Table of contents

This work uses Monte Carlo radiation transport simulation to assess the potential benefits of gold nanoparticles (AuNP) in the treatment of neovascular age-related macular degeneration with stereotactic radiosurgery. Clinically, a 100 kVp x-ray beam of 4 mm diameter is aimed at the macula to deliver an ablative dose in a single fraction. In the transport model, AuNP accumulated at the bottom of the macula are targeted with a source representative of the clinical beam in order to provide enhanced dose to the diseased macular endothelial cells. It is observed that, because of the AuNP, the dose to the endothelial cells can be significantly enhanced, allowing for greater sparing of optic nerve, retina and other neighboring healthy tissue. For 20 nm diameter AuNP concentration of 32 mg g⁻¹, which has been shown to be achievable in vivo, a dose enhancement ratio (DER) of 1.97 was found to be possible, which could potentially be increased through appropriate optimization of beam quality and/or AuNP targeting. A significant enhancement in dose is seen in the vicinity of the AuNP layer within 30 μm, peaked at the AuNP-tissue interface. Different angular tilting of the 4 mm beam results in a similar enhancement. The DER inside and in the penumbra of the 4 mm irradiation-field are almost the same while the actual delivered dose is more than one order of magnitude lower outside the field leading to normal tissue sparing. The prescribed dose to macular endothelial cells can be delivered using almost half of the radiation allowing reduction of dose to the neighboring organs such as retina/optic nerve by 49% when compared to a treatment without AuNP.

Tumor acute hypoxia has a dynamic component that is also, at least partially, coherent. Using blood oxygen level dependent magnetic resonance imaging, we observed coherent oscillations in hemoglobin saturation dynamics in cell line xenograft models of head and neck squamous cell carcinoma. We posit a well-established biochemical nonlinear oscillatory mechanism called the glycolytic oscillator as a potential cause of the coherent oscillations in tumors. These data suggest that metabolic changes within individual tumor cells may affect the local tumor microenvironment including oxygen availability and therefore radiosensitivity. These individual cells can synchronize the oscillations in patches of similar intermediate glucose levels. These alterations have potentially important implications for radiation therapy and are a potential target for optimizing the cancer response to radiation.

PET/CT plays an important role in radiotherapy planning for lung tumors. Several segmentation algorithms have been proposed for PET tumor segmentation. However, most of them do not take into account respiratory motion and are not well validated. The aim of this work was to evaluate a semi-automated contrast-oriented algorithm (COA) for PET tumor segmentation adapted to retrospectively gated (4D) images. The evaluation involved a wide set of 4D-PET/CT acquisitions of dynamic experimental phantoms and lung cancer patients. In addition, segmentation accuracy of 4D-COA was compared with four other state-of-the-art algorithms. In phantom evaluation, the physical properties of the objects defined the gold standard. In clinical evaluation, the ground truth was estimated by the STAPLE (Simultaneous Truth and Performance Level Estimation) consensus of three manual PET contours by experts. Algorithm evaluation with phantoms resulted in: (i) no statistically significant diameter differences for different targets and movements ($\Delta \phi =0.3\pm 1.6$ mm); (ii) reproducibility for heterogeneous and irregular targets independent of user initial interaction and (iii) good segmentation agreement for irregular targets compared to manual CT delineation in terms of Dice Similarity Coefficient (DSC  =  $0.66\pm 0.04$), Positive Predictive Value (PPV  =  $0.81\pm 0.06$) and Sensitivity (Sen.  =  $0.49\pm 0.05$). In clinical evaluation, the segmented volume was in reasonable agreement with the consensus volume (difference in volume (%Vol)  =  $40\pm 30$, DSC  =  $0.71\pm 0.07$ and PPV  =  $0.90\pm 0.13$). High accuracy in target tracking position ($\Delta$ME) was obtained for experimental and clinical data ($\Delta$ME${{}_{\text{exp}}}=0\pm 3$ mm; $\Delta$ME${{}_{\text{clin}}}=0.3\pm 1.4$ mm). In the comparison with other lung segmentation methods, 4D-COA has shown the highest volume accuracy in both experimental and clinical data. In conclusion, the accuracy in volume delineation, position tracking and its robustness on highly irregular target movements, make this algorithm a useful tool for 4D-PET based volume definition for radiotherapy planning of lung cancer and may help to improve the reproducibility in PET quantification for therapy response assessment and prognosis.

