Characterization of the metabolomic profile of renal cell carcinoma by high resolution magic angle spinning proton magnetic resonance spectroscopy.

BACKGROUND: Renal cell carcinoma (RCC) is a metabolic disease, with subtypes exhibiting aberrations in different metabolic pathways. Metabolomics may offer greater sensitivity for revealing disease biology. We investigated the metabolomic profile of RCC using high-resolution magic angle spinning (HRMAS) proton magnetic resonance spectroscopy (1HMRS). METHODS: Surgical tissue samples were obtained from our frozen tissue bank, collected from radical or partial nephrectomy. Specimens were fresh-frozen, then stored at -80 °C until analysis. Tissue HRMAS-1HMRS was performed. A MatLab-based curve fitting program was used to process the spectra to produce relative intensities for 59 spectral regions of interest (ROIs). Comparisons of the metabolomic profiles of various RCC histologies and benign tumors, angiomyolipoma, and oncocytoma, were performed. False discovery rates (FDR) were used from the response screening to account for multiple testing; ROIs with FDR p < 0.05 were considered potential predictors of RCC. Wilcoxon rank sum test was used to compare median 1HMRS relative intensities for those metabolites that may differentiate between RCC and benign tumor. Logistic regression determined odds ratios for risk of malignancy based on the abundance of each metabolite. RESULTS: Thirty-eight RCC (16 clear cell, 11 papillary, 11 chromophobe), 10 oncocytomas, 7 angiomyolipomas, and 13 adjacent normal tissue specimens (matched pairs) were analyzed. Candidate metabolites for predictors of malignancy based on FDR p-values include histidine, phenylalanine, phosphocholine, serine, phosphocreatine, creatine, glycerophosphocholine, valine, glycine, myo-inositol, scyllo-inositol, taurine, glutamine, spermine, acetoacetate, and lactate. Higher levels of spermine, histidine, and phenylalanine at 3.15 to 3.13 parts per million (ppm) were associated with decreased risk of RCC (OR 4 × 10-5, 95% CI 7.42 × 10-8, 0.02), while 2.84 to 2.82 ppm increased the risk of malignant pathology (OR 7158.67, 95% CI 6.3, 8.3 × 106). The specific metabolites characterizing this region remain to be identified. Tumor stage did not affect metabolomic profile of malignant tumors, suggesting that metabolites are dependent on histologic subtype. CONCLUSIONS: HRMAS-1HMRS identified metabolites that may predict RCC. We demonstrated that those in the 3.14 to 3.13 ppm ROI were present in lower levels in RCC, while higher levels of metabolites in the 2.84 to 2.82 ppm ROI were associated with substantially increased risk of RCC. Further research in a larger population is required to validate these findings.

Single-spoke binning: Reducing motion artifacts in abdominal radial stack-of-stars imaging.

PURPOSE: To increase the effectiveness of respiratory gating in radial stack-of-stars MRI, particularly when imaging at high spatial resolutions or with multiple echoes. METHODS: Free induction decay (FID) navigators were integrated into a three-dimensional gradient echo radial stack-of-stars pulse sequence. These navigators provided a motion signal with a high temporal resolution, which allowed single-spoke binning (SSB): each spoke at each phase encode step was sorted individually to the corresponding motion state of the respiratory signal. SSB was compared with spoke-angle binning (SAB), in which all phase encode steps of one projection angle were sorted without the use of additional navigator data. To illustrate the benefit of SSB over SAB, images of a motion phantom and of six free-breathing volunteers were reconstructed after motion-gating using either method. Image sharpness was quantitatively compared using image gradient entropies. RESULTS: The proposed method resulted in sharper images of the motion phantom and free-breathing volunteers. Differences in gradient entropy were statistically significant (p = 0.03) in favor of SSB. The increased accuracy of motion-gating led to a decrease of streaking artifacts in motion-gated four-dimensional reconstructions. To consistently estimate respiratory signals from the FID-navigator data, specific types of gradient spoiler waveforms were required. CONCLUSION: SSB allowed high-resolution motion-corrected MR imaging, even when acquiring multiple gradient echo signals or large acquisition matrices, without sacrificing accuracy of motion-gating. SSB thus relieves restrictions on the choice of pulse sequence parameters, enabling the use of motion-gated radial stack-of-stars MRI in a broader domain of clinical applications.

