Tomographic detection of photon pairs produced from high-energy x-rays for the monitoring of radiotherapy dosing

ABSTRACT Measuring the radiation dose reaching a patient’s body is difficult. Here we report a technique for the tomographic reconstruction of the location of photon pairs originating from

the annihilation of positron–electron pairs produced by high-energy X-rays travelling through tissue. We used Monte Carlo simulations on pre-recorded data from tissue-mimicking phantoms and

from a patient with a brain tumour to show the feasibility of this imaging modality, which we named ‘pair-production tomography’, for the monitoring of radiotherapy dosing. We simulated

three image-reconstruction methods, one applicable to a pencil X-ray beam scanning through a region of interest, and two applicable to the excitation of tissue volumes via broad beams (with

temporal resolution sufficient to identify coincident photon pairs via filtered back projection, or with higher temporal resolution sufficient for the estimation of a photon’s

time-of-flight). In addition to the monitoring of radiotherapy dosing, we show that image contrast resulting from pair-production tomography is highly proportional to the material’s atomic

number. The technique may thus also allow for element mapping and for soft-tissue differentiation. SIMILAR CONTENT BEING VIEWED BY OTHERS TIME OF FLIGHT DUAL PHOTON EMISSION COMPUTED

TOMOGRAPHY Article Open access 11 November 2020 QUASI-REAL-TIME RANGE MONITORING BY IN-BEAM PET: A CASE FOR 15O Article Open access 01 November 2023 SIMULTANEOUS MULTI-NUCLIDE IMAGING VIA

DOUBLE-PHOTON COINCIDENCE METHOD WITH PARALLEL HOLE COLLIMATORS Article Open access 25 June 2021 MAIN There is a long history of using X-rays for detection1. Besides industrial inspection2,

X-rays hold a uniquely important position in medicine3. Compared with other medical imaging techniques such as magnetic resonance imaging (MRI)4,5,6 and positron emission tomography (PET)7,

X-ray imaging systems are advantageous in their low cost, high speed, high resolution and high sensitivity to dense or high-atomic-number materials. A major breakthrough in X-ray imaging was

the invention of tomographic images8,9. By acquiring two-dimensional (2D) X-ray attenuation images from many different angles around the patient, a 3D computed tomography (CT)10,11 image

can be reconstructed8,12. CT revolutionized modern medicine by enabling diagnoses that were previously simply impossible. Since the invention of CT, many technological developments in the

hardware and reconstruction methods have markedly improved its speed, image quality and versatility13,14,15. Nonetheless, the underlying mechanism for image formation remains the same. X-ray

CT imaging signals are produced by a mixture of physical interactions, including Rayleigh scatter, photoelectric effect and Compton scatter. These fundamentally different interactions

result in different attenuation patterns with regard to the material properties. The problem is further complicated by the bremsstrahlung poly-energetic X-rays commonly available for

diagnosis and therapy. As a result, it is difficult to obtain clean material information from the X-ray CT images. Dual-energy and photon-counting CT help improve material differentiation,

but they are limited in the number of differentiable basis materials and do not directly quantify material atomic numbers16. Another weakness of CT is poor soft-tissue contrast due to the

similarity in densities and the diminishing photoelectric effect with low-atomic-number materials. Phase-contrast CT can improve soft-tissue contrast, but it requires either coherent X-ray

sources or additional optical elements such as Talbot-Lau gratings. Owing to these additional complexities, significant research and development are still needed to make phase-contrast CT a

clinically viable modality17,18,19. For tomographic reconstruction, attenuation signals from sufficient X-ray beam angles are required. In specific cases, with machine learning20 and sparse

regularization21 methods, the requirement can be relaxed to predict useable images, but a general solution for sparse-view tomographic image reconstruction does not exist22. Furthermore, CT

reconstruction based on the Radon transform has global support, meaning that truncated projections with a partial view of the patient would inevitably introduce inaccuracies whose magnitude

depends on the degree of truncation and reconstruction method. One undesired consequence of the data sufficiency requirement is the exposure of a large patient volume to the imaging dose

regardless of the size of the region of interest (ROI). Another major application of X-rays in modern medicine is radiotherapy of cancer. The ionizing radiation from high-energy X-rays can

break DNA strands which, if not repaired, can lead to cell death. By exploiting the differential repair mechanisms of cancer and normal cells, and the additional therapeutic contrast due to

conformal dose distribution, radiotherapy has been a mainstay modality in cancer treatment. It is estimated that 60% of cancer patients and 40% of the curative cases in the United States use

radiotherapy as either one of or the only treatment method23. X-ray-based radiotherapy is an open-loop treatment, meaning that the delivered 3D dose in the patient is not directly verified.

