics approaches in breast cancer detection. Further, we gave an account of several computational models (machine learning, deep learning, and robotics), which have been developed and can serve as alternative techniques for breast cancer diagnostics imaging. This review will be helpful to academia, medical practitioners, and others for further study in this area to improve the biomedical breast cancer imaging diagnosis.

Increased sensitivity to traumatic axonal injury on postconcussion diffusion tensor imaging scans in National Football League players by using premorbid baseline scans

OBJECTIVE

Statistical challenges exist when using diffusion tensor imaging (DTI) to assess traumatic axonal injury (TAI) in individual concussed athletes. The authors examined active professional American football players over a 6-year time period to study potential TAI after concussion and assess optimal methods to analyze DTI at the individual level.

METHODS

Active American professional football players recruited prospectively were assessed with DTI, conventional MRI, and standard clinical workup. Subjects underwent an optional preseason baseline scan and were asked to undergo a scan within 5 days of concussion during gameplay. DTI from 25 age- and sex-matched controls were obtained. Both semiautomated region-of-interest analysis and fully automated tract-based spatial statistics (TBSS) were used to examine DTI at individual and group levels. Statistical differences were assessed comparing individual DTI data to baseline imaging versus a normative database. Group-level comparisons were also performed to determine if longer exposure to professional-level play or prior concussion cause white matter microstructural integrity changes.

RESULTS

Forty-nine active professional football players were recruited into the study. Of the 49 players, 7 were assessed at baseline during the preseason and after acute concussion. An additional 18 players were assessed after acute concussion only. An additional 24 players had only preseason baseline assessments. The results suggest DTI is more sensitive to suspected TAI than conventional MRI, given that 4 players demonstrated decreased fractional anisotropy (FA) in multiple tracts despite normal conventional MRI. Furthermore, the data suggest individual assessment of DTI data using baseline premorbid imaging is more sensitive than typical methods of comparing data to a normative control group. Among all subjects with baseline data, 1 reduced FA tract (± 2.5 standard deviations) was found using the typical normative database reference versus 10 statistically significant (p < 0.05) reduced FA tracts when referencing internal control baseline data. All group-level comparisons were statistically insignificant (p > 0.05).

CONCLUSIONS

Baseline premorbid DTI data for individual DTI analysis provides increased statistical sensitivity. Specificity using baseline imaging also increases because numerous potential etiologies for reduced FA may exist prior to a concussion. These data suggest that there is a high potential for false-positive and false-negative assessment of DTI data using typical methods of comparing an individual to normative groups given the variability of FA values in the normal population.

New boundaries of liver imaging: from morphology to function

From an invisible organ to one of the most explored non-invasively, the liver is, today, one of the cornerstones for current cross-sectional imaging techniques and minimally invasive procedures. After the achievements of US, CT and, most recently, MRI in providing highly accurate morphological and structural information about the organ, a significant scientific development has gained momentum for the last decades, coupling morphology to liver function and contributing far most to what we know today as precision medicine. In fact, dedicated tailor-made investigations are now possible in order to detect and, most of all, quantify physiopathological processes with unprecedented certitude. It is the intention of this review to provide a better insight to the reader of several functional imaging techniques applied to liver imaging. Contrast enhanced imaging, diffusion weighted imaging, elastography, spectral computed tomography and fat and iron assessment techniques are commonly performed clinically. Diffusion kurtosis imaging, magnetic resonance spectroscopy, T1 relaxometry and radiomics remain largely limited to advanced clinical research. Each of them has its own value and place on the diagnostic armamentarium and provide unique qualitative and quantitative information regarding the pathophysiology of diseases, contributing at a large scale to model therapeutic decisions and patient follow-up. Therefore, state-of-the-art liver imaging acts today as a non-invasive surrogate biomarker of many focal and diffuse liver diseases.

