one or more exemplary embodiments are now described in detail herein below . referring to the drawings , like numbers indicate like parts throughout the views . as used in the description herein , the meaning of “ a ,” “ an ,” and “ the ” includes plural reference unless the context clearly dictates otherwise . also , as used in the description herein , the meaning of “ in ” includes “ in ” and “ on ” unless the context clearly dictates otherwise . finally , as used in the description herein , the meanings of “ and ” and “ or ” include both the conjunctive and disjunctive and may be used interchangeably unless the context clearly dictates otherwise . ranges may be expressed herein as from “ about ” one particular value , and / or to “ about ” another particular value . when such a range is expressed , another embodiment includes from the one particular value and / or to the other particular value . similarly , when values are expressed as approximations , by use of the antecedent “ about ,” it will be understood that the particular value forms another embodiment . it will be further understood that the endpoints of each of the ranges are significant both in relation to the other endpoint , and independently of the other endpoint . fig1 illustrates one embodiment of the invented controlled cardiac ct / micro - ct system . based on the clinical requirements ( 010 ), the image quality requirements ( 020 ) are defmed , which are translated to the ct data requirements ( 030 ). then , a single -/ multi - slice / cone - beam data acquisition system ( 040 ) starts collecting data . the acquired data ( 050 ) is constantly processed by a data completeness checker ( 060 ) to reveal the data missing status in reference to the data requirements ( 030 ). the cardiac motion is monitored by an ecg device ( 070 ) which produces the cardiac motion signal ( 080 ). such a signal may be also extracted from a data / image analyzer ( 090 ). based on the cardiac motion signal ( 080 ), the cardiac motion pattern will be estimated / predicted ( 100 ). if a sufficient amount of data has not been accumulated yet , according to our source rotation control strategy ( 110 ) the x - ray source will be optimally steered as needed by a control servo system ( 120 ) at variable source rotation velocity / acceleration ( 130 ) to fill in the missing data gaps . finally , image reconstruction ( 140 ) is completed for further analysis and visualization ( 150 ) the computing resources are important for implementation of these functionalities but they are not shown here for clarity . fig2 illustrates one embodiment of the source rotation control strategy . the objective functions ( 010 ) are set in reference to a cardiac motion model ( 020 ). the off - line design ( 030 ) is first performed to optimize the control strategies in various settings based on the cardiac motion modeling . the real - time estimation / prediction ( 040 ) is done to capture the trend of the cardiac motion , which is also based on the cardiac motion modeling . the off - line optimized guidelines and the real - time analysis results are combined to update our control law ( 050 ) in real - time . under the control law , the control servo system ( 060 ) steers the source rotation at a variable velocity / acceleration ( 070 ) for the data acquisition system ( 080 ) to collect projection data up to the ct data requirements . at any instant , acquired data ( 090 ) are analyzed by the data completeness checker ( 100 ) to update the data completeness status ( 110 ) with respect to the ct data requirements ( 200 ). the computing resources are not shown here for brevity . it is underlined that the references of ours cited therein provide more details on our invention , and should be considered as an integrated part of this patent application . also , the description in this part should be interpreted as exemplary instead of restrictive . we recognize that variants of this technology are clearly possible in the spirit of this application including the cited references of ours . it is recognized that the cardiac motion is certainly complicated by other factors , especially the respiratory motion . however , in the following we will focus on the cardiac motion for two reasons . primarily , the methodology applies similarly to the combination of cardiac and respiratory motions . secondarily , the cardiac motion is the dominating component to be corrected for cardiac ct in most cases . for a heart in a periodic cardiac motion , it is relatively easy to verify its regularity and estimate its period . for example , it can be done based on analysis of a number of cycles of the ecg signal of a patient . we will start with the case of periodic motion based control as a basis for quasi - periodic motion based control , which is our desired model for cardiac ct . to convey the idea without unnecessary complication , we will first focus on one volume level in the expanding or contracting phase of the cardiac motion . let v ( t )= v ( t + p ) be the heart volume which is periodic with period p , and v ( t 1 ) be the heart level that we are interested in and a l = l - 1 p ⁢ 2 ⁢ π , l = 1 , … ⁢ , p , be p angles at which the x - ray source has to be on . it is recognized that for the same cardiac volume level the structure of the heart may be slightly different in the expanding and contracting phases . without loss of generality , we assume such a difference is substantial and requires that the heart of a pre - specified volume must be sampled at the consistent phases of the ecg signal ; for example , in the expanding phase . ideally , the problem might be solved with a pre - specified constant source velocity . recall that v ( t )= v ( t + p ) is the heart volume , α l , l = 1 , 2 , . . . , p , p evenly spaced angles and s ( t ) the source angle . the problem we have to solve is to find a source angle profile s ( t ) as a function of time t so that for every angle α l , l = 1 , . . . , p , at every level v ( t i ), i = 1 . . . , q , there exists a time t such that s ( t )= α l and v ( t )= v ( t i ). in other words , the source angle is α l when the volume level is at v ( t i ) for all i , l or the source covers all the angles α l , l = 1 , . . . , p exactly at all the levels v ( t i ), i = 1 , . . . , q . fig3 illustrates cardiac ct implemented using a constant source rotation velocity and the same set of projection angles for all the volumetric levels of interest . further , among all the solutions s ( t )&# 39 ; s , find one that requires the minimum time to cover all the angles for all the levels . however , the solution may not always exist in this setting . to illustrate the situation , we provide a numerical example . let the heart volume be v ( t )= r 1 + r 2 sin ( 2πt ) for some constants r 1 , r 2 . suppose the volume that we are interested in is at r 1 + r 2 sin [ 2π ( 5 / 8 + m )] for integers m . without lose of generality , let us assume that the x - ray source must scan the given heart volume at only three angles 2π ( l − 1 )/ 3 , l = 1 , 2 , 3 . this implies for some integers m , α ( 5 / 8 + m )= 2π ( l − 1 )/ 3 , mod 2π , l = 1 , 2 , 3 . it can be shown that there does not exist a constant x - ray source s ( t )= αt so that these equations can be satisfied simultaneously . approximate solutions however do exist . to quantify the error , for example , we can define the minimum error between the given angles and all possible x - ray source angles at a given heart volume as e = min min max { α ( 5 / 8 + m )− 2π ( l − 1 )/ 3 , mod 2π }. the following table shows e , converted to degrees , and the corresponding optimal α * and m *. for instance , if the error is required to be no larger than 0 . 631 °, the optimal α *, among all possible values ( 0 , ∞), is 0 . 316π that achieves the minimum error at t = 82 + 5 / 8 ( unit : s ). clearly , the more accurate the solution is , the longer time the scan takes . the optimal α * is not unique . for example , α *+ 2kπ is also a solution , but the minimum time m + 5 / 8 is unique . nevertheless , we can modify the problem to allow a constant but not pre - fixed velocity solution with some angle offsets : to find a source angle profile s ( t ) as a fuinction of time t so that for every i at every l , there exists a corresponding time t such that s ( t )= α i , l = α l + θ i , and v ( t )= v ( t i ), where θ i is independent of the level 1 . the interpretation is that at any level i , projections have to be taken at p evenly spaced angles α i , l = α l + θ i , l = 1 , . . . , p , with the same offset angle θ i . however , the offset angle θ i at level i may not be the same as the offset angle θ j at level j . further , among all the solutions s ( t )&# 39 ; s , find one that requires the minimum time . as shown in fig4 , the difference lies in that it does not require that p evenly spaced angles at level i are the same as that at level j . this makes perfect sense for practical applications where an accurate image of level v ( t i ) is reconstructed as long as p is large enough independent of whether the same angles are used for levels i and j . surprisingly , this seeming complicated problem is solved by a constant source rotation velocity s ( t ) [ 9 - 11 ]. let k & gt ; 0 be any integer and consider s ⁡ ( t ) = 2 ⁢ π p ⁢ ⁢ pk ⁢ t . then s ( t i +( l − 1 ) pk )= α i , l = α l + θ i . this solves the problem , and further the solution with k = 1 is the minimum time solution . the solution can be also implemented by using a variable velocity x - ray source rotation [ 9 - 11 ]. the requirement is s ( t 1 +( l − 1 ) kp )= α l which is an interpolation problem and k balances the minimum time needed and the maximum velocity and acceleration . one solution would be a polynomial solution s ( t )= s 0 + s 1 t + s 2 t 2 + . . . + s p − 1 t p − 1 so that s 0 + s 1 t 1 + s 2 t 1 2 + . . . + s p − 1 t 1 p − 1 = α 1 s 0 + s 1 ( t 1 +( l − 2 ) kp )+ s 2 ( t 1 +( l − 2 ) kp ) 2 + . . . + s p − 1 ( t 1 +( l − 2 ) kp ) p − 1 = α l − 1 s 0 + s 1 ( t 1 +( l − 1 ) kp )+ s 2 ( t 1 +( l − 1 ) kp ) 2 + . . . + s p − 1 ( t 1 +( l − 1 ) kp ) p − 1 = α l this polynomial solution not only matches exact p angles precisely at the level v ( t l ) but also reaches the minimum time when k = 1 . recall again that a polynomial solution is just an example and any other function can also be a solution as long as the required interpolation condition is satisfied . recall v ( t ) is periodic if and only if it is completely determined by the first period , i . e ., v ( t )| tε [( l − 1 ) p , lp ) = v ( t −( l − 1 ) p )| t −( l − 1 ) pε [ 0 , p ) . in reality , the cardiac motion may not be periodic , especially under diseased conditions . as a result , the period for one cycle may be different from that for another cycle . however , despite the variability in the period , the cardiac volume usually alternates between the minimum and maximum volumes monotonically . approximately , such a motion pattern can be characterized by a class of functions referred to as quasi - periodic functions . let 0 = p 0 & lt ; p 1 & lt ; . . . & lt ; p l & lt ; . . . be a monotonically increasing sequence [ 9 - 11 ). the interval p l + 1 − p l is the lth “ period ” of v ( t ), i . e ., the time to finish that specific cycle . in general , p l + 1 − p l ≠ p l − p l − 1 . however , we assume that v ( t ) is completely determined by its first period in the following sense . let v 1 ( t )= v ( t ), tε [ 0 , p 1 ), then v ⁡ ( t ) = v 1 ⁡ ( p 1 - p 0 p l - p l - 1 ⁢ ( t - p l - 1 ) ) t ∈ [ p l - 1 , p l ) . simply put , v ( t ) is not periodic but its appropriately scaled ( expanded or compressed ) version matches its profile in the first period . for such functions , the techniques that we proposed for period functions can be applied with some modifications . in particular , if all the periods are known , a control scheme can be calculated α priori that determines the order and time when the angle α l at the level v ( t i ) should be imaged . the problem is that periods of a quasi - periodic heart motion are unknown α priori and have to be estimated online . nevertheless , all the related major issues can be addressed as follows . how to estimate periods ? suppose the period is time varying but does not change too much from one cycle to the next , it can be estimated , for example , from the ecg signal . keep in mind , however , measurement of ecg signals is always corrupted by noises with some delay . this affects the accuracy of the estimation but can be addressed properly . for example , this can be done as follows . suppose for the ( 1 − 1 ) th period , p l − 1 − p l − 2 , is estimated , and we need to estimate the lth period p l − p l − 1 or equivalently to estimate v ⁡ ( t _ ) = v 1 ⁡ ( 1 α l ⁢ ( t _ - p l - 1 ) ) is obtained , this gives rise to the information on α 1 =( t − p l − 1 )/ v − 1 ( v ( t )), where v − 1 denotes the inverse function of v . to reduce the effect of noises , an average of several such observations could be used . if necessary , other estimation and prediction algorithms that were developed by