EP2770911A1

EP2770911A1 - Method for producing optimised tomography images

Info

Publication number: EP2770911A1
Application number: EP12784529.5A
Authority: EP
Inventors: Frank-Detlef Scholle; Joachim Hütter
Original assignee: Piramal Imaging SA
Current assignee: Life Molecular Imaging SA
Priority date: 2011-10-25
Filing date: 2012-10-24
Publication date: 2014-09-03
Also published as: IL231618A0; CA2853188A1; CN104144649A; ZA201402962B; DE102011085180A1; IN2014MN00918A; AU2012330480A1; MX2014004276A; RU2014120903A; US20140294275A1; BR112014009244A2; SG11201401780UA; BR112014009244A8; KR20140131500A; JP2014535048A; WO2013060716A1

Abstract

The present invention relates to the technical field of imaging methods, in particular for diagnostic purposes. The subject matter of the present invention is a method for producing optimised tomography images, a computer program product for performing the method according to the invention on a computer, and the optimised images produced by means of the method according to the invention.

Description

Method for generating optimized tomography images

The present invention relates to the technical field of imaging methods, in particular for diagnostic purposes. The present invention is a method for generating optimized tomography images, a computer program product for performing the erfmdungs proper method on a computer and the generated by means of the inventive method optimized recordings.

In today's medicine, various imaging techniques are used to visualize anatomical and functional structures in living humans or animals and thus to assess the state of health. In contrast to the projection methods, such as the usual X-ray image, in which structures which lie one behind the other in the beam path of the X-rays are superimposed in the image, tomographic methods allow the generation of sectional images and three-dimensional representations (3D images). A sectional image reflects the internal structures of the examined body as they were after cutting out a thin layer. A 3D representation shows how the examined structures are spatially present.

In computed tomography (CT), for example, X-ray absorption profiles of the body to be examined are generated from many directions. From these absorption profiles, the degree of absorption can then be calculated for each volume element of the body and sectional images and 3D representations can be constructed.

While the morphological / anatomical structure of a body can be represented by means of computed tomography, optionally with the use of contrast agents, positron emission tomography (PET), for example, allows the representation of biochemical functionalities of an organism. In PET, a radiolabeled tracer is applied to the body of a patient. The tracer selectively binds to certain biomolecules, and by recording the radiation emitted by the tracer, the activity of the biomolecules in the body can be visualized. After the administration of a tracer it takes some time until the tracer has reached a desired distribution in the body. Usually, the tracer is administered intravenously and thus passes through the bloodstream to the desired destination. Some of the administered tracer molecules bind specifically to the desired target areas, while another part distributes nonspecifically. In order to obtain tomography images with a high signal-to-noise ratio, it often makes sense to wait after the administration with the images until a large part of the nonspecifically binding or distributed tracer molecules has left the body region to be examined, since the non-specifically binding tracer molecules contribute to the background signal in the PET frames.

Depending on the tracer used and the physiological parameters of the patient being examined, there is a window of time after administration of the tracer and before its removal from the body region under consideration or its metabolic degradation, in which an optimal signal-to-noise ratio can be achieved.

Taking PET scans takes some time because positron emission tomography is based on the detection of a variety of annihilation events. The more events that are registered, the higher the number of data used for reconstruction and the higher the signal-to-noise ratio. The number of events can in principle be influenced both by the amount of tracer administered and the duration of the scan.

The burden of the body with radioactive substances should, however, be kept as low as possible in order to avoid side effects. To minimize side effects, the amount of tracer administered should therefore be minimized.

The extension of the scan duration is also limited. On the one hand, the examined body area should not move during the recording, since movements in the recordings lead to a false representation of the tracer distribution. Motionless persistence, however, is a burden on the patient. Some movements, such as respiratory movements and cardiac muscle movements, can not be avoided when taking measurements on living organisms. On the other hand, restrict factors such as the half-life of the radioactive isotopes of the tracer and / or the degradation of the tracer in the body its temporal detectability and / or informative value

Many different factors play a role in the development of a new tracer. The aim of the development is to provide a tracer that delivers specific biochemical information to the body under investigation with a high signal-to-noise ratio and low body stress. Any increase in the signal-to-noise ratio resulting from improvements in the measurement and containment technique would be a valuable contribution that could help minimize the burden on the body of a tracer.

