Investigations of hypoxia-induced deoxyhemoglobin as a contrast agent for cerebral perfusion imaging.

The assessment of resting perfusion measures (mean transit time, cerebral blood flow, and cerebral blood volume) with magnetic resonance imaging currently requires the presence of a susceptibility contrast agent such as gadolinium. Here, we present an initial comparison between perfusion measures obtained using hypoxia-induced deoxyhemoglobin and gadolinium in healthy study participants. We hypothesize that resting cerebral perfusion measures obtained using precise changes of deoxyhemoglobin concentration will generate images comparable to those obtained using a clinical standard, gadolinium. Eight healthy study participants were recruited (6F; age 23-60). The study was performed using a 3-Tesla scanner with an eight-channel head coil. The experimental protocol consisted of a high-resolution T1-weighted scan followed by two BOLD sequence scans in which each participant underwent a controlled bolus of transient pulmonary hypoxia, and subsequently received an intravenous bolus of gadolinium. The resting perfusion measures calculated using hypoxia-induced deoxyhemoglobin and gadolinium yielded maps that looked spatially comparable. There was no statistical difference between methods in the average voxel-wise measures of mean transit time, relative cerebral blood flow and relative cerebral blood volume, in the gray matter or white matter within each participant. We conclude that perfusion measures generated with hypoxia-induced deoxyhemoglobin are spatially and quantitatively comparable to those generated from a gadolinium injection in the same healthy participant.

Does breathing pattern affect cerebrovascular reactivity?

NEW FINDINGS: What is the central question of this study? Is cerebrovascular reactivity affected by isocapnic changes in breathing pattern? What is the main finding and its importance? Cerebrovascular reactivity does not change with isocapnic variations in tidal volume and frequency. ABSTRACT: Deviations of arterial carbon dioxide tension from resting values affect cerebral blood vessel tone and thereby cerebral blood flow. Arterial carbon dioxide tension also affects central respiratory chemoreceptors, adjusting respiratory drive. This coincidence raises the question: does respiratory drive also affect the cerebral blood flow response to carbon dioxide? A change in cerebral blood flow for a given change in the arterial carbon dioxide tension is defined as cerebrovascular reactivity (CVR). Two studies have reached conflicting conclusions on this question, using voluntary control of breathing as a disturbing factor during measurements of CVR. Here, we address some of the methodological limitations of both studies by using sequential gas delivery and targeted control of carbon dioxide and oxygen to enable a separation of the effects of carbon dioxide on CVR from breathing vigour. We confirm that there is no detectable superimposed effect of breathing efforts on CVR.

Convolutional Neural Networks to Assess Steno-Occlusive Disease Using Cerebrovascular Reactivity.

Cerebrovascular Reactivity (CVR) is a provocative test used with Blood oxygenation level-dependent (BOLD) Magnetic Resonance Imaging (MRI) studies, where a vasoactive stimulus is applied and the corresponding changes in the cerebral blood flow (CBF) are measured. The most common clinical application is the assessment of cerebral perfusion insufficiency in patients with steno-occlusive disease (SOD). Globally, millions of people suffer from cerebrovascular diseases, and SOD is the most common cause of ischemic stroke. Therefore, CVR analyses can play a vital role in early diagnosis and guiding clinical treatment. This study develops a convolutional neural network (CNN)-based clinical decision support system to facilitate the screening of SOD patients by discriminating between healthy and unhealthy CVR maps. The networks were trained on a confidential CVR dataset with two classes: 68 healthy control subjects, and 163 SOD patients. This original dataset was distributed in a ratio of 80%-10%-10% for training, validation, and testing, respectively, and image augmentations were applied to the training and validation sets. Additionally, some popular pre-trained networks were imported and customized for the objective classification task to conduct transfer learning experiments. Results indicate that a customized CNN with a double-stacked convolution layer architecture produces the best results, consistent with expert clinical readings.