In this work we develop a computer-aided diagnosis (CAD) scheme for classification of pulmonary disease for grating-based x-ray radiography. In addition to conventional transmission radiography, the grating-based technique provides a dark-field imaging modality, which utilizes the scattering properties of the x-rays. This modality has shown great potential for diagnosing early stage emphysema and fibrosis in mouse lungs in vivo. The CAD scheme is developed to assist radiologists and other medical experts to develop new diagnostic methods when evaluating grating-based images. The scheme consists of three stages: (i) automatic lung segmentation; (ii) feature extraction from lung shape and dark-field image intensities; (iii) classification between healthy, emphysema and fibrosis lungs. A study of 102 mice was conducted with 34 healthy, 52 emphysema and 16 fibrosis subjects. Each image was manually annotated to build an experimental dataset. System performance was assessed by: (i) determining the quality of the segmentations; (ii) validating emphysema and fibrosis recognition by a linear support vector machine using leave-one-out cross-validation. In terms of segmentation quality, we obtained an overlap percentage (Ω) 92.63  ±  3.65%, Dice Similarity Coefficient (DSC) 89.74  ±  8.84% and Jaccard Similarity Coefficient 82.39  ±  12.62%. For classification, the accuracy, sensitivity and specificity of diseased lung recognition was 100%. Classification between emphysema and fibrosis resulted in an accuracy of 93%, whilst the sensitivity was 94% and specificity 88%. In addition to the automatic classification of lungs, deviation maps created by the CAD scheme provide a visual aid for medical experts to further assess the severity of pulmonary disease in the lung, and highlights regions affected.

Respiratory-induced organ motion is a technical challenge to PET imaging. This motion induces displacements and deformation of the organs tissues, which need to be taken into account when reconstructing the spatial radiation activity. Classical image-based methods that describe motion using deformable image registration (DIR) algorithms cannot fully take into account the non-reproducibility of the respiratory internal organ motion nor the tissue volume variations that occur during breathing. In order to overcome these limitations, various biomechanical models of the respiratory system have been developed in the past decade as an alternative to DIR approaches. In this paper, we describe a new method of correcting motion artefacts in PET image reconstruction adapted to motion estimation models such as those based on the finite element method. In contrast with the DIR-based approaches, the radiation activity was reconstructed on deforming tetrahedral meshes. For this, we have re-formulated the tomographic reconstruction problem by introducing a time-dependent system matrix based calculated using tetrahedral meshes instead of voxelized images. The MLEM algorithm was chosen as the reconstruction method. The simulations performed in this study show that the motion compensated reconstruction based on tetrahedral deformable meshes has the capability to correct motion artefacts. Results demonstrate that, in the case of complex deformations, when large volume variations occur, the developed tetrahedral based method is more appropriate than the classical DIR-based one. This method can be used, together with biomechanical models controlled by external surrogates, to correct motion artefacts in PET images and thus reducing the need for additional internal imaging during the acquisition.

In geometric calibration of cone-beam computed tomography (CBCT), sphere-like objects such as balls are widely imaged, the positioning information of which is obtained to determine the unknown geometric parameters. In this process, the accuracy of the detector location of CB projection of the center of the ball, which we call the center projection, is very important, since geometric calibration is sensitive to errors in the positioning information. Currently in almost all the geometric calibration using balls, the center projection is invariably estimated by the center of the support of the projection or the centroid of the intensity values inside the support approximately. Clackdoyle's work indicates that the center projection is not always at the center of the support or the centroid of the intensity values inside, and has given a quantitative analysis of the maximum errors in evaluating the center projection by the centroid. In this paper, an exact method is proposed to calculate the center projection, utilizing both the detector location of the ellipse center and the two axis lengths of the ellipse. Numerical simulation results have demonstrated the precision and the robustness of the proposed method. Finally there are some comments on this work with non-uniform density balls, as well as the effect by the error occurred in the evaluation for the location of the orthogonal projection of the cone vertex onto the detector.

With the advent of MR guided radiotherapy the relevance of Monte Carlo radiation transport simulations in the presence of strong magnetic fields (B-fields) is increasing. While new tests are available to benchmark these simulation algorithms for internal consistency, their application to known codes such as EGSnrc, PENELOPE, and GEANT4 is yet to be provided. In this paper a method is provided to apply the Fano cavity test as a benchmark for a generic implementation of B-field effects in PENELOPE. In addition, it is investigated whether violation of the conditions for the Fano test can partially explain the change in the response of ionization chambers in the presence of strong B-fields.

