Download Energy selective computed tomography: a potential revolution for

Energy selective computed tomography: a potential revolution for radiotherapy
Hugo Bouchard
Radiation Dosimetry Team, National Physical Laboratory, UK
Computed tomography (CT) imaging is a technique where a series of X-ray images, acquired
at many different angles around the patient, are processed mathematically to generate a 3D map
of the patient anatomy. This contributed to a revolution in diagnostic medicine, as doctors had
a non-invasive technique to discover what might previously have been possible only through
major surgery, if at all. In radiotherapy, for example, CT imaging is routinely used to define
precisely which regions should receive large doses and to locate the radiosensitive tissues that
would be damaged by too large a dose. However the delivery of radiotherapy depends in an
even more critical way on CT imaging, since the preparation of treatment plans relies entirely
on certain physical properties of tissues in the patient [1]. In general, CT data does not provide
these physical properties in a direct manner; instead it provides information on the way tissues
interact with X-rays. Only by using our scientific understanding of X-ray interactions can we
determine from CT data the tissues’ physical properties on which successful radiotherapy
depends. In principle, one can determine most physical properties of tissues from their
elemental composition and mass density. However, knowing these elemental properties in the
whole human body at once is not possible. Thanks to Sir Godfrey Hounsfield, CT imaging
provides one approach to solving this problem. The technique is at the core of diagnostic
imaging and radiotherapy and, most of the time, data provided by conventional CT is sufficient
to achieve acceptable tissue differentiation for radiotherapy [2].
The idea of using two distinctive energies to acquire CT data was proposed in the seventies
and referred to as “energy-selective computed tomography” (ESCT) [3]. Spectral CT (SCT),
dual-energy CT (DECT) and dual-source CT (DSCT) can all be referred to as ESCT. These
techniques provide multiple and complementary patient information for each position in space
(i.e., voxel), allowing the reconstruction of physical properties of human tissues with more
accuracy than conventional single-energy CT (SECT), which provides only a single piece of
information per voxel. The goal of ESCT is to go beyond the limits of SECT, which information
is dominated by the mass density, or more precisely, the number of electrons per unit volume
contained in human tissues. While some medical applications require highly sensitive methods
to differentiate human tissues, ESCT can determine the presence of a contrast agent and
characterize tissue more precisely than SECT. Apart from providing benefits in diagnostic
imaging, ESCT can also improve radiotherapy. As recently shown, DECT is highly promising
for proton therapy [4-5], where knowing the range of the beam with millimetric precision is
crucial for patient safety and treatment success.
The main idea behind DECT and DSCT is to use two distinct X-ray energy spectra to acquire
CT scans simultaneously. In practice, DSCT uses two X-ray tubes operated at different kVp
and placed perpendicularly to one another [6], while DECT uses a single X-ray tube with rapid
kVp switching technology which periodically alternates between the two energies [7]. In
theory, SCT is not limited to only two energies; the energy discrimination is performed at the
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detector level rather than at the source. That is, SCT uses a conventional X-ray source but relies
on advanced detector technology which can distinguish photon energies [8]. Despite work
beginning in the eighties [7], the complete integration of simultaneous dual energy acquisition
into a commercial CT scanner appeared less than a decade ago [9]. Currently, there are three
manufacturers providing ESCT solutions: Siemens Healthcare (DSCT) [10], GE Healthcare
(DECT) [11] and Philips Healthcare (SCT) [12].
Figure: Advanced tissue characterization using energy selective computed tomography.