Computational technology for nasal cartilage-related clinical research and application

Surgeons need to understand the effects of the nasal cartilage on facial morphology, the function of both soft tissues and hard tissues and nasal function when performing nasal surgery. In nasal cartilage-related surgery, the main goals for clinical research should include clarification of surgical goals, rationalization of surgical methods, precision and personalization of surgical design and preparation and improved convenience of doctor-patient communication. Computational technology has become an effective way to achieve these goals. Advances in three-dimensional (3D) imaging technology will promote nasal cartilage-related applications, including research on computational modelling technology, computational simulation technology, virtual surgery planning and 3D printing technology. These technologies are destined to revolutionize nasal surgery further. In this review, we summarize the advantages, latest findings and application progress of various computational technologies used in clinical nasal cartilage-related work and research. The application prospects of each technique are also discussed.

Multiparametric Functional Magnetic Resonance Imaging for Evaluating Renal Allograft Injury

Kidney transplantation is the treatment of choice for patients with end-stage renal disease, as it extends survival and increases quality of life in these patients. However, chronic allograft injury continues to be a major problem, and leads to eventual graft loss. Early detection of allograft injury is essential for guiding appropriate intervention to delay or prevent irreversible damage. Several advanced MRI techniques can offer some important information regarding functional changes such as perfusion, diffusion, structural complexity, as well as oxygenation and fibrosis. This review highlights the potential of multiparametric MRI for noninvasive and comprehensive assessment of renal allograft injury.

Heavy Metal-Induced Cerebral Small Vessel Disease: Insights into Molecular Mechanisms and Possible Reversal Strategies

Heavy metals are considered a continuous threat to humanity, as they cannot be eradicated. Prolonged exposure to heavy metals/metalloids in humans has been associated with several health risks, including neurodegeneration, vascular dysfunction, metabolic disorders, cancer, etc. Small blood vessels are highly vulnerable to heavy metals as they are directly exposed to the blood circulatory system, which has comparatively higher concentration of heavy metals than other organs. Cerebral small vessel disease (CSVD) is an umbrella term used to describe various pathological processes that affect the cerebral small blood vessels and is accepted as a primary contributor in associated disorders, such as dementia, cognitive disabilities, mood disorder, and ischemic, as well as a hemorrhagic stroke. In this review, we discuss the possible implication of heavy metals/metalloid exposure in CSVD and its associated disorders based on in-vitro, preclinical, and clinical evidences. We briefly discuss the CSVD, prevalence, epidemiology, and risk factors for development such as genetic, traditional, and environmental factors. Toxic effects of specific heavy metal/metalloid intoxication (As, Cd, Pb, Hg, and Cu) in the small vessel associated endothelium and vascular dysfunction too have been reviewed. An attempt has been made to highlight the possible molecular mechanism involved in the pathophysiology, such as oxidative stress, inflammatory pathway, matrix metalloproteinases (MMPs) expression, and amyloid angiopathy in the CSVD and related disorders. Finally, we discussed the role of cellular antioxidant defense enzymes to neutralize the toxic effect, and also highlighted the potential reversal strategies to combat heavy metal-induced vascular changes. In conclusion, heavy metals in small vessels are strongly associated with the development as well as the progression of CSVD. Chelation therapy may be an effective strategy to reduce the toxic metal load and the associated complications.

Deep Cerebellar Nuclei Segmentation via Semi-Supervised Deep Context-Aware Learning from 7T Diffusion MRI

Deep cerebellar nuclei are a key structure of the cerebellum that are involved in processing motor and sensory information. It is thus a crucial step to accurately segment deep cerebellar nuclei for the understanding of the cerebellum system and its utility in deep brain stimulation treatment. However, it is challenging to clearly visualize such small nuclei under standard clinical magnetic resonance imaging (MRI) protocols and therefore precise segmentation is not feasible. Recent advances in 7 Tesla (T) MRI technology and great potential of deep neural networks facilitate automatic patient-specific segmentation. In this paper, we propose a novel deep learning framework (referred to as DCN-Net) for fast, accurate, and robust patient-specific segmentation of deep cerebellar dentate and interposed nuclei on 7T diffusion MRI. DCN-Net effectively encodes contextual information on the patch images without consecutive pooling operations and adding complexity via proposed dilated dense blocks. During the end-to-end training, label probabilities of dentate and interposed nuclei are independently learned with a hybrid loss, handling highly imbalanced data. Finally, we utilize self-training strategies to cope with the problem of limited labeled data. To this end, auxiliary dentate and interposed nuclei labels are created on unlabeled data by using DCN-Net trained on manual labels. We validate the proposed framework using 7T B0 MRIs from 60 subjects. Experimental results demonstrate that DCN-Net provides better segmentation than atlas-based deep cerebellar nuclei segmentation tools and other state-of-the-art deep neural networks in terms of accuracy and consistency. We further prove the effectiveness of the proposed components within DCN-Net in dentate and interposed nuclei segmentation.

