Irène Buvat

Scatter correction in scintigraphy : the state of the art

In scintigraphy, the detection of scattered photons degrades both visual image analysis and quantitative accuracy. Many methods have been proposed and are still under investigation to cope with scattered photons. The main features of the problem of scattering in radionuclide imaging are presented first, to provide a sound foundation for a critical review of the existing scatter correction techniques. These are described using a classification relating to their aims and principles. Their theoretical potentials are analysed, as well as the difficulties of their practical implementation. Finally, the problems of their evaluation and comparison are discussed.

18F-FDG PET-Derived Textural Indices Reflect Tissue-Specific Uptake Pattern in Non-Small Cell Lung Cancer

Purpose Texture indices (TI) calculated from 18F-FDG PET tumor images show promise for predicting response to therapy and survival. Their calculation involves a resampling of standardized uptake values (SUV) within the tumor. This resampling can be performed differently and significantly impacts the TI values. Our aim was to investigate how the resampling approach affects the ability of TI to reflect tissue-specific pattern of metabolic activity. Methods 18F-FDG PET were acquired for 48 naïve-treatment patients with non-small cell lung cancer and for a uniform phantom. We studied 7 TI, SUVmax and metabolic volume (MV) in the phantom, tumors and healthy tissue using the usual relative resampling (RR) method and an absolute resampling (AR) method. The differences in TI values between tissue types and cancer subtypes were investigated using Wilcoxon’s tests. Results Most RR-based TI were highly correlated with MV for tumors less than 60 mL (Spearman correlation coefficient r between 0.74 and 1), while this correlation was reduced for AR-based TI (r between 0.06 and 0.27 except for RLNU where r = 0.91). Most AR-based TI were significantly different between tumor and healthy tissues (pvalues <0.01 for all 7 TI) and between cancer subtypes (pvalues<0.05 for 6 TI). Healthy tissue and adenocarcinomas exhibited more homogeneous texture than tumor tissue and squamous cell carcinomas respectively. Conclusion TI computed using an AR method vary as a function of the tissue type and cancer subtype more than the TI involving the usual RR method. AR-based TI might be useful for tumor characterization.

Understanding changes in tumor textural indices in PET: a comparison between visual assessment and index values in simulated and patient data.

The use of texture indices to characterize tumor heterogeneity from PET images is being increasingly investigated in retrospective studies, yet the interpretation of PET-derived texture index values has not been thoroughly reported. Furthermore, the calculation of texture indices lacks a standardized methodology, making it difficult to compare published results. To allow for texture index value interpretation, we investigated the changes in value of 6 texture indices computed from simulated and real patient data. Methods: Ten sphere models mimicking different activity distribution patterns and the 18F-FDG PET images from 54 patients with breast cancer were used. For each volume of interest, 6 texture indices were measured. The values of texture indices and how they changed as a function of the activity distribution were assessed and compared with the visual assessment of tumor heterogeneity. Results: Using the sphere models and real tumors, we identified 2 sets of texture indices reflecting different types of uptake heterogeneity. Set 1 included homogeneity, entropy, short-run emphasis, and long-run emphasis, all of which were sensitive to the presence of uptake heterogeneity but did not distinguish between hyper- and hyposignal within an otherwise uniform activity distribution. Set 2 comprised high-gray-level-zone emphasis and low-gray-level-zone emphasis, which were mostly sensitive to the average uptake rather than to the uptake local heterogeneity. Four of 6 texture indices significantly differed between homogeneous and heterogeneous lesions as defined by 2 nuclear medicine physicians (P < 0.05). All texture index values were sensitive to voxel size (variations up to 85.8% for the most homogeneous sphere models) and edge effects (variations up to 29.1%). Conclusion: Unlike a previous report, our study found that variations in texture indices were intuitive in the sphere models and real tumors: the most homogeneous uptake distribution exhibited the highest homogeneity and lowest entropy. Two families of texture index reflecting different types of uptake patterns were identified. Variability in texture index values as a function of voxel size and inclusion of tumor edges was demonstrated, calling for a standardized calculation methodology. This study provides guidance for nuclear medicine physicians in interpreting texture indices in future studies and clinical practice.