Compared with a closed-loop system, an open-loop system is intrinsically less safe and less accurate due to the lack of direct feedback. In vivo radiation dose deep inside the patient’s

body is difficult to measure. Implanted dosimeters require an undesirable interventional procedure and still only measure point doses24. Cerenkov imaging is limited to superficial

locations25. X-ray induced acoustic CT (XACT) has shown promise to measure 3D in vivo dosimetry. However, XACT applications are hampered by the acoustic boundaries, low resolution and

signal-to-noise ratio (SNR), and mandatory ultrasound receiver arrays that interfere with the X-ray beam path26. Because of the fundamental impediments, these in vivo dosimetry methods are

unlikely to meet the general needs of 3D in vivo dosimetry to close the radiotherapy open loop. To meet the challenges and expand the applicability of X-ray tomography, we introduce a

distinctly new 3D X-ray image-formation method: pair-production tomography (P2T) imaging. P2T is similar to PET in terms of how imaging is done: they both measure coincident annihilation

photons emitted from positron annihilation. The only difference is the source of positrons: PET introduces positrons with radioactive tracers, while P2T introduces positrons through

mega-voltage X-ray-induced pair production. P2T is similar to CT in terms of what it is imaging: the image signals in both depend on the material composition. They differ in image-formation

mechanisms: CT measures X-ray transmission, while P2T measures pair-production signals. With its unique image-formation method, we show that P2T provides a direct verification of 3D

radiotherapy dose. It also serves as an imaging modality with a contrast different from that of CT, provides a clean linear relationship to the material atomic number, and P2T images can be

formed even with partial-view and sparse-view projections. A previous study used pair production for one-sided point material detection27, where the radiation source and a single detector

module are located on the same side of the object, and only a single point can be measured at a time. Different from the previous work, we use coincidence information to form 3D P2T images,

which substantially expands the capacity for medical applications. PRINCIPLES OF PAIR-PRODUCTION TOMOGRAPHY IMAGING Figure 1a illustrates the major X-ray interactions in the P2T energy range

(for example, 10 MV bremsstrahlung source, which is commonly used in radiotherapy). In the photoelectric effect, an incident photon vanishes after striking a bound electron, resulting in

the ejection of the electron and a vacancy in the inner shell. To stabilize the atom, an outer shell electron fills the vacancy and converts the energy lost to characteristic radiation X-ray

or an Auger electron. In Compton scattering, an incident photon is scattered by a charged particle, typically an electron, and transfers part of the photon energy to the recoiling electron.

The pair production occurs in a Coulomb force field, typically near a nucleus, where an incident X-ray of sufficiently high energy (at least 1.022 MeV) is annihilated and produces a

positron and an electron. Subsequently, the electron dissipates energy through successive interactions with the medium before being absorbed by the medium. However, as the positron loses its

kinetic energy and comes to a near stop, it encounters an electron with nearly simultaneous annihilation of the positron and the electron, and their conversion into two annihilation photons

moving in opposite directions with an energy of around 511 keV. The pair-production attenuation coefficient is linear to the material atomic number28. The P2T formation process is

illustrated in Fig. 1b. An X-ray beam typically used for radiotherapy introduces the pair-production electron-positron pair in a subject placed at the centre of a ring-detector array. Before

producing the two time-coincident 511 keV annihilation photons, the positron would travel for a median distance of 4.6 mm for a 10 MV beam (Supplementary Fig. 1). The two annihilation

photons travel in opposite directions, captured by two detectors on the ring. A 3D map of the event locations can then be reconstructed on the basis of a collection of the signals. The

photon contamination from photoelectric and Compton interactions is effectively reduced via a coincidence time window and an energy window. Figure 1c shows the energy distribution of

detected photons ranging from 0 to 1 MeV, with a zoom-in view of 0.511 MeV ± 10% energy range (Fig. 1d,e). The 511 keV photons comprise 0.91% of the total photons before applying filters

(Fig. 1c), 15.4% after applying a ±10% energy window filter (Fig. 1d) and 77.6% after applying the ±10% energy as well as a 1 ns coincidence time filter (Fig. 1e). Two P2T excitation

approaches are investigated in this study (Fig. 1f). The volume excitation (VE) approach excites the entire imaging field simultaneously at each view angle. In scanning pencil beam

excitation (SPBE), the imaging field is excited sequentially. Note that the imaging field can be full view or partial view of the imaged object. Full-view imaging covers the entire imaged

object, while partial-view imaging only irradiates the ROI without exposing the majority of the imaged object. SPBE affords additional geometrical information for tomographic reconstruction

by pinpointing the pair-production location at the intersection of the pencil beam and the detector coincidence line. SIMULATION, RECONSTRUCTION AND POST PROCESSING A general-purpose Monte

Carlo (MC) package, Geant4 (ref. 29), was used to characterize P2T. A ring-detector array with a total of 1,440 detector elements and a diameter of 240 cm were assumed. For simplicity, we

set the ring detector to have only a single row with 10 cm width in the _z_-direction (for example, patient superior-inferior direction), which covers 4.17% of the solid angles. A ±10%

energy window was assumed. The photon detection time can be computed as the sum of the photon releasing time, the photon travelling time and the detector response time. The primary photons

within each pencil beam are released in sequence, with time intervals following a uniform distribution. The detector response time was simulated as a Gaussian distribution with a standard

deviation equal to the time resolution of the detector. We studied three imaging acquisition and reconstruction methods: filtered back projection (FBP), scanning pencil beam (SPB) and time

of flight (TOF). Coincident events were identified as two energy-eligible photons (within the energy window) arriving at two detector elements within the coincidence time of 1 ns. Two

coincident events define a line of response (LOR): the line connecting the two detector elements, indicating that the annihilation event happened on the LOR. Once LORs are identified, they

are re-binned to sinogram and then reconstructed using FBP with the Michigan Image Reconstruction Toolbox (MIRT)30 (the FBP method). The SPB imaging method uses the scanning pencil beam

excitation method and excites the imaging ROI sequentially using thin pencil beams. With the known excitation path, SPB locates each annihilation event as the intersection of the

corresponding LOR and the pencil beam path (the SPB method). For detectors with a high time resolution (such as 20 ps), the range of the annihilation event along the LOR can be narrowed down

