Dual- and multi-energy CT for particle stopping-power estimation: current state, challenges and potential

Abstract

Range uncertainty has been a key factor preventing particle radiotherapy from reaching its full physical potential. One of the main contributing sources is the uncertainty in estimating particle stopping power (ρs) within patients. Currently, the ρs distribution in a patient is derived from a single- energy CT (SECT) scan acquired for treatment planning by converting CT number (μHU) of each voxel to ρs using a Hounsfield look-up table (HLUT), also known as the CT calibration curve. μHU and ρs share a linear relationship with electron density but fundamentally differ in additional physical properties, such as photon attenuation cross section or effective atomic number and stopping number or mean excitation energy, respectively. Due to this degeneracy, the HLUT approach is particularly sensitive to differences in elemental composition between real human tissues and tissue surrogates as well as tissue variations within and among individual patients. The use of dual-energy CT (DECT) for ρs prediction has been shown to be effective in reducing the uncertainty in ρs estimation compared to SECT. The acquisition of CT data over different x-ray spectra yields additional information on the material elemental composition. Recently, multi-energy CT (MECT) has been explored to deduct material-specific information with higher dimensionality, which has the potential to further improve the accuracy of ρs estimation. Even though various DECT and MECT methods have been proposed and evaluated over the years, these approaches are still only scarcely implemented in routine clinical practice. In this topical review, we aim at accelerating this translation process by providing: 1) a comprehensive review of the existing DECT/MECT methods for ρs estimation with their respective strengths and weaknesses; 2) a general review of uncertainties associated with DECT/MECT methods; 3) a general review of different aspects related to clinical implementation of DECT/MECT methods; 4) other potential advanced DECT/MECT applications beyond ρs estimation.

Dual‐energy CT‐based stopping power prediction for dental materials in particle therapy

“…SECT overestimated the SPR values of tooth dentin and tooth enamel compared to measured SPR values, whereas DECT slightly underestimated SPR values with deviations <1%. Since an SPR accuracy within 1% can be realistically reached using DECT in idealized situations, 11 the DECT‐predicted SPR accuracy of the tooth surrogates was within the expected uncertainty. Moreover, the relative residuals of DECT‐predicted SPR values were in the same order of magnitude as the uncertainty of the measured SPR values of the tooth surrogates (0.2%) 16 .…”

“…Several different technical approaches for acquiring DECT image data have emerged with unique features and compromises to be balanced for each application, 9 comprising dual-source CT, sequential acquisition CT (SACT), twin-beam CT, fast kVp-switching CT, and dual-layer spectral CT (DLCT). [8][9][10][11] In various studies conducted with tissue surrogates or biological tissues as well as in patient analyses, DECT showed improved SPR prediction for particle therapy compared to SECT. 8,[12][13][14][15][16][17][18] Besides human tissue, the usage of DECT in particle therapy planning may also be advantageous for non-tissue implant materials.…”

“…It thus increases the available quantitative data and possibilities for material characterization compared to classical single‐energy CT (SECT). Several different technical approaches for acquiring DECT image data have emerged with unique features and compromises to be balanced for each application, 9 comprising dual‐source CT, sequential acquisition CT (SACT), twin‐beam CT, fast kVp‐switching CT, and dual‐layer spectral CT (DLCT) 8–11 . In various studies conducted with tissue surrogates or biological tissues as well as in patient analyses, DECT showed improved SPR prediction for particle therapy compared to SECT 8,12–18 .…”

Prediction of proton stopping power ratios using dual‐energy CT basis material decomposition

“…21,29 Many DECT-based SPR prediction methods have been suggested, as summarized in various review publications. 4,17,30 To date, only one commercial SPR prediction algorithm, for image-based DECT (Direct-SPR, Siemens Healthineers, Forchheim, Germany), has been released, 31,32 and introduced into the clinic. 33 Previously suggested SPR prediction methods for MECT scanners with projection-space processing have used combinations of VM images, 20,34,35 or RED and EAN images 18,36 to factor the SPR into RED and the relative stopping number (RSN = SPR/RED).…”

“…The possibility to obtain additional information about the patient's tissues by performing multiple attenuation measurements with different x‐ray energies (multi‐energy CT; MECT) has been known since the invention of CT in the early 1970s 16 . MECT enables more sophisticated SPR prediction methods compared to SECT and has been shown to improve SPR prediction, 17 for both adult and pediatric tissues, 15 as well as for implant materials 18,19 . MECT with two energy measurements is usually called dual‐energy CT (DECT) and has until the recent introduction of CT scanners with photon counting detectors (PCCT) 20 been the only commercially available form of MECT 21 .…”