International
Tables for Crystallography Volume B Reciprocal space Edited by U. Shmueli © International Union of Crystallography 2006 
International Tables for Crystallography (2006). Vol. B, ch. 2.5, pp. 316320

2.5.6. Threedimensional reconstruction ^{6}
In electron microscopy we obtain a twodimensional image – a projection of a threedimensional object (Fig. 2.5.6.1): The projection direction is defined by a unit vector and the projection is formed on the plane x perpendicular to The set of various projections may be assigned by a discrete or continuous set of points on a unit sphere (Fig. 2.5.6.2). The function reflects the structure of an object, but gives information only on coordinates of points of its projected density. However, a set of projections makes it possible to reconstruct from them the threedimensional (3D) distribution (Radon, 1917; DeRosier & Klug, 1968; Vainshtein et al., 1968; Crowther, DeRosier & Klug, 1970; Gordon et al., 1970; Vainshtein, 1971a; Ramachandran & Lakshminarayanan, 1971; Vainshtein & Orlov, 1972, 1974; Gilbert, 1972a; Herman, 1980). This is the task of the threedimensional reconstruction of the structure of an object:

The projection sphere and projection of along τ onto the plane . The case represents orthoaxial projection. Points indicate a random distribution of τ. 
Besides electron microscopy, the methods of reconstruction of a structure from its projections are also widely used in various fields, e.g. in Xray and NMR tomography, in radioastronomy, and in various other investigations of objects with the aid of penetrating, backscattered or their own radiations (Bracewell, 1956; Deans, 1983; Mersereau & Oppenheim, 1974).
In the general case, the function (2.5.6.1) (the subscript indicates dimension) means the distribution of a certain scattering density in the object. The function is the twodimensional projection density; one can also consider onedimensional projections of two (or three) dimensional distributions. In electron microscopy, under certain experimental conditions, by functions and we mean the potential and the projection of the potential, respectively [the electron absorption function μ (see Section 2.5.4) may also be considered as `density']. Owing to a very large depth of focus and practical parallelism of the electron beam passing through an object, in electron microscopy the vector τ is the same over the whole area of the irradiated specimen – this is the case of parallel projection.
The 3D reconstruction (2.5.6.2) can be made in the real space of an object – the corresponding methods are called the methods of direct threedimensional reconstruction (Radon, 1917; Vainshtein et al., 1968; Gordon et al., 1970; Vainshtein, 1971a; Ramachandran & Lakshminarayanan, 1971; Vainshtein & Orlov, 1972, 1974; Gilbert, 1972a).
On the other hand, threedimensional reconstruction can be carried out using the Fourier transformation, i.e. by transition to reciprocal space. The Fourier reconstruction is based on the well known theorem: the Fourier transformation of projection of a threedimensional object is the central (i.e. passing through the origin of reciprocal space) twodimensional plane cross section of a threedimensional transform perpendicular to the projection vector (DeRosier & Klug, 1968; Crowther, DeRosier & Klug, 1970; Bracewell, 1956). In Cartesian coordinates a threedimensional transform is The transform of projection along z is In the general case (2.5.6.1) of projecting the plane along the vector τ Reconstruction with Fourier transformation involves transition from projections at various to cross sections , then to construction of the threedimensional transform by means of interpolation between in reciprocal space, and transition by the inverse Fourier transformation to the threedimensional distribution : Transition (2.5.6.2) or (2.5.6.6) from twodimensional electronmicroscope images (projections) to a threedimensional structure allows one to consider the complex of methods of 3D reconstruction as threedimensional electron microscopy. In this sense, electron microscopy is an analogue of methods of structure analysis of crystals and molecules providing their threedimensional spatial structure. But in structure analysis with the use of Xrays, electrons, or neutrons the initial data are the data in reciprocal space in (2.5.6.6), while in electron microscopy this role is played by twodimensional images [(2.5.6.2), (2.5.6.6)] in real space.