Comparison between Diffusion-Weighted Sequences with Selective and Non-Selective Fat Suppression in the Evaluation of Crohn's Disease Activity: Are They Equally Useful?

Background: We compared the efficiency of two MRI diffusion weighted imaging (DWI) techniques: DWI with SPIR (DWI_SPIR) and DWI with STIR (DWI_STIR), to estimate their eligibility for quantitative assessment of Crohn’s disease activity in children and adults. Methods: In inflamed terminal ileum segments (n = 32 in adults, n = 46 in children), Magnetic Resonance Index of Activity (MaRIA) was calculated, ADC values of both DWI techniques were measured, and the corresponding Clermont scores calculated. ADC values of both DWI techniques were compared between both and within each patient group, assessing their mutual correlation. Correlations between MaRIA and the corresponding ADC values, and Clermont scores based on both DWI techniques were estimated. Results: No correlation between ADC of DWI_SPIR and DWI_STIR was observed (rho = 0.27, p = 0.13 in adults, rho = 0.20, p = 0.17 in children). The correlation between MaRIA and Clermont scores was strong in both techniques—in SPIR, rho = 0.93; p < 0.0005 in adults, rho = 0.98, p < 0.0005 in children, and, in STIR, rho = 0.89; p < 0.0005 in adults, rho = 0.95, p < 0.0005 in children. The correlation between ADC and MaRIA was moderate negative for DWI_STIR (rho = 0.93, p < 0.0005 in adults, rho = 0.95, p < 0.0005 in children), but, in DWI_STIR, no correlation between ADC and MaRIA score was observed in adults (rho = −0.001, p = 0.99), whereas children presented low negative correlation (rho = −0.374, p = 0.01). Conclusions: DWI_STIR is not suitable for quantitative assessment of Crohn’s disease activity both in children and adult patients.

Brain damage by trauma

Traumatic brain injury (TBI) represents a major clinical and economic challenge for health systems worldwide, and it is considered one of the leading causes of disability in young adults. The recent development of brain-computer interface (BCI) tools to target cognitive and motor impairments has led to the exploration of these techniques as potential therapeutic tools in patients with TBI. However, little evidence has been gathered so far to support applicability and efficacy of BCIs for TBI in a clinical setting. In the present chapter, results from studies using BCI approaches in conscious patients with TBI or in animal models of TBI as well as an overview of future directions in the use of BCIs to treat cognitive symptoms in this patient population will be presented.

Children with epilepsy demonstrate macro- and microstructural changes in the thalamus, putamen, and amygdala

PURPOSE

Despite evidence for macrostructural alteration in epilepsy patients later in life, little is known about the underlying pathological or compensatory mechanisms at younger ages causing these alterations. The aim of this work was to investigate the impact of pediatric epilepsy on the central nervous system, including gray matter volume, cerebral blood flow, and water diffusion, compared with neurologically normal children.

METHODS

Inter-ictal magnetic resonance imaging data was obtained from 30 children with epilepsy ages 1-16 (73% F, 27% M). An atlas-based approach was used to determine values for volume, cerebral blood flow, and apparent diffusion coefficient in the cerebral cortex, hippocampus, thalamus, caudate, putamen, globus pallidus, amygdala, and nucleus accumbens. These values were then compared with previously published values from 100 neurologically normal children using a MANCOVA analysis.

