Accurate diagnosis of intracranial hemorrhage represents a frequent challenge for the practicing radiologist. The purpose of this article is to provide the reader with a synoptic overview of the imaging characteristics of intracranial hemorrhage, using text, tables, and figures to illustrate time-dependent changes. We examine the underlying physical, biological, and biochemical factors of evolving hematoma and correlate them with the aspect on cross-sectional imaging techniques. On CT scanning, the appearance of intracranial blood is determined by density changes which occur over time, reflecting clot formation, clot retraction, clot lysis and, eventually, tissue loss. However, MRI has become the technique of choice for assessing the age of an intracranial hemorrhage. On MRI the signal intensity of intracranial hemorrhage is much more complex and is influenced by multiple variables including: (a) age, location, and size of the lesion; (b) technical factors (e.g., sequence type and parameters, field strength); and (c) biological factors (e.g., pO2, arterial vs. venous origin, tissue pH, protein concentration, presence of a blood-brain barrier, condition of the patient). We discuss the intrinsic magnetic properties of sequential hemoglobin degradation products. The differences in evolution between extra- and intracerebral hemorrhages are addressed and illustrated.

Quantification of haemodynamic parameters with a deconvolution analysis of bolus-tracking data is an ill-posed problem which requires regularization. In a previous study, simulated data without structural errors were used to validate two methods for a pixel-by-pixel analysis: standard-form Tikhonov regularization with either the L-curve criterion (LCC) or generalized cross validation (GCV) for selecting the regularization parameter. However, problems of image artefacts were reported when the methods were applied to patient data. The aim of this study was to investigate the nature of these problems in more detail and evaluate strategies of optimization for routine application in the clinic. In addition we investigated to which extent the calculation time of the algorithm can be minimized. In order to ensure that the conclusions are relevant for a larger range of clinical applications, we relied on patient data for evaluation of the algorithms. Simulated data were used to validate the conclusions in a more quantitative manner. We conclude that the reported problems with image quality can be removed by appropriate optimization of either LCC or GCV. In all examples this could be achieved with LCC without significant perturbation of the values in pixels where the regularization parameter was originally selected accurately. GCV could not be optimized for the renal data, and in the CT data only at the cost of image resolution. Using the implementations given, calculation times were sufficiently short for routine application in the clinic.

Magnetic resonance (MR) imaging after ultra-small super paramagnetic iron oxide (USPIO) injection and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) for preoperative axillary lymph node staging in patients with breast cancer were evaluated using histopathologic findings as the reference standard. USPIO-enhanced MR and FDG-PET were performed in ten patients with breast cancer who were scheduled for surgery and axillary node resection. T2-weighted fast spin echo, T1-weighted three-dimensional (3D) gradient echo, T2*-weighted gradient echo and gadolinium-enhanced T1-weighted 3D gradient echo with spectral fat saturation were evaluated. MR imaging before USPIO infusion was not performed. The results were correlated with FDG-PET (acquired with dedicated PET camera, visual analysis) and histological findings. The histopathologic axillary staging was negative for nodal malignancy in five patients and positive in the remaining five patients. There was one false positive finding for USPIO-enhanced MR and one false negative finding for FDG-PET. A sensitivity (true positive rate) of 100%, specificity (true negative rate) of 80%, positive predictive value of 80%, and negative predictive value of 100% were achieved for USPIO-enhanced MR and of 80%, 100%, 100%, 80% for FDG-PET, respectively. The most useful sequences in the detection of invaded lymph nodes were in the decreasing order: gadolinium-enhanced T1-weighted 3D gradient echo with fat saturation, T2*-weighted 2D gradient echo, T1-weighted 3D gradient echo and T2-weighted 2D spin echo. In our study, USPIO-enhanced T1 gradient echo after gadolinium injection and fat saturation emerged as a very useful sequence in the staging of lymph nodes. The combination of USPIO-enhanced MR and FDG-PET achieved 100% sensitivity, specificity, PPV and NPV. If these results are confirmed, the combination of USPIO MR with FDG-PET has the potential to identify the patient candidates for axillary dissection versus sentinel node lymphadenectomy.

Pixelwise deconvolution analysis of DCE MR data in patients with breast cancer can provide preoperative information regarding TBF. These results also support the hypothesis that there is increased TBF in HER2-positive tumors.

Duret hemorrhages are delayed, secondary brainstem hemorrhages. They occur in craniocerebral trauma victims with rapidly evolving descending transtentorial herniation. Diagnosis is made on computed tomography of the brain. In most cases the outcome is fatal. On the basis of our observations we believe that arterial hypertension and advanced age are risk factors for the development of Duret hemorrhage.

In theory, the application of the two-compartment exchange model (2CXM) to data from a dynamic contrast-enhanced (DCE) MRI exam allows the estimation of the plasma flow, plasma volume, extraction flow and extravascular-extracellular volume. The aim of this paper was to explore whether simulations based on the 2CXM could provide useful information on the trustworthiness of the results. The deviations from the input values of the haemodynamic quantities were estimated for a 'reference tissue' with a clear bi-phasic response and four 'limit tissues' with more challenging 2CXM fitting properties. The impact of the instrumental factors sampling step (T(s)), acquisition window (T(acq)) and contrast-to-noise ratio (CNR) was investigated. Each factor was varied separately, while keeping the other ones at a value above concern. Measurement guidelines to ensure that all deviations fell within a predefined range (±20%) could not be derived, but simulations for fixed T(s) and T(acq) were found to provide a practical tool for studying the error behaviour to be expected from a given experimental set-up and for comparing measurement protocols. At the level of an individual DCE exam, a bootstrap version of the simulation approach was shown to lead to a useful estimate of the errors on the fitted parameters.

Purpose:To test the feasibility of using a second-bolus injection, added to a routine breast MRI examination, to measure regional perfusion and permeability in human breast tumors. Materials and Methods:In 30 patients with breast tumors, first a routine whole-breast T1-DCE sequence was applied, and the slice where the lesion enhanced maximally was located. At that slice position, T1-weighted MR images were acquired at 0.3-second resolution using a second-bolus dynamic inversion recovery (IR)-prepared turbo field echo (TFE) sequence. A pixel-by-pixel model-independent deconvolution of the relative signal enhancement was performed to estimate the tumor blood flow (TBF), tumor volume of distribution (TVD), mean transit time (MTT), extraction flow product (EF), and extraction fraction (E). In addition to this pilot study, a first appraisal of its sensitivity to tissue type was made on the basis of a small patient cohort. Results:In the malignant tumors, the parametric maps clearly delineated tumors from the breast tissue and enabled visualization of the heterogeneity. The deconvolution analysis provided objective parametric maps of tumor perfusion with a mean TBF (84 Ϯ 48 mL/100 mL/minute) in malignant tumors in the high range of literature values. Conclusion:In terms of these perfusion values, our method appears promising to quantitatively characterize tumor pathophysiology. However, the number of patients was limited, and the separation between malignant and benign groups was not clear-cut. Additional parameters generated through compartment modeling may improve the tumor differentiation.

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