Visualizing metabolomics data with R.

In communicating scientific results, convincing data visualization is of utmost importance. Especially in metabolomics, results based on large numbers of dimensions and variables necessitate particular attention in order to convey their message unambiguously to the reader; also, in the era of open science, traceability and reproducibility are becoming increasingly important. This article describes the use of the R programming language to visualize published metabolomics data resulting from ex vivo NMR spectroscopy and mass spectrometry experiments with a special focus on reproducibility, including example figures as well as associated R code for ease of reuse. Examples include various types of plots (bar plots, swarm plots, and violin plots; volcano plots, heatmaps, Euler diagrams, Kaplan-Meier survival plots) and annotations (groupings, intragroup line connections, significance brackets, text annotations). Advantages of code-generated plots as well as advanced techniques and best practices are discussed.

Drug sensitivity profiling of 3D tumor tissue cultures in the pediatric precision oncology program INFORM.

The international precision oncology program INFORM enrolls relapsed/refractory pediatric cancer patients for comprehensive molecular analysis. We report a two-year pilot study implementing ex vivo drug sensitivity profiling (DSP) using a library of 75-78 clinically relevant drugs. We included 132 viable tumor samples from 35 pediatric oncology centers in seven countries. DSP was conducted on multicellular fresh tumor tissue spheroid cultures in 384-well plates with an overall mean processing time of three weeks. In 89 cases (67%), sufficient viable tissue was received; 69 (78%) passed internal quality controls. The DSP results matched the identified molecular targets, including BRAF, ALK, MET, and TP53 status. Drug vulnerabilities were identified in 80% of cases lacking actionable (very) high-evidence molecular events, adding value to the molecular data. Striking parallels between clinical courses and the DSP results were observed in selected patients. Overall, DSP in clinical real-time is feasible in international multicenter precision oncology programs.

Patient-by-Patient Deep Transfer Learning for Drug-Response Profiling Using Confocal Fluorescence Microscopy of Pediatric Patient-Derived Tumor-Cell Spheroids.

Image-based phenotypic drug profiling is receiving increasing attention in drug discovery and precision medicine. Compared to classical end-point measurements quantifying drug response, image-based profiling enables both the quantification of drug response and characterization of disease entities and drug-induced cell-death phenotypes. Here, we aim to quantify image-based drug responses in patient-derived 3D spheroid tumor cell cultures, tackling the challenges of a lack of single-cell-segmentation methods and limited patient-derived material. Therefore, we investigate deep transfer learning with patient-by-patient fine-tuning for cell-viability quantification. We fine-tune a convolutional neural network (pre-trained on ImageNet) with 210 control images specific to a single training cell line and 54 additional screen -specific assay control images. This method of image-based drug profiling is validated on 6 cell lines with known drug sensitivities, and further tested with primary patient-derived samples in a medium-throughput setting. Network outputs at different drug concentrations are used for drug-sensitivity scoring, and dense-layer activations are used in t-distributed stochastic neighbor embeddings of drugs to visualize groups of drugs with similar cell-death phenotypes. Image-based cell-line experiments show strong correlation to metabolic results ( R ≈ 0.7 ) and confirm expected hits, indicating the predictive power of deep learning to identify drug-hit candidates for individual patients. In patient-derived samples, combining drug sensitivity scoring with phenotypic analysis may provide opportunities for complementary combination treatments. Deep transfer learning with patient-by-patient fine-tuning is a promising, segmentation-free image-analysis approach for precision medicine and drug discovery.

High Resolution Magic Angle Spinning Proton NMR Study of Alzheimer's Disease with Mouse Models.

Alzheimer's disease (AD) is a crippling condition that affects millions of elderly adults each year, yet there remains a serious need for improved methods of diagnosis. Metabolomic analysis has been proposed as a potential methodology to better investigate and understand the progression of this disease; however, studies of human brain tissue metabolomics are challenging, due to sample limitations and ethical considerations. Comprehensive comparisons of imaging measurements in animal models to identify similarities and differences between aging- and AD-associated metabolic changes should thus be tested and validated for future human non-invasive studies. In this paper, we present the results of our highresolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR) studies of AD and wild-type (WT) mouse models, based on animal age, brain regions, including cortex vs. hippocampus, and disease status. Our findings suggest the ability of HRMAS NMR to differentiate between AD and WT mice using brain metabolomics, which potentially can be implemented in in vivo evaluations.

