The physiological noise in the resting brain, which arises from fluctuations in metabolic-linked brain physiology and subtle brain pulsations, was investigated in six healthy volunteers using oxygenation-sensitive dual-echo spiral MRI at 3.0 T. In contrast to the system and thermal noise, the physiological noise demonstrates a signal strength dependency and, unique to the metabolic-linked noise, an echo-time dependency. Variations of the MR signal strength by changing the flip angle and echo time allowed separation of the different noise components and revealed that the physiological noise at 3.0 T (1) exceeds other noise sources and (2) is significantly greater in cortical gray matter than in white matter regions. The SNR in oxygenation-sensitive MRI is predicted to saturate at higher fields, suggesting that noise measurements of the resting brain at 3.0 T and higher may provide a sensitive probe of functional information. Several biophysical models (1,2) and recent investigations of the field dependency in MRI (3-8) strongly suggest improvements in the signal-to-noise ratio (SNR) and the contrast-to-noise ratio (CNR) at higher magnetic fields. In a recent work (8), however, we have shown that the physiological noise demonstrates an MR signal strength dependency and exceeds the thermal noise and scanner noise at 3.0 T. Consequently, the physiological noise counteracts the gain in SNR at higher fields. In the present study, a model for the intrinsic noise in oxygenation-sensitive MRI of the human brain is developed. In corresponding experiments the MR-signal strength was modulated by variations in the flip angle (␣) and echo-time (TE) to separate individual noise contributions in the resting brain. Results were compared with the predicted noise contributions and corresponding implications on neuroimaging modalities are discussed. THEORYThe signal and the intrinsic noise in high-field MRI have been shown to be quadratic and linear in B 0 , respectively, resulting in an SNR proportional to the strength of the magnetic field (3,4). Whereas these earlier studies characterize the dominant noise by thermally generated random noise from the subject and scanner electronics and assume that coil losses are negligible (4), we expand this model by considering physiological noise as a further significant contribution to the total image noise:Here, T is the thermal noise from the subject and scanner electronics and S describes other system noise that includes drift and imperfections in RF, gradient, and shim subsystems. 0 , the square-law sum of T and S , is considered the raw noise and has been shown to be proportional to B 0 (4) but independent of the MR-signal strength. The term P in Eq.[1] describes the physiological noise, which arises from fluctuations in the basal cerebral metabolism (CMRO 2 ), blood flow (CBF), and blood volume (CBV), but also from cardiac and respiratory functions that cause quasiperiodic oscillations in the vascular system (9 -11), motion from subtle brain pulsatility (12), and magnetic field modula...

Changes in glucose consumption, lactate production, and blood oxygenation were measured during prolonged neuronal activation (4-6 min) in human primary visual cortex using dynamic magnetic resonance spectroscopy and imaging. A decrease of steady-state glucose by 40% because of enhanced use by 21% was accompanied by a transient accumulation of lactate with a peak value of 170% 2.5 min after stimulation onset. Rapid blood hyperoxygenation indicating "uncoupling" of blood flow and oxidative metabolism was followed by a return to basal levels over 3 min. Thus, initial nonoxidative glucose consumption during functional activation is gradually complemented by a slower adjustment of oxidative phosphorylation that "recouples" perfusion and oxygen consumption at a new equilibrium.