RESULTS

Most brain volumes of children with epilepsy followed a pattern similar to typically developing children, except for significantly larger putamen and amygdala. Cerebral blood flow was also comparable between the groups, except for the putamen, which demonstrated decreased blood flow in children with epilepsy. Diffusion (apparent diffusion coefficient) showed a trend towards higher values in children with epilepsy, with significantly elevated diffusion within the thalamus in children with epilepsy compared with neurologically normal children.

CONCLUSION

Children with epilepsy show statistically significant differences in volume, diffusion, and cerebral blood flow within their thalamus, putamen, and amygdala, suggesting that epilepsy is associated with structural changes of the central nervous system influencing brain development and potentially leading to poorer neurocognitive outcomes.

High-field mr diffusion-weighted image denoising using a joint denoising convolutional neural network

BACKGROUND

Low signal-to-noise ratio (SNR) has been a major limiting factor for the application of higher-resolution diffusion-weighted imaging (DWI). Most of the conventional denoising models suffer from the drawbacks of shallow feature extraction and hand-crafted parameter tuning. Although multiple studies have shown the promising applications of image denoising using convolutional neural networks (CNNs), none of them have considered denoising multiple b-value DWIs using a multichannel CNN model.

PURPOSE

To present a joint denoising CNN (JD-CNN) model to improve the SNR of multiple b-value DWI.

STUDY TYPE

Retrospective technical development.

POPULATION

Twenty healthy rats and two rats with clinically confirmed focal cortical dysplasia were included to evaluate the performance of the proposed method.

FIELD STRENGTH/SEQUENCE

11.7T MRI, a multiple b-values DWI sequence.

ASSESSMENT

The total variation (TV) and BM3D denoising methods were also performed on the same dataset for comparison. Peak SNR (PSNR) and normalized mean square error (NMSE) were calculated for the assessment of image qualities.

STATISTICAL TESTS

A paired Student's t-test was conducted to compare the diffusion parameter measurements between different approaches. P < 0.01 was considered statistically significant.

RESULTS

Simulation results showed substantial improvement of image quality after JD-CNN denoising (PSNR of original image: 23.15 ± 1.77; PSNR of denoised image: 42.94 ± 2.12). The proposed method outperforms the state-of-the-art methods on high b-value DWIs in terms of PSNR (TV: 33.51 ± 0.83, BM3D: 35.12 ± 0.94, JD-CNN: 46.52 ± 0.98). In addition, the NMSE of the estimated apparent diffusion coefficient (ADC) reduces from 0.72 ± 0.13 to 0.45 ± 0.06 (P < 0.01) with the application of the JD-CNN model.

DATA CONCLUSION

The proposed method is able to remove noise with a wide range of noise levels in multiple b-value DWI and improve the diffusion parameter estimation. This shows potential clinical promise.

LEVEL OF EVIDENCE

2 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:1937-1947.

Elimination of residual aliasing artifact that resembles brain lesion on multi-oblique diffusion-weighted echo-planar imaging with parallel imaging using virtual coil acquisition

BACKGROUND

Single-shot diffusion-weighted echo-planar imaging (ssDW-EPI) acquired with parallel imaging and a multi-oblique scan plane may suffer from residual aliasing artifacts, resembling lesions on the calculated apparent diffusion coefficient (ADC) map.

PURPOSE

To combine ssDW-EPI and virtual coil acquisition and develop a self-reference reconstruction method to eliminate the residual aliasing artifact on multi-oblique ssDW-EPI sequence with parallel imaging and multiple signal averaging.

STUDY TYPE

Prospective.

SUBJECTS

Three healthy subjects and 50 stroke patients.

FIELD STRENGTH/SEQUENCE

Conventional ssDW-EPI with parallel imaging, and proposed ssDW-EPI with virtual coil acquisition at 1.5T.

ASSESSMENT

The efficacy of the proposed method was evaluated in 50 stroke patients by comparing the ssDW-EPI with conventional parallel imaging reconstructions. The extent of residual aliasing artifacts were rated on a 5-point Likert scale by three independent raters. Only the data without residual aliasing artifacts on conventional ssDW-EPI were included for the assessment of signal-to-noise ratio (SNR), ghost-to-signal ratio (GSR), and ADC.