The above considerations apply in a similar manner to other Tomo grafic- method, in particular for those methods in which aids for signal generation or signal amplification such as tracers, contrast agents or fluorescent dyes are administered to the body to be examined.

It would be desirable to be able to produce high signal-to-noise ratio tomography scans with the patient being exposed to both the dose of radiation exposed to the body and / or the amount of adjuvant applied and the duration of the study minimize

The previous considerations were primarily related to the generation of static

Snapshots of anatomical and / or functional structures.

However, they are also particularly true for the temporal tracking of processes in a body, where body here includes both the body of a human or animal and a lifeless object such as a measuring phantom or a material sample. In the case of the production of images, the dynamic behavior represent an applied aid in a body area, measurements are made on the considered body area over a longer period. From this valuable information on the timing of physiological processes can be gained.

Subsequently, the measurement data are subdivided into a plurality of time ranges, the signal intensities in each volume element are determined for the individual time ranges and a signal intensity-time curve is generated Here, the problem arises that the subdivision of the total measurement time into increasingly shorter sections leads to an increasingly higher temporal resolution, but the shortening of the time periods results in a more noisy signal.

So you get either a high spatial resolution with low noise with little or no temporal information or a high temporal resolution at low spatial resolution.

It would therefore be desirable to be able to compensate at least partially for the loss of spatial resolution due to the increase in temporal resolution.

According to the invention, the stated tasks are achieved by linking the spatial measurement data with associated temporal information, taking into account physiological boundary conditions

A first subject of the present invention is a method for generating optimized tomography images, comprising at least the steps:

a) providing a data set representing an area in the body of a patient during a measurement time, the representation of the body area in the data set being subdivided into a plurality of discrete subareas, wherein the measurement time in the data set is subdivided into a plurality of discrete measurement intervals, each one Partial area is assigned to each measurement interval, a discrete Stnikturwert;

b) establishing boundary conditions about the expected time course of a structure size in the region of the body during the measurement time; c) calculating optimized structure values for each individual subarea on the basis of structural values of the individual subarea at temporally successive measurement intervals taking into account the boundary conditions; d) Output of an optimized data set which represents an area in the body at arbitrary times during the measurement time and which is based on the optimized structure values A tomographic image is understood to mean a set of data that represents an area in a body during a period of time. The term tomography image should not be limited to a sectional image but should also include data sets that represent a body area in three dimensions. The representation of the body region is based on a feature size and corresponding structure values, which are described in more detail below.

The method according to the invention comprises at least the following steps: a) provision of a data record which represents an area in a body during a measuring time, the representation of the body area in the data set being subdivided into a plurality of discrete partial areas, the measuring time in the data set being in a plurality is subdivided by discrete measurement intervals, wherein each subregion is assigned a discrete structure value for each measurement interval; b) establishing boundary conditions about the expected time course of a structure size in the region of the body during the measurement time; c) calculating optimized structure values for each individual subarea on the basis of structural values of the individual subarea at temporally successive measurement intervals taking into account the boundary conditions; d) Output of an optimized data set which represents the body or an area in the body at arbitrary times within the measuring time and which is based on the optimized structure values.

The method according to the invention generates from a first data set, which represents an area in a body during a measuring time, a second, optimized data set, which represents an area in the body during freely selectable times within the measuring time

The second, optimized data record is characterized by the following points: the noise component is reduced compared to the first data set,

Image blurs, such as are inevitable in longer scans, are reduced, and the spatial resolution is closer to the physical resolution of the

Scan device,

Displacements, compressions, strains, rotations, etc., which may be contained in the first data set during the measuring time, are generally reduced,

representations of the body area can be generated at freely selectable times within the measuring time,

it is possible to specifically emphasize or suppress morphological and / or physiological functions.