(to, for example, 3 mm), according to the time difference of the two photons (the TOF method). In addition to the reconstructed images, the ground-truth (GT) image was created as the

voxel-wise tally of positron annihilation events. We considered two different detector time resolutions: 20 ps or 300 ps, representing the upper limits of experimental Cerenkov31,32 and

state-of-the-art commercial scintillator detectors33, respectively. The TOF information of the 20 ps detector allows directly locating an annihilation event with 3 mm resolution. A TOF of

300 ps results in a 45 mm range, which in itself is inadequate resolution. However, when a large number of annihilation events are recorded, such as in high-dose radiotherapy, useful images

can be reconstructed in a statistical manner using FBP. SPB increases resolution to 2 mm by using a 2 mm excitation beam even with a 300 ps detector. Therefore, we only considered TOF

reconstruction for a 20 ps detector and SPB or FBP for a 300 ps detector in this study. Similar to PET, the attenuation to annihilation photons is compensated for by weighting the coincident

photon-pair counting on the basis of their respective radiological path lengths. Besides attenuation, the signal intensity of P2T also depends on the fluence intensity of the imaging beams.

For quantitative imaging, the P2T images are normalized by the fluence to correct for bias due to variation in the excitation X-ray beam fluence. Details on the fluence and attenuation

correction methods can be found in Methods. A summary of the correction methods, imaging approaches and the assumed detector time resolution used for all P2T images can be found in Table 1.

P2T LINEARITY WITH HIGH-_Z_ ELEMENTS Both the CT and P2T image intensities are determined by the cross-section of physical interactions, or the attenuation coefficient, which is a function

of the atomic number _Z_, the density _ρ_ and the photon energy hν. In P2T with MV X-ray as the source, both Compton scatter and pair production contribute to the interaction. However, since

the P2T detectors remove the majority of Compton scatter photons, the P2T image intensity overwhelmingly depends on the probability of pair-production interaction, which is linearly

proportional to _ρZ_. Consequently, the P2T image contrast should follow a simple linear relationship with _ρZ_. If _ρ_ is known, then the atomic number _Z_ is determined. Details on the

linear relationship can be found in Methods. In comparison, the CT image signals using a kV source are produced by a mixture of Rayleigh scatter, photoelectric effect and Compton scatter,

the mixture being both material and energy dependent. The Compton attenuation coefficient is approximately _Z_ independent, and the photoelectric effect is approximately proportional to _Z_3

with sharp discontinuities at the _K_-edges, which give CT excellent sensitivity to materials with mid to high atomic numbers. On the other hand, the convolution of poly-energetic X-rays

with nonlinear cross-intersections inevitably renders multiple material differentiation tasks underdetermined. The linearity of P2T contrast to _Z_ were evaluated on an elliptical

water-equivalent phantom with 10 inserts, among which 7 inserts were made up of water and 5% of high-_Z_ elements (ranging from 53 to 83), including iodine, barium, gadolinium, ytterbium,

tantalum, gold and bismuth. The minor and major axes of the phantom were 20 cm and 24 cm, respectively. P2T MC simulation utilized a total of 56.6 billion primary particles in 20 equally

distributed co-planar fan beams, with full coverage of the phantom in each beam. For SPB, the pencil beam size was 0.2 × 0.2 cm2. The pencil beams were excited sequentially, and together all

pencil beams covered the entire phantom at each of the 20 beam angles. MC CT simulation utilized a total of 72 billion primary particles in 360 equally distributed co-planar fan beams. The

CT detector pixel size was 0.2 cm by 0.2 cm, the source to detector distance was 100 cm and the source to isocentre distance was 66.7 cm. The beam energies of P2T and CT were 10 MV and 120 

kVp, respectively. X-rays of 10 MV have a typical poly-energetic bremsstrahlung X-ray spectrum for radiotherapy. The 120 kVp X-ray spectrum is typical of a diagnostic hot cathode system. The

reconstructed image resolution was 0.2 cm. The CT and P2T images are presented in panels a and c in Fig. 2, respectively. Linear regressions of the increased contrast to water on the atomic

number _Z_ are presented in Fig. 2b. The CT has a higher contrast for the high-_Z_ materials due to the _Z_3 photoelectric cross-section, but the relationship between CT image intensity and

the atomic number is nonlinear. For example, although gadolinium has a lower atomic number than ytterbium, tantalum, gold and bismuth, its _K_-edge energy at 50 keV is closer to the peak of

the 120 kVp CT spectrum. Consequently, the CT contrast of gadolinium is substantially higher than those of the other materials. In comparison, the P2T image intensities show the expected

linear relationship with the atomic number. The _r_2 values of CT, P2T GT, P2T FBP, P2T SPB and P2T TOF images are 0.23, 0.99, 0.48, 0.93 and 0.84, respectively. Apart from P2T FBP, where

the image contrast is obscured by excessive noise, all other P2T images show a strong linear relationship with _Z_. P2T LINEARITY WITH TISSUE EQUIVALENT MATERIALS Besides high_-Z_

nanoparticle imaging, P2T for human tissue imaging is evaluated on the same elliptical phantom containing 10 different tissue-mimicking inserts, including air, lung inhale, lung exhale,

adipose, breast, water, muscle, liver, trabecular bone and dense bone (Fig. 3a), under the same geometry and energy setup. Figure 3b shows the image contrast of the P2T from different

reconstruction methods compared with CT on the standard phantom, with error bars showing the standard deviations. The dashed lines show the theoretical values of P2T contrast, defined as the

increments in _ρZ_eff of each material relative to water, where _ρ_ is the material mass density and _Z_eff is the effective atomic number of a composite material34 (Supplementary Table 1).