STATISTICAL TESTS

The interobserver agreements for examining residual aliasing artifacts were measured by the intraclass correlation coefficient (ICC). A two-sample t-test was performed for comparing SNR, GSR, and ADC.

RESULTS

There was a perfect agreement (ICC = 1.00) in the examination of residual aliasing artifacts on images obtained using the proposed method. The incidence rates of the residual aliasing artifact on the ADC maps obtained from the scanner console and proposed method were 60% (ie, 30 out of 50) and 0%, respectively. The proposed method offers significantly lower GSR than conventional parallel imaging reconstruction (P < 0.001). There was no significant difference in SNR (P = 0.20-0.51) and ADC values (P = 0.20-0.94) between conventional parallel imaging reconstructions and the proposed method.

DATA CONCLUSION

It appears that our method could effectively eliminate artifacts and significantly improve the GSR of b = 0 T₂ WI and b > 0 DWI, as well as permit ADC measurement consistent with conventional techniques. Our method may be beneficial to clinical assessment of the brain that utilizes multi-oblique ssDW-EPI.

LEVEL OF EVIDENCE

1 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;51:1442-1453.

In-Vivo Retention of 5-Fluorouracil Using 19F Magnetic Resonance Chemical Shift Imaging in Colorectal Cancer in a Murine Model

Colorectal cancer is the third leading cause of cancer death worldwide. 5-Fluorouracil (5-FU) is one of the most commonly used chemotherapies for treatment of solid tumours, including colorectal cancer. The efficacy of treatment is dependent on tumour type and can only be determined six weeks after beginning chemotherapy, with only 40–50% of patients responding positively to the 5-FU therapy. In this paper, we demonstrate the potential of using Magnetic Resonance (MR) Chemical Shift Imaging (CSI) for in-vivo monitoring of 5-FU tumor-retention in two different colorectal tumour types (HT-29 & H-508). Time curves for 5-FU signals from the liver and bladder were also acquired. We observed significant differences (p < 0.01) in 5-FU signal time dependencies for the HT-29 and H-508 tumours. Retention of 5-FU occurred in the H-508 tumour, whereas the HT-29 tumour is not expected to retain 5FU due to the observation of the negative b time constant indicating a decline in 5FU within the tumour. This study successfully demonstrates that CSI may be a useful tool for early identification of 5-FU responsive tumours based on observed tumour retention of the 5-FU.

Diffusion-Weighted Magnetic Resonance Imaging for Early Detection of Chemotherapy Resistance in Non-Small Cell Lung Cancer

Background

The aim of this study was to examine the role of magnetic resonance imaging-diffusion weighted imaging (MRI-DWI) in the early detection of chemotherapy resistance in non-small cell lung cancer (NSCLC) patients.

Material/Methods

MRI-DWI and computed tomography (CT) were carried out in 75 patients with newly diagnostic NSCLC before and after first, second, fourth, and sixth cycles of chemotherapy. Resistance to chemotherapy was assessed based on the change in the largest tumor diameter after chemotherapy. Diffusion of water molecule in each lesion was quantitatively measured by apparent diffusion coefficient (ADC). The diagnostic results of DWI after first and second cycle of chemotherapy were analyzed by the area under receiver operating characteristics curve (ROC).

Results

Among the patients, 43 patients were chemo-resistance while 32 patients were chemo-sensitive. The ADC changing rate between second and first cycle of chemotherapy was significantly higher in chemo-sensitive patients compared with chemo-resistance patients (t=3.236, P=0.002). The ROC showed cutoff values of the ADC changing rate after first and second cycles of chemotherapy for resistance/sensitive discrimination were 23.6% and 5.56%, respectively. DWI after first and second cycles of therapy showed sensitivities of 55.8% and 55.8%, specificities of 65.6% and 87.5%, and area under ROC of 0.568 and 0.733, respectively.

Conclusions

ADC changing rate between first and second cycles of chemotherapy could sensitively distinguish chemo-sensitive and chemo-resistant tumors at earlier stages, which may direct treatment adjustment and improve the prognosis of patients.