Such technology is highly appealing for radiotherapy. At the treatment planning phase, we need
to characterize the tissue with sufficient information to predict how the beam is transported in
the patient and what will be the resulting radiation dose distribution. From this knowledge, we
can use computational strategies to maximize the dose to tumours and minimize the dose to
healthy tissues. This allows optimizing the beam configuration to give the best possible
therapeutic dose delivery and this is done for each patient based on CT imaging and dose
calculation models. There exist several types of radiotherapy beams: photons, electrons,
protons, etc. The scattering and absorption properties of these beams, known as “interaction
cross sections”, need to be mapped in the patient as a function of energy in each voxel in order
to compute dose precisely. When using SECT, the single piece of information only allows the
reconstruction of one main piece of the puzzle, and assumptions about the other pieces must
be made to predict patient dose distributions. With ESCT, we no longer need to make these
assumptions because the dual or multiple information allows the reconstruction of all pieces of
the puzzle and characterize human tissue for our needs. The use of ESCT is not expected to
impact all types of treatment in the same manner; some types of beam are much more sensitive
to this information than others. Low-energy photons (brachytherapy) and proton beams (proton
therapy) are good examples where ESCT is expected to have a big impact, as tissue properties
other than the electron densities play an important role in the behaviour of the interaction cross
sections (e.g., the effective atomic number, the mean excitation energy, etc.). What makes
proton therapy different from common radiotherapy which uses photons (i.e., X-ray or gammarays) is the fact that the beam stops at a certain range in the patient instead of being gradually
attenuated until leaving the body with low intensity. For some conditions where critical organs
are nearby, like brain and skull base, knowing exactly where the beam stops is crucial. While
ESCT is expected to improve dose calculation [13-17], there are other promising applications
for radiotherapy [18-22]: improved tumour and organ at risk definition during treatment
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planning, reduction of metal implant artefacts, improved diagnostics and tumour localization
using functional imaging, characterization of organ motion using 4D CT, etc.
No one has yet shown the benefits of one ESCT method over the other, likely because the
technologies are too recent. Although one could expect the benefits of ESCT to reach their
peak by maximizing the amount of extracted information on X-ray attenuation in human
tissues, in which case SCT appears more promising, this might not be the case due to inherent
limitations of what can be uncovered from X-ray attenuation properties. Indeed, it is possible
that the gain of uncorrelated information achieved by using more than two energies is marginal,
due to the nature of X-ray imaging or to what is technologically achievable. In other words,
using two energies could be sufficient for our current clinical needs. Therefore the
performances of SCT, DECT and DSCT rely on a series of parameters, such as the X-ray source
filtration [23], the detector mode of operation [24], etc., and are yet to be optimized for specific
medical applications. Only time will tell which technology is better and for which applications.
At the present time, the remarkable evolution from SECT to ESCT is already convincing and
radiotherapy patients should benefit from this milestone. Why did it take 30 years to achieve
ESCT commercially: was it mainly technology challenges [9], or the coexistence of MR
imaging? Indeed, MRI is a fierce competitor to ESCT, and while its principles of operation are
completely different (i.e., information about the atomic nuclei is obtained rather than on the
electronic structure), there is no doubt regarding its advantages over CT for diagnostic imaging.
However, MRI cannot be used reliably to quantify the tissue properties required for
radiotherapy treatment planning; it can only be used reliably for localizing organs, tumours or
other objects. For now, initial applications of ESCT have been mainly focused on diagnostic
imaging because the needs of radiation therapy are distinct and that market is smaller.
Therefore, further effort and development will be required before ESCT can be exploited to
maximize the benefit to cancer patients.
References
1. Hounsfield, G. N. (1973). Computerized transverse axial scanning (tomography): Part 1.
Description of system. The British journal of radiology, 46(552), 1016-1022.
2. Schneider, U., Pedroni, E., & Lomax, A. (1996). The calibration of CT Hounsfield units
for radiotherapy treatment planning. Physics in medicine and biology, 41(1), 111.
3. Alvarez, R. E., & Macovski, A. (1976). Energy-selective reconstructions in X-ray
computerised tomography. Physics in medicine and biology, 21(5), 733.
4. Bourque, A. E., Carrier, J. F., & Bouchard, H. (2014). A stoichiometric calibration method
for dual energy computed tomography. Physics in medicine and biology, 59(8), 2059.
5. Hünemohr, N., Paganetti, H., Greilich, S., Jäkel, O., & Seco, J. (2014). Tissue
decomposition from dual energy CT data for MC based dose calculation in particle therapy.
Medical physics, 41(6), 061714.
6. Petersilka, M., Bruder, H., Krauss, B., Stierstorfer, K., & Flohr, T. G. (2008). Technical
principles of dual source CT. European journal of radiology, 68(3), 362-368.
7. Kalender, W. A., Perman, W. H., Vetter, J. R., & Klotz, E. (1986). Evaluation of a prototype
dual‐energy computed tomographic apparatus. I. Phantom studies. Medical physics, 13(3),
334-339.