our and other teams [ 13 - 19 ] can be used for controlled cardiac ct , including algorithms that are based on the hammerstein model , non - parametric model , variable gain model and approximate linear model , respectively . how to design the control scheme based on the estimates of the periods ? there is always a chance that the estimation error is larger than a pre - specified threshold . for example , if in the lth period the error is larger than the threshold , then the images taken during the lth period may become non - usable . we can address this problem using a monitoring mechanism in the system that compares the predicted and the actual periods ( see fig1 ). if a larger error occurs , the missing data has to be re - sampled later , using a similar control scheme to fill in the missing gaps in the desirable projection region . in the solution given above , the variable k is used to balance the minimum time required and maximum velocity and acceleration . as k increases , the required scan time increases and at the same time the maximum velocity / acceleration of s ( t ) decreases . addition of k in the solution is important practically . there are other ways to take velocity / acceleration into consideration . for instance , instead of usual polynomial interpolation , fejer or other similar types of interpolations [ 12 , 13 ] that have constraints on its derivatives can be used . since velocity and acceleration are the first and second order derivatives respectively , fejer interpolation effectively sets constraints on the allowable maximum velocity and acceleration . it is important to emphasize again that hardware constraints must be taken into account in practice , which can be achieved by balancing between the scan time and the maximum speed and maximum acceleration using our invented control scheme , as demonstrated in fig5 . to illustrate the method , we give an example . consider a heart volume v ( t )= 1 − 0 . 5 cos ( 2 * π * t / h ) for three unknown periods h = 1 , 0 . 8 and 0 . 5 . this is no longer periodic but quasi - periodic . suppose that three heart levels v ( t )= 0 . 64 , 1 , 1 . 5 and three angles at 0 , 120 and 240 degrees are specified . by using the method discussed above with a polynomial interpolation , fig6 shows the computer simulation results based on the estimates of the unknown periods . clearly , all angles at all three heart levels are imaged . the goal is achieved using a variable velocity x - ray source . a constant velocity x - ray source either fails to do that or takes too long to even have a good approximate solution , as discussed in table 1 . as another embodiment , ccct can be implemented using a monitoring mechanism and a control mechanism which is manual , semi - automatic , automatic , or in a mixed mode . this combined setup monitors the data completeness status , identifies missing projections , and adjusts the source rotation velocity / acceleration of the scanner to make up these missing projections in an optimal or heuristic way . for example , for each cardiac state / phase , covered and not covered projection angles can be graphically displayed in a disk like panel , in which the current heart motion frequency , the source angular position , velocity and acceleration can be also graphically shown . based on this type of visual input , an operator can steer the source using , for example , arrow keys , subject to some physical constraints . a sample heuristic rule for steering the source is that the source be driven to cover the missing projection that takes the shortest source motion time . more sophisticated rules can be constructed in a similar spirit . this type of rules can be automated as preferred ; for example , using an interpolation based control strategy similar to what discussed above . another very useful problem is the non - exact matching case . in the above analysis , the p angles have to be exactly covered at q volume levels . in reality , the exact angles and precise levels are not necessary as long as the errors are small . by non - exact matching , we mean at each level images do not have to reflect the volume level v ( α l t i + p l − 1 ) exactly as long as it stays in a small neighborhood of the targeted level . likewise , the projection angles do not have to be exactly at α l but it suffices for them to be in a small neighborhood