The Influence of Bowel Preparation on ADC Measurements: Comparison between Conventional DWI and DWIBS Sequences

Background and objectives: The aim of the study was to assess whether there were differences between apparent diffusion coefficient (ADC) values of diffusion-weighted imaging (DWI) and diffusion-weighted imaging with background body signal suppression (DWIBS) sequences in non-prepared and prepared bowels before and after preparation with an enteric hyperosmolar agent, to assess whether ADC measurements have the potential to avoid bowel preparation and whether ADC-DWIBS has advantages over ADC-DWI. Materials and Methods: 106 adult patients without evidence of inflammatory bowel disease (IBD) underwent magnetic resonance (MR) enterography before and after bowel preparation. ADC-DWI and ADC-DWIBS values were measured in the intestinal and colonic walls demonstrating high signal intensity (SI) at DWI tracking images of b = 800 s/mm² before and after preparation. Results: There were significant difference (p < 0.0001) in both ADC-DWI and ADC-DWIBS results between non-prepared and prepared jejunum for DWI being 1.09 × 10⁻³ mm²/s and 1.76 × 10⁻³ mm²/s, respectively, and for DWIBS being 0.91 × 10⁻³ mm²/s and 1.75 × 10⁻³ mm²/s, respectively. Both ADC-DWI and DWIBS also showed significant difference between non-prepared and prepared colon (p < 0.0001), with DWI values 1.41 × 10⁻³ mm²/s and 2.13 × 10⁻³ mm²/s, and DWIBS—1.01 × 10⁻³ mm²/s and 2.04 × 10⁻³ mm²/s, respectively. No significant difference between ADC-DWI and ADC-DWIBS was found in prepared jejunum (p = 0.84) and prepared colon (p = 0.58), whereas a significant difference was found in non-prepared jejunum and non-prepared colon (p = 0.0001 in both samples). Conclusions: ADC between DWI and DWIBS does not differ in prepared bowel walls but demonstrates a difference in non-prepared bowel. ADC in non-prepared bowel is lower than in prepared bowel and possible overlap with the ADC range of IBD is possible in non-prepared bowel. ADC-DWIBS has no advantage over ADC-DWI in regard to IBD assessment.

The usefulness of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) sequences visual assessment in the early diagnosis of axial spondyloarthritis

The aim of the study was to compare the diagnostic efficacy of the visual assessment of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) sequences compared to the STIR sequence in the diagnostics of active sacroiliitis in the course of axial spondyloarthritis (axSpA). The study group consisted of 49 patients who had undergone multiparametric magnetic resonance imaging of the sacroiliac joints (SIJs) due to clinical suspicion of axSpA. Two independent observers retrospectively assessed four quadrants of the SIJs for the presence of subchondral bone marrow oedema/osteitis with the use of modified SPARCC score in sequences: STIR, DWI (with ADC map) and DCE. Diagnostic efficiency parameters were calculated for DWI and DCE sequence separately, using STIR sequence as a reference. Inter-observer agreement was evaluated with the use of κ coefficient. Patients' clinical symptoms were analysed to identify the group fulfilling the imaging arm of the ASAS criteria for axSpA. Overall, 46.9% (n = 23) of patients fulfilled the imaging arm of ASAS criteria for axial spondyloarthritis. DWI with ADC map: accuracy 95.6%, sensitivity 99.4%, specificity 54.0%. DCE sequence: accuracy 96.8%, sensitivity 98.4%, specificity 79.5%. The highest level of inter-observer agreement was achieved for STIR sequence (κ = 0.888), slightly lower for DCE sequence (κ = 0.773) and the lowest for DWI with ADC (κ = 0.674). Visual assessment of the DWI and DCE sequences has high accuracy and sensitivity of bone marrow oedema/osteitis detection, but the specificity and inter-observer agreement are poor, especially for the DWI sequence with ADC maps.

