Characterization of positioning uncertainties in PET-CT-MR trimodality solutions for radiotherapy.

The purpose of this study was to evaluate the positioning uncertainties of two PET/CT-MR imaging setups, C1 and C2. Because the PET/CT data were acquired on the same hybrid device with automatic image registration, experiments were conducted using CT-MRI data. In C1, a transfer table was used, which allowed the patient to move from one imager to another while maintaining the same position. In C2, the patient stood up and was positioned in the same radiotherapy treatment position on each imager. The two setups provided a set of PET/CT and MR images. The accuracy of the registration software was evaluated on the CT-MRI data of one patient using known translations and rotations of MRI data. The uncertainties on the two setups were estimated using a phantom and a cohort of 30 patients. The accuracy of the positioning uncertainties was evaluated using descriptive statistics and a t-test to determine whether the mean shift significantly deviated from zero (p < 0.05) for each setup. The maximum registration errors were less than 0.97 mm and 0.6° for CT-MRI registration. On the phantom, the mean total uncertainties were less than 2.74 mm and 1.68° for C1 and 1.53 mm and 0.33° for C2. For C1, the t-test showed that the displacements along the z-axis did not significantly deviate from zero (p = 0.093). For C2, significant deviations from zero were present for anterior-posterior and superior-inferior displacements. The mean total uncertainties were less than 4 mm and 0.42° for C1 and less than 1.39 mm and 0.27° for C2 in the patients. Furthermore, the t-test showed significant deviations from zero for C1 on the anterior-posterior and roll sides. For C2, there was a significant deviation from zero for the left-right displacements.This study shows that transfer tables require careful evaluation before use in radiotherapy.

Correction: Brochet et al. A Quantitative Comparison between Shannon and Tsallis-Havrda-Charvat Entropies Applied to Cancer Outcome Prediction. Entropy 2022, 24, 436.

Alexandre Huat, Sébastien Thureau, David Pasquier, Isabelle Gardin, Romain Modzelewski, David Gibon, Juliette Thariat and Vincent Grégoire were not included as authors in the original publication [...].

A Quantitative Comparison between Shannon and Tsallis-Havrda-Charvat Entropies Applied to Cancer Outcome Prediction.

In this paper, we propose to quantitatively compare loss functions based on parameterized Tsallis-Havrda-Charvat entropy and classical Shannon entropy for the training of a deep network in the case of small datasets which are usually encountered in medical applications. Shannon cross-entropy is widely used as a loss function for most neural networks applied to the segmentation, classification and detection of images. Shannon entropy is a particular case of Tsallis-Havrda-Charvat entropy. In this work, we compare these two entropies through a medical application for predicting recurrence in patients with head-neck and lung cancers after treatment. Based on both CT images and patient information, a multitask deep neural network is proposed to perform a recurrence prediction task using cross-entropy as a loss function and an image reconstruction task. Tsallis-Havrda-Charvat cross-entropy is a parameterized cross-entropy with the parameter α. Shannon entropy is a particular case of Tsallis-Havrda-Charvat entropy for α=1. The influence of this parameter on the final prediction results is studied. In this paper, the experiments are conducted on two datasets including in total 580 patients, of whom 434 suffered from head-neck cancers and 146 from lung cancers. The results show that Tsallis-Havrda-Charvat entropy can achieve better performance in terms of prediction accuracy with some values of α.

Anthropometer3D: Automatic Multi-Slice Segmentation Software for the Measurement of Anthropometric Parameters from CT of PET/CT.

Anthropometric parameters like muscle body mass (MBM), fat body mass (FBM), lean body mass (LBM), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) are used in oncology. Our aim was to develop and evaluate the software Anthropometer3D measuring these anthropometric parameters on the CT of PET/CT. This software performs a multi-atlas segmentation of CT of PET/CT with extrapolation coefficients for the body parts beyond the usual acquisition range (from the ischia to the eyes). The multi-atlas database is composed of 30 truncated CTs manually segmented to isolate three types of voxels (muscle, fat, and visceral fat). To evaluate Anthropomer3D, a leave-one-out cross-validation was performed to measure MBM, FBM, LBM, VAT, and SAT. The reference standard was based on the manual segmentation of the corresponding whole-body CT. A manual segmentation of one CT slice at level L3 was also used. Correlations were analyzed using Dice coefficient, intra-class coefficient correlation (ICC), and Bland-Altman plot. The population was heterogeneous (sex ratio 1:1; mean age 57 years old [min 23; max 74]; mean BMI 27 kg/m2 [min 18; max 40]). Dice coefficients between reference standard and Anthropometer3D were excellent (mean+/-SD): muscle 0.95 ± 0.02, fat 1.00 ± 0.01, and visceral fat 0.97 ± 0.02. The ICC was almost perfect (minimal value of 95% CI of 0.97). All Bland-Altman plot values (mean difference, 95% CI and slopes) were better for Anthropometer3D compared to L3 level segmentation. Anthropometer3D allows multiple anthropometric measurements based on an automatic multi-slice segmentation. It is more precise than estimates using L3 level segmentation.