of α l . to this end , at each level i , let the error bound δ & gt ; 0 , the corresponding times t − i , l ≦ t i ≦ t i , l can be found max t ∈ [ α l ⁢ t _ i , l + p l - 1 , ⁢ α l ⁢ t ⁢ _ ⁢ i , l + p l - 1 ] ⁢  v ⁡ ( t ) - v ⁡ ( α l ⁢ t i + p l - 1 )  ≤ δ furthermore , let us define τ i , l =[ α l t i , l + p l − 1 , α l t i , l + p l − 1 ] and δ i , l ⁡ [ min ⁢ ⁢ v ⁡ ( t ) t ∈ [ α l ⁢ t _ i , l + p l - 1 , ⁢ α l ⁢ t _ i , l + p l - 1 ] , max ⁢ ⁢ v ⁡ ( t ) ] the neighborhood of v ( α l t i + p l − 1 ) is δ i , l with the corresponding permissible time interval τ i , l . that is , v ( α l t i + p l − 1 ) εδ i , l , max tετ i , l | v ( t )− v ( α l t i + p l − 1 )|≦ δ and τ i , l is the time interval in which the images for any angle α l , l = 1 , . . . , p can be taken at the level i . by the same token , in a non - exact case , the volume level i can be defined as l i ={ δ i , l , δ i , 2 , . . . , δ i , p . . . } or in time domain t i ={ τ i , l , τ i , 2 , . . . , τ i , p , . . . }. now , the non - exact match problem can be defined as to find an x - ray source angular position s ( t ) that satisfies | s ( t )− α l ≦ ε for tετ i , k , where ε & gt ; 0 is the given angle error bound . the resultant s ( t ) significantly reduces the total data acquisition time needed to cover all the projection angles at each of the desired cardiac volume levels . the control algorithms can be accordingly developed , for example , using the numerical search technique . as far as the imaging part is concerned , we may , for example , use the feldkamp cone - beam image reconstruction algorithm or the generalized feldkamp algorithm developed by wang et al . [ 20 , 21 ]. this algorithm can be written as : g ⁡ ( x , y , z ) = 1 2 ⁢ ∫ 0 2 ⁢ π ⁢ d 2 ⁡ ( β ) ( d ⁡ ( β ) - s ) 2 ⁢ ∫ - ∞ ∞ ⁢ r ⁡ ( p , ζ , β ) ⁢ h ⁡ ( d ⁡ ( β ) ⁢ t d ⁡ ( β ) - s - p ) ⁢ d ⁡ ( β ) d 2 ⁡ ( β ) + p 2 + ζ 2 ⁢ ⅆ p ⁢ ⅆ β , where g ( x , y , z ) is a reconstructed 3d image , d ( β ) the distance between the source and the z - axis of the reconstruction system in which an object of interest η ( x , y , z ) is located , β the source rotation angle relative to the z axis , r ( p , ζ , β ) cone - beam projection data of η ( x , y , z ), { ζ = d ⁡ ( β ) ⁢ z d ⁡ ( β ) - s p = d ⁡ ( β ) ⁢ t d ⁡ ( β ) - s , ⁢ ⁢ { t = x ⁢ ⁢ cos ⁢ ⁢ β - y ⁢ ⁢ sin ⁢ ⁢ β s = - x ⁢ ⁢ sin ⁢ ⁢ β - y ⁢ ⁢ cos ⁢ ⁢ β . let us assume that d ( β ) is a constant , the approximate reconstruction procedure consists of the following three steps : r ′ ⁡ ( p , ζ , β ) = d d 2 + p 2 + ζ 2 ⁢ r ⁡ ( p , ζ , β ) ; 2 . filter the weighted data q ( p , ζ , β )= r ′( p , ζ , β )* h ( p ); 3 . back - project the filtered data g ⁡ ( x , y , z ) = ∫ 0 2 ⁢ π ⁢ d 2 2 ⁢ ( d - s ) 2 ⁢ q ⁡ ( p , ζ , β ) ⁢ ⁢ ⅆ β . note that the algorithm does allow non - uniform sampling patterns along a scanning circle . if the cone - angle becomes larger , more sophisticated algorithms may be adapted , including both exact and approximate algorithms with circular or non - circular scanning trajectories [ 22 - 28 ]. collected ccct data can be fed into any representative ct algorithm after appropriate data interpolation , which should be clear to those who practice in the cardiac ct field . as another example of the method for cone - beam ccct , we can use the grangeat - type reconstruction [ 29 - 31 ]. while traditional half - scan cone - beam algorithms are in the feldkamp framework , grangeat - type half - scan cone - beam algorithms were subsequently developed in the circular and helical scanning cases . the grangeat - type framework promises better quality reconstruction from an incomplete dataset after missing data are appropriately estimated . we can utilize the explicit radon space information available in collected cone - beam data , perform appropriate data filling in the shadow zone of the radon space , and suppress various artifacts in the final reconstruction . in the circular half - scan case , the original grangeat formula can be modified into the following half - scan version : ∂ ∂ ρ ⁢ rf ( ρ ⁢ h ρ ) = ∑ i = 1 2 ⁢ ω i ( ρ ⁢ h ρ ) ⁢ 1 cos 2 ⁢ β ⁢ ∂ ∂ s ⁢ ∫ - ∞ ∞ ⁢ so sa ⁢ xf ( s ( ρ ⁢ h ρ ) ) ⁢ ⁢ ⅆ t , where ⁢ ⁢ ψ 1 ⁡ ( ρ , θ , φ ) = φ + sin - 1 ⁡ [ ρ so ⁢ ⁢ sin ⁢ ⁢ θ ] ⁢ ⁢ and ⁢ ⁢ ψ 2 ⁡ ( ρ , θ , φ ) = φ + π - sin - 1 ⁡ [ ρ so ⁢ ⁢ sin ⁢ ⁢ θ ] are two meridian plane angle functions , depending on a characteristic point in