Multi-scale texture analysis: from 18F-FDG PET images to pathological slides

Characterizing tumor heterogeneity using texture indices derived from PET images has shown promise in predicting treatment response and patient survival in some types of cancer. Yet, the relationship between PET-derived texture indices, precise tracer distribution, and biologic heterogeneity needs to be clarified. We investigated this relationship using PET images, autoradiographic images, and histologic images. Methods: Three mice bearing orthotopically implanted mammary tumors derived from transgenic MMTV-PyMT mice were scanned with 18F-FDG PET/CT. The tumors were then sliced, and the slices were imaged with autoradiography and stained with hematoxylin and eosin. Six texture indices derived from the PET images, autoradiographic images, and histologic images were compared for their ability to capture heterogeneity on different scales. Results: The PET-derived indices correlated significantly with the autoradiography-derived ones (R = 0.57–0.85), but the values differed in magnitude. The histology-derived indices correlated poorly with the autoradiography- and PET-derived ones (R = 0.06–0.54). All indices were slightly to moderately influenced by the difference in voxel size and spatial resolution in the autoradiographic images. The autoradiography-derived indices differed significantly (P < 0.05) between regions with a high density of cells and regions with a low density and between regions with different spatial arrangements of cells. Conclusion: Heterogeneity derived in vivo from PET images accurately reflects the heterogeneity of tracer uptake derived ex vivo from autoradiographic images. Various tumor-cell densities and spatial cell distributions seen on histologic images can be distinguished using texture indices derived from autoradiographic images despite the difference in voxel size and spatial resolution. Yet, tumor texture derived from PET images only coarsely reflects the spatial distribution and density of tumor cells.

Extraction of functional volumes from medical dynamic volumetric data sets

A method based on factor analysis is presented to process dynamic volumetric (t + 3D) data sets acquired for flow, excretion, or metabolic studies. It estimates a reduced number of underlying physiological kinetics and their associated spatial distributions, corresponding to functional volumes, using dedicated algorithms. The global (t + 3D) approach is shown to be superior to the conventional one, which repeats estimations on each (t + 2D) data set, obtained for each slice or projection of the volume.

A comparative study of scatter correction methods for scintigraphic images

Phantom studies have demonstrated that factor analysis of medical image sequences using target apex-seeking (FAMIS-TAS) applied to spectral scintigraphic image sequences is an efficient adaptive scatter correction method. We assessed the improvement in quality of clinical images using FAMIS-TAS as compared with two other scatter correction techniques: conventional 20% photopeak window (PW) and scatter window subtraction (SWS). Thirty normal technetium-99m hydroxymethylene diphosphonate bone scans were processed. Bone to soft tissue contrasts and signal-to-noise and contrast-to-noise ratios were measured. The overall image quality was evaluated using an observer testing questionnaire submitted to four physicians. Quantitative parameters showed that FAMIS-TAS images displayed the best bone to soft tissue contrasts and contrast-tonoise ratios, but the lowest signal-to-noise ratios. PW images presented the lowest contrasts and contrast-tonoise ratios, and the highest signal-to-noise ratios. SWS gave intermediate results. According to the observer testing results, PW images showed the lowest bone to soft tissue contrasts and the highest signal-to-noise ratios. FAMIS-TAS images showed the lowest signal-to-noise ratios. The images processed by the three methods displayed the same anatomical information.

Strategies to Inhibit ABCB1- and ABCG2-Mediated Efflux Transport of Erlotinib at the Blood–Brain Barrier: A PET Study on Nonhuman Primates

The tyrosine kinase inhibitor erlotinib poorly penetrates the blood–brain barrier (BBB) because of efflux transport by P-glycoprotein (ABCB1) and breast cancer resistance protein (ABCG2), thereby limiting its utility in the treatment of non–small cell lung cancer metastases in the brain. Pharmacologic strategies to inhibit ABCB1/ABCG2-mediated efflux transport at the BBB have been successfully developed in rodents, but it remains unclear whether these can be translated to humans given the pronounced species differences in ABCG2/ABCB1 expression ratios at the BBB. We assessed the efficacy of two different ABCB1/ABCG2 inhibitors to enhance brain distribution of 11C-erlotinib in nonhuman primates as a model of the human BBB. Methods: Papio anubis baboons underwent PET scans of the brain after intravenous injection of 11C-erlotinib under baseline conditions (n = 4) and during intravenous infusion of high-dose erlotinib (10 mg/kg/h, n = 4) or elacridar (12 mg/kg/h, n = 3). Results: Under baseline conditions, 11C-erlotinib distribution to the brain (total volume of distribution [VT], 0.22 ± 0.015 mL/cm3) was markedly lower than its distribution to muscle tissue surrounding the skull (VT, 0.86 ± 0.10 mL/cm3). Elacridar infusion resulted in a 3.5 ± 0.9-fold increase in 11C-erlotinib distribution to the brain (VT, 0.81 ± 0.21 mL/cm3, P < 0.01), reaching levels comparable to those in muscle tissue, without changing 11C-erlotinib plasma pharmacokinetics. During high-dose erlotinib infusion, 11C-erlotinib brain distribution was also significantly (1.7 ± 0.2-fold) increased (VT, 0.38 ± 0.033 mL/cm3, P < 0.05), with a concomitant increase in 11C-erlotinib plasma exposure. Conclusion: We successfully implemented ABCB1/ABCG2 inhibition protocols in nonhuman primates resulting in pronounced increases in brain distribution of 11C-erlotinib. For patients with brain tumors, such inhibition protocols may ultimately be applied to create more effective treatments using drugs that undergo efflux transport at the BBB.

