Topics in Magnetic Resonance Imaging最新文献

Abstract: This report presents imaging from a mediastinal mass in a patient with colon cancer. At baseline and surveillance chest computed tomography examinations, it was characterized as a pericardial cyst. However, during chemotherapy, complications arose and this mass was further characterized with a chest MRI. It was then decided to be removed, and histopathology confirmed the diagnosis of a hemangioma.

Objective: To compare observer confidence for myocardial scar detection using 3 different late gadolinium enhancement (LGE) data sets by 2 observers with different levels of experience.

Materials and methods: Forty-one consecutive patients, who were referred for 3D dark-blood LGE MRI before implantable cardioverter-defibrillator implantation or ablation therapy and who underwent 2D bright-blood LGE MRI within a time frame of 3 months, were prospectively included. From all 3D dark-blood LGE data sets, a stack of 2D short-axis slices was reconstructed. All acquired LGE data sets were anonymized and randomized and evaluated by 2 independent observers with different levels of experience in cardiovascular imaging (beginner and expert). Confidence in detection of ischemic scar, nonischemic scar, papillary muscle scar, and right ventricular scar for each LGE data set was scored using a using a 3-point Likert scale (1 = low, 2 = medium, or 3 = high). Observer confidence scores were compared using the Friedman omnibus test and Wilcoxon signed-rank post hoc test.

Results: For the beginner observer, a significant difference in confidence regarding ischemic scar detection was observed in favor of reconstructed 2D dark-blood LGE compared with standard 2D bright-blood LGE (p = 0.030) while for the expert observer, no significant difference was found (p = 0.166). Similarly, for right ventricular scar detection, a significant difference in confidence was observed in favor of reconstructed 2D dark-blood LGE compared with standard 2D bright-blood LGE (p = 0.006) while for the expert observer, no significant difference was found (p = 0.662). Although not significantly different for other areas of interest, 3D dark-blood LGE and its derived 2D dark-blood LGE data set showed a tendency to score higher for all areas of interest at both experience levels.

Conclusions: The combination of dark-blood LGE contrast and high isotropic voxels may contribute to increased observer confidence in myocardial scar detection, independent of observer's experience level but in particular for beginner observers.

Abstract: Functional 1H magnetic resonance spectroscopy (fMRS) is a derivative of dynamic MRS imaging. This modality links physiologic metabolic responses with available activity and measures absolute or relative concentrations of various metabolites. According to clinical evidence, the mitochondrial glycolysis pathway is disrupted in many nervous system disorders, especially Alzheimer disease, resulting in the activation of anaerobic glycolysis and an increased rate of lactate production. Our study evaluates fMRS with J-editing as a cutting-edge technique to detect lactate in Alzheimer disease. In this modality, functional activation is highlighted by signal subtractions of lipids and macromolecules, which yields a much higher signal-to-noise ratio and enables better detection of trace levels of lactate compared with other modalities. However, until now, clinical evidence is not conclusive regarding the widespread use of this diagnostic method. The complex machinery of cellular and noncellular modulators in lactate metabolism has obscured the potential roles fMRS imaging can have in dementia diagnosis. Recent developments in MRI imaging such as the advent of 7 Tesla machines and new image reconstruction methods, coupled with a renewed interest in the molecular and cellular basis of Alzheimer disease, have reinvigorated the drive to establish new clinical options for the early detection of Alzheimer disease. Based on the latter, lactate has the potential to be investigated as a novel diagnostic and prognostic marker for Alzheimer disease.

Purpose: Previous work used phantoms to calibrate the nonlinear relationship between the gadolinium contrast concentration and the intensity of the magnetic resonance imaging signal. This work proposes a new nonlinear calibration procedure without phantoms and considers the variation of contrast agent mass minimum combined with the multiple input blood flow system. This also proposes a new single-input method with meaningful variables that is not influenced by reperfusion or noise generated by aliasing. The reperfusion in the lung is usually neglected and is not considered by the indicator dilution method. However, in cases of lung cancer, reperfusion cannot be neglected. A new multiple input method is formulated, and the contribution of the pulmonary artery and bronchial artery to lung perfusion can be considered and evaluated separately.

Methods: The calibration procedure applies the minimum variation of contrast agent mass in 3 different regions: (1) pulmonary artery, (2) left atrium, and (3) aorta. It was compared with four dimensional computerized tomography with iodine, which has a very high proportional relationship between contrast agent concentration and signal intensity.

Results: Nonlinear calibration was performed without phantoms, and it is in the range of phantom calibration. It successfully separated the contributions of the pulmonary and bronchial arteries. The proposed multiple input method was verified in 6 subjects with lung cancer, and perfusion from the bronchial artery, rich in oxygen, was identified as very high in the cancer region.

