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. 2016 May 6;29(4):260–268. doi: 10.1177/1971400916648338

Usefulness of brain atlases in neuroradiology: Current status and future potential

Wieslaw L Nowinski 1,✉
PMCID: PMC4978331  PMID: 27154190

Abstract

Human brain atlases, although prevalent in medical education and stereotactic and functional neurosurgery, are not yet applied practically in neuroradiology. In a step towards introducing brain atlases to neuroradiology, we discuss nine different situations of potential atlas use: (1) to support interpretation of brain scans with clearly visible structures (to increase confidence of non-neuroradiologists); (2) to delineate and label scans of low anatomical content (with indiscernible or poorly visible anatomy); (3) to assist in generating the structured report; (4) to assist in interpreting small deep lesions, since an atlas’s anatomical parcellation is higher than that of the interpreted scan; (5) to approximate distorted due to pathology (and unknown to the interpreter) anatomy and label it; (6) to cope with data explosion; (7) to assist in the interpretation of functional scans (to label the activation foci with the underlying anatomy and Brodmann’s areas); (8) to support ischemic stroke image handling by means of atlases of anatomy and blood supply territories; and (9) to communicate image interpretation results (diagnosis) to others. The usefulness of the atlas for automatic structure identification, localisation, delineation, labelling and quantification, as well as for reporting and communication, potentially increases the interpreter’s efficiency and confidence, as well as expedites image interpretation.

Keywords: Brain atlas, neuroradiology, image interpretation, image quantification, diagnosis, communication

Introduction

Human brain atlases are applicable in medical education, research and clinical applications. In order to exploit a wide spectrum of atlas applications, we have created 35 brain atlases, grouped into three families: print material-based, imaging-based and population-based.1

The brain atlases are prevalent in medical education, and numerous excellent atlases have been created for this purpose, including ADAM,2 Cerefy,3,4 Digital Anatomist5 and Voxel-Man6 (e.g. see Nowinski1 and Nowinski et al.7 for overviews). We have also developed brain atlases on mobile platforms, including iPad,8 iPhone9 and Android.10

We have introduced electronic brain atlases to stereotactic and functional neurosurgery11–13 (see the overview on our contribution to this field in Nowinski13). Our atlases have been adopted by 13 surgical companies, including Medtronic, Brainlab and Elekta, and installed in more than 1550 surgical workstations worldwide.

The atlases are applicable in medical research; we have proposed atlas-assisted solutions in human brain mapping14–16 and also in analysis of schizophrenia images.17 Recently, we have employed atlases in neurology,18,19 including linking neuroanatomy, neuroradiology and neurology together.20 Although, typically, atlases have been employed in a single plane, we have proposed multiple plane11 and multiple atlas use21–23 (in stroke and neurosurgery).

Encouraged by the acceptance of brain atlases, we expected their application in neuroradiology as well. In 2002, we envisaged the following advantages of atlas use in radiology: (1) providing a faster and more efficient scan interpretation, (2) facilitating communicating information about the interpreted scans from the neuroradiologist to other clinicians and medical students, (3) increasing confidence of general radiologists and residents in interpreting brain scans in urgent situations and (4) expediting learning of neuroanatomy and scan interpretation.24

Furthermore in 2005, we overoptimistically predicted that in 2010 ‘almost every scanner, radiological workstation and PACS workstation will contain an atlas’.25

Today, brain atlases are still not available in PACS and radiologic workstations, despite the interest in our brain atlases licensed to 67 companies and institutions and being distributed in about 100 countries, as well as their recognition for almost two decades from the leading radiologic societies (Magna cum Laude RSNA 2009, Magna cum Laude ECR 2000 and Summa cum Laude ASNR 1997, 2008, 2012 and 2014, among other awards).

Therefore, this raises the question of what the reasons of this situation are. We believe that there are at least three main reasons hampering the use of brain atlases in neuroradiology. First, the atlases are unavailable directly in PACS and radiologic workstations, probably due mainly to regulatory issues. Second, radiology requires automatic, fast, accurate and validated atlas-to-scan warping, which is not available yet. Third, there is unfamiliarity of brain atlas usefulness, benefits and potential by the community. The last point is addressed here and illustrated with examples.

