Topics in Magnetic Resonance Imaging最新文献

Abstract: Recent advances in technology, particularly in the field of magnetic resonance imaging, have brought forth new sequences, including vessel wall imaging (VWI). Traditionally, the workup for intracranial vascular pathology has always turned to luminal imaging using computed tomography angiography, magnetic resonance angiography, or digital subtraction angiography. Since its introduction, VWI has enabled researchers and practicing clinicians to better understand disease processes and manage patients to the best standard of care possible. Spontaneous recanalization in acute ischemic stroke (AIS) is a known but understudied phenomenon. Available literature has looked at this phenomenon and postulated the occurrence based on conventional cross-sectional imaging and angiography; however, objective evidence pointing to the occurrence of this phenomenon is scarce. We would like to share our experience using VWI in a patient who was clinically suspected to have a middle cerebral artery syndrome at onset, with resolution of the symptoms 3 hours after initial presentation. VWI showed vessel wall enhancement at the suspected vessel involved, with evidence of acute infarcts at the vascular territory supplied. A presumptive diagnosis of AIS with spontaneous recanalization was made. Our experience could potentially aid in the understanding of spontaneous recanalization in patients with AIS, particularly in the postulation of the pathophysiology.

Objectives: Automated whole brain segmentation from magnetic resonance images is of great interest for the development of clinically relevant volumetric markers for various neurological diseases. Although deep learning methods have demonstrated remarkable potential in this area, they may perform poorly in nonoptimal conditions, such as limited training data availability. Manual whole brain segmentation is an incredibly tedious process, so minimizing the data set size required for training segmentation algorithms may be of wide interest. The purpose of this study was to compare the performance of the prototypical deep learning segmentation architecture (U-Net) with a previously published atlas-free traditional machine learning method, Classification using Derivative-based Features (C-DEF) for whole brain segmentation, in the setting of limited training data.

Materials and methods: C-DEF and U-Net models were evaluated after training on manually curated data from 5, 10, and 15 participants in 2 research cohorts: (1) people living with clinically diagnosed HIV infection and (2) relapsing-remitting multiple sclerosis, each acquired at separate institutions, and between 5 and 295 participants' data using a large, publicly available, and annotated data set of glioblastoma and lower grade glioma (brain tumor segmentation). Statistics was performed on the Dice similarity coefficient using repeated-measures analysis of variance and Dunnett-Hsu pairwise comparison.

Results: C-DEF produced better segmentation than U-Net in lesion (29.2%-38.9%) and cerebrospinal fluid (5.3%-11.9%) classes when trained with data from 15 or fewer participants. Unlike C-DEF, U-Net showed significant improvement when increasing the size of the training data (24%-30% higher than baseline). In the brain tumor segmentation data set, C-DEF produced equivalent or better segmentations than U-Net for enhancing tumor and peritumoral edema regions across all training data sizes explored. However, U-Net was more effective than C-DEF for segmentation of necrotic/non-enhancing tumor when trained on 10 or more participants, probably because of the inconsistent signal intensity of the tissue class.

Conclusions: These results demonstrate that classical machine learning methods can produce more accurate brain segmentation than the far more complex deep learning methods when only small or moderate amounts of training data are available (n ≤ 15). The magnitude of this advantage varies by tissue and cohort, while U-Net may be preferable for deep gray matter and necrotic/non-enhancing tumor segmentation, particularly with larger training data sets (n ≥ 20). Given that segmentation models often need to be retrained for application to novel imaging protocols or pathology, the bottleneck associated with large-scale manual annotation could be avoided with classical machine learning algorithms, such as C-DEF.

Objectives: Magnetic resonance imaging (MRI) is increasingly used in postmortem fetal imaging. Several factors influence the quality of MRI in this setting, such as small size, autolytic and maceration changes, and temperature. Knowing the fetal temperature at the time of scanning can improve the MRI interpretation. Temperature can be calculated using diffusion-weighted imaging with measurements of the apparent diffusion coefficient (ADC) in the cerebrospinal fluid (CSF). However, this is complicated by small ventricle size and hemorrhage and, therefore, may be unreliable in postmortem imaging. The current study evaluated the feasibility and reliability of using the ADC for temperature measurements of the vitreous body compared to that of CSF.