In the present paper it is shown that the condition of isotropy of the secondary particle field (Charged Particle Isotropy, CPI) is an essential requirement to apply the Fano test in the presence of B-fields. Simulations in PENELOPE are performed with (B  =  0.0 T) and (B  =  1.5 T) for cylindrical cavity geometry. The secondary particle field consists of electrons generated from a mono-energetic source (E  =  0.5–4.0 MeV) with a uniform source density and different angular distributions; isotropic, mono-directional, and Compton. In realistic photon fields the secondary radiation field has a non-isotropic angular distribution due to the Compton process. Based on the simulations for the Compton angular distribution (non-CPI), the response change of the cavity model in a uniform radiation field in the presence of B-fields is investigated.

For the angular distributions that violate the CPI condition and B  =  1.5 T, the deviations from 1 are considerable, which emphasizes the requirement of CPI. For the isotropic angular distributions obeying this requirement, both the results for B  =  0.0 T and B  =  1.5 T shows deviations from the predictions for E  ⩾  1.5 MeV with values up to 1.0% for E  =  4.0 MeV. Nevertheless, due to the high correlation in the deviation for B  =  0.0 T and B  =  1.5 T, the accuracy of the PENELOPE code for the simulation of the change in detector response in the presence of B-fields is within 0.3%. The effect of the B-field on the detector response for non-isotropic angular distributions suggests that violation of CPI is a major contribution to the response change of ionization chambers in the presence of B-fields.

Proton range verification based on prompt gamma imaging is increasingly considered in proton therapy. Tissue heterogeneity normal to the beam direction or near the end of range may considerably degrade the ability of prompt gamma imaging to detect proton range shifts. The goal of this study was to systematically investigate the accuracy and precision of range detection from prompt gamma emission profiles for various fractions for intensity modulated proton therapy of prostate cancer, using a comprehensive clinical dataset of 15 different CT scans for 5 patients.

Monte Carlo simulations using Geant4 were performed to generate spot-by-spot dose distributions and prompt gamma emission profiles for prostate treatment plans. The prompt gammas were scored at their point of emission. Three CT scans of the same patient were used to evaluate the impact of inter-fractional changes on proton range. The range shifts deduced from the comparison of prompt gamma emission profiles in the planning CT and subsequent CTs were then correlated to the corresponding range shifts deduced from the dose distributions for individual pencil beams. The distributions of range shift differences between prompt gamma and dose were evaluated in terms of precision (defined as half the 95% inter-percentile range IPR) and accuracy (median). In total about 1700 individual proton pencil beams were investigated.

The IPR of the relative range shift differences between the dose profiles and the prompt gamma profiles varied between  ±1.4 mm and  ±2.9 mm when using the more robust profile shifting analysis. The median was found smaller than 1 mm. Methods to identify and reject unreliable spots for range verification due to range mixing were derived and resulted in an average 10% spot rejection, clearly improving the prompt gamma–dose correlation.

This work supports that prompt gamma imaging can offer a reliable indicator of range changes due to anatomical variations and tissue heterogeneity in scanning proton treatment of prostate cancer patients when considering prompt gamma emission profiles.

For high-resolution, iterative 3D PET image reconstruction the efficient implementation of forward-backward projectors is essential to minimise the calculation time. Mathematically, the projectors are summarised as a system response matrix (SRM) whose elements define the contribution of image voxels to lines-of-response (LORs). In fact, the SRM easily comprises billions of non-zero matrix elements to evaluate the tremendous number of LORs as provided by state-of-the-art PET scanners. Hence, the performance of iterative algorithms, e.g. maximum-likelihood-expectation-maximisation (MLEM), suffers from severe computational problems due to the intensive memory access and huge number of floating point operations.

Here, symmetries occupy a key role in terms of efficient implementation. They reduce the amount of independent SRM elements, thus allowing for a significant matrix compression according to the number of exploitable symmetries. With our previous work, the PET REconstruction Software TOolkit (PRESTO), very high compression factors (>300) are demonstrated by using specific non-Cartesian voxel patterns involving discrete polar symmetries. In this way, a pre-calculated memory-resident SRM using complex volume-of-intersection calculations can be achieved. However, our original ray-driven implementation suffers from addressing voxels, projection data and SRM elements in disfavoured memory access patterns. As a consequence, a rather limited numerical throughput is observed due to the massive waste of memory bandwidth and inefficient usage of cache respectively.