Functional Therapeutic Target Validation Using Pediatric Zebrafish Xenograft Models.

The survival rate among children with relapsed tumors remains poor, due to tumor heterogeneity, lack of directly actionable tumor drivers and multidrug resistance. Novel personalized medicine approaches tailored to each tumor are urgently needed to improve cancer treatment. Current pediatric precision oncology platforms, such as the INFORM (INdividualized Therapy FOr Relapsed Malignancies in Childhood) study, reveal that molecular profiling of tumor tissue identifies targets associated with clinical benefit in a subgroup of patients only and should be complemented with functional drug testing. In such an approach, patient-derived tumor cells are exposed to a library of approved oncological drugs in a physiological setting, e.g., in the form of animal avatars injected with patient tumor cells. We used molecularly fully characterized tumor samples from the INFORM study to compare drug screen results of individual patient-derived cell models in functional assays: (i) patient-derived spheroid cultures within a few days after tumor dissociation; (ii) tumor cells reisolated from the corresponding mouse PDX; (iii) corresponding long-term organoid-like cultures and (iv) drug evaluation with the corresponding zebrafish PDX (zPDX) model. Each model had its advantage and complemented the others for drug hit and drug combination selection. Our results provide evidence that in vivo zPDX drug screening is a promising add-on to current functional drug screening in precision medicine platforms.

iTReX: Interactive exploration of mono- and combination therapy dose response profiling data.

High throughput screening methods, measuring the sensitivity and resistance of tumor cells to drug treatments have been rapidly evolving. Not only do these screens allow correlating response profiles to tumor genomic features for developing novel predictors of treatment response, but they can also add evidence for therapy decision making in precision oncology. Recent analysis methods developed for either assessing single agents or combination drug efficacies enable quantification of dose-response curves with restricted symmetric fit settings. Here, we introduce iTReX, a user-friendly and interactive Shiny/R application, for both the analysis of mono- and combination therapy responses. The application features an extended version of the drug sensitivity score (DSS) based on the integral of an advanced five-parameter dose-response curve model and a differential DSS for combination therapy profiling. Additionally, iTReX includes modules that visualize drug target interaction networks and support the detection of matches between top therapy hits and the sample omics features to enable the identification of druggable targets and biomarkers. iTReX enables the analysis of various quantitative drug or therapy response readouts (e.g. luminescence, fluorescence microscopy) and multiple treatment strategies (drug treatments, radiation). Using iTReX we validate a cost-effective drug combination screening approach and reveal the application's ability to identify potential sample-specific biomarkers based on drug target interaction networks. The iTReX web application is accessible at https://itrex.kitz-heidelberg.de.

Screening human lung cancer with predictive models of serum magnetic resonance spectroscopy metabolomics.

The current high mortality of human lung cancer stems largely from the lack of feasible, early disease detection tools. An effective test with serum metabolomics predictive models able to suggest patients harboring disease could expedite triage patient to specialized imaging assessment. Here, using a training-validation-testing-cohort design, we establish our high-resolution magic angle spinning (HRMAS) magnetic resonance spectroscopy (MRS)-based metabolomics predictive models to indicate lung cancer presence and patient survival using serum samples collected prior to their disease diagnoses. Studied serum samples were collected from 79 patients before (within 5.0 y) and at lung cancer diagnosis. Disease predictive models were established by comparing serum metabolomic patterns between our training cohorts: patients with lung cancer at time of diagnosis, and matched healthy controls. These predictive models were then applied to evaluate serum samples of our validation and testing cohorts, all collected from patients before their lung cancer diagnosis. Our study found that the predictive model yielded values for prior-to-detection serum samples to be intermediate between values for patients at time of diagnosis and for healthy controls; these intermediate values significantly differed from both groups, with an F1 score = 0.628 for cancer prediction. Furthermore, values from metabolomics predictive model measured from prior-to-diagnosis sera could significantly predict 5-y survival for patients with localized disease.

CT-based attenuation correction of whole-body radiotherapy treatment positioning devices in PET/MRI hybrid imaging.