Brain atlas usefulness

For more than two decades, we have created 35 brain atlases (and several prototypes), having a wide spectrum of applications.1 Figure 1 illustrates some of these atlases. The atlas is understood here as content (e.g. 2D, 3D or probabilistic) with a browser enabling the user to explore it and dedicated functionality to support the required application. The use of an atlas depends on the situation, the problem to be addressed, the interpreter’s skills, the required outcome and the type of scan, among others. Table 1 summarises nine different situations of atlas use in neuroradiology: (1) to support interpretation of brain scans with clearly visible structures (to increase confidence of non-neuroradiologists); (2) to delineate and label scans of low anatomical content (with indiscernible or poorly visible anatomy); (3) to assist in generating the structured report; (4) to assist in interpreting small deep lesions by the atlas of a higher anatomical parcellation than that of the interpreted scan; (5) to approximate anatomy (that is typically unclear or even unknown to the interpreter and surgeon) distorted by a lesion causing mass effect and label it; (6) to cope with data explosion; (7) to assist in interpretation of functional scans (to label the activation foci with the underlying anatomy and Brodmann’s areas26); (8) to support ischemic stroke image handling by means of atlases of anatomy and blood supply territories; and (9) to communicate image interpretation results (diagnosis). All these nine atlas applications have been developed as proof of concept prototypes and presented earlier at RSNA, ECR and ASNR annual meetings as computer exhibits for handling brain scans, excluding structural reporting developed for hand radiographs.

Figure 1.

Figure 1.

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Examples of various brain atlases constructed by us: (a) coronal plate from the electronic Talairach atlas; (b) axial plate from the electronic Talairach atlas with Brodmann’s areas on the left and gyri on the right side; (c) Talairach atlas and referentially oriented Talairach atlas51 combined together and extended into in 3D; (d) axial plate of the atlas of blood supply territories; (e) coronal colour-coded plate from the electronic Schaltenbrand–Wahren atlas; (f) coronal contour plate from the electronic Schaltenbrand–Wahren atlas; and (g) 3D atlas of the brain, head and neck (with about 3000 components).

Table 1.

Summary of atlas usefulness in neuroradiology.

No. Situation Problem Type of scan Atlas use
1 Scan interpretation assistance Low confidence of the interpreter (such as resident or general radiologist interpreting brain scans on duty) Any structural scan The atlas provides labels (names of structures)
2 Interpretation of scans of low anatomical content Indiscernible (or poorly visible) anatomy PET, SPECT, low-quality CT The atlas delineates (indiscernible or poorly visible) structures and labels them
3 Structural reporting Time-consuming Any The atlas assists in generating the structured report by speeding it up and increasing confidence
4 Small deep lesions Insufficient scan contrast for small deep lesions Any The atlas delineates nuclei involved in the lesion and labels them, thanks to its anatomical parcellation higher than that of the interpreted scan
5 Lesions causing mass effect Distorted anatomy typically unclear or even unknown to the interpreter (and the surgeon) Any The atlas approximates distorted anatomy and labels it
6 Vascular imaging with high MDCT Data explosion; impossibility of interpreting images in a standard way High MDCTA, 320 CTA The atlas guides the radiologist to identify approximately (due to vascular variability) the vessels in the MIP (projected) image. To facilitate identification, any vascular content of the atlas can be interactively selected.
7 Functional image interpretation Lack of anatomic regions fMRI, PET, SPECT The atlas labels the activation foci with the underlying anatomy and Brodmann’s areas.
8 Ischemic stroke image interpretation Lack of anatomic and functional quantification of the infarct and penumbra DWI, PWI The atlases of anatomy and blood supply territories label the infarct and penumbra; automatically quantify them with the names of all structures/territories, their percentage and volume of occupancy; and automatically calculate the infarct-to-MCA ratio.
9 Communication of image interpretation results. Implementation of a paradigm change from volume-based to value-based radiology Any The atlas facilitates efficient communication of image interpretation results (diagnosis) from the neuroradiologist to other clinicians, residents, medical students, patients and/or their families
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PET: positron emission tomography; SPECT: single photon emission computed tomography; CT: computed tomography; MDCT, multiple detector computed tomography; fMRI: functional magnetic resonance imaging; DWI: diffusion-weighted imaging; PWI: perfusion-weighted imaging.