The first data set results from measurements made on a human or animal or other body. Preferably, the measurements have been made on a living organism.

For example, the first set of data is a sequence of PET reconstructions, CT scans, MRI scans, or comparable scans. Each single shot was taken within one measurement interval. The sequence shows the frames at successive time intervals or measuring intervals. The terms "sequence" and "time sequence" are used synonymously here.

All measuring intervals combined give the measuring time

The first and second data sets may be a three-dimensional representation. However, it can also be a two-dimensional representation, ie a sectional image. Regardless of whether it is a two- or three-dimensional representation, the following also refers to the representation of a spatial area. The representation of the spatial area in the dataset is quantized, that is, the spatial area is divided into a discrete number of partial areas (area elements or volume elements), each individual area being characterized by its coordinates in space Do not change measuring time. They do not change when the area of the body is at the Recording of the measured values for generating the first data record during the measuring time with respect to the measuring device has not been moved. First, for the sake of simplicity, it is assumed that neither movement of the region nor movements within the region of the body took place during the measurement time, so that the coordinates of the individual subregions are constant during the measurement time.

A structure value is assigned to the individual subareas for each measurement interval. The structure values characterize the state of the subarea within the considered measurement interval. The state of each subarea is determined by a number of quantities. At least one size, referred to herein as a feature size, is considered in the method of the invention. It is also conceivable to consider multiple magnitudes. For example, feature sizes may be such as X-ray absorption (CT), number of decay events per time (PET), MR relaxation times, and so on. To clarify the above definitions, the computer tomography and the positron emission Tomo graphy are exemplified. Computed tomography images are spatial data sets composed of a discrete number of volume elements, each individual volume element being characterized by its coordinates in space and an absorption value. Usually the absorption width represents a gray level, for example "black" having the lowest absorption level (gray level 0). and "white" represents the highest degree of absorption (eg gray level 99 in the case of 100 gray levels). Thus, the spatial data sets can be represented graphically. The feature size considered in the case of CT is the absorbance of the tissue for X-radiation.

In the case of PET, the decays of the radionuclides used are detected over the measurement time. The spatial data sets can then be reconstructed for any time intervals which subdivide the entire measuring time. Every single volume element is characterized by its coordinates in space and a decay rate. The method according to the invention requires a plurality of spatial data sets, each of which represents the state of the examined body region at a time interval from one another. The temporal distance from each other can be constant or variable; It is important that the time interval between each other and the duration of the individual data records are known. Furthermore, the time intervals and the durations are either during the measurement or, as in the case of PET, to be chosen during the reconstruction in such a way that the temporal changes of the considered structural value of interest are temporally resolved. The time intervals and the durations should therefore be smaller than the considered temporal changes of the structure value.

Step a) of the method according to the invention represents the provision of a first data set. Since this data set results from measurements, i. has been empirically generated, it has a noise component. In particular, PET frames have a significant noise due to the statistics of the decay events, which is the higher the shorter the period of time during which annihilation events are registered to produce a PET shot.

The reduction of the noise component succeeds according to the invention by linking the spatial measurement data with the associated temporal information taking into account physiological boundary conditions.

These boundary conditions are set up in step b) of the process according to the invention. Step b) can take place before or after step a), i. the designation of the steps with a) and b) does not necessarily mean that first step a) and then step b) takes place

The boundary conditions determine the laws that follow the temporal course of the structure size in the area of the body. The temporal course of the structure size is not arbitrary but it inevitably follows the laws, for example, by the anatomy, morphology and / or physiology of the body area and use a tracer or contrast agent are determined by the physical and chemical properties of the tracer or contrast agent. For example, it is extremely unlikely that the absorbance in the computer tomography of a patient increases and decreases oscillatory as a structure size after a single application of a contrast agent