The ground-truth and reconstructed P2T images and the CT image are shown in Fig. 3c. Among the three P2T reconstruction methods, FBP provides the lowest SNR, making it more difficult to

discern materials with _ρZ_eff similar to water. The SPB image is comparable to the TOF image without requiring a high detector time resolution. The SPB and TOF images are noisier than the

ground-truth image due to the low detector coverage of the solid angles. For low-_Z_ materials, the photoelectric component in CT is negligible, and the contrast is approximately linear to

_ρ_, while P2T is linear to _ρZ_eff. The _Z_eff factor offers greater contrast (the corresponding rods indicated by the red arrows in Fig. 3c are more visible in P2T images) for materials

including the lung inhale, lung exhale, adipose and breast tissue. Specifically for the breast tissue with a 1% difference in density to water but a 13.6% difference in _Z_eff, this

translates to a 13.6× increase in the contrast. P2T ALLOWS PARTIAL-VIEW AND SPARSE-VIEW IMAGING The data sufficiency condition of P2T is distinctly different from that of CT, which requires

the voxel to be reconstructed on the line connecting two points on the source trajectory35. The requirement is translated into densely sampled full-view projections around the image subject.

P2T is intrinsically compatible with partial-view and sparse-view imaging as the pair-production event detections are separable from each other. Even by locally irradiating an ROI, P2T can

extract information from an interior patient sub-volume. Note that the fluence correction and attenuation correction in P2T reconstruction still require the X-ray attenuation coefficients of

the entire patient volume, but the corrections are insensitive to minor structures. One CT scan with the lowest possible dose could serve for fluence correction of repeated P2T acquisitions

on the same imaging object. Figure 4a shows a comparison of 20-beam full-view P2T images (top) and 2-beam partial-view P2T images (bottom). The ROIs in this case are the three inserts (5%

iodine, ytterbium and bismuth from left to right) at the bottom of the phantom. The full-view P2T simulation utilized a total of 56.6 billion primary particles in 20 equally distributed

co-planar beams, with full coverage of the phantom in each beam. The partial-view P2T utilized a total of 10.6 billion primary particles in 2 opposing beams, with partial beam coverage of

the area indicated by the yellow dashed lines in Fig. 4a (top left). Despite irradiating only 20% of the whole volume and using only two beams, the 2-beam partial-view images are comparable

to the 20-beam full-view P2T images within the ROI. More importantly, the imaging dose is limited to the irradiated volume. In the specific case, the maximal imaging dose is around 3.3 cGy,

assuming 100% detector efficiency. Note that the estimated imaging dose is inversely proportional to the detector efficiency assuming fixed image SNRs. The three imaging acquisition methods

(FBP, TOF and SPB) were simulated using the same number of particles and therefore have the same imaging dose. Figure 4b shows the image intensity of the 3 inserts normalized by their

average values. The ground-truth, SPB and TOF images show similar image intensity values between the 2-beam partial-view images and the 20-beam full-view images. The variations in the FBP

images are due to statistical imaging noise. REAL-TIME RADIATION-DOSE MONITORING Radiotherapy treatment uses high-energy X-rays (for example, 10 MV X-rays) to kill cancer cells. The same

energy beams are conducive for P2T. The radiotherapy dose is closely related to the total energy released per unit mass (TERMA). TERMA is the energy loss of primary photons as they interact

in the medium. It is proportional to the fluence intensity, the energy of the primary photon and the total attenuation coefficient. The radiation dose is the local energy deposition from

both primary photons and secondary particles. The dose can be computed either through Monte Carlo simulation or by convolving TERMA with energy deposition kernels36 to account for the energy

spread owing to the finite travelling of secondary particles. In this study, we used Monte Carlo simulations to compute dosing. The number of pair-production interactions is proportional to

the fluence intensity and the pair-production attenuation coefficient. The P2T images, defined as the number of annihilation events, show the number of pair-production interactions

convoluted with a kernel representing the statistical probabilities of positron travelling before annihilation within the medium. We tested the feasibility of obtaining P2T images using

pair-production signals produced by radiotherapy beams in a patient with glioblastoma multiforme. The dose calculation was performed using Geant4 for an intensity-modulated radiotherapy

(IMRT) plan with 7-equal-spacing co-planar beams. The pencil beam size for dose calculation was 0.5 × 0.5 cm2. The dose voxel size was 0.25 × 0.25 × 0.25 cm3. The source is a 10 MV

poly-energetic X-ray point source. The dose calculation simulated 108 X-ray photons within each pencil beam. A dose matrix was constructed on the basis of the dose calculation result, which

converts the x-ray fluence intensity to dose distribution within the patient body. The dose matrix was used to create a treatment plan for the IMRT delivery technique37,38, where the

radiation dose distribution was optimized to achieve prescription dose within the target volume and minimal dose to the surrounding normal tissues, using convex optimization algorithms39,40.