Diffusion-weighted imaging (DWI) highlights SDHB-related tumours: A pilot study

OBJECTIVE

There is consensus that asymptomatic carriers of SDHB mutations should undergo periodic surveillance imaging. MRI has the advantage of avoiding radiation exposure but its sensitivity and specificity for detecting phaeochromocytoma and paraganglioma (PPGL) are dependent on sequences performed and expertise of reporting radiologists. We aim to highlight the additional value of diffusion-weighted imaging (DWI) for MR based surveillance, demonstrating DWI's ability to identify small PPGLs at all body sites.

DESIGN

We presented DWI sequences taken as part of SDHB surveillance to a radiologist, expert in reporting PPGL screening scans. Areas of high signal on DWI were interrogated using other standard MRI sequences.

PATIENTS

We reviewed the MRI scans for 18 SDHB mutation carriers with a total of 18 histologically proven SDHB-related tumours and 12 presumed PGLs/metastatic deposits.

RESULTS

The DWI sequences identified all 30 lesions. False-positive lesions were excluded by standard sequences. The tumours detected by DWI ranged in size from 5 to 52 mm. PPGLs were identified on DWI in the abdomen (n = 14), adrenal gland (n = 1), thorax (n = 3), neck (n = 2) and bladder (n = 2). Additionally, other SDHB-related tumours (GIST, RCC) were also highlighted by DWI, as were metastatic deposits in the liver and bone.

CONCLUSIONS

These preliminary data suggest that DWI has high sensitivity and can identify even small SDHB-related tumours. If these findings are confirmed in larger series, for all SDH subunits, it will provide reassurance about identifying small SDH-related tumours, without exposing patients to the consequences of radiation-based imaging and will secure the role of MRI for surveillance imaging.

A Simplified Crossing Fiber Model in Diffusion Weighted Imaging

Diffusion MRI (dMRI) is a vital source of imaging data for identifying anatomical connections in the living human brain that form the substrate for information transfer between brain regions. dMRI can thus play a central role toward our understanding of brain function. The quantitative modeling and analysis of dMRI data deduces the features of neural fibers at the voxel level, such as direction and density. The modeling methods that have been developed range from deterministic to probabilistic approaches. Currently, the Ball-and-Stick model serves as a widely implemented probabilistic approach in the tractography toolbox of the popular FSL software package and FreeSurfer/TRACULA software package. However, estimation of the features of neural fibers is complex under the scenario of two crossing neural fibers, which occurs in a sizeable proportion of voxels within the brain. A Bayesian non-linear regression is adopted, comprised of a mixture of multiple non-linear components. Such models can pose a difficult statistical estimation problem computationally. To make the approach of Ball-and-Stick model more feasible and accurate, we propose a simplified version of Ball-and-Stick model that reduces parameter space dimensionality. This simplified model is vastly more efficient in the terms of computation time required in estimating parameters pertaining to two crossing neural fibers through Bayesian simulation approaches. Moreover, the performance of this new model is comparable or better in terms of bias and estimation variance as compared to existing models.

Image quality and diagnostic accuracy of complex-averaged high b value images in diffusion-weighted MRI of prostate cancer

PURPOSE

To evaluate the impact of complex-averaging on image quality (IQ) and diagnostic accuracy of acquired and calculated high b value (aHBV, cHBV) images in diffusion-weighted prostate MRI.

MATERIALS AND METHODS

This retrospective study included 84 patients who underwent multiparametric prostate MRI at 3 Tesla without endorectal coil. DWIs were acquired at three different b values which included two lower b values (b = 50,900 s/mm²) and one higher b value (aHBV at 2000 s/mm²). The acquired data were postprocessed to generate two different types of trace-weighted images-using conventional magnitude-averaging and complex-averaging. Using lower b values (b = 50,900 s/mm²) from both conventional and complex-averaged image sets, cHBV images (b = 2000 s/mm²) and ADC maps were derived. All image sets were reviewed by two radiologists in different reading sessions to assess image quality and PIRADS. The diagnostic accuracy of different image sets for the detection of prostate lesions was performed by correlating PIRADS and Gleason scores.