If a tracer or contrast agent is administered, it will enter the body region under consideration and leave it again after a dwell time. Apart from recirculation peaks, the metrological tracking of the tracer or contrast agent should therefore show an increase in signal with subsequent signal decay (Main peak). In addition, in each case at most a further signal increase with subsequent signal drop due to eg extravasation, leakage in tumors, specific or unspecific enrichment can occur (secondary maxium), whereby the secondary maximum is temporally downstream of the main maximum

The boundary conditions determine the limits within which a structure can move and which temporal changes of the structure value are compatible with natural laws. Boundary conditions can be, for example:

Time constant of the tracer or contrast agent in the species considered for dilution in blood volume after application

Time constant of the tracer or contrast agent in the species considered for elimination from the blood

Typical time courses for the concentration of a tracer or contrast agent.

For example, after application of the tracer or contrast agent, it may only be one

Signal increase with subsequent drop in the vessel portion in vivo and in addition in each case at most an increase and decrease due to e.g.

Extravasation (when tracers or contrast agents are small enough to penetrate vessel walls), leakage into tumors, specific or nonspecific

Enrichment, etc.

These time courses can also be described by a pharmacokinetic modeling. In step c) of the inventive method, optimized structural values are calculated for each individual subsection. Step c) requires the presence of a first data set and of boundary conditions, so that step c) can take place only after the steps a) and b). The calculation is based on the measured structural values and taking into account the boundary conditions. For the calculation of the optimized structure values, measured structural values are correlated to temporally successive measurement intervals

The calculation can be carried out in various ways. Hereinafter, two preferred embodiments will be described in more detail. /. Sectional smoothing

In a first preferred embodiment of the method according to the invention, the following mathematical operations are carried out for each individual subarea: cl) subdividing the measuring time into a plurality of sections, the shorter the larger the change of the structural values in one

Range of the measuring time The sections must contain at least one measuring interval. At z. As the computed tomography or magnetic resonance imaging this is to be considered in the measurement of the data set. c2) Means of the structure values within each section, if there is more than one measurement time range in the selected temporal section. Alternatively, instead of the averaging in a section, a corresponding data record with the time length of the considered section can be reconstructed, as for example possible in the case of PET. c3) fitting a compensation curve into the averaged structure values, the compensation curve providing optimized structure values

The steps cl) to c3) are carried out successively in the order given. In Figure 1, the calculation is illustrated graphically and explained in more detail in the example described below.

The size of the sections is adapted to the existing measured structure values In the areas of the measuring time in which large changes in the structure values are to be found, the sections are shorter than in the areas of the measuring time in which the structure values are less from one measuring interval to the next measuring interval change strongly. The decisive factor is therefore the first derivation of the structure values according to time. The larger this is, the shorter the sections are.

Preferably, the size of each section is inversely proportional to the amount of first derivative of the structure values by time. The sections can be chosen such that two sections each adjoin one another; it is also conceivable to design the sections in such a way that two or more sections each overlap. Preferably, the sections are designed such that in each case two temporally successive sections overlap in their edge regions. In a particularly preferred embodiment, two temporally successive sections overlap each in one edge point

As soon as the sections have been defined, an averaging of the structural values within each section takes place. Averaging is understood to mean the formation of known mathematical mean values such as, for example, the arithmetic or geometric or harmonic or quadratic mean or weighted mean. The choice of the respective mean value depends above all on the considered structure size and the existing boundary conditions. Usually, the arithmetic mean is formed.

The average values are preferably assigned to the middle of the respective time segment, so that a mean value curve results which represents the average structural values as a function of time. However, it is also conceivable to associate the average values in each case with the first or last or another time of the corresponding time segment.

A compensation curve is fitted in the mean value curve. The compensation curve is selected on the basis of the boundary conditions set up in step b) of the method according to the invention. The compensation curve is adjusted so that the deviations between the mean value curve and the compensation curve are as small as possible. It is also a weighted adjustment conceivable. Weighting means that the compensation curve in the area of the higher-weighted structure values may have a smaller deviation from the mean value curve than in the area of the lower-weighted structure values. For example, spline functions are suitable as compensation curves. Depending on the boundary conditions, apart from recirculation peaks, for example, a global maximum for the application of a tracer or contrast agent and, if appropriate, in each case a local maximum at zJB. present extravasation, leakage in tumors, specific or unspecific enrichment in the mathematical function.