Noise properties, the signal-to-noise ratio (SNR), contrast-tonoise ratio (CNR), and signal responses were compared during functional activation of the human brain at 1.5 and 3.0 T. At the higher field spiral gradient-echo (GRE) brain images revealed an average gain in SNR of 1.7 in fully relaxed and 2.2 in images with a repetition time (TR) of 1.5 sec. The tempered gain at longer TRs reflects the fact that the physiological noise depends on the signal strength and becomes a larger fraction of the total noise at 3.0 T. Activation of the primary motor and visual cortex resulted in a 36% and 44% increase of "activated pixels" at 3.0 T, which reflects a greater sensitivity for the detection of activated gray matter at the higher field. The gain in the CNR exhibited a dependency on the underlying tissue, i.e., an increase of 1.8؋ in regions of particular high activation-induced signal changes (presumably venous vessels) and of 2.2؋ in the average activated areas. These results demonstrate that 3.0 T provides a clear advantage over MRI modalities are often limited by the signal-to-noise ratio (SNR) and the contrast-to-noise ratio (CNR). Both SNR and CNR have been shown to increase with magnetic field strength B 0 (1,2). Consequently, the "optimal field strength" and the field dependency in blood oxygenation level dependent (BOLD) MRI have been the subject of various investigations (1,3-6). For many MRI applications a magnetic field strength of 1.5 Tesla (T) seems to represent a good compromise. Functional MRI (fMRI), however, is particularly dependent on good SNR and CNR properties, since typically observed BOLD signal changes at 1.5 T are on the order of a few percent and often exceed the intrinsic noise only slightly. Several biophysical models of activation-induced changes of the oxygenation-sensitive MRI signals have proposed that the changes in the relaxation rate ⌬R* 2 and subsequently the BOLD effect are proportional to B 0 for large vessels and proportional to B 0 2 for small vessels and capillaries (7,8). Thus, higher fields may provide an important improvement in fMRI. Indeed, recent investigations have demonstrated a superlinear increase in the BOLD CNR with the field strength (1,4,5), suggesting that high field fMRI methods may be able to resolve oxygenation changes in small vessels and capillaries, which are spatially localized near the origin of the neuronal activity.In the present study, various BOLD-relevant properties were compared at 1.5 T and 3.0 T. In order to establish identical BOLD-sensitivities, we investigated the T* 2 relaxation times for gray matter at each field strength and scaled the corresponding echo time (TE), and the excitation angle at 3.0 T. We compared intrinsic noise contributions and the SNR in gradient-echo (GRE) images and examined activation-induced BOLD responses during visual and motor activation at both fields in terms of spatial extent, the mean z-score, and the CNR of "activated voxels." T* 2 -maps from various brain sections were calculated to investigate spatial aspect...

The physiological noise in the resting brain, which arises from fluctuations in metabolic-linked brain physiology and subtle brain pulsations, was investigated in six healthy volunteers using oxygenation-sensitive dual-echo spiral MRI at 3.0 T. In contrast to the system and thermal noise, the physiological noise demonstrates a signal strength dependency and, unique to the metabolic-linked noise, an echo-time dependency. Variations of the MR signal strength by changing the flip angle and echo time allowed separation of the different noise components and revealed that the physiological noise at 3.0 T (1) exceeds other noise sources and (2) is significantly greater in cortical gray matter than in white matter regions. The SNR in oxygenation-sensitive MRI is predicted to saturate at higher fields, suggesting that noise measurements of the resting brain at 3.0 T and higher may provide a sensitive probe of functional information. Several biophysical models (1,2) and recent investigations of the field dependency in MRI (3-8) strongly suggest improvements in the signal-to-noise ratio (SNR) and the contrast-to-noise ratio (CNR) at higher magnetic fields. In a recent work (8), however, we have shown that the physiological noise demonstrates an MR signal strength dependency and exceeds the thermal noise and scanner noise at 3.0 T. Consequently, the physiological noise counteracts the gain in SNR at higher fields. In the present study, a model for the intrinsic noise in oxygenation-sensitive MRI of the human brain is developed. In corresponding experiments the MR-signal strength was modulated by variations in the flip angle (␣) and echo-time (TE) to separate individual noise contributions in the resting brain. Results were compared with the predicted noise contributions and corresponding implications on neuroimaging modalities are discussed. THEORYThe signal and the intrinsic noise in high-field MRI have been shown to be quadratic and linear in B 0 , respectively, resulting in an SNR proportional to the strength of the magnetic field (3,4). Whereas these earlier studies characterize the dominant noise by thermally generated random noise from the subject and scanner electronics and assume that coil losses are negligible (4), we expand this model by considering physiological noise as a further significant contribution to the total image noise:Here, T is the thermal noise from the subject and scanner electronics and S describes other system noise that includes drift and imperfections in RF, gradient, and shim subsystems. 0 , the square-law sum of T and S , is considered the raw noise and has been shown to be proportional to B 0 (4) but independent of the MR-signal strength. The term P in Eq.[1] describes the physiological noise, which arises from fluctuations in the basal cerebral metabolism (CMRO 2 ), blood flow (CBF), and blood volume (CBV), but also from cardiac and respiratory functions that cause quasiperiodic oscillations in the vascular system (9 -11), motion from subtle brain pulsatility (12), and magnetic field modula...

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