A piglet model for detection of hypoxic-ischemic brain injury with magnetic resonance imaging.

BACKGROUND: Early detection of hypoxic-ischemic (HI) injury in the asphyxic newborn is important because present prognostic factors are inadequate. Furthermore, therapeutic interventions may have additional benefit if initiated in time. PURPOSE: To assess whether the use of a combined protocol including conventional magnetic resonance imaging (MRI), diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), and proton MR spectroscopy (MRS) could detect pathological findings in a piglet model 7 hours after HI. MATERIAL AND METHODS: Ten piglets were submitted to HI for 30 min followed by reoxygenation with 21% O2 for 7 hours. MRI at 1.5T was done prior to and 7 hours after the HI. Single-voxel proton MRS was performed, and apparent diffusion coefficient (ADC) and fractional anisotropy (FA) were measured in the basal ganglia. MRS identified N-acetylaspartate (NAA), choline (Cho), creatine (Cr), and lactate (Lac). Histology and microtubule-associated protein 2 (MAP-2) staining was performed in the basal ganglia at the end of the experiment. RESULTS: Compared to baseline, ADC, NAA/Cho, and NAA/Cr were significantly reduced after 7 hours (P<0.001, P=0.01, and P=0.05, respectively) and FA values were increased (P<0.025). The ratios of Lac/Cho and Lac/NAA were significantly higher after 7 hours compared to baseline (P<0.001). Presence of necrosis correlated well with reduced ADC (R(S)=0.91) and presence of Lac (R(S)=0.80). Histology and MAP-2 staining showed more than 90% necrosis in eight piglets, 60% in one piglet, and no necrosis in one piglet. CONCLUSION: Diffusion MRI and proton MRS can detect HI injury in the piglet brain 7 hours after hypoxia. DWI and MRS can be used to give useful prognostic information. This piglet model may potentially be used to mimic clinical situations and is suitable for further research investigating HI injury.

Aqueductal Stroke Volume: Comparisons with Intracranial Pressure Scores in Idiopathic Normal Pressure Hydrocephalus.

BACKGROUND AND PURPOSE: Aqueductal stroke volume from phase-contrast MR imaging has been proposed for predicting shunt response in normal pressure hydrocephalus. However, this biomarker has remained controversial in use and has a lack of validation with invasive intracranial monitoring. We studied how aqueductal stroke volume compares with intracranial pressure scores in the presurgical work-up and clinical score, ventricular volume, and aqueduct area and assessed the patient's response to shunting. MATERIALS AND METHODS: Phase-contrast MR imaging was performed in 21 patients with probable idiopathic normal pressure hydrocephalus. Patients were selected for shunting on the basis of pathologically increased intracranial pressure pulsatility. Patients with shunts were offered a second MR imaging after 12 months. Ventricular volume and transverse aqueductal area were calculated, as well as clinical symptom score. RESULTS: No correlations between aqueductal stroke volume and preoperative scores of mean intracranial pressure or mean wave amplitudes were observed. Preoperative aqueductal stroke volume was not different between patients with shunts and conservatively treated patients (P = .69) but was correlated with ventricular volume (R = 0.60, P = .004) and aqueductal area (R = 0.58, P = .006) but not with the severity or duration of clinical symptoms. After shunting, aqueductal stroke volume (P = .006) and ventricular volume (P = .002) were reduced. A clinical improvement was seen in 16 of 17 patients who had shunts (94%). CONCLUSIONS: Aqueductal stroke volume does not reflect intracranial pressure pulsatility or symptom score, but rather aqueduct area and ventricular volume. The results do not support the use of aqueductal stroke volume for selecting patients for shunting.

An automatic procedure for normalization of cerebral blood volume maps in dynamic susceptibility contrast-based glioma imaging.

To characterize gliomas from dynamic susceptibility contrast (DSC)-based cerebral blood volume (CBV) maps, a CBV value from a normal-appearing region of interest is typically identified manually and used to normalize the CBV maps. This method is user-dependent and time-consuming. We propose an alternative approach based on automatic identification of normal-appearing first-pass curves from brain tissue. Our results in 101 patients suggest similar or better diagnostic accuracy values than the manual approach.

Histogram analysis of MR imaging-derived cerebral blood volume maps: combined glioma grading and identification of low-grade oligodendroglial subtypes.

BACKGROUND AND PURPOSE: Inclusion of oligodendroglial tumors may confound the utility of MR based glioma grading. Our aim was, first, to assess retrospectively whether a histogram-analysis method of MR perfusion images may both grade gliomas and differentiate between low-grade oligodendroglial tumors with or without loss of heterozygosity (LOH) on 1p/19q and, second, to assess retrospectively whether low-grade oligodendroglial subtypes can be identified in a population of patients with high-grade and low-grade astrocytic and oligodendroglial tumors. MATERIALS AND METHODS: Fifty-two patients (23 women, 29 men; mean age, 52 years; range, 19-78 years) with histologically confirmed gliomas were imaged by using dynamic susceptibility contrast MR imaging at 1.5T. Relative cerebral blood volume (rCBV) maps were created, and 4 neuroradiologists defined the glioma volumes independently. Averaged over the 4 observers, a histogram-analysis method was used to assess the normalized histogram peak height of the glioma rCBV distributions. RESULTS: Of the 52 patients, 22 had oligodendroglial tumors. The histogram method was able to differentiate high-grade gliomas (HGGs) from low-grade gliomas (LGGs) (Mann-Whitney U test, P < .001) and to identify low-grade oligodendroglial subtypes (P = .009). The corresponding intraclass correlation coefficients were 0.902 and 0.801, respectively. The sensitivity and specificity in terms of differentiating low-grade oligodendroglial tumors without LOH on 1p/19q from the other tumors was 100% (6/6) and 91% (42/46), respectively. CONCLUSION: With histology as a reference, our results suggest that histogram analysis of MR imaging-derived rCBV maps can differentiate HGGs from LGGs as well as low-grade oligodendroglial subtypes with high interobserver agreement. Also, the method was able to identify low-grade oligodendroglial tumors without LOH on 1p/19q in a population of patients with astrocytic and oligodendroglial tumors.