Particular attention should be paid to the beginning of the compensation curve. Immediately after application of a tracer or contrast agent rapid changes of high When selecting the compensation curve calculation, it must be ensured that the compensation curve for the times before the middle first period of time reflects the structure value development in a meaningful way

For example, in a simple variant, the beginning of the curve can be extrapolated with the aid of the slope of the first two mean values.

The mathematical optimization method known to the mathematician can be used to fit the compensation curve (see, for example: JA Snyman: Practical Mathematical Optimization, Springer-Verlag 2005 / C. Daniel et al.: Fitting equations to data; 2nd ed., Wiley 1980 / P Dierckx: Curve and Surface Fitting with Splines, Oxford Science Publications 1996).

The compensation curve provides optimized structure values at arbitrary times within the measurement interval, since the compensation curve represents a continuous time curve and does not consist of discrete values

The result is a data set with optimized structure values at freely selectable times within the measurement interval. Due to the boundary conditions taken into account, in the optimized data record that has been taken there is information which makes it possible to specifically highlight or suppress morphological and / or physiological structures within the data record.

This possibility is given in the following embodiment in an optimal manner, with corresponding operations are also possible in the present embodiment.

2. Adapt to a mathematical model

In a second preferred embodiment of the method according to the invention, a mathematical model is used to calculate the optimized feature width in step c).

This embodiment of the method according to the invention comprises the following steps: c1) providing a mathematical model which describes the temporal behavior of the structure value in the regions of the body; c2) for each subarea: adapting at least one parameter of the model to the measured structure values and determining a model function which optimally reproduces the temporal course of the measured structure values as a result of a mathematical optimization method, the model function being optimized

Provides structure values and wherein the optimization process also optimizes model parameters are obtained.

The mathematical model represents the boundary conditions that have been set up in step b) of the method according to the invention.

As a mathematical model, depending on the examined body area and the physical-biological-chemical properties of a possibly applied auxiliary means such as e.g. a tracer or contrast agent - preferably a single or multi-compartment model.

Such models are well known to those skilled in the art of pharmacokinetics (see, eg, Molecular Imaging: Computer Reconstruction and Practice, Proceedings of the NATO Advanced Study Institute on Molecular Imaging from Physical Principles to Computer Reconstruction and Practice, Springer-Verlag 2006 / Physiologically based pharmacokinetic modeling; by MB Reddy et al. \ Wiley-Interscience 2005 / Peter L. Bonate: Pharmacokinetic-Pharmacodynamic Modeling and Simulation; 2 ^nd Ed., Springer-Verlag 2011).

In such models, the considered Kötperbereich is considered as a built up of one or more compartments body. A compartment in the model is used for each temporal change of the structure value. For example, after a bolus application in the blood of a patient, a tracer distributes itself in a manner and speed characteristic of the patient and the tracer and is gradually eliminated and, if necessary, metabolized.

For example, another compartment is needed for the model if the tracer can leave and extravasate the vasculature due to its physiochemical properties. For all effects or physiological functions which lead to a temporal change of the structure value in the considered data set, a compartment in the model function is to be provided. In order to simulate the temporal behavior of the structure values with the help of the model in the best possible way, various mathematical methods can be used. For example, a model function can be obtained by solving the differential equations that can be established for the model, as is the case in pharmacokinetic modeling.

However, the model function can also be obtained by simulating the time evolution of the considered structure values over the measurement time. By varying the mode functional parameters, a mathematical adaptation of the model function to the temporal behavior of the structure values is possible.