Correction to: Tumor fragmentation estimated by volume surface ratio of tumors measured on 18F-FDG PET/CT is an independent prognostic factor of diffuse large B-cell lymphoma.

A unit error concerning the tumor volume surface ratio (TVSR) is present throughout the article. The unit reported is "cm" but is actually "mm".

Tumor fragmentation estimated by volume surface ratio of tumors measured on 18F-FDG PET/CT is an independent prognostic factor of diffuse large B-cell lymphoma.

INTRODUCTION: Our aim was to study the prognostic value of two new 18F-FDG PET biomarkers in diffuse large B-cell lymphoma (DLBCL). We examined the total tumor surface (TTS), describing the tumor-host interface, and the tumor volume surface ratio (TVSR), corresponding to the ratio between the total metabolic tumor volume (TMTV) and TTS, describing the tumor fragmentation. METHODS: We retrospectively included 215 patients with DLBCL. Patients underwent initial 18F-FDG PET/CT before R-CHOP (73%) or intensified R-CHOP (R-ACVBP) regimens (27%). The TMTV was measured using a fixed threshold value of 41% of SUVmax. To calculate TTS and TVSR, the surface was measured using an in-house software based on the marching cube algorithm. Spearman's rank correlation coefficient (ρ) was computed between TMTV, TTS, and TVSR, and ROC analysis was performed. Survival functions at 5 years were studied using a Kaplan-Meier method and uni/multivariate Cox analysis. RESULTS: TVSR was poorly correlated with TMTV (ρ = 0.5) and TTS (ρ = 0.26), while TTS was highly correlated with TMTV (ρ = 0.94) and was, therefore, excluded from the analysis. TMTV had the highest area under the ROC curve (0.711) and the best sensitivity (0.797), while TVSR had the best specificity (0.745). The optimal cut-off values to predict 5-year OS were 222 cm3 for TMTV and 6.0 mm for TVSR. Patients with high TMTV and TVSR had significantly worse prognosis in Kaplan-Meier and Cox univariate analysis. In a multivariate Cox analysis combining the International Prognostic Index (IPI), the type of chemotherapy, TMTV, and TVSR, all parameters were independent and significant prognostic factors (HR [95%CI]: IPI 1.4 [1.1-1.8], type of chemotherapy 4.5 [2.0-10.5], TMTV 2.8 [1.4-5.5], TVSR 2.1 [1.3-3.4]). A synergistic effect between TMTV and TVSR was observed in a Kaplan-Meier analysis combining the two parameters. CONCLUSIONS: TVSR measured on the initial 18F-FDG PET is an independent prognostic factor in DLBCL and has an additional prognostic value when combined with TMTV, IPI score and chemotherapy.

Automatic Measurement of the Total Visceral Adipose Tissue From Computed Tomography Images by Using a Multi-Atlas Segmentation Method.

BACKGROUND: The visceral adipose tissue (VAT) volume is a predictive and/or prognostic factor for many cancers. The objective of our study was to develop an automatic measurement of the whole VAT volume using a multi-atlas segmentation (MAS) method from a computed tomography. METHODS: A total of 31 sets of whole-body computed tomography volume data were used. The reference VAT volume was defined on the basis of manual segmentation (VATMANUAL). We developed an algorithm, which measured automatically the VAT volumes using a MAS based on a nonrigid volume registration algorithm coupled with a selective and iterative method for performance level estimation (SIMPLE), called VATMAS_SIMPLE. The results were evaluated using intraclass correlation coefficient and dice similarity coefficients. RESULTS: The intraclass correlation coefficient of VATMAS_SIMPLE was excellent, at 0.976 (confidence interval, 0.943-0.989) (P < 0.001). The dice similarity coefficient of VATMAS_SIMPLE was also good, at 0.905 (SD, 0.076). CONCLUSIONS: This newly developed algorithm based on a MAS can measure accurately the whole abdominopelvic VAT.

18F-FDG-PET partial volume effect correction using a modified recovery coefficient approach based on functional volume and local contrast: physical validation and clinical feasibility in oncology.