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8. Schlomka, J., Roessl, E., Dorscheid, R., Dill, S., Martens, G., Istel, T., Baumer, C.,
Herrmann, C., Steadman, R., Zeitler, G., & Proksa, R. (2008). Experimental feasibility of
multi-energy photon-counting K-edge imaging in pre-clinical computed tomography.
Physics in medicine and biology, 53(15), 4031.
9. Flohr, T. G., McCollough, C. H., Bruder, H., Petersilka, M., Gruber, K., Süβ, C., ... &
Ohnesorge, B. M. (2006). First performance evaluation of a dual-source CT (DSCT)
system. European radiology, 16(2), 256-268.
10. http://www.healthcare.siemens.co.uk/computed-tomography/dual-source-ct
11. http://www3.gehealthcare.com/en/products/categories/computed_tomography/revolution_
gsi
12. http://www.usa.philips.com/healthcare-product/HCNOCTN284/iqon-spectral-ct
13. Bazalova, M., Carrier, J. F., Beaulieu, L., & Verhaegen, F. (2008). Tissue segmentation in
Monte Carlo treatment planning: a simulation study using dual-energy CT images.
Radiotherapy and Oncology, 86(1), 93-98.
14. Bazalova, M., Carrier, J. F., Beaulieu, L., & Verhaegen, F. (2008). Dual-energy CT-based
material extraction for tissue segmentation in Monte Carlo dose calculations. Physics in
medicine and biology, 53(9), 2439.
15. Landry, G., Granton, P. V., Reniers, B., Öllers, M. C., Beaulieu, L., Wildberger, J. E., &
Verhaegen, F. (2011). Simulation study on potential accuracy gains from dual energy CT
tissue segmentation for low-energy brachytherapy Monte Carlo dose calculations. Physics
in medicine and biology, 56(19), 6257.
16. Landry, G., Reniers, B., Granton, P. V., van Rooijen, B., Beaulieu, L., Wildberger, J. E., &
Verhaegen, F. (2011). Extracting atomic numbers and electron densities from a dual source
dual energy CT scanner: experiments and a simulation model. Radiotherapy and Oncology,
100(3), 375-379.
17. Landry, G., Seco, J., Gaudreault, M., & Verhaegen, F. (2013). Deriving effective atomic
numbers from DECT based on a parameterization of the ratio of high and low linear
attenuation coefficients. Physics in medicine and biology, 58(19), 6851.
18. Johnson, T. R., Krauss, B., Sedlmair, M., Grasruck, M., Bruder, H., Morhard, D., ... &
Becker, C. R. (2007). Material differentiation by dual energy CT: initial experience.
European radiology, 17(6), 1510-1517.
19. Thieme, S. F., Becker, C. R., Hacker, M., Nikolaou, K., Reiser, M. F., & Johnson, T. R.
(2008). Dual energy CT for the assessment of lung perfusion—correlation to scintigraphy.
European journal of radiology, 68(3), 369-374.
20. Graser, A., Johnson, T. R., Chandarana, H., & Macari, M. (2009). Dual energy CT:
preliminary observations and potential clinical applications in the abdomen. European
radiology, 19(1), 13-23.
21. Landry, G., Parodi, K., Wildberger, J. E., & Verhaegen, F. (2013). Deriving concentrations
of oxygen and carbon in human tissues using single-and dual-energy CT for ion therapy
applications. Physics in medicine and biology, 58(15), 5029.
22. Schmid, S., Landry, G., Thieke, C., Verhaegen, F., Ganswindt, U., Belka, C., ... & Dedes,
G. (2015). Monte Carlo study on the sensitivity of prompt gamma imaging to proton range
variations due to interfractional changes in prostate cancer patients. Physics in Medicine
and Biology, 60(24), 9329.
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23. Primak, A. N., Giraldo, J. R., Liu, X., Yu, L., & McCollough, C. H. (2009). Improved dualenergy material discrimination for dual-source CT by means of additional spectral
filtration. Medical physics, 36(4), 1359-1369.
24. Baumer, C., Martens, G., Menser, B., Roessl, E., Schlomka, J. P., Steadman, R., & Zeitler,
G. (2008). Testing an energy-dispersive counting-mode detector with hard X-rays from a
synchrotron source. Nuclear Science, IEEE Transactions on, 55(3), 1785-1790.
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