In electron microscopy the 3D reconstruction methods are, mainly, used for studying biological structures (symmetric or asymmetric associations of biomacromolecules), the quaternary structure of proteins, the structures of muscles, spherical and rodlike viruses, bacteriophages, and ribosomes.
An exact reconstruction is possible if there is a continuous set of projections corresponding to the motion of the vector over any continuous line connecting the opposite points on the unit sphere (Fig. 2.5.6.2). This is evidenced by the fact that, in this case, the cross sections which are perpendicular to τ in Fourier space (2.5.6.4) continuously fill the whole of its volume, i.e. give (2.5.6.3) and thereby determine .
In reality, we always have a discrete (but not continuous) set of projections . The set of is, practically, obtained by the rotation of the specimen under the beam through various angles (Hoppe & Typke, 1979) or by imaging of the objects which are randomly oriented on the substrate at different angles (Kam, 1980; Van Heel, 1984). If the object has symmetry, one of its projections is equivalent to a certain number of different projections.
The object is finite in space. For function and any of its projections there holds the normalization condition where Ω is the total `weight' of the object described by the density distribution . If one assumes that the density of an object is constant and that inside the object = constant = 1, and outside it , then Ω is the volume of an object. The volume of an object, say, of molecules, viruses and so on, is usually known from data on the density or molecular mass.
In practice, an important case is where all the projection directions are orthogonal to a certain straight line: (Fig. 2.5.6.3). Here the axis of rotation or the axis of symmetry of an object is perpendicular to an electron beam. Then the threedimensional problem is reduced to the twodimensional one, since each cross section is represented by its onedimensional projections. The direction of vector τ is defined by the rotational angle ψ of a specimen: In this case, the reconstruction is carried out separately for each level : and the threedimensional structure is obtained by superposition of layers (Vainshtein et al., 1968; Vainshtein, 1978).
In direct methods of reconstruction as well as in Fourier methods the space is represented as a discrete set of points on a twodimensional net or on a threedimensional lattice. It is sometimes expedient to use cylindrical or spherical coordinates. In twodimensional reconstruction the onedimensional projections are represented as a set of discrete values , at a certain spacing in . The reconstruction (2.5.6.9) is carried out over the discrete net with nodes . The net side A should exceed the diameter of an object ; the spacing . Then (2.5.6.8) transforms into the sum For oblique projections the above sum is taken over all the points within the strips of width a along the axis (Fig. 2.5.6.4).
The resolution δ of the reconstructed function depends on the number h of the available projections. At approximately uniform angular distribution of projections, and diameter equal to D, the resolution at reconstruction is estimated as The reconstruction resolution δ should be equal to or somewhat better than the instrumental resolution d of electron micrographs , the real resolution of the reconstructed structure being d. If the number of projections h is not sufficient, i.e. , then the resolution of the reconstructed structure is δ (Crowther, DeRosier & Klug, 1970; Vainshtein, 1978).
In electron microscopy the typical instrumental resolution d of biological macromolecules for stained specimens is about 20 Å; at the object with diameter Å the sufficient number h of projections is about 20. If the projections are not uniformly distributed in projection angles, the resolution decreases towards for such τ in which the number of projections is small.
Properties of projections of symmetric objects . If the object has an Nfold axis of rotation, its projection has the same symmetry. At orthoaxial projection perpendicular to the Nfold axis the projections which differ in angle at are identical: This means that one of its projections is equivalent to N projections. If we have h independent projections of such a structure, the real number of projections is hN (Vainshtein, 1978). For a structure with cylindrical symmetry one of its projections fully determines the threedimensional structure.
Many biological objects possess helical symmetry – they transform into themselves by the screw displacement operation , where p is the number of packing units in the helical structure per q turns of the continuous helix. In addition, the helical structures may also have the axis of symmetry N defining the pitch of the helix. In this case, a single projection is equivalent to projections (Cochran et al., 1952).