Materials and methods: Two lambs were scanned postmortem at five different time points over 28 hours. Furthermore, 10 stillborn fetuses were scanned once, at 4 to 62 hours after birth. The temperature was measured with a digital thermometer and calculated using the ADCs of the vitreous body (lambs and fetuses) and CSF (fetuses).

Results: There was an excellent correlation between measured and calculated temperatures in vitreous bodies of lambs (r = 0.997, P < 0.001) and fetuses (r = 0.970, P < 0.001). The correlation between measured and calculated temperatures in the CSF was poor (r = 0.522, P = 0.122).

Conclusion: The calculation of the temperature based on the ADC of the vitreous body is feasible and reliable for postmortem fetal imaging.

Background: Magnetic resonance elastography (MRE) allows noninvasive assessment of intracranial tumor mechanics and may thus be predictive of intraoperative conditions. Variations in the use of technical terms complicate reading of current literature, and there is need of a review using consolidated nomenclature.

Objectives: We present an overview of current literature on MRE relating to human intracranial neoplasms using standardized nomenclature suggested by the MRE guidelines committee. We then discuss the implications of the findings, and suggest approaches for future research.

Method: We performed a systematic literature search in PubMed, Embase, and Web of Science; the articles were screened for relevance and then subjected to full text review. Technical terms were consolidated.

Results: We identified 12 studies on MRE in patients with intracranial tumors, including meningiomas, glial tumors including glioblastomas, vestibular schwannomas, hemangiopericytoma, central nervous system lymphoma, pituitary macroadenomas, and brain metastases. The studies had varying objectives that included prediction of intraoperative consistency, histological separation, prediction of adhesiveness, and exploration of the mechanobiology of tumor invasiveness and malignancy. The technical terms were translated using standardized nomenclature. The literature was highly heterogeneous in terms of image acquisition techniques, post-processing, and study design and was generally limited by small and variable cohorts.

Conclusions: MRE shows potential in predicting tumor consistency, adhesion, and mechanical homogeneity. Furthermore, MRE provides insight into malignant tumor behavior and its relation to tissue mechanics. MRE is still at a preclinical stage, but technical advances, improved understanding of soft tissue rheological impact, and larger samples are likely to enable future clinical introduction.

Abstract: Numerous factors make the initial diagnostic evaluation of children with suspected arterial ischemic stroke (AIS) a relatively unsettling challenge, even for the experienced stroke specialist. The low frequency of pediatric AIS, diversity of unique age-oriented stroke phenotypes, and unconventional approaches required for diagnosis and treatment all contribute difficulty to the process. This review aims to outline important features that differentiate pediatric AIS from adult AIS and provide practical strategies that will assist the stroke specialist with diagnostic decision making in the initial phase of care.

Background and purpose: The diagnosis of childhood arteriopathy is complex. We present a Web-based, evidence-backed classification system to return the most likely cause(s) of a pediatric arterial ischemic stroke. This tool incorporates a decision-making algorithm that considers a patient's clinical and imaging features before returning a differential diagnosis, including the likelihood of various arteriopathy subtypes.

Methods: The Vascular Effects of Infection in Pediatric Stroke study prospectively enrolled 355 children with arterial ischemic stroke (2010-2014). Previously, a central panel of experts classified the stroke etiology. To create this tool, we used the 174 patients with definite arteriopathy and spontaneous cardioembolic stroke as the "derivation cohort" and the 34 with "possible" arteriopathy as the "test cohort." Using logistic regression models of clinical and imaging characteristics associated with each arteriopathy subtype in the derivation cohort, we built a decision framework that we integrated into a Web interface specifically designed to create a probabilistic differential diagnosis. We applied the Web-based tool to the "test cohort."

Results: The differential diagnosis returned by our tool was in complete agreement with the experts' opinions in 20.6% of patients. We observed a partial agreement in 41.2% of patients and an overlap in 29.4% of patients. The tool disagreed with the experts on the diagnoses of 3 patients (8.8%).

Conclusions: Our tool yielded an overlapping differential diagnosis in most patients that defied definitive classification by experts. Although it needs to be validated in an independent cohort, it helps facilitate high-quality, and timely diagnoses of arteriopathy in pediatric patients.