The treatment plan optimization produces an optimized fluence map, dictating the number of particles in each pencil beam to achieve the optimized treatment plan. Assuming that the detector

efficiency is 10% to collect a coincident photon pair, we simulated 10% of the particles needed to deliver a 2 Gy fraction treatment, which amounts to a total of 221.2 billion primary

particles. The radiotherapy treatment dose (Fig. 5a) and the TERMA (Fig. 5b) are compared with P2T images (Fig. 5c). The image resolution is 0.25 × 0.25 cm2. All images were normalized by

the mean intensity value within the target and were displayed as iso-intensity colour-wash images superimposed on the CT image. The target and normal tissues are contoured with different

colours. Through optimization, the radiation dose was pushed towards the prescription dose of 2 Gy within the target and was tailored to avoid important normal tissues, including the

brainstem, chiasm and eyes. Figure 5d shows the cumulative intensity volume histograms (cIVHs) of dose, TERMA and P2T ground truth. The cIVH lines indicate the volume percentage of a

structure receiving intensity values greater than a threshold. As closely linked physical quantities, the dose, TERMA and P2T are also correlated, as shown in the intensity maps and the cIVH

lines. The highest intensity values are achieved within the target and the ring structure (a 1.5 cm shell surrounding the target). Sparing of normal tissues, including the brainstem, eyes,

optical nerves and the majority of the brain, is verified. In radiotherapy treatment, the number of particles and the radiation dose is 2–3 orders of magnitude greater than that for imaging,

producing high SNR images even with FBP reconstruction. The differences between the ground-truth and the reconstructed images are largely due to a low detection resolution in the patient’s

superior-inferior direction. The ground truth was computed with a 0.25 cm resolution in this direction, while the P2T images are effectively weighted sums of multiple image slices due to the

10 cm detector height. Note that although current radiotherapy treatment combines multiple pencil beams as an aperture for efficient delivery, SPB is a viable option with existing hardware

using multileaf collimator (MLC)41 or with a magnetic scanning beam system under development42. DISCUSSION We report an X-ray tomography method, P2T, for radiotherapy dose verification and

material imaging. Owing to the distinct image formation mechanism based on high-energy X-ray pair production, P2T provides three unique features that are distinct from the capabilities of

X-ray CT. First, P2T intensities are closely related to dose for in vivo dosimetry. Unlike X-ray-induced radiation acoustic imaging43 or Cerenkov imaging25, in vivo dosimetry using P2T is

not limited by anatomical locations and acoustic boundaries. Second, compared with CT with the photoelectric component, P2T is not as sensitive to high-_Z_ materials. Nonetheless, the P2T

image intensity is linear to the atomic number after fluence correction, helping disambiguate the difficult atomic-number mapping task. The current application for multiple imaging contrast

differentiation is limited by the few elements approved for clinical use, while the other elements in Fig. 2a, including tantalum, ytterbium, gold and bismuth, are still in the preclinical

stage as potential imaging contrast agents. For non-contrast use, the linearity to effective atomic numbers leads to a remarkable increase in P2T contrast for certain low-atomic-number soft

tissues, complementing CT’s sensitivity to mid and high-_Z_ materials. Third, unlike CT that generally exposes a large volume to imaging dose from many angles, P2T can image a partial volume

with as few as one beam (the sparse-view study used only two beams and 20% of the full-view), allowing geometrical control over the imaging dose distribution. These highly generalizable

features can profoundly influence a broad range of detection and diagnosis applications. Despite the theoretical analysis of P2T in this study, its acquisition is within the realm of

existing technologies. P2T benefits from decades of technological development of PET, which provides the energy and timing windows to remove non-pair-production photon contamination.

Moreover, for emission-guided radiotherapy, the integration of a high-energy source and a PET detector ring was recently demonstrated44. We note that MV X-rays produced by linear

accelerators are not readily available in a typical diagnostic department for general imaging applications. Therefore, the early development of the P2T will probably start at radiation

oncology as a means for dose verification and image-guided radiation therapy. The role of P2T may later expand to the imaging realm as a complementing technology to CT, with future

integration of therapy and diagnosis. We implemented three reconstruction methods for P2T. FBP is the least technically demanding, which can be acquired with a regular PET detector and

medical linac. Currently, the FBP images are only useful in high-dose therapeutic mode, and the low-SNR FBP images under low-dose P2T acquisition are only included as a reference. Given its

poor image quality, iterative reconstruction methods using total variation are unlikely to improve the image quality by much. The low-dose FBP P2T may be possible if using a different

detector setup with higher geometric efficiency. On the other hand, the SPB and TOF methods result in substantially higher SNRs compared with the FBP method with the same geometry setup. We

point out that ultrafast TOF detectors are an active area of research, with many technical challenges to balance time resolution and detecting efficiency. In the study, we simulated

detectors with a 20 ps time resolution to localize the annihilation event with an accuracy of 3 mm. The 20 ps time resolution detector is consistent with the roadmap of PET detection using

prompt Cherenkov emission31,32 or ultrafast emitting quantum-confined systems45,46, but both still require significant engineering development to be practical47. On the other hand, the SPB

does not require fast TOF detectors and is readily achievable using current technology. It largely relaxes the required time resolution due to the known excitation path. We assumed a 300 ps

time resolution for the SPB-based and FBP reconstruction, which is commercially available for PET33. The sequential pencil beams required for SPB are also feasible with current technologies,