Individual protein molecules are described by point groups of symmetry of type N or . Spherical viruses have icosahedral symmetry 532 with two, three and fivefold axes of symmetry. The relationship between vectors τ of projections is determined by the transformation matrix of the corresponding point group (Crowther, Amos et al., 1970).
Modelling. If several projections are available, and, especially, if the object is symmetric, one can, on the basis of spatial imagination, recreate approximately the threedimensional model of the object under investigation. Then one can compare the projections of such a model with the observed projections, trying to draw them as near as possible. In early works on electron microscopy of biomolecules the tentative models of spatial structure were constructed in just this way; these models provide, in the case of the quaternary structure of protein molecules or the structure of viruses, schemes for the arrangement of protein subunits. Useful subsidiary information in this case can be obtained by the method of optical diffraction and filtration.
This method is also called the synthesis of projection functions. Let us consider a twodimensional case and stretch along each onedimensional projection (Fig. 2.5.6.5) by a certain length b; thus, we obtain the projection function Let us now superimpose h functions The continuous sum over the angles of projection synthesis is this is the convolution of the initial function with a rapidly falling function (Vainshtein, 1971b). In (2.5.6.15), the approximation for a discrete set of h projections is also written. Since the function approaches infinity at , the convolution with it will reproduce the initial function , but with some background B decreasing around each point according to the law . At orthoaxial projection the superposition of cross sections arranged in a pile gives the threedimensional structure .
Radon operator. Radon (1917; see also Deans, 1983) gave the exact solution of the problem of reconstruction. However, his mathematical work was for a long time unknown to investigators engaged in reconstruction of a structure from images; only in the early 1970s did some authors obtain results analogous to Radon's (Ramachandran & Lakshminarayanan, 1971; Vainshtein & Orlov, 1972, 1974; Gilbert, 1972a).
The convolution in (2.5.6.15) may be eliminated using the Radon integral operator, which modifies projections by introducing around each point the negative values which annihilate on superposition the positive background values. The onedimensional projection modified with the aid of the Radon operator has the form Now is calculated analogously to (2.5.6.14), not from the initial projections L but from the modified projection :
The reconstruction of highsymmetry structures, in particular helical ones, by the direct method is carried out from one projection making use of its equivalence to many projections. The Radon formula in discrete form can be obtained using the double Fourier transformation and convolution (Ramachandran & Lakshminarayanan, 1971).
These methods have been derived for the twodimensional case; consequently, they can also be applied to threedimensional reconstruction in the case of orthoaxial projection.
Let us discretize by a net of points ; then we can construct the system of equations (2.5.6.10).
When h projections are available the condition of unambiguous solution of system (2.5.6.10) is: . At we can, in practice, obtain sufficiently good results (Vainshtein, 1978).
Methods of reconstruction by iteration have also been derived that cause some initial distribution to approach one satisfying the condition that its projection will resemble the set . Let us assign on a discrete net as a zeroorder approximation the uniform distribution of mean values (2.5.6.7) The projection of the qth approximation at the angle (used to account for discreteness) is .
The next approximation for each point jk is given in the method of `summation' by the formula where is the number of points in a strip of the projection . One cycle of iterations involves running around all of the angles (Gordon et al., 1970).
When carrying out iterations, we may take into account the contribution not only of the given projection, but also of all others. In this method the process of convergence improves. Some other iteration methods have been elaborated (Gordon & Herman, 1971; Gilbert, 1972b; Crowther & Klug, 1974; Gordon, 1974).