Discussion

In the typical post-processing of radiologic scans, a devised method and a developed application lead to a solution (such as scan interpretation or decision making). In the atlas-assisted post-processing of radiologic scans, a triple containing the method, model and application leads to a solution. The better the model (map, atlas) in terms of its content (anatomic parcellation) and quality, the more efficient the solution.

We have stated three main reasons hindering the use of brain atlases in neuroradiology: atlas unavailability directly in PACS and radiologic workstations; lack of automatic, fast, accurate and validated warping; and unfamiliarity by the community of brain atlas usefulness, benefits and potential.

Numerous methods have been developed for atlas-to-scan warping (and comparisons of some of them are given in Nowinski et al.27 and Klein et al.28). Rapid procedures, such as the Fast Talairach Transformation27 that warps the atlas in less than five seconds on a standard PC based on piece-wise linear transformation, are appropriate for certain procedures only, for instance deep brain stimulation. This is because the accuracy between non-linear and piece-wise linear warping for deep structures is practically negligible.29 However, high degrees of freedom warping may be unacceptably long to be applied routinely, in addition to other shortcomings, such as limitation to normal scans or specific types of scans.28 Another problem is atlas warping against a tumour causing mass effect. Although some solutions to this problem have been proposed, such as Nowinski and Belov,30 its validation remains a challenge.

The most obvious and typical use of an individualised (i.e. mapped to a patient’s specific scan) atlas is to support the interpretation of brain scans with clearly visible structures (Figure 2). Then, the atlas provides labels (the names of structures). Although this feature may be seldom required by neuroradiologists (unless the atlas provides a higher neuroanatomic parcellation than a typical brain scan does, as discussed below), it can be vital to increase the confidence of general radiologists or residents interpreting brain scans on duty. For instance, when visiting some radiology departments in China, the use of print atlases at radiologic workstations was observed to assist in scan interpretation. The electronic atlas superimposed on the scan (or at least displayed next to it) provides much more convenient and efficient support.

Figure 2.

Figure 2.

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Atlas-assisted labelling of magnetic resonance imaging (MRI) scan. Left: original MRI axial image; right: image labelled with names of structures, including Brodmann’s areas.

For some types of scans, such as single-photon emission computed tomography, positron emission tomography (PET) or low-quality (or energy) computed tomography (CT), the underlying neuroanatomy is poorly visible or even (almost) indiscernible. In such instances, the atlas superimposed on the scan delineates structures and labels them (Figure 3). Note that the atlas can delineate structures by means of colour (Figure 3) or outline (Figure 4). The border between this and the previous situation is interpretation of PET/CT scans, as the underlying brain anatomy is not clearly visible and the scans are typically interpreted by general radiologists rather than neuroradiologists. In these circumstances, the use of a brain atlas could potentially increase the use of PET/CT in brain studies.

Figure 3.

Figure 3.

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Delineating and labelling of poorly visible neuroanatomy by the anatomic atlas (in colour) superimposed on a scan.

Figure 4.

Figure 4.

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Small thalamic lesion (left) interpreted simultaneously on axial, coronal and sagittal planes (right) by a highly anatomically parcellated atlas.

Generation of a structured report can be expedited by atlases or templates. The benefits include: (1) automatic scan labelling by the neuroradiologist and transferring the labelled scan to others, including clinicians, general practitioners, nurses, medical students and even patients and their families; (2) rapid inputting of the anatomical names to the report by clicking on the corresponding structures; and (3) atlas-driven selection/checking of relevant fields in the report (e.g. basal ganglia, arterial system, dural sinuses and/or cranial nerves).