OBJECTIVE: To implement computed tomography (CT)-based attenuation maps of radiotherapy (RT) positioning hardware and radiofrequency (RF) coils to enable hybrid positron emission tomography/magnetic resonance imaging (PET/MRI)-based RT treatment planning. MATERIALS AND METHODS: The RT positioning hardware consisted of a flat RT table overlay, coil holders for abdominal scans, coil holders for head and neck scans and an MRI compatible hip and leg immobilization device. CT images of each hardware element were acquired on a CT scanner. Based on the CT images, attenuation maps of the devices were created. Validation measurements were performed on a PET/MR scanner using a 68Ge phantom (48 MBq, 10 min scan time). Scans with each device in treatment position were performed. Then, reference scans containing only the phantom were taken. The scans were reconstructed online (at the PET/MRI scanner) and offline (via e7tools on a PC) using identical reconstruction parameters. Average reconstructed activity concentrations of the device and reference scans were compared. RESULTS: The device attenuation maps were successfully implemented. The RT positioning devices caused an average decrease of reconstructed PET activity concentration in the range between -8.3 ± 2.1% (mean ± SD) (head and neck coil holder with coils) to -1.0 ± 0.5% (abdominal coil holder). With attenuation correction taking into account RT hardware, these values were reduced to -2.0 ± 1.2% and -0.6 ± 0.5%, respectively. The results of the offline and online reconstructions were nearly identical, with a difference of up to 0.2%. CONCLUSION: The decrease in reconstructed activity concentration caused by the RT positioning devices is clinically relevant and can successfully be corrected using CT-based attenuation maps. Both the offline and online reconstruction methods are viable options.

Algorithms for joint activity-attenuation estimation from positron emission tomography scatter.

BACKGROUND: Attenuation correction in positron emission tomography remains challenging in the absence of measured transmission data. Scattered emission data may contribute missing information, but quantitative scatter-to-attenuation (S2A) reconstruction needs to input the reconstructed activity image. Here, we study S2A reconstruction as a building block for joint estimation of activity and attenuation. METHODS: We study two S2A reconstruction algorithms, maximum-likelihood expectation maximization (MLEM) with one-step-late attenuation (MLEM-OSL) and a maximum-likelihood gradient ascent (MLGA). We study theoretical properties of these algorithms with a focus on convergence and convergence speed and compare convergence speeds and the impact of object size in simulations using different spatial scale factors. Then, we propose joint estimation of activity and attenuation from scattered and nonscattered (true) emission data, combining MLEM-OSL or MLGA with scatter-MLEM as well as trues-MLEM and the maximum-likelihood transmission (MLTR) algorithm. RESULTS: Shortcomings of MLEM-OSL inhibit convergence to the true solution with high attenuation; these shortcomings are related to the linearization of a nonlinear measurement equation and can be linked to a new numerical criterion allowing geometrical interpretations in terms of low and high attenuation. Comparisons using simulated data confirm that while MLGA converges largely independent of the attenuation scale, MLEM-OSL converges if low-attenuation data dominate, but not with high attenuation. Convergence of MLEM-OSL can be improved by isolating data satisfying the aforementioned low-attenuation criterion. In joint estimation of activity and attenuation, scattered data helps avoid local minima that nonscattered data alone cannot. Combining MLEM-OSL with trues-MLEM may be sufficient for low-attenuation objects, while MLGA, scatter-MLEM, and MLTR may additionally be needed with higher attenuation. CONCLUSIONS: The performance of S2A algorithms depends on spatial scales. MLGA provides lower computational complexity and convergence in more diverse setups than MLEM-OSL. Finally, scattered data may provide additional information to joint estimation of activity and attenuation through S2A reconstruction.

Magnetic Resonance Spectroscopy-based Metabolomic Biomarkers for Typing, Staging, and Survival Estimation of Early-Stage Human Lung Cancer.

Low-dose CT has shown promise in detecting early stage lung cancer. However, concerns about the adverse health effects of radiation and high cost prevent its use as a population-wide screening tool. Effective and feasible screening methods to triage suspicious patients to CT are needed. We investigated human lung cancer metabolomics from 93 paired tissue-serum samples with magnetic resonance spectroscopy and identified tissue and serum metabolomic markers that can differentiate cancer types and stages. Most interestingly, we identified serum metabolomic profiles that can predict patient overall survival for all cases (p = 0.0076), and more importantly for Stage I cases alone (n = 58, p = 0.0100), a prediction which is significant for treatment strategies but currently cannot be achieved by any clinical method. Prolonged survival is associated with relative overexpression of glutamine, valine, and glycine, and relative suppression of glutamate and lipids in serum.