This method is based on the Fourier projection theorem [(2.5.6.3) –(2.5.6.5)]. The reconstruction is carried out according to scheme (2.5.6.6) (DeRosier & Klug, 1968; Crowther, DeRosier & Klug, 1970; Crowther, Amos et al. 1970; DeRosier & Moore, 1970; Orlov, 1975). The threedimensional Fourier transform is found from a set of twodimensional cross sections on the basis of the Whittaker–Shannon interpolation. If the object has helical symmetry (which often occurs in electron microscopy of biological objects, e.g. on investigating bacteriophage tails, muscle proteins) cylindrical coordinates are used. Diffraction from such structures with c periodicity and scattering density is defined by the Fourier–Bessel transform: The inverse transformation has the form so that and are the mutual Bessel transforms
Owing to helical symmetry, (2.5.6.22), (2.5.6.23) contain only those of the Bessel functions which satisfy the selection rule (Cochran et al., 1952) where N, q and p are the helix symmetry parameters, . Each layer l is practically determined by the single function with the lowest n; the contribution of other functions is neglected. Thus, the Fourier transformation of one projection of a helical structure, with an account of symmetry and phases, gives the threedimensional transform (2.5.6.23). We can introduce into this transform the function of temperaturefactor type filtering the `noise' from large spatial frequencies.
In the general case of 3D reconstruction from projections the projection vector τ occupies arbitrary positions on the projection sphere (Fig. 2.5.6.2). Then, as in (2.5.6.15), we can construct the threedimensional spatial synthesis. To do this, let us transform the twodimensional projections by extending them along τ as in (2.5.6.13) into threedimensional projection functions .
Analogously to (2.5.6.15), such a threedimensional synthesis is the integral over the hemisphere (Fig. 2.5.6.2) this is the convolution of the initial function with (Vainshtein, 1971b).
To obtain the exact reconstruction of we find, from each , the modified projection (Vainshtein & Orlov, 1974; Orlov, 1975)
By extending along τ we transform them into . Now the synthesis over the angles gives the threedimensional function
The approximation for a discrete set of angles is written on the right. In this case we are not bound by the coaxial projection condition which endows the experiment with greater possibilities; the use of object symmetry also profits from this. To carry out the 3D reconstruction (2.5.6.25) or (2.5.6.27) one should know all three Euler's angles ψ, θ, α (Fig. 2.5.6.2).
The projection vectors should be distributed more or less uniformly over the sphere (Fig. 2.5.6.2). This can be achieved by using special goniometric devices.
Another possibility is the investigation of particles which, during the specimen preparation, are randomly oriented on the substrate. This, in particular, refers to asymmetric ribosomal particles. In this case the problem of determining these orientations arises.
The method of spatial correlation functions may be applied if a large number of projections with uniformly distributed projection directions is available (Kam, 1980). The space correlation function is the averaged characteristic of projections over all possible directions which is calculated from the initial projections or the corresponding sections of the Fourier transform. It can be used to find the coefficients of the object density function expansion over spherical harmonics, as well as to carry out the 3D reconstruction in spherical coordinates.
Another method (Van Heel, 1984) involves the statistical analysis of image types, subdivision of images into several classes and image averaging inside the classes. Then, if the object is rotated around some axis, the 3D reconstruction is carried out by the iteration method.
If such a specimen is inclined at a certain angle with respect to the beam, then the images of particles in the preferred orientation make a series of projections inclined at an angle β and having a random azimuth. The azimuthal rotation is determined from the image having zero inclination.
If particles on the substrate have a characteristic shape, they may acquire a preferable orientation with respect to the substrate, their azimuthal orientation α being random (Radermacher et al., 1987).
In the general case, the problem of determining the spatial orientations of randomly distributed identical threedimensional particles with an unknown structure may be solved by measuring their twodimensional projections (Fig. 2.5.6.1) if the number i of such projections is not less than three, (Vainshtein & Goncharov, 1986a,b; Goncharov et al. 1987; Goncharov, 1987). The direction of the vector along which the projection is obtained is set by the angle (Fig. 2.5.6.2).
The method is based on the analysis of onedimensional projections of twodimensional projections where α is the angle of the rotation about vector τ in the p plane.
Lemma 1. Any two projections and (Fig. 2.5.6.6) have common (identical) onedimensional projections : Vectors and (Fig. 2.5.6.3) determine plane h in which they are both lying. Vector is normal to plane h and parallel to axis of the onedimensional projection ; both and axes along which the projections and are constructed are perpendicular to .