The determination of a model function by adaptation to a mathematical model is preferably carried out in the inventive method using the simulation approach. The result is a model function that optimally reproduces the temporal behavior of the structure values in the mathematical sense. The model function provides optimized structure values at arbitrary times within the measurement interval the model function represents a continuous time curve and does not consist of discrete values. For each subarea of the scanned body, a set of optimized parameters, which indicates the influence of each compartment on the temporal course of the structure value, results from the named method variant

Thus, it is possible to highlight the contributions of the individual compartments, vennindern or omit altogether.

This can be done by not using all the optimized values of the model parameters determined by the adaptation calculation in the calculation of the data set for any time within the measurement time. By limiting the value range of one or more parameters, the contribution of one or more compartments can be influenced in a targeted manner.

Thus, for example, in contrast-enhanced MR tomography on the patient, the contrasting of the vascular system in the output data set can be suppressed or highlighted as required. The result of the model adaptation is thus a data set with optimized structure values and a data set with associated mode parameters, with which the optimized data record can be output in various variants useful for understanding the examination data.

For the sake of simplicity, it has been assumed above that the body region did not move in the generation of the first, measured value-based data record with respect to the measuring device. If, on the other hand, it has moved, temporal changes in the structural values are not attributable solely to changes in the structural or functional state of the body region under consideration, but also to the fact that the subareas considered shift over the measuring device over time. If these temporal changes of the structure value are not compatible with the boundary conditions, they are reduced or eliminated by the described method. This is especially true for structural changes caused by movements that are faster than the considered temporal changes of the structure value or which have oscillatory character, such as the movement of the heart muscle.

Since unwanted movements of the body during the scanning process can always lead to a falsification of the representation of the scanned body, it is fundamentally advantageous to be able to recognize them already in the first data set based on measured values and to reduce or eliminate them. However, if the first data set has too large a spatial noise component, then a motion correction can also be performed on the basis of the optimized data set, i. after carrying out the method according to the invention, unless the movement is already sufficiently reduced by the fiction, contemporary method.

In step d) of the method according to the invention, the output of an optimized data set takes place. The optimized data set represents an area in the examined body. Usually, the range in step d) coincides with the range in step a). However, it is also conceivable that the area in step d) represents only a partial area of the area from step a). It is conceivable that partial areas have been discarded in the course of or following the calculation of the optimized structural values in step c) or by a motion correction , This applies in particular to edge regions of the data set that may not spatially coincide due to motion in all measurement time intervals.

The optimized data set is based on the optimized structure values from step c). Therefore, step d) can take place only after step c).

The optimized data set may be output in the form of one or more two- or three-dimensional representations of the area of the body on a screen or as an expression. Likewise, it is conceivable that the output takes place on a data carrier in the form of machine-readable data.

The optimized data set which has been produced by means of the method according to the invention is likewise an object of the present invention. A further subject of the present invention is a computer program product with program code stored on a machine-readable carrier for carrying out the method according to the invention on a computer.

The method according to the invention is suitable for optimizing all known 3D images or tomography images, for example for optimizing SPECT, PET, CT or MRT images, or measurement data from a 3D or 4D ultrasound method or optical tomography ( See relevant literature such as: Ashok Kliurana, Nirvikar Dahiya: 3D & 4D Vitrasound - A Text and Atlas, Jaypee Brothers Medical Pubiishers (P) Ltd., 2004, R. Weissleder et al., Molecular Imaging - Principles and Practice, Peopie's Medical Publishing House, USA, 2010; GB Saha; Basics of PET Imaging, 2nd Edition, Springer 2010; SA Jackson, RM Thomas; CT, MRI, Ultrasound at a Glance, Elsevier 2009; Olaf Dössel: Medical Imaging, Springer-Verlag Berlin Heidelberg New York, 2000). Surprisingly, with the aid of the method according to the invention, significantly reduced noise tomography images can be generated from a sequence of measured tomography images without the kinetics of the measurement data being lost, such as, for example, the so-called MIP (Maximum Intensity Projection) or Averaging over all individual scans. Movements which occur during the measuring time in the scanned body or in subregions of the scanned body are reduced by the method according to the invention in many cases, which is particularly advantageous in highly noisy data sets image blurring, as they are unavoidable in static recordings with only one record per total measurement time are reduced by the method according to the invention and the spatial resolution is closer to the physically possible resolution of the scanning device.