Metabolomic prostate cancer fields in HRMAS MRS-profiled histologically benign tissue vary with cancer status and distance from cancer.

In this article, we review the state of the field of high resolution magic angle spinning MRS (HRMAS MRS)-based cancer metabolomics since its beginning in 2004; discuss the concept of cancer metabolomic fields, where metabolomic profiles measured from histologically benign tissues reflect patient cancer status; and report our HRMAS MRS metabolomic results, which characterize metabolomic fields in prostatectomy-removed cancerous prostates. Three-dimensional mapping of cancer lesions throughout each prostate enabled multiple benign tissue samples per organ to be classified based on distance from and extent of the closest cancer lesion as well as the Gleason score (GS) of the entire prostate. Cross-validated partial least squares-discriminant analysis separations were achieved between cancer and benign tissue, and between cancer tissue from prostates with high (≥4 + 3) and low (≤3 + 4) GS. Metabolomic field effects enabled histologically benign tissue adjacent to cancer to distinguish the GS and extent of the cancer lesion itself. Benign samples close to either low GS cancer or extensive cancer lesions could be distinguished from those far from cancer. Furthermore, a successfully cross-validated multivariate model for three benign tissue groups with varying distances from cancer lesions within one prostate indicates the scale of prostate cancer metabolomic fields. While these findings could, at present, be potentially useful in the prostate cancer clinic for analysis of biopsy or surgical specimens to complement current diagnostics, the confirmation of metabolomic fields should encourage further examination of cancer fields and can also enhance understanding of the metabolomic characteristics of cancer in myriad organ systems. Our results together with the success of HRMAS MRS-based cancer metabolomics presented in our literature review demonstrate the potential of cancer metabolomics to provide supplementary information for cancer diagnosis, staging, and patient prognostication.

MLAA-based attenuation correction of flexible hardware components in hybrid PET/MR imaging.

BACKGROUND: Accurate PET quantification demands attenuation correction (AC) for both patient and hardware attenuation of the 511 keV annihilation photons. In hybrid PET/MR imaging, AC for stationary hardware components such as patient table and MR head coil is straightforward, employing CT-derived attenuation templates. AC for flexible hardware components such as MR-safe headphones and MR radiofrequency (RF) surface coils is more challenging. Registration-based approaches, aligning CT-based attenuation templates with the current patient position, have been proposed but are not used in clinical routine. Ignoring headphone or RF coil attenuation has been shown to result in regional activity underestimation values of up to 18%. We propose to employ the maximum-likelihood reconstruction of attenuation and activity (MLAA) algorithm to estimate the attenuation of flexible hardware components. Starting with an initial attenuation map not including flexible hardware components, the attenuation update of MLAA is applied outside the body outline only, allowing to estimate hardware attenuation without modifying the patient attenuation map. Appropriate prior expectations on the attenuation coefficients are incorporated into MLAA. The proposed method is investigated for non-TOF PET phantom and 18F-FDG patient data acquired with a clinical PET/MR device, using headphones or RF surface coils as flexible hardware components. RESULTS: Although MLAA cannot recover the exact physical shape of the hardware attenuation maps, the overall attenuation of the hardware components is accurately estimated. Therefore, the proposed algorithm significantly improves PET quantification. Using the phantom data, local activity underestimation when neglecting hardware attenuation was reduced from up to 25% to less than 3% under- or overestimation as compared to reference scans without hardware present or to CT-derived AC. For the patient data, we found an average activity underestimation of 7.9% evaluated in the full brain and of 6.1% for the abdominal region comparing the uncorrected case with MLAA. CONCLUSIONS: MLAA is able to provide accurate estimations of the attenuation of flexible hardware components and can therefore be used to significantly improve PET quantification. The proposed approach can be readily incorporated into clinical workflow.

Numerical algorithms for scatter-to-attenuation reconstruction in PET: empirical comparison of convergence, acceleration, and the effect of subsets.