The corresponding lemma in the Fourier space states:
Lemma 2. Any two plane transforms, and intersect along the straight line (Fig. 2.5.6.7); the onedimensional transform is the transform of .
Thus in order to determine the orientations of a threedimensional particle it is necessary either to use projections in real space or else to pass to the Fourier space (2.5.6.5).
Now consider real space. The projections are known and can be measured but angles of their rotation about vector (Fig. 2.5.6.8) are unknown and should be determined. Let us choose any two projections and and construct a set of onedimensional projections and by varying angles and . In accordance with Lemma 1, there exists a onedimensional projection, common for both and , which determines angles and along which and should be projected for obtaining the identical projection (Fig. 2.5.6.5). Comparing and and using the minimizing function it is possible to find such a common projection . (A similar consideration in Fourier space yields .)

Plane projections of a threedimensional body. The systems of coordinates in planes (a) and (b) are chosen independently of one another. 
The mutual spatial orientations of any three noncoplanar projection vectors , , can be found from three different twodimensional projections , and by comparing the following pairs of projections: and , and , and and , and by determining the corresponding , and . The determination of angles , and reduces to the construction of a trihedral angle formed by planes , and . Then the projections with the known can be complemented with other projections and the corresponding values of ω can be determined. Having a sufficient number of projections and knowing the orientations , it is possible to carry out the 3D reconstruction of the object [see (2.5.6.27); Orlov, 1975; Vainshtein & Goncharov, 1986a; Goncharov et al., 1987].
References
Bracewell, B. N. (1956). Strip integration in radio astronomy. Austr. J. Phys. 9, 198–217.Google ScholarCochran, W., Crick, F. H. C. & Vand, V. (1952). The structure of synthetic polypeptides. 1. The transform of atoms on a helix. Acta Cryst. 5, 581–586.Google Scholar
Crowther, R. A., Amos, L. A., Finch, J. T., DeRosier, D. J. & Klug, A. (1970). Three dimensional reconstruction of spherical viruses by Fourier synthesis from electron micrographs. Nature (London), 226, 421–425.Google Scholar
Crowther, R. A., DeRosier, D. J. & Klug, A. (1970). The reconstruction of a threedimensional structure from projections and its application to electron microscopy. Proc. R. Soc. London Ser. A, 317, 319–340.Google Scholar
Crowther, R. A. & Klug, A. (1974). Three dimensional image reconstruction on an extended field – a fast, stable algorithm. Nature (London), 251, 490–492.Google Scholar
DeRosier, D. J. & Klug, A. (1968). Reconstruction of three dimensional structures from electron micrographs. Nature (London), 217, 130–134.Google Scholar
DeRosier, D. J. & Moore, P. B. (1970). Reconstruction of threedimensional images from electron micrographs of structure with helical symmetry. J. Mol. Biol. 52, 355–369.Google Scholar
Deans, S. R. (1983). The Radon transform and some of its applications. New York: John Wiley.Google Scholar
Gilbert, P. F. C. (1972a). The reconstruction of a threedimensional structure from projections and its application to electron microscopy. II. Direct methods. Proc. R. Soc. London Ser. B, 182, 89–102.Google Scholar
Gilbert, P. F. C. (1972b). Iterative methods for the threedimensional reconstruction of an object from projections. J. Theor. Biol. 36, 105–117.Google Scholar
Goncharov, A. B. (1987). Integral geometry and 3Dreconstruction of arbitrarily oriented identical particles from their electron micrographs. Sov. Phys. Crystallogr. 32, 663–666.Google Scholar