RESULTS

Complex-averaging did not impact ADC values of the prostate lesions compared to magnitude-averaging (P = 0.08). Complex-averaging improved image quality of acquired high b value and calculated high b value images (P < 0.0001). Complex-averaging also improved the level of confidence (LOC) of the acquired high b value for both readers (P < 0.0001, P < 0.05), but only for reader A in calculated high b value (P < 0.0001). The image quality of calculated high b value images was not significantly different than acquired high b value images. The dataset combining complex-averaging and calculated high b value provided the highest diagnostic accuracy (but not statistically significant) for detection of the significant prostate lesion compared to the magnitude-averaged acquired high b value (79.55% vs. 72.73%; P = 0.317). The mean acquisition time for b = 2000 s/mm² sequence (aHBV) was 6 min 30 s (± 1 min 16 s) out of a total of 28 min 31 s (± 4 min 26 s) for the entire mp-MRI protocol (approximately 25% of total scan time).

CONCLUSION

Complex-averaging provides better image quality and level of confidence without significant impact on ADC values and diagnostic accuracy for detection of the significant prostate lesions . The calculated high b value images are also comparable to (and can substitute) the acquired high b value images which can help in reducing the imaging time.

Profile of children with cerebral palsy spectrum disorder and a normal MRI study

OBJECTIVE

This study looks at what profile can be expected in children with cerebral palsy spectrum disorder (CP) and a normal MRI.

METHODS

The data were excerpted from the Canadian Cerebral Palsy Registry database. Only patients who had undergone MRI were included in the analysis. Neuroimaging classification was ascertained by university-based pediatric neuroradiologists and split into 2 categories: normal and abnormal MRIs. Six factors were then compared between those 2 groups: prematurity, perinatal adversity, presence of more than 1 comorbidity, CP subtype, bimanual dexterity (Manual Ability Classification System [MACS]), and gross motor function (Gross Motor Function Classification System [GMFCS]).

RESULTS

Participants with no perinatal adversity were 5.518 times more likely to have a normal MRI (p < 0.0001, 95% confidence interval [CI] 4.153-7.330). Furthermore, participants with dyskinetic, ataxic/hypotonic, and spastic diplegic forms of CP were 2.045 times more likely to have a normal MRI than those with hemiplegia, triplegia, and quadriplegia (p < 0.0001, 95% CI 1.506-2.778). No significant difference was found in prematurity, GMFCS levels, MACS levels, and the number of comorbidities.

CONCLUSIONS

Normal MRIs were associated with lack of perinatal adversity as well as with the dyskinetic, ataxic/hypotonic, and spastic diplegic CP subtypes. As MRI normality is not strongly associated with the severity of CP, continuous follow-up in children with normal imaging appears warranted. Further advanced imaging modalities, as well as strong consideration for metabolic and genetic testing, may provide additional insights into causal pathways in this population.

Renal Allograft Rejection: Noninvasive Ultrasound- and MRI-Based Diagnostics

To date, allogeneic kidney transplantation remains the best available therapeutic option for patients with end-stage renal disease regarding overall survival and quality of life. Despite the advancements in immunosuppressive drugs and protocols, episodes of acute allograft rejection, a sterile inflammatory process, continue to endanger allograft survival. Since effective treatment for acute rejection episodes is available, instant diagnosis of this potentially reversible graft injury is imperative. Although histological examination by invasive core needle biopsy of the graft remains the gold standard for the diagnosis of ongoing rejection, it is always associated with the risk of causing substantial graft injury as a result of the biopsy procedure itself. At the same time, biopsies are not immediately feasible for a considerable number of patients taking anticoagulants due to the high risk of complications such as bleeding and uneven distribution of pathological changes within the graft. This can result in the wrong diagnosis due to the small size of the tissue sample taken. Therefore, there is a need for a tool that overcomes these problems by being noninvasive and capable of assessing the whole organ at the same time for specific and fast detection of acute allograft rejection. In this article, we review current state-of-the-art approaches for noninvasive diagnostics of acute renal transplant inflammation, i.e., rejection. We especially focus on nonradiation-based methods using magnetic resonance imaging (MRI) and ultrasound.