The use of scattered coincidences for attenuation correction of positron emission tomography (PET) data has recently been proposed. For practical applications, convergence speeds require further improvement, yet there exists a trade-off between convergence speed and the risk of non-convergence. In this respect, a maximum-likelihood gradient-ascent (MLGA) algorithm and a two-branch back-projection (2BP), which was previously proposed, were evaluated. METHODS: MLGA was combined with the Armijo step size rule; and accelerated using conjugate gradients, Nesterov's momentum method, and data subsets of different sizes. In 2BP, we varied the subset size, an important determinant of convergence speed and computational burden. We used three sets of simulation data to evaluate the impact of a spatial scale factor. RESULTS AND DISCUSSION: The Armijo step size allowed 10-fold increased step sizes compared to native MLGA. Conjugate gradients and Nesterov momentum lead to slightly faster, yet non-uniform convergence; improvements were mostly confined to later iterations, possibly due to the non-linearity of the problem. MLGA with data subsets achieved faster, uniform, and predictable convergence, with a speed-up factor equivalent to the number of subsets and no increase in computational burden. By contrast, 2BP computational burden increased linearly with the number of subsets due to repeated evaluation of the objective function, and convergence was limited to the case of many (and therefore small) subsets, which resulted in high computational burden. CONCLUSION: Possibilities of improving 2BP appear limited. While general-purpose acceleration methods appear insufficient for MLGA, results suggest that data subsets are a promising way of improving MLGA performance.

Attenuation correction in emission tomography using the emission data--A review.

The problem of attenuation correction (AC) for quantitative positron emission tomography (PET) had been considered solved to a large extent after the commercial availability of devices combining PET with computed tomography (CT) in 2001; single photon emission computed tomography (SPECT) has seen a similar development. However, stimulated in particular by technical advances toward clinical systems combining PET and magnetic resonance imaging (MRI), research interest in alternative approaches for PET AC has grown substantially in the last years. In this comprehensive literature review, the authors first present theoretical results with relevance to simultaneous reconstruction of attenuation and activity. The authors then look back at the early history of this research area especially in PET; since this history is closely interwoven with that of similar approaches in SPECT, these will also be covered. We then review algorithmic advances in PET, including analytic and iterative algorithms. The analytic approaches are either based on the Helgason-Ludwig data consistency conditions of the Radon transform, or generalizations of John's partial differential equation; with respect to iterative methods, we discuss maximum likelihood reconstruction of attenuation and activity (MLAA), the maximum likelihood attenuation correction factors (MLACF) algorithm, and their offspring. The description of methods is followed by a structured account of applications for simultaneous reconstruction techniques: this discussion covers organ-specific applications, applications specific to PET/MRI, applications using supplemental transmission information, and motion-aware applications. After briefly summarizing SPECT applications, we consider recent developments using emission data other than unscattered photons. In summary, developments using time-of-flight (TOF) PET emission data for AC have shown promising advances and open a wide range of applications. These techniques may both remedy deficiencies of purely MRI-based AC approaches in PET/MRI and improve standalone PET imaging.

Scattered PET data for attenuation-map reconstruction in PET/MRI.

PURPOSE: In hybrid medical imaging devices combining positron emission tomography (PET) with magnetic resonance imaging, PET attenuation correction remains challenging. Known approaches to estimating attenuation (µ-)maps from PET emission data, especially maximum-likelihood reconstruction of activity and attenuation (MLAA), take into account true coincidences only and exhibit two kinds of ambiguities: First, the attenuation sinogram can only be determined up to a constant offset (sinogram ambiguity). Second, the attenuation sinogram is unknown outside of the support of the activity sinogram and does not completely define a µ-map (image-space ambiguity). In this work, the authors aim at using additional information from scattered coincidences to resolve these ambiguities--information that is unavailable using true coincidences. METHODS: The authors propose a two-level scheme for combining measurements of true and scattered coincidences. On the top level, scatter-to-attenuation (S2A) reconstruction recovers the µ-map from a measurement of scattered coincidences and results available from trues-based algorithms. On the lower level, S2A reconstruction is implemented by iterative scatter simulation and a proposed (simplified) S2A back-projection. The S2A back-projection is based on determining possible scattering locations in image space from energy measurements (via Compton-scattering angles) and summing contributions from scattered coincidences in image space. S2A back-projection is validated with GATE simulations of activity-attenuation configurations with both sinogram and image-space ambiguities. The authors further evaluate the impact of asymmetric source and activity distributions, extended source distributions, and energy uncertainty to demonstrate the limitations of the simplified noniterative S2A back-projection approach. Feasibility of the iterative S2A reconstruction is evaluated in a low-resolution, analytical 2D problem. RESULTS: S2A back-projection of scattered coincidences with scattered-photon energies in the range of 248-478 keV provides image-space information about the attenuation distribution, even in challenging cases of perfect spherical symmetry of attenuation and activity distributions as well as attenuation outside of the activity support. Realistic energy uncertainties (5% and 10% full width at half maximum at 511 keV) deteriorate spatial image resolution in the proposed noniterative method. The iterative S2A reconstruction is able to recover the full µ-map (errors less than 3.7 × 10(-5)/cm) as well as the unknown scaling factor (error smaller than 0.0005%) from scattered coincidences and an activity distribution with unknown scaling. CONCLUSIONS: Scattered coincidences provide information to complement existing PET attenuation-correction approaches such as MLAA. The proposed scatter-to-attenuation back-projection and reconstruction may constitute a missing piece for resolving ambiguities in the simultaneous reconstruction of activity and attenuation, and improving the quality of µ-maps reconstructed from PET emission data.