Goncharov, A. B., Vainshtein, B. K., Ryskin, A. I. & Vagin, A. A. (1987). Threedimensional reconstruction of arbitrarily oriented identical particles from their electron photomicrographs. Sov. Phys. Crystallogr. 32, 504–509.Google Scholar
Gordon, R. (1974). A tutorial on ART (algebraic reconstruction techniques). IEEE Trans. Nucl. Sci. NS21, 78–93.Google Scholar
Gordon, R., Bender, R. & Herman, G. T. (1970). Algebraic reconstruction techniques (ART) for threedimensional electron microscopy and Xray photography. J. Theor. Biol. 29, 471–481.Google Scholar
Gordon, R. & Herman, G. T. (1971). Reconstruction of pictures from their projections. Commun. ACM, 14, 759–768.Google Scholar
Herman, G. T. (1980). Image reconstruction from projection: the fundamentals of computerized tomography. New York: Academic Press.Google Scholar
Hoppe, W. & Typke, D. (1979). Threedimensional reconstruction of aperiodic objects in electron microscopy. In Advances in structure research by diffraction method. Oxford: Pergamon Press.Google Scholar
Kam, Z. (1980). Threedimensional reconstruction of aperiodic objects. J. Theor. Biol. 82, 15–32.Google Scholar
Mersereau, R. M. & Oppenheim, A. V. (1974). Digital reconstruction of multidimensional signals from their projections. Proc. IEEE, 62(10), 1319–1338.Google Scholar
Orlov, S. S. (1975). Theory of threedimensional reconstruction. II. The recovery operator. Sov. Phys. Crystallogr. 20, 429–433.Google Scholar
Radermacher, M., McEwen, B. & Frank, J. (1987). Threedimensional reconstruction of asymmetrical object in standard and high voltage electron microscopy. Proc. Microscop. Soc. Canada, XII Annual Meet., pp. 4–5.Google Scholar
Radon, J. (1917). Über die Bestimmung von Funktionen durch ihre Integralwerte längs gewisser Mannigfaltigkeiten. (On the determination of functions from their integrals along certain manifolds). Ber. Verh. Saechs. Akad. Wiss. Leipzig Math. Phys. Kl. 69, 262–277.Google Scholar
Ramachandran, G. N. & Lakshminarayanan, A. V. (1971). Threedimensional reconstruction from radiographs and electron micrographs: application of convolutions instead of Fourier transforms. Proc. Natl Acad. Sci. USA, 68(9), 2236–2240.Google Scholar
Vainshtein, B. K. (1971a). The synthesis of projecting functions. Sov. Phys. Dokl. 16, 66–69.Google Scholar
Vainshtein, B. K. (1971b). Finding the structure of objects from projections. Sov. Phys. Crystallogr. 15, 781–787.Google Scholar
Vainshtein, B. K. (1978). Electron microscopical analysis of the threedimensional structure of biological macromolecules. In Advances in optical and electron microscopy, Vol. 7, edited by V. E. Cosslett & R. Barer, pp. 281–377. London: Academic Press.Google Scholar
Vainshtein, B. K., Barynin, V. V. & Gurskaya, G. V. (1968). The hexagonal crystalline structure of catalase and its molecular structure. Sov. Phys. Dokl. 13, 838–841.Google Scholar
Vainshtein, B. K. & Goncharov, A. B. (1986a). Determination of the spatial orientation of arbitrarily arranged identical particles of unknown structure from their projections. Sov. Phys. Dokl. 287, 278–283.Google Scholar
Vainshtein, B. K. & Goncharov, A. B. (1986b). Proceedings of the 11th International Congress on Electron Microscopy, Kyoto, Vol. 1, pp. 459–460.Google Scholar
Vainshtein, B. K. & Orlov, S. S. (1972). Theory of the recovery of functions from their projections. Sov. Phys. Crystallogr. 17, 213–216.Google Scholar
Vainshtein, B. K. & Orlov, S. S. (1974). General theory of direct 3D reconstruction. Proceedings of International Workshop, Brookhaven National Laboratory, pp. 158–164.Google Scholar
Van Heel, M. (1984). Multivariate statistical classification of noisy images (randomly oriented biological macromolecules). Ultramicroscopy, 13, 165–184.Google Scholar