In this work, an advantageous symmetry-driven evaluation of the forward-backward projectors is proposed to overcome these inefficiencies. The polar symmetries applied in PRESTO suggest a novel organisation of image data and LOR projection data in memory to enable an efficient single instruction multiple data vectorisation, i.e. simultaneous use of any SRM element for symmetric LORs. In addition, the calculation time is further reduced by using simultaneous multi-threading (SMT). A global speedup factor of 11 without SMT and above 100 with SMT has been achieved for the improved CPU-based implementation while obtaining equivalent numerical results.

Anatomical landmark detection plays an important role in medical image analysis, e.g. for registration, segmentation and quantitative analysis. Among the various existing methods for landmark detection, regression-based methods have recently attracted much attention due to their robustness and efficiency. In these methods, landmarks are localised through voting from all image voxels, which is completely different from the classification-based methods that use voxel-wise classification to detect landmarks. Despite their robustness, the accuracy of regression-based landmark detection methods is often limited due to (1) the inclusion of uninformative image voxels in the voting procedure, and (2) the lack of effective ways to incorporate inter-landmark spatial dependency into the detection step. In this paper, we propose a collaborative landmark detection framework to address these limitations. The concept of collaboration is reflected in two aspects. (1) Multi-resolution collaboration. A multi-resolution strategy is proposed to hierarchically localise landmarks by gradually excluding uninformative votes from faraway voxels. Moreover, for informative voxels near the landmark, a spherical sampling strategy is also designed at the training stage to improve their prediction accuracy. (2) Inter-landmark collaboration. A confidence-based landmark detection strategy is proposed to improve the detection accuracy of 'difficult-to-detect' landmarks by using spatial guidance from 'easy-to-detect' landmarks. To evaluate our method, we conducted experiments extensively on three datasets for detecting prostate landmarks and head & neck landmarks in computed tomography images, and also dental landmarks in cone beam computed tomography images. The results show the effectiveness of our collaborative landmark detection framework in improving landmark detection accuracy, compared to other state-of-the-art methods.

Several solid phantom materials have been tested regarding their suitability as water substitutes for dosimetric measurements in brachytherapy with ¹⁹²Ir as a typical high energy photon emitter. The radial variations of the spectral photon fluence, of the total, primary and scattered photon fluence and of the absorbed dose to water in the transversal plane of the tested cylindrical phantoms surrounding a centric and coaxially arranged Varian GammaMed afterloading ¹⁹²Ir brachytherapy source were Monte-Carlo simulated in EGSnrc. The degree of water equivalence of a phantom material was evaluated by comparing the radial dose-to-water profile in the phantom material with that in water. The phantom size was varied over a large range since it influences the dose contribution by scattered photons with energies diminished by single and multiple Compton scattering. Phantom axis distances up to 10 cm were considered as clinically relevant. Scattered photons with energies reaching down into the 25 keV region dominate the photon fluence at source distances exceeding 3.5 cm.

The tested phantom materials showed significant differences in the degree of water equivalence. In phantoms with radii up to 10 cm, RW1, RW3, Solid Water, HE Solid Water, Virtual Water, Plastic Water DT, and Plastic Water LR phantoms show excellent water equivalence with dose deviations from a water phantom not exceeding 0.8%, while Original Plastic Water (as of 2015), Plastic Water (1995), Blue Water, polyethylene, and polystyrene show deviations up to 2.6%. For larger phantom radii up to 30 cm, the deviations for RW1, RW3, Solid Water, HE Solid Water, Virtual Water, Plastic Water DT, and Plastic Water LR remain below 1.4%, while Original Plastic Water (as of 2015), Plastic Water (1995), Blue Water, polyethylene, and polystyrene produce deviations up to 8.1%. PMMA plays a separate role, with deviations up to 4.3% for radii not exceeding 10 cm, but below 1% for radii up to 30 cm.

As suggested by the results of the dose simulations and the values of the linear attenuation coefficient, μ, over a large energy range, the balanced content of inorganic additives in a phantom material is regarded as the key feature, providing water equivalence with regard to the attenuation of the primary photons, the release of low-energy photons by Compton scattering, and their attenuation by a combination of the photoelectric and Compton effects.

This study aims at the experimental determination of the detector-specific 1D lateral dose response function K(x) and of its associated rotational symmetric counterpart K(r) for a set of high-resolution detectors presently used in narrow-beam photon dosimetry. A combination of slit-beam, radiochromic film, and deconvolution techniques served to accomplish this task for four detectors with diameters of their sensitive volumes ranging from 1 to 2.2 mm. The particular aim of the experiment was to examine the existence of significant negative portions of some of these response functions predicted by a recent Monte-Carlo-simulation (Looe et al2015Phys. Med. Biol. 60 6585–607).