Sensitivity encoded silicon photomultiplier--a new sensor for high-resolution PET-MRI.

Detectors for simultaneous positron emission tomography and magnetic resonance imaging in particular with sub-mm spatial resolution are commonly composed of scintillator crystal arrays, readout via arrays of solid state sensors, such as avalanche photo diodes (APDs) or silicon photomultipliers (SiPMs). Usually a light guide between the crystals and the sensor is used to enable the identification of crystals which are smaller than the sensor elements. However, this complicates crystal identification at the gaps and edges of the sensor arrays. A solution is to use as many sensors as crystals with a direct coupling, which unfortunately increases the complexity and power consumption of the readout electronics. Since 1997, position-sensitive APDs have been successfully used to identify sub-mm crystals. Unfortunately, these devices show a limitation in their time resolution and a degradation of spatial resolution when placed in higher magnetic fields. To overcome these limitations, this paper presents a new sensor concept that extends conventional SiPMs by adding position information via the spatial encoding of the channel sensitivity. The concept allows a direct coupling of high-resolution crystal arrays to the sensor with a reduced amount of readout channels. The theory of sensitivity encoding is detailed and linked to compressed sensing to compute unique sparse solutions. Two devices have been designed using one- and two-dimensional linear sensitivity encoding with eight and four readout channels, respectively. Flood histograms of both devices show the capability to precisely identify all 4 × 4 LYSO crystals with dimensions of 0.93 × 0.93 × 10 mm(3). For these crystals, the energy and time resolution (MV ± SD) of the devices with one (two)-dimensional encoding have been measured to be 12.3 · (1 ± 0.047)% (13.7 · (1 ± 0.047)%) around 511 keV with a paired coincidence time resolution (full width at half maximum) of 462 · (1 ± 0.054) ps (452 · (1 ± 0.078) ps).

Study-parameter impact in quantitative 90-Yttrium PET imaging for radioembolization treatment monitoring and dosimetry.

A small positron-generating branch in 90-Yttrium ((90)Y) decay enables post-therapy dose assessment in liver cancer radioembolization treatment. The aim of this study was to validate clinical (90)Y positron emission tomography (PET) quantification, focusing on scanner linearity as well as acquisition and reconstruction parameter impact on scanner calibration. Data from three dedicated phantom studies (activity range: 55.2 MBq-2.1 GBq) carried out on a Philips Gemini TF 16 PET/CT scanner were analyzed after reconstruction with up to 361 parameter configurations. For activities above 200 MBq, scanner linearity could be confirmed with relative error margins 4%. An acquisition-time-normalized calibration factor of 1.04 MBq·s/CNTS was determined for the employed scanner. Stable activity convergence was found in hot phantom regions with relative differences in summed image intensities between -3.6% and +2.4%. Absolute differences in background noise artifacts between - 79.9% and + 350% were observed. Quantitative accuracy was dominated by subset size selection in the reconstruction. Using adequate segmentation and optimized acquisition parameters, the average activity recovery error induced by the axial scanner sensitivity profile was reduced to +2.4%±3.4% (mean ± standard deviation). We conclude that post-therapy dose assessment in (90)Y PET can be improved using adapted parameter setups.