A new correction method for gamma camera non-uniformity due to energy response variability

We present a new uniformity correction (Fourier energy correction) which is designed to correct for gamma camera non-uniformity caused by variations of the energy response function within a wide spectral range. A convolution model is used to describe the spatial distortions of the energy response function. The model is solved in Fourier space. A preliminary flood acquisition is required to obtain energy-dependent Fourier weights which are used to correct subsequent acquisitions. The influence of the parameters involved in the correction procedure is studied and the Fourier energy correction is compared to a conventional multiplicative energy correction for different acquisition geometries. The Fourier energy correction appears especially useful when the energy information associated with each detected photon is analysed using a fine sampling, or when windows different from the photopeak window are used.

A post-reconstruction harmonization method for multicenter radiomic studies in PET

Several reports have shown that radiomic features are affected by acquisition and reconstruction parameters, thus hampering multicenter studies. We propose a method that, by removing the center effect while preserving patient-specific effects, standardizes features measured from PET images obtained using different imaging protocols. Methods: Pretreatment 18F-FDG PET images of patients with breast cancer were included. In one nuclear medicine department (department A), 63 patients were scanned on a time-of-flight PET/CT scanner, and 16 lesions were triple-negative (TN). In another nuclear medicine department (department B), 74 patients underwent PET/CT on a different brand of scanner and a different reconstruction protocol, and 15 lesions were TN. The images from department A were smoothed using a gaussian filter to mimic data from a third department (department A-S). The primary lesion was segmented to obtain a lesion volume of interest (VOI), and a spheric VOI was set in healthy liver tissue. Three SUVs and 6 textural features were computed in all VOIs. A harmonization method initially described for genomic data was used to estimate the department effect based on the observed feature values. Feature distributions in each department were compared before and after harmonization. Results: In healthy liver tissue, the distributions significantly differed for 4 of 9 features between departments A and B and for 6 of 9 between departments A and A-S (P < 0.05, Wilcoxon test). After harmonization, none of the 9 feature distributions significantly differed between 2 departments (P > 0.1). The same trend was observed in lesions, with a realignment of feature distributions between the departments after harmonization. Identification of TN lesions was largely enhanced after harmonization when the cutoffs were determined on data from one department and applied to data from the other department. Conclusion: The proposed harmonization method is efficient at removing the multicenter effect for textural features and SUVs. The method is easy to use, retains biologic variations not related to a center effect, and does not require any feature recalculation. Such harmonization allows for multicenter studies and for external validation of radiomic models or cutoffs and should facilitate the use of radiomic models in clinical practice.

A score combining baseline neutrophilia and primary tumor SUV peak measured from FDG PET is associated with outcome in locally advanced cervical cancer

PurposeWe investigated whether a score combining baseline neutrophilia and a PET biomarker could predict outcome in patients with locally advanced cervical cancer (LACC).MethodsPatients homogeneously treated with definitive chemoradiation plus image-guided adaptive brachytherapy (IGABT) between 2006 and 2013 were analyzed retrospectively. We divided patients into two groups depending on the PET device used: a training set (TS) and a validation set (VS). Primary tumors were semi-automatically delineated on PET images, andxa011 radiomics features were calculated (LIFEx software). A PET radiomic index was selected using the time-dependent area under the curve (td-AUC) for 3-year local control (LC). We defined the neutrophil SUV grade (NSGxa0=xa00, 1 or 2) score as the number of risk factors among (i) neutrophilia (neutrophil count >7xa0G/L) and (ii) high risk defined from the PET radiomic index. The NSG prognostic value was evaluated for LC and overall survival (OS).ResultsData from 108 patients were analyzed. Estimated 3-year LC was 72% in the TS (nxa0=xa069) and 65% in the VS (nxa0=xa039). In the TS, SUVpeak was selected as the most LC-predictive biomarker (td-AUCxa0=xa00.75), and was independent from neutrophilia (pxa0=xa00.119). Neutrophilia (HRxa0=xa02.6), high-risk SUVpeak (SUVpeakxa0>xa010, HRxa0=xa04.4) and NSGxa0=xa02 (HRxa0=xa09.2) were associated with low probability of LC in TS. In multivariate analysis, NSGxa0=xa02 was independently associated with low probability of LC (HRxa0=xa07.5, pxa0<xa00.001) and OS (HRxa0=xa05.8, pxa0=xa00.001) in the TS. Results obtained in the VS (HRxa0=xa05.2 for OS and 3.5 for LC, pxa0<xa00.02) were promising.ConclusionThis innovative scoring approach combining baseline neutrophilia and a PET biomarker provides an independent prognostic factor to consider for further clinical investigations.