Conclusions: Nonlinear calibration of the contrast agent without phantoms is possible. Separate contributions of the pulmonary artery and aorta can be determined.

Abstract: In this case report we describe the case of a 66-year old man with subacute gait difficulties, with a progression to confusion coma with multiple generalised epileptic seizures during the following days. Biochemical analysis showed hyperglycaemia, cerebrospinal fluid (CSF) testing showed a mild lymphocytic pleocytosis and an elevated protein and lactate. Broad-spectrum antibiotics and antiviral therapy where initiated. However, all other CSF testing remained negative. Magnetic resonance imaging of the brain showed remarkably symmetric hyperintense T2 white matter lesions most noticable in the corpus callosum. The lesion pattern was suggestive of a metabolic or toxic encephalopathy, the preponderance for the corpus callosum was furthermore suggestive for Marchiafava-Bignami disease (MDB), as was the clinical course since admission of the patient. A high dose IV substitution of vitamin B1, B6 and B12 was started and antibiotic and antiviral therapy was discontinued. After one day the patient showed progressive regaining of consciousness and he returned to premorbid functioning in a matter of 1-2 weeks. MRI of the brain after 1 week showed notable improvement of the white matter lesions. At routine follow-up two weeks later he presented with icterus and a diagnosis of Epstein-Barr virus (EBV) hepatitis was made, lymph node biopsies showed an EBV positive diffuse large cell B-cell lymphoma (DLCBL). MDB is mostly associated with severe alcoholism, with malnourishment being the second leading cause, however there are case reports describing MDB in patients with chronically poorly controlled diabetes mellitus. We hypothesize that his condition may have been precipitated by his poorly controlled diabetes mellitus. However it is also possible that weight loss (probably related to the DLCBL diagnosis) might have contributed to a state of malnourishment and therefore played a role in the aetiology as well.

Objectives: 7T small animal magnetic resonance imaging (MRI) was used to analyze the growth characteristics of hepatic alveolar echinococcosis (HAE).

Methods: A mouse model of HAE was established by intraperitoneal injection of alveolar Echinococcus tissue suspension. Ten mouse models successfully inoculated by ultrasound screening were selected. The mouse model was scanned with T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI) sequence by 7T small animal MRI. Size, morphology, boundary, signal, and relationship with surrounding tissues of the lesions were recorded as characteristic alterations. Mice were killed at the end of the experiment, and the pathological specimens were taken for routine hematoxylin and eosin staining.

Results: Lesions were mainly located in the right lobe of the liver. The multivesicular structure is the characteristic manifestation of this disease. In the liver, lesions invaded the portal vein and were mainly distributed at the hepatic hilum. The left branch of the portal vein was mainly invaded. The mean diameter of the lesions in the left lobe of the liver was larger than in other parts of the liver. The mean diameter of the cystic solid lesions was greater than the multilocular cystic lesions. HAE showed hypointense on T1WI, hyperintense on T2WI, and hypointense on DWI; the marginal zone of the lesion showed hyperintensity on DWI and grew toward the hilum. The MRI features of intraperitoneal lesions were similar to those of intrahepatic lesions. Intraperitoneal lesions increased faster than intrahepatic lesions in the same period.

Conclusion: Polyvesicular structure is a characteristic manifestation of hepatic alveolar echinococcosis in mice. The noninvasive monitoring of liver HAE in mice by 7T small animal MRI provides a visual basis for the diagnosis and treatment integration of HAE.

Objectives: Diffusion-weighted imaging may be useful as part of a postmortem magnetic resonance imaging protocol. However, apart from the effect of temperature on apparent diffusion coefficient (ADC), normal postmortem ADC changes can influence the interpretation. Therefore, this study was conducted to evaluate the correlation between normal ADC changes and postmortem intervals (PMIs) and develop a reference standard for postmortem changes after temperature correction.

Materials and methods: Six premature lambs were scanned at different PMIs. ADC values were measured at different parenchymal locations. Correlation and linear regression between ADC values and PMI were analyzed for all locations, both uncorrected and corrected for temperature.

Results: All locations showed a significant negative correlation between the PMI and ADC value, with (R2 = 0.581-0.837, P < 0.001) and without (R2 = 0.183-0.555, P < 0.001-0.018) temperature correction.

Conclusions: The postmortem interval is negatively correlated with ADC values in the brain. A correlation coefficient for the PMI can be calculated after temperature correction to predict ADC changes. However, further research is required to evaluate its clinical application in humans.

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.