Acoustocerebrography

Acoustocerebrography (ACG) refers to a diagnostic method in medicine that is used to diagnose pathological changes and illnesses in the brain and the central nervous system. ^[1] It can also be applied as a means to diagnose and monitor intracranial pressure, for example as incorporated into continuous brain monitoring device. As a method of transcranial, acoustic spectroscopy, ACG is based on Molecular Acoustics.^[2] It allows for the non-invasive examination of the brain’s cell and molecular structure. ACG can use microphones, accelerometers or multi-frequency ultrasound (i.e. sound waves) to monitor changes. This methodology does not use any radiation and is completely free of any side effects. ACG also facilitates blood flow analysis as well as the detection of obstructions in cerebral blood flow.

Passive and active Acoustocerebrography

Passive Acoustocerebrography

All brain tissue is influenced by blood circulating in the brain’s vascular system. With each heartbeat, blood circulates in the skull, following a recurring pattern according to the oscillation produced. This oscillation’s effect, in turn, depends on the brain’s size, form, structure and its vascular system. Thus, every heartbeat stimulates minuscule motion in the brain tissue as well as cerebrospinal fluid and therefore produces minimal changes in intracranial pressure. These changes can be monitored and measured in the skull. Today, mostly passive sensors like accelerometers are used to identify these signals correctly. ^[3] Sometimes highly sensitive microphones are utilized. ^{[4] [5] [6]}

With a digital signal, it becomes possible to study the patterns of the blood flow moving inside the skull. These patterns form unique signatures that can be analyzed with specially designed algorithms, identifying them either as an inconspicuous, “normal” pattern or as a pattern showing an “abnormal” behavior.

Active Acoustocerebrography

In active ACG applications, a multi-frequency ultrasonic signal is used to detect and classify adverse changes at the cellular or molecular level.^[7] In addition to all of the advantages that passive ACG provides, with active ACG it is possible to conduct a spectral analysis of the acoustic signals received. These spectrum analyses not only display changes in the brain’s vascular system, but also those in its cellular and molecular structures. One common application of active ACG is the Transcranial Doppler test. More recently, its color version (TCCD) has been deployed. These ultrasonic procedures measure blood flow velocity within the brain’s blood vessels. They are used to diagnose embolisms, stenoses and vascular constrictions, for example, in the aftermath of a subarachnoid hemorrhage.

Fields of Application

Contrary to applications that provide only momentary images, such as MRI and CT, the results of ACG procedures can be obtained continuously, thus facilitating effortless and non-invasive real-time monitoring. This can be especially helpful during the acute phase directly after a stroke or a traumatic brain injury. The measured data is mathematically processed continuously and displayed on a monitoring device. The computer-aided analysis of the signals enables the physician/nursing staff to precisely interpret the results immediately after device setup. Furthermore, ACG allows for preventive detection of pathological changes in brain tissue.

References

1. Computer Aided Multispectral Ultrasound Diagnostics Brain Health Monitoring System based on Acoustocerebrography, Bogdan et al. (2015)
2. Molekularakustik - Eine Einführung in die Zusammenhänge zwischen Ultraschall und Molekülstruktur in Flüssigkeiten und Gasen, Werner Schaaffs (1963)
3. Cranial Accelerometry Can Detect Cerebral Vasospasm Caused by Subarachnoid Hemorrhage, Smith et al. (2015)
4. Recording and processing aneurysmal vibration signals in dogs, Sun et al. (1988)
5. Acoustic recordings from experimental saccular aneurysms in dogs, Sekhar et al. (1990)
6. Detection and Analysis of Cranial Bruit, Kosugi et al. (1987)
7. On ultrasound classification of stroke risk factors from randomly chosen respondents using non-invasive multispectral ultrasonic brain measurements and adaptive profiles, Wrobel et al. (2015)