such as MLC41 or scanning photon beams42. METHODS SIMULATION OF THE DETECTION SIGNAL We assumed that the primary photons within each pencil beam are released from the source following a

uniform distribution with an average releasing rate _R_. At the time of the incident, photon generation _t_incident is: $$t_{{{{\mathrm{incident}}}}}\left( {n,b} \right) =

t_{{{{\mathrm{incident}}}}}\left( {n - 1,b} \right) + \tilde t,\,\tilde t\sim U(0,\,2/R)$$ where _n_ is the index of the released primary photon, _b_ is the index of the pencil beam. In the

volume excitation, the first primary photons of all pencil beams are released altogether. In scanning pencil beam excitation, the first primary photon of one pencil beam only starts after

all photons are released in the previous pencil beam. When a qualifying photon (within the energy resolution window) passes through the ring detector, the colliding detector module records

the travelling time _t_travel of the detection event since the primary photon was generated and departed from the source, on the basis of the MC simulation in Geant4. The global time

_t_global of the detection is then computed by adding the travelling time _t_travel to the generation time of the corresponding incident photon _t_incident: $$t_{{{{\mathrm{global}}}}} =

t_{{{{\mathrm{incident}}}}} + t_{{{{\mathrm{travel}}}}}.$$ The detector response time _t_response is simulated as a Gaussian distribution with variance _σ_2 = Δ_T_2, where Δ_T_ is the time

resolution of the detector. The simulated detection time _t_detection of the photon is: $$t_{{{{\mathrm{detection}}}}} = t_{{{{\mathrm{global}}}}} +

t_{{{{\mathrm{response}}}}},\,t_{{{{\mathrm{response}}}}}\sim N(0,\,{{{\mathrm{{\Delta}}}}}T^2)$$ For each beam, the detected signals were discarded for all detector modules receiving

primary photons due to the difficulty of identifying the annihilation photons from the primary photons with energy near 511 keV. Some simplifications in detector geometry and properties were

made in this study. For simplicity, we assumed an ideal point source without leakage. In reality, a shielding structure is required to remove X-ray photons from source leakage (~1% of

primary X-ray photons), so that they would not interfere with the P2T signals. We also assumed an ideal ring detector without electronic noise and cross-talks in adjacent detector elements.

In reality, the detector response would decrease the image SNR and resolution. In addition, the photon may travel through a few detector modules before generating a signal, causing parallax

error. The parallax error could be avoided with a more advanced detector using depth-of-interaction information48,49. We also assumed an ideal geometry for CT. The poly-energetic CT source

was modelled as a single point source with 4.3 mm Al filtration, and we assumed an ideal detector response with no cross-talks. The simulation was based on a very thin fan-beam geometry (5 

mm fan beam) and an anti-scatter grid was not simulated. The impacts of non-ideal geometry on image quality can be found in the literature50,51,52,53. Despite assuming an ideal detector

response, the SNRs of the reconstructed images are still lower than the ground-truth P2T images. The low SNR can be attributed to a wide detector module (a single detector module is 10 cm

long in the patient longitudinal direction) and an extremely low detector geometric efficiency: the ring detector only covers 4.17% of the 4π space, leading to only (4.17%)2 = 0.17%

efficiency for collecting coincident photon pairs. Using detectors with multiple rows and extending the longitudinal coverage to 2 m (such as in the EXPLORER project for total-body PET

scanner54) could improve the image resolution and raise the detector geometric efficiency by 2 orders of magnitude. These improvements are expected to improve the SNR, shorten the imaging

time and reduce the radiation dose. We used the general-purpose Monte Carlo package Geant4 (ref. 29) to study the P2T performance. Geant4 includes numerous well-validated physical models and

is flexible for different applications, but as a CPU-based software package, it is extremely slow. To accelerate the simulation, we developed an automated and distributed computation

framework that allows asynchronous and scalable computation divided at the unit of individual pencil beams. A total of over 280 logical CPU cores were utilized for simulation. The simulation

time for the full-view phantom simulation, partial-view phantom simulation and radiotherapy imaging simulation are 30 h, 7 h and 6 d, respectively. Further acceleration may be achieved by

using the graphic processing unit (GPU)-based MC code with simplified physical models55. FBP RECONSTRUCTION From the simulated detector signals, coincident events were identified as two

energy-eligible photons (within the energy window) arriving at two detector modules within the coincidence time _t_coincidence. Two coincident events define a LOR, the line connecting the

two detector modules, indicating that the annihilation event happened on the LOR. With all LORs identified, a rebinning algorithm was applied to convert the list-mode pair-wise detector data

to the sinogram data. The list-mode data of 1,440 detectors were histogrammed into sinograms having 227 radial bins and 1,440 angles. FBP reconstruction was applied to the sinogram data

using the MIRT30, where a plain ramp filter was applied on the Fourier transform of the sinogram at each angle before applying an inverse Fourier transform and back projection. SPB

RECONSTRUCTION SPB uses SPBE, and the locations of annihilation events can be further tracked down to the area where the incident beam passes through. The pencil beam width was 2 mm in this

study. The SPB-based reconstruction was applied to the list-mode pair-wise detector data. The intersection of each detector pair’s corresponding LOR and pencil beam path locates the

annihilation event. The SPB reconstruction tallied the intersections within each voxel from all detector-pair data. Each intersection point was locally convolved with a Gaussian kernel (_σ_ 