the radon space . the scanning angle ψ varies from 0 to π + 2γ m , where γ m is the cone angle . in the circular full - scan case , for any characteristic point not in the shadow zone or not on its surface there exist a pair of detector planes specified by the above two angle functions . however , in our half - scan case , such the dual planes are not always possible . when the dual planes are found , we are in a doubly sampled zone . when one of them is missing due to the half - scan , we are in a singly sampled zone . relative to the full - scan , the shadow zone is increased due to the reduction in the amount of cone - beam data . in the context of this invention , we can adapt our half - scan grangeat - type reconstruction for cardiac ct from data collected from x - ray source positions pseudo - randomly distributed along a scanning circle . this adaptation can be done in a number of ways . the main challenge is how to design an best interpolation strategy so that the image quality , especially the temporal resolution , can be optimized . for that purpose , we can model time - varying radon information as well to interpolate for missing data in both the spatial and temporal domains . we can use various linear interpolation methods to estimate missing derivative data in the shadow zone corresponding to different cardiac phases . the heuristics behind our choice of the linear interpolation approach is that the derivative radon data of many geometrically regular objects , such as ellipsoids or tetrahedrons , are piece - wise linear along the ρ direction . therefore , the linear interpolation method may help effectively recover missing data . then , we can equip this interpolation method with some adaptive capabilities . that is , the slope of the linear interpolation may vary region by region , according to prior knowledge such as the boundaries of the cardiac components derived from earlier reconstructions . the essential feature of this invention is to synchronize the x - ray source rotation and data acquisition optimally so that the dynamics of a beating heart can be captured with minimum distortion . this type of synchronization can be implemented in a number of ways . in a simple case , we can use an arbitrary but pre - selected source rotation speed . in a complicated case , we have to steer the source rotation with a variable acceleration . without loss of generality , here we compare the results obtained with the existing schemes of fixed source rotation with our method for a selectable source rotation speed [ 11 ]. several numerical tests were carried out using two cardiac phantoms . they are the thorax phantom and the motion phantom . the thorax phantom consists of parts simulating lungs , heart , aorta , ribs , spine , stemum and shoulders , respectively . there are 271 basic objects in the thorax phantom , constructed by spheres , cylinders and boxes . a heart is represented by an ellipsoid . the aorta is composed of three cylinders . for simplicity , the motion of the left ventricle is divided into 5 phases , depicting the basic systolic and diastolic processes , as shown in fig7 . the period of the cardiac motion was set to 0 . 6 s with the motion amplitude being 1 cm along each semi - axis of the heart . the motion phantom contained three rows of cylindrical patterns with the diameters being 1 , 2 and 3 mm , respectively . the second and fourth columns were vibrated along the horizontal direction according to a sinusoid function . the motion amplitude was set to 5 mm . other relevant parameters include the heart period p = 0 . 6 s , the source rotation period pc = 0 . 5 s , temporal resolution τ = 0 . 1 s , allowing an ideal combination of data segments . the circular fan - beam scanning was assumed with 400 detectors and 360 views . a 256 by 256 image matrix was reconstructed . for the thorax phantom , the reconstructed images are shown in fig8 corresponding to the cases of multi - segment combinations without any gap (( a ), τ = 0 . 1 s ), with gaps (( b ), τ & lt ; 0 . 1 ) and with overlaps (( c ), τ & gt ; 0 . 