A brain atlas with an anatomical parcellation higher than that of the interpreted scan can assist in identification of invisible structures. For instance, deep grey nuclei, such as the thalamic nuclei, are practically indiscernible with current routine imaging. However, the thalamus plays a critical role, and identification of its individual nuclei is of great importance. In our multi-atlas database,7,31 we employ the Schaltenbrand–Wahren brain atlas32 with the Hassler parcellation of the thalamus. As this anatomic parcellation is much higher than that of the interpreted scan, the atlas is able to identify the nuclei involved in a thalamic lesion and label them (and simultaneous use of multiple planes also increases confidence), as illustrated in Figure 4. Similarly in structural scans, cortical areas or white matter tracts involved in lesions can be determined and labelled by means of the electronic atlases of Brodmann’s areas31 or brain connections,33 respectively.

Although neuroradiologists are familiar with normal brain anatomy, the anatomy distorted by a lesion producing a mass effect may cause difficulties in understanding it, especially in 3D, and consequently in interpreting it. The same difficulty faces the surgeon operating on the case. The individualised atlas approximates the distorted anatomy and labels it, as illustrated in Figure 5, by means of the method proposed in Nowinski and Belov.30 This fast and simplified method is based on geometric modelling. However, a more accurate approach requires physical modelling. Realistic modelling of the pathology process, along with its validation, remains a challenge because of at least three factors: (1) difficulty in accurate mathematical modelling (which requires unknown physical properties of tissues); (2) lack of an accurate gold standard for validation (because of lesion extent differences between histology and diagnostic imaging, as well as within various pulse sequences); and (3) because of lesion infiltration into the parenchyma.

Figure 5.

Figure 5.

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Brain tumour interpretation. Left: original scan; right: scan with the superimposed individualised atlas and some structures labelled.

Modern multiple detector computed tomography scanners generate huge amounts of data. For instance, a 320-CT scanner generates about 6000–7000 images in a minute. Interpreting so many images in the classic manner by viewing them one-by-one is not practically feasible. The CTA scans can be converted into maximum intensity projection (MIP) images. Because of the complexity of the human vasculature and the two-dimensional nature of projections, the interpretation of these images can be problematic. The atlas can guide the neuroradiologist to identify (due to vascular variability) the vessels approximately in the MIP image. In order to facilitate identification, any vascular content of the atlas can be interactively selected and displayed for interpretation (e.g. arteries, veins, dural sinuses, left or right hemisphere or any group or individual vessels). Moreover, the use of an atlas with vascular variants, such as Nowinski et al.,34 can be potentially helpful.

Accurate registration of the vascular atlas with the scan within an acceptable time may be difficult, if indeed it is possible at all (with the current state-of-the-art technology). A practical solution (as presented in Figure 6) is to place the MIP image and the vascular atlas (or its relevant content) with the same projection side by side.

Figure 6.

Figure 6.

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Atlas-guided interpretation of 320-CTA scans. Left: maximum intensity projection image; right: corresponding vascular atlas image (both images have the same view (projection)). Note that the 320-CTA and the vascular atlas are not spatially registered (which may be difficult and time consuming, if feasible at all without employing the atlas without vascular variants) but rather placed side by side with the same view.

Brain atlases are prevalent in human brain mapping.14–16,35 Probably the most popular research tool for human brain mapping is the Talairach Daemon,35 which, by means of the hidden (to the user) Talairach brain atlas,36 provides labels for the given Talairach coordinates of the activation loci.