= 2 mm in this study) to reduce image noises and artefacts. TOF RECONSTRUCTION For detectors with a high time resolution (such as 20 ps), the location of the annihilation event can be

computed from the flying time of the two photons. The travelling distance difference Δ_d_ between the two photons is \({\Delta}d = c{\Delta}t\), where _c_ is the speed of light and Δ_t_ is

the time difference of the two photons when arriving at the detectors. The location of the annihilation event on the LOR can then be derived from the travelling distance difference Δ_d_. The

located point was locally convolved with a Gaussian kernel (_σ_ = 2 mm in this study) to reduce image noises and artefacts. Note that the image resolution \({{{\mathrm{{\Delta}}}}}R =

c{{{\mathrm{{\Delta}}}}}T/2\) is limited by the detector time resolution Δ_T_. ATTENUATION CORRECTION Before arriving at the detectors, the two coincident photons may be absorbed or

scattered as they travel through the imaging subject. To compensate for the attenuation, the detector data need to be corrected accordingly. For FBP, the attenuation correction was performed

on the sinogram: $$P_\mathrm{c}\left( i \right) = P_\mathrm{r}\left( i \right){{{\mathrm{exp}}}}\left( {\mathop {\int}\limits_i {\mu _{511}\,\mathrm{d}l} } \right),$$ where _P_c is the

corrected sinogram and _P_r is the raw sinogram obtained directly from the list-mode detector data. _i_ is the index of the sinogram. _μ_511 is the attenuation coefficient of the imaging

subject for 511 keV X-ray. d_l_ stands for differential of the variable _l_, which is the path length of the X-ray photon. The attenuation correction factor \({{{\mathrm{exp}}}}({\mathop

{\int}\limits_i {\mu _{511}\,\mathrm{d}l} })\) is an integral over the path of the coincident photons associated with the _i_th element of the sinogram. After the attenuation correction, the

corrected sinogram _P_c was used for FBP reconstruction. For the SPB-based method and the TOF method, the attenuation correction factor was computed as the same line integral over the path

of the coincident photons. During reconstruction, the voxel-wise tallies were weighted by the correction factor of each identified detector pair. FLUENCE CORRECTION FOR QUANTITATIVE IMAGING

The total nuclear pair-production cross-section per atom _α_a is28: $$\alpha _\mathrm{a} = \sigma _0Z^2\mathop {\smallint }\limits_0^1 Pd\left( {\frac{{T^ + }}{{h\nu - 2m_0c^2}}} \right) =

\sigma _0Z^2\bar P\tilde \propto Z^2$$ where _T_+ is the positron energy, hν is the energy of the incident photon, _Z_ is the atomic number, _P_ is a function of hν and _Z_, _m_0 is the mass

of electron and positron, _σ_0 is a constant. The attenuation coefficient _α_ of nuclear pair production is therefore proportional to the atomic number _Z_ and density _ρ_: $$\alpha = \rho

\alpha _a\frac{{N_0}}{A} \propto \rho Z,$$ where _A_ is the atomic mass number and _N_0 is the Avogadro constant. A simplification was made using the property that _Z_/_A_ is close to 1 for

most elements. The P2T image signal is proportional to the attenuation coefficient of the imaging material and the X-ray fluence intensity _f_: $$I \propto f\alpha \propto f\rho Z$$ The

fluence intensity at image voxel _v_ from pencil beam _b_ is $$f_{v,b} = f_{r,b}\frac{{\left( {p_r - p_s} \right)^2}}{{\left( {p_v - p_s} \right)^2}}\exp \left( { - \mathop {\smallint

}\limits_{p_s}^{p_v} \mu _\mathrm{{10MV}}\,\mathrm{d}l} \right),$$ where _p__s_, _p__v_ and _p__r_ are the locations of the source, image voxel _v_ and reference point _r_, respectively. The

reference point _r_ is outside the imaging subject and on the line segment connecting the source and the voxel _v_. _f__r,b_ is the fluence intensity at the reference point from pencil beam

_b_, and _f__v,b_ is the fluence intensity at image voxel _v_ from pencil beam _b_. _μ_10MV is the attenuation coefficient of the imaging subject for 10 MV X-ray. The integral was computed

using Siddon’s ray tracing algorithm56 with the matRad toolbox57. The total fluence intensity at image voxel _v_ is $$f_v = \mathop {\sum }\limits_b f_{v,b}.$$ The dependence of the image

intensity on the incident X-ray fluence intensity can be removed with the fluence correction. The corrected image intensity \(\tilde I\) is proportional to the product of density and the

atomic number, obtained by voxel-wise scaling of the P2T image by the fluence intensity _f_: $$\tilde I_v = I_v/f_v \propto \rho _vZ_v.$$ Note that the fluence correction was only performed

for quantitative imaging applications and did not apply to the P2T images obtained during radiotherapy treatments. P2T IMAGE CONTRAST FOR COMPOUNDS The Bragg’s additivity rule applies to the

pair-production mass attenuation coefficient of compounds \(\left( {\frac{\alpha }{\rho }} \right)_{\mathrm{comp}}\): $$\left( {\frac{\alpha }{\rho }} \right)_{\mathrm{comp}} = \mathop