1 s ), respectively . fig8 ( a ) has the best quality because the data segment was ideal . fig8 ( b ) is unsatisfactory and fig8 ( c ) is a little distorted due to the gaps and overlaps . this simulation shows that better image can be only achieved in ideal segment . however , temporal resolution is fixed and can not be improved in current cardiac ct . in the following , three types of simulations are provided . in the first simulation , we consider the ge light speed scanner because it can offer more choices of velocities than others . six fixed velocities were assumed , including 0 . 5 , 0 . 6 , 0 . 7 , 0 . 8 , 0 . 9 , and 1 . 0 s per rotation as provided by ge light speed scanner . we reconstructed images based on these velocities and compared these images with ones reconstructed using the selectable velocity method . when the heart rate was 78 bpm ( beats per minute ) or the heart period was 0 . 77 s . none of the available source rotation periods 0 . 5 s , 0 . 6 s , 0 . 7 s , 0 . 8 s , 0 . 9 s and 1 . 0 s would provide an ideal multi - segment combination . hence , needed multi - segments must be assembled from available data after necessary interpolation . fig9 shows the reconstructed image over 5 heart cycles using the current and our methods . clearly , the image reconstructed by the selected rotation period 0 . 43 s ( fig9 ( g )) is better than the images reconstructed with fixed periods 0 . 5 , 0 . 6 , 0 . 7 , 0 . 8 , 0 . 9 , and 1 . 0 s ( fig9 ( a )-( f )) because the data segment was ideal with the selected source rotation velocity but it was not ideal with any fixed velocity . in the second simulation , different velocities were selected by selecting different adjustable parameters in the formula . generally speaking , the faster the rotation is , the better the temporal resolution is . for example , the setting of p = 0 . 6 s , and m = 1 implies pc = 0 . 5 s . if m = 2 , then pc = 0 . 33 s . some representative images are illustrated in fig1 . fig1 ( b ) is better than fig1 ( a ) because the velocity was higher for fig1 ( b ). on the other hand , commercially available ct scanners do not allow a free choice of the source rotation velocity to optimize the image quality . the simulation shows that faster rotation would result in better images using our method . in the third simulation , the images were reconstructed from scans of different temporal spans . for a fixed source rotation velocity , even if ideal data segments were collected , temporal resolution would not be improved further by prolonging the scanning time . on the other hand , a higher temporal resolution would be achieved using our selectable velocity method if a longer scanning time was taken . some simulation results are given in fig1 . fig1 ( b ) is much better than fig1 ( a ) because the total time was longer for fig1 ( b ). this example shows that the image quality can always be improved by extending the total scanning time for our selectable velocity method . on the other hand , the image quality could not be improved by prolonging scanning time for a fixed velocity ct . as mentioned above and demonstrated in our simulation , the our variable velocity method also outperformed the fixed velocity method in terms of image quality in both the periodic and quasi - periodic cases . the major merits with the variable velocity method include a significantly reduced scanning time and a unique capability of dealing with a quasi - periodic cardiac motion pattern . once a better dataset is collected using any of our proposed techniques , the image reconstruction process is not so much different , since it is nothing but image reconstruction from projections at pseudo - randomly distributed angular positions . therefore , we will not present more simulation results here , for sake of brevity .
cardiac computed tomography has been a hot topic for years because of the clinical importance of cardiac diseases and the rapid evolution of ct systems . in this application , we disclose a novel strategy for controlled cardiac ct that may effectively reduce image artifacts due to cardiac and respiratory motions and reduce the scan time . our approach is radically different from existing ones and is based on controlling the x - ray source rotation velocity and powering status in reference to the cardiac motion . by such a control - based intervention the data acquisition process can be optimized for cardiac ct in the cases of periodic and quasi - periodic cardiac motions . specifically , we present the corresponding coordination / control schemes for either exact or approximate matches between the ideal and actual source positions .