Depending on requirements, it is possible to generate representations of a body region in which morphological and / or physiological structures are specifically emphasized or suppressed. This allows, for example, the creation of better diagnoses.

The invention is explained in more detail in the description of the figures (FIGS. 1 to 4) and by way of example without being limited thereto.

example

The following explanation of the method according to the invention is carried out for the case of the section-wise smoothing. Let a temporal course of a structure value for a discrete spatial subregion be given from a tomographic PET data set, as shown in FIG

At the beginning of this time course, a signal drop is to be recognized, as expected after application and flooding of the tracer in vivo. Thereafter, the curve apparently passes through a maximum before it drops to a low value at the end of the scan time. Superimposed on all that is not atypical for PET data is noise due to the statistics of the decay events.

Such a course would be expected for a thrombus tracer, which could have a major peak in the data curve due to the flooding and leaching of the tracer after application and another maximum due to possible accumulation of the tracer in or on any existing thrombi in the vascular space. Accordingly, the boundary conditions for this case are chosen with a major and a minor maximum in the structural value-time curve The lengths of the sections which are required for the section-wise smoothing are entered in FIG. 1b. They can be roughly read from the measurement curve. Rapid change of the structure value at the beginning of the curve requires short sections, whereas long sections have to be chosen for the secondary maximum extending over a longer period of time. For measurements that are not carried out for the first time in the combination tracer or contrast agent and species tested, the possible changes in the structure value and thus also the section lengths are known and can be selected accordingly. In the case of adaptation of the measured data to a pharmacological model, the same applies.

Next, the structure values per section located in the different temporal sections are averaged and corrected according to the selected boundary conditions for a main maximum and at most a secondary maximum, if necessary, in the amount of the value. In the present track curve, therefore, the slightly higher average of the penultimate Section (Minute 44-52) down to the mean of the third last section (minute 36-44) down, since there may be no further maximum in the curve due to the boundary conditions except the much larger secondary maximum at less than 20 minutes.

Finally, mathematically, a compensation curve was laid through the calculated mean values of the sections (see FIG. 1 c), thus creating an optimized data record

FIGS. 2 to 4 show, by way of example, a section of a measurement data set in the anatomically customary planes. FIG. 2 shows the data set without processing using the method according to the invention. In comparison, the noise reduction with the method according to the invention can be seen in FIG. 3 on the basis of structures which can be easily recognized and substantially fewer individual spots. In FIG. 4, the structure recognizable in FIG. 3 is confirmed. By averaging over all measuring time intervals, however, the dataset illustrated in FIG. 4 does not allow any conclusions to be drawn about the kinetics of the tracer distribution in the scanbody, in contrast to the dataset from FIG. figure description

Figure 1: Representation of an exemplary time course of the tracer concentration during an in vivo PET scan in a discrete portion of a PET data set

a) without noise reduction by the method according to the invention, b) without noise reduction by the method according to the invention and with additionally indicated suitable sections for the section averaging after step c2) of the section-wise smoothing (horizontal bars) and c) after application of the method according to the invention.

The section bars in FIG. 1b are each entered at the level of the value obtained from the section average. The start of the PET scan was done immediately after application of the tracer

Figure 2: representation of the anatomical views

(a) transversal,

(b) coronal and

(c) sagittal

from an in vivo 3 D PET scan.

The scan was performed on a C nomolgus monkey after application of a thrombus tracer from PET tracer research with a small animal PET scanner. Shown is the measurement data record number 28 of 60 successive scans without noise reduction by the inventive method. The measurement duration of each measurement data set is 1 minute. The measurement of all data records took place one after the other without a break. The planes for the views shown are identical to those of Figure 3a-c and Figure 4a-c. The crosses recognizable in the figures represent the cursor position in the computer program product according to the invention with which the figures were created.