In a 6 MV photon slit beam formed by the Siemens Artiste collimation system and a 0.5 mm wide slit between 10 cm thick lead blocks serving as the tertiary collimator, the true cross-beam dose profile D(x) at 3 cm depth in a large water phantom was measured with radiochromic film EBT3, and the detector-affected cross-beam signal profiles M(x) were recorded with a silicon diode, a synthetic diamond detector, a miniaturized scintillation detector, and a small ionization chamber. For each detector, the deconvolution of the convolution integral M(x)  =  K(x)  ∗  D(x) served to obtain its specific 1D lateral dose response function K(x), and K(r) was calculated from it. Fourier transformations and back transformations were performed using function approximations by weighted sums of Gaussian functions and their analytical transformation.

The 1D lateral dose response functions K(x) of the four types of detectors and their associated rotational symmetric counterparts K(r) were obtained. Significant negative curve portions of K(x) and K(r) were observed in the case of the silicon diode and the diamond detector, confirming the Monte-Carlo-based prediction (Looe et al2015Phys. Med. Biol. 60 6585–607). They are typical for the perturbation of the secondary electron field by a detector with enhanced electron density compared with the surrounding water. In the cases of the scintillation detector and the small ionization chamber, the negative curve portions of K(x) practically vanish. It is planned to use the measured functions K(x) and K(r) to deconvolve clinical narrow-beam signal profiles and to correct the output factor values obtained with various high-resolution detectors.

The ability to monitor tumor motion without implanted markers is clinically advantageous for lung image-guided radiotherapy (IGRT). Existing markerless tracking methods often suffer from overlapping structures and low visibility of tumors on kV projection images. We introduce the short arc tumor tracking (SATT) method to overcome these issues. The proposed method utilizes multiple kV projection images selected from a nine-degree imaging arc to improve tumor localization, and respiratory-correlated 4D cone-beam CT (CBCT) prior knowledge to minimize the effects of overlapping anatomies. The 3D tumor position is solved as an optimization problem with prior knowledge incorporated via regularization. We retrospectively validated SATT on 11 clinical scans from four patients with central tumors. These patients represent challenging scenarios for markerless tumor tracking due to the inferior adjacent contrast. The 3D trajectories of implanted fiducial markers were used as the ground truth for tracking accuracy evaluation. In all cases, the tumors were successfully tracked at all gantry angles. Compared to standard pre-treatment CBCT guidance alone, trajectory errors were significantly smaller with tracking in all cases, and the improvements were the most prominent in the superior-inferior direction. The mean 3D tracking error ranged from 2.2–9.9 mm, which was 0.4–2.6 mm smaller compared to pre-treatment CBCT. In conclusion, we were able to directly track tumors with inferior visibility on kV projection images using SATT. Tumor localization accuracies are significantly better with tracking compared to the current standard of care of lung IGRT. Future work involves the prospective evaluation and clinical implementation of SATT.

This study aims at improving the accuracy of temperature simulation for temperature-controlled radio frequency ablation (RFA). We proposed a new voltage-calibration method in the simulation and investigated the feasibility of a hyperbolic bioheat equation (HBE) in the RFA simulation with longer durations and higher power. A total of 40 RFA experiments was conducted in a liver-mimicking phantom. Four mathematical models with multipolar electrodes were developed by the finite element method in COMSOL software: HBE with/without voltage calibration, and the Pennes bioheat equation (PBE) with/without voltage calibration. The temperature-varied voltage calibration used in the simulation was calculated from an experimental power output and temperature-dependent resistance of liver tissue. We employed the HBE in simulation by considering the delay time $\tau$ of 16 s. First, for simulations by each kind of bioheat equation (PBE or HBE), we compared the differences between the temperature-varied voltage-calibration and the fixed-voltage values used in the simulations. Then, the comparisons were conducted between the PBE and the HBE in the simulations with temperature-varied voltage calibration. We verified the simulation results by experimental temperature measurements on nine specific points of the tissue phantom. The results showed that: (1) the proposed voltage-calibration method improved the simulation accuracy of temperature-controlled RFA for both the PBE and the HBE, and (2) for temperature-controlled RFA simulation with the temperature-varied voltage calibration, the HBE method was 0.55 °C more accurate than the PBE method. The proposed temperature-varied voltage calibration may be useful in temperature field simulations of temperature-controlled RFA. Besides, the HBE may be used as an alternative in the simulation of long-duration high-power RFA.