In our solution,14 the electronic version of the Talairach atlas extended with Brodmann’s areas is explicitly displayed, and the user can interactively label the activation loci supported by image thresholding (without needing to convert the scan into the Talairach space, measure coordinates of the activation loci, send them to the Talairach Daemon, and receive the names in order to link them manually with the interpreted activations). We use the electronic Talairach atlas, endorsed by Prof. Talairach himself (who wrote the foreword to our electronic atlas31), which is continuously enhanced and extended.37,38

In the above-mentioned examples, the atlas has been exploited for identification, localisation, delineation, reporting and labelling. The atlas is also applicable for quantitative assessment, which we have demonstrated for stroke. The current state-of-the-art technology for handing ischemic stroke images takes into account the size of the infarct and penumbra, but not their locations. The use of atlas proposed in Nowinski et al.23 takes into account pathology location and the structures involved. By using the atlases of anatomy and blood supply territories, it is feasible to: (1) label the infarct and penumbra; (2) automatically quantify the infarct and penumbra with names of all structures/territories, their percentage and volume of occupancy; and (3) automatically calculate the infarct-to-MCA ratio (applied for making decision in thrombolysis39,40), along with the ratios for other territories.

The role of efficient communication of the results of image interpretation (diagnosis) from the neuroradiologist to other clinicians, general practitioners, nurses, residents, medical students and especially to patients and their families has been increasing, particularly in a recent paradigm change from volume-based to value-based radiology. The atlas helps the neuroradiologist to explain not only a complicated neuroanatomy in 3D to the patient and/or his/her family, but also pathology, along with resulting disorders. Suitable for this purpose is a family of our interactive, completely parcellated and labelled brain atlases with normal anatomy in 3D (The Human Brain in 1492/1969/2953 Pieces41–45; Figure 7) and the atlas of neurologic disorders,46–49 including its mobile version.50 The content of the brain atlas extended into the head and neck comprises: the brain divided into the left and right hemispheres, cerebrum, cerebellum and brainstem; the cerebellum divided into the left and right hemispheres; the brainstem divided into the left and right parts, and parcellated into the midbrain, pons and medulla; the cerebral cortex parcellated into lobes, gyri and gyri with sulci, as well as 3D Brodmann’s areas; the spinal cord; white matter parcellated into cerebral, posterior fossa and deep white matter; grey matter nuclei; ventricular system; intracranial vasculature (arteries, veins, and dural sinuses) parcellated into more than 1300 vessels, the smallest being 0.08 mm in diameter; intracranial arteries grouped into the internal carotid, anterior cerebral, middle cerebral, posterior cerebral, vertebral and basilar arteries and the circle of Willis; intracranial veins grouped into superficial, deep and posterior fossa veins; dural sinuses; extracranial vasculature grouped into arteries and veins; white matter tracts grouped into associations, commissures, projections and posterior fossa tracts; cranial nerves (CN) grouped into CN I–CN XII, along with the nuclei with more than 630 components; head muscles grouped into extra-ocular, facial, masticatory and other muscles; glands grouped into mouth and other glands; the skull parcellated into 29 bones; visual and auditory systems; and the skin.

Figure 7.

Figure 7.

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An MRI scan labelled, along with the corresponding structures in 3D (in the right hemisphere) also labelled.

Another important atlas-based application worth mentioning (though not implemented yet) is the use of a probabilistic atlas as ‘normative data’. The deviation of a patient’s scan from the probabilistic norm could then be quantified or drug treatment monitored and assessed.

Conclusion

Although brain atlases are prevalent in medical education, as well as in stereotactic and functional neurosurgery, as they are useful in medical research and neurology, their acceptance in neuroradiology is low, and they are unavailable in radiologic and PACS workstations. This work analysing and illustrating atlas usefulness in nine situations (for identification, localisation, delineation, labelling, quantification, reporting and communication) may potentially accelerate the process of adopting brain atlases in neuroradiology.

Acknowledgements

I am very grateful to numerous individuals, listed as the co-authors in the references, who contributed to the atlas development work. In addition, I am indebted to the following professionals and institutions for collaboration and providing scans: Dr. R. Nick Bryan of the Department of Radiology, Johns Hopkins Hospital, Baltimore, MD (Figure 2); Dr. Satoshi Minoshima of the Department of Radiology, University of Washington, Seattle, WA (Figure 3); and Dr. William Orrison of the Nevada Imaging Centers, Las Vegas, NV (Figure 6).

Funding

The atlas development work was funded by ASTAR, Singapore.

Conflict of interest

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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