Figure 3: representation of the anatomical views

(a) transversal,

(b) coronal and

(c) sagittal from an in vivo 3D PET scan.

The scan was performed on a cynomolgus monkey after application of a thrombus tracer from PET tracer research with a small animal PET scanner. Shown is the measurement data set number 28 of 60 successive scans after application of the inventive method. The measurement duration of each measurement data set is 1 minute. The measurement of all data records took place one after the other without a break. The planes for the views shown are identical to those of Figure 2a-c and Figure 4a-c. The crosses recognizable in the figures represent the cursor position in the computer program product according to the invention with which the figures were created.

Illustration of anatomical views

(a) transversal,

(b) coronal and

(c) sagittal

from an in vivo 3D PET scan.

The scan was performed on a cynomolgus monkey after application of a thrombus tracer from PET tracer research with a small animal PET scanner. Shown is the averaging of all 60 individual data sets that were scanned during the total measurement time. The measurement duration of each measurement data set is 1 minute. The measurement of all data records took place one after the other without a break. The individual data sets have not been processed by the method according to the invention. The planes for the views shown are identical to those of Figure 2a-c and Figure 3a-c. The crosses recognizable in the figures represent the cursor position in the Computeiprograinmprodukt invention, with which the figures were created.

Claims

claims

A method of generating optimized tomography scans comprising at least the steps of:

a) providing a data set representing an area in the body of a patient during a measurement time, the representation of the body area in the data set being subdivided into a plurality of discrete subareas, wherein the measurement time in the data set is subdivided into a plurality of discrete measurement intervals, each one Subarea for each measurement interval a discrete structure value is assigned; b) Establishing constraints on the expected time course of a feature size in the region of the body during the measurement time; c) calculating optimized structure values for each individual subarea on the basis of structural values of the individual subarea at temporally successive measurement intervals taking into account the boundary conditions; d) Output of an optimized data set which represents an area in the body at arbitrary times during the measurement time and which is based on the optimized structure values

2. The method according to claim 1, characterized in that the following operations are carried out for each subarea in step c): cl) subdividing the measuring time into a plurality of sections, wherein the individual sections are shorter, the greater the change in the structural values in a range of measuring time; c2) averaging the structure values for each subarea within each section; c3) fitting a compensation curve into the averaged structure values, wherein the compensation curve yields optimized structure values. Method according to claim 2, characterized in that the greetings of each section in step cl) are inversely proportional to the amount of the first derivative of the

Structure values after the time is method according to claim 2 or 3, characterized in that the sections in step cl) are designed so that in each case two temporally successive sections overlap in their edge regions. A method according to claim 1, characterized in that in step c) the following operations are performed: c1) providing a mathematical model which determines the temporal behavior of the

Describes structure value in the areas of the body; c2) for each subarea: adapting at least one parameter of the model to the measured structure values and determining a mode function that optimally reproduces the temporal course of the measured structure values as a result of a mathematical optimization method, wherein the model function supplies optimized structure values and wherein the optimization method also optimizes model parameters be won; A method according to claim S, characterized in that the mathematical model is a single or multi-compartment pharmacokinetic model. Method according to any one of claims 1 to 6, characterized in that the first data set results from measurements made on a living organism , Method according to one of claims 1 to 6, characterized in that the first data set results from measurements made on a non-living object. Method according to one of claims 1 to 8, characterized in that it is the first record to SPECT, PET, CT, MRI Aumahmen, or a measurement data set from a 3D or 4D ultrasound method or the optical tomography These

Method according to one of claims 1 to 9, characterized in that structure values are selectively changed in the optimized data set on the basis of the boundary conditions in order to highlight or suppress morphological and or physiological structures.

Optimized data set generated by a method according to one of claims 1 to 10.

A computer program product having program code means for execution. Method according to one of claims 1 to 10 on a computer system.