Accurate tumor segmentation in [18F]-fluorodeoxyglucose positron emission tomography is crucial for tumor response assessment and target volume definition in radiation therapy. Evaluation of segmentation methods from clinical data without ground truth is usually based on physicians' manual delineations. In this context, the simultaneous truth and performance level estimation (STAPLE) algorithm could be useful to manage the multi-observers variability. In this paper, we evaluated how this algorithm could accurately estimate the ground truth in PET imaging.

Complete evaluation study using different criteria was performed on simulated data. The STAPLE algorithm was applied to manual and automatic segmentation results. A specific configuration of the implementation provided by the Computational Radiology Laboratory was used.

Consensus obtained by the STAPLE algorithm from manual delineations appeared to be more accurate than manual delineations themselves (80% of overlap). An improvement of the accuracy was also observed when applying the STAPLE algorithm to automatic segmentations results.

The STAPLE algorithm, with the configuration used in this paper, is more appropriate than manual delineations alone or automatic segmentations results alone to estimate the ground truth in PET imaging. Therefore, it might be preferred to assess the accuracy of tumor segmentation methods in PET imaging.

Respiratory triggered four dimensional cone-beam computed tomography (RT 4D CBCT) is a novel technique that uses a patient's respiratory signal to drive the image acquisition with the goal of imaging dose reduction without degrading image quality. This work investigates image quality and dose using patient-measured respiratory signals for RT 4D CBCT simulations. Studies were performed that simulate a 4D CBCT image acquisition using both the novel RT 4D CBCT technique and a conventional 4D CBCT technique. A set containing 111 free breathing lung cancer patient respiratory signal files was used to create 111 pairs of RT 4D CBCT and conventional 4D CBCT image sets from realistic simulations of a 4D CBCT system using a Rando phantom and the digital phantom, XCAT. Each of these image sets were compared to a ground truth dataset from which a mean absolute pixel difference (MAPD) metric was calculated to quantify the degradation of image quality. The number of projections used in each simulation was counted and was assumed as a surrogate for imaging dose. Based on 111 breathing traces, when comparing RT 4D CBCT with conventional 4D CBCT, the average image quality was reduced by 7.6% (Rando study) and 11.1% (XCAT study). However, the average imaging dose reduction was 53% based on needing fewer projections (617 on average) than conventional 4D CBCT (1320 projections). The simulation studies have demonstrated that the RT 4D CBCT method can potentially offer a 53% saving in imaging dose on average compared to conventional 4D CBCT in simulation studies using a wide range of patient-measured breathing traces with a minimal impact on image quality.

Prior-image-based reconstruction (PIBR) methods leveraging patient-specific anatomical information from previous imaging studies and/or sequences have demonstrated dramatic improvements in dose utilization and image quality for low-fidelity data. However, a proper balance of information from the prior images and information from the measurements is required (e.g. through careful tuning of regularization parameters). Inappropriate selection of reconstruction parameters can lead to detrimental effects including false structures and failure to improve image quality. Traditional methods based on heuristics are subject to error and sub-optimal solutions, while exhaustive searches require a large number of computationally intensive image reconstructions. In this work, we propose a novel method that prospectively estimates the optimal amount of prior image information for accurate admission of specific anatomical changes in PIBR without performing full image reconstructions. This method leverages an analytical approximation to the implicitly defined PIBR estimator, and introduces a predictive performance metric leveraging this analytical form and knowledge of a particular presumed anatomical change whose accurate reconstruction is sought. Additionally, since model-based PIBR approaches tend to be space-variant, a spatially varying prior image strength map is proposed to optimally admit changes everywhere in the image (eliminating the need to know change locations a priori). Studies were conducted in both an ellipse phantom and a realistic thorax phantom emulating a lung nodule surveillance scenario. The proposed method demonstrated accurate estimation of the optimal prior image strength while achieving a substantial computational speedup (about a factor of 20) compared to traditional exhaustive search. Moreover, the use of the proposed prior strength map in PIBR demonstrated accurate reconstruction of anatomical changes without foreknowledge of change locations in phantoms where the optimal parameters vary spatially by an order of magnitude or more. In a series of studies designed to explore potential unknowns associated with accurate PIBR, optimal prior image strength was found to vary with attenuation differences associated with anatomical change but exhibited only small variations as a function of the shape and size of the change. The results suggest that, given a target change attenuation, prospective patient-, change-, and data-specific customization of the prior image strength can be performed to ensure reliable reconstruction of specific anatomical changes.