{\sum }\limits_i \left( {\frac{\alpha }{\rho }} \right)_if_i$$ where _f__i_ and \(({\frac{\alpha }{\rho }})_i\) are the weight fraction and the pair-production mass attenuation coefficient

of element _i_, respectively. After fluence correction, the P2T image intensity \(\tilde I\) is proportional to the pair-production attenuation coefficient of compounds \(\left( \alpha

\right)_{\mathrm{comp}}\). Using the property that \(\left( {\frac{\alpha }{\rho }} \right)_i \propto Z_i\), then $$\tilde I \propto \left( \alpha \right)_{\mathrm{comp}} \propto \left( \rho

\right)_{\mathrm{comp}}\mathop {\sum }\limits_i Z_if_i,$$ where _Z__i_ is the atomic number of element _i_, and (_ρ_)comp is the density of the compounds. Let _Z_eff be the effective atomic

number $$Z_{\mathrm{eff}} = \mathop {\sum }\limits_i Z_if_i,$$ then the P2T image intensity \(\tilde I\) is proportional to \(\left( \rho \right)_{\mathrm{comp}}Z_{\mathrm{eff}}\): $$\tilde

I \propto \left( \rho \right)_{\mathrm{comp}}Z_{\mathrm{eff}}.$$ Therefore, \(\left( \rho \right)_{\mathrm{comp}}Z_{\mathrm{eff}}\) provides a theoretical value of P2T image contrast. The

material composition58, density and effective atomic number of the 10 inserts in the standard phantom are listed in Supplementary Table 1. RADIOTHERAPY DOSE AND P2T The radiotherapy dose is

closely related to a concept named TERMA, which is defined for photons as $${{{\mathrm{TERMA}}}} = {\int} {f(E)E\frac{{\mu (E)}}{\rho }dE}$$ where _f_ is the fluence intensity, _E_ is the

photon energy, _ρ_ is the material density and _μ_ is the attenuation coefficient, including contributions from Rayleigh, photoelectric, Compton and pair-production interactions. Radiation

dose can be computed through direct Monte Carlo simulation or through an analytical approach, where TERMA is convoluted with energy deposition kernels using the collapsed cone convolution

algorithm36. These Monte-Carlo precomputed energy deposition kernels account for the energy spread due to finite travelling of secondary particles, with different dose spreads under

different materials and photon energies. The P2T image collected during radiotherapy is also proportional to the fluence intensity (no fluence correction applied): $$I \propto {\int}

{f(E)\alpha (E)dE}$$ where _α_(_E_) is the pair-production attenuation coefficient. Most human tissues are approximately water-equivalent under the high-energy X-rays used in radiotherapy.

Therefore, the P2T image intensity and the TERMA are proportional to the fluence intensity. Consequently, the P2T image is strongly correlated with the dose distribution and can be used for

in vivo dose monitoring. POSITRON TRAVELLING BEFORE ANNIHILATION Supplementary Fig. 1 shows histograms of the positron travelling distances before annihilation and the initial positron

energies from pair production. The median of the positron travelling distances before annihilation was 4.6 mm, and the median of the initial positron kinetic energy was 1.1 MeV. The finite

positron travels contribute to the theoretical image resolution of raw P2T images. On the other hand, the positron travelling distances histogram is determined by the incident photon energy

spectrum. Super-resolution P2T images can be recovered through deconvolution with the precomputed kernels. REPORTING SUMMARY Further information on research design is available in the Nature

Research Reporting Summary linked to this article. DATA AVAILABILITY The main data supporting the results in this study are available within the paper and its Supplementary Information. All

phantom data used to generate simulation data in this study are available on Zenodo with the identifier https://doi.org/10.5281/zenodo.6330603. The raw and analysed P2T detector data

generated during the study are too large to be publicly shared, yet they are available for research purposes from the corresponding author on reasonable request. CODE AVAILABILITY All codes

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for use in radiological protection: reference values - ICRP Publication 89. _Ann. ICRP_ https://doi.org/10.1016/S0146-6453(03)00002-2 (2002). Download references ACKNOWLEDGEMENTS This

research is supported by DOE Grant Nos. DE-SC0017057 (K.S.) and DE-SC0017687 (K.S.), NIH Grant Nos. R01CA188300 (K.S.), R43CA183390 (K.S.) and R44CA183390 (K.S.). AUTHOR INFORMATION AUTHORS

AND AFFILIATIONS * Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA, USA Qihui Lyu, Ryan Neph & Ke Sheng Authors * Qihui Lyu View author

publications You can also search for this author inPubMed Google Scholar * Ryan Neph View author publications You can also search for this author inPubMed Google Scholar * Ke Sheng View

author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS All authors contributed extensively to the study. K.S. conceived the study and designed the

experiments. Q.L. designed and implemented Monte Carlo modelling and image reconstruction, and analysed the results for P2T and CT. R.N. implemented Monte Carlo modelling for dose

calculations and the distributed CPU computing framework for acceleration. All authors discussed the results and implications, and commented on the manuscript. CORRESPONDING AUTHOR

Correspondence to Ke Sheng. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. PEER REVIEW PEER REVIEW INFORMATION _Nature Biomedical Engineering_ thanks

Petr Bruza, Cynthia McCollough and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. ADDITIONAL INFORMATION

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from high-energy X-rays for the monitoring of radiotherapy dosing. _Nat. Biomed. Eng_ 7, 323–334 (2023). https://doi.org/10.1038/s41551-022-00953-8 Download citation * Received: 25 January

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