BACKGROUND: 2-deoxy-2-[18F]fluoro-D-glucose 18F-FDG uptake within tumors reflects the glucose consumption of malignant tumors, i.e., the tumor activity. Thus, 18F-FDG uptake measurements enable improved therapeutic monitoring of patients in chemo- or radiotherapy treatment through the detection of changes in tumor uptake via quantitative measurements of the lesion standard uptake values (SUVs) or activity concentrations. A major bias that affects positron emission tomography (PET) image quantitation is the partial volume effect (PVE), which most strongly affects the smallest structures due to the poor spatial resolution of PET. Thus, PVE corrections are important when 18F-FDG-PET images are used as a quantitative tool for monitoring responses to therapy. The aim of this paper was to propose a PVE correction based on a modified recovery coefficient method (termed FARCAS) that considers the functional volumes and local contrasts of lesions that are automatically determined using a semi-automatic iterative segmentation algorithm. METHODS: The FARCAS method consists of establishing a set of calibration curves based on the mathematical fitting of the RC values as a function of the automatically determined functional lesion volume and local lesion contrast. We set up our method using data from a cylindrical phantom that included spheres of different volumes (range: 0.43 to 97.8 mL) and contrasts (range: 1.7 to 22.9), and we assessed the method using both cylindrical and anthropomorphic phantom data that also included spheres of different volumes and contrasts. FARCAS was also compared with conventional RC methods that only considered the lesion functional volume, either automatically determined (RCVa) or using the ground truth volume (RCVgt). Finally, the clinical feasibility of FARCAS and its evaluation on tumor classification were also assessed on 24 NSCLC lesions. RESULTS: Whatever the phantom considered, for the spheres with contrast <5, FARCAS obtained comparable results to RCVgt and better than RCVa. For the spheres with contrast >5, FARCAS and RCVa were not statistically different, neither for the cylindrical and nor the anthropomorphic phantom. For the cylindrical phantom FARCAS yielded corrections that were not statistically different to those of RCVa for the smallest spheres (V<2 mL), but statistically superior for the larger spheres (V≥2 mL). RCVgt maintained a non-statistically superior accuracy. Regarding the anthropomorphic data, FARCAS was statistically more accurate than RCVa but not RCVgt. As main findings regarding the clinical data, FARCAS modified the classifications of five of 24 NSCLC lesions based on quantitative PERCIST criteria. CONCLUSIONS: The PVE correction proposed in this paper allows the accurate quantification of the PVE-corrected SUV, allowing also an automatic definition of the Metabolic Target Volume (MTV). Our results revealed that the PVE correction based on FARCAS is a better approach than conventional RC to significantly reduce the impact of PVE on lesion quantification, thus improving the evaluation of tumor response to treatment based on PET-CT images.

Adjuvant I-131 Lipiodol After Resection or Radiofrequency Ablation for Hepatocellular Carcinoma.

BACKGROUND AND OBJECTIVES: High rates of recurrence have been observed after curative treatment for hepatocellular carcinoma (HCC). The main aim of this study was to establish the influence of adjuvant transarterial radioembolization-based I-131 lipiodol on survival and recurrence. METHODS: Between 2004 and 2010, 38 patients were treated with adjuvant I-131 lipiodol therapy, at a dosage of 2220 MBq, within 4 months after surgery. This treated cohort was compared to a control cohort consisting of 42 consecutive patients operated prior to the time the I-131 lipiodol treatment became available. RESULTS: Recurrence-free survival in the control and in the I-131 lipiodol cohort was 12.6 and 18.7 months, respectively (HR = 1.871, p = 0.025). At 2 and 5 years, the cumulative incidence of a first recurrence or death was, respectively, 50 % and 61 % in the treated cohort versus 69 % and 74 % in the control cohort. Median overall survival was 55 and 29 months, respectively (p = 0.051). Among patients with a recurrence at 2 years, more patients had already experienced such recurrence at 1 year in the control cohort (70 % vs 33 %, p = 0.014). CONCLUSIONS: Adjuvant I-131 lipiodol improves disease-free survival in patients with HCC.

High FDG uptake areas on pre-radiotherapy PET/CT identify preferential sites of local relapse after chemoradiotherapy for locally advanced oesophageal cancer.

PURPOSE: The high failure rates in the radiotherapy (RT) target volume suggest that patients with locally advanced oesophageal cancer (LAOC) would benefit from increased total RT doses. High 2-deoxy-2-[(18)F]fluoro-D-glucose (FDG) uptake (hotspot) on pre-RT FDG positron emission tomography (PET)/CT has been reported to identify intra-tumour sites at increased risk of relapse after RT in non-small cell lung cancer and in rectal cancer. Our aim was to confirm these observations in patients with LAOC and to determine the optimal maximum standardized uptake value (SUVmax) threshold to delineate smaller RT target volumes that would facilitate RT dose escalation without impaired tolerance. METHODS: The study included 98 consecutive patients with LAOC treated by chemoradiotherapy (CRT). All patients underwent FDG PET/CT at initial staging and during systematic follow-up in a single institution. FDG PET/CT acquisitions were coregistered on the initial CT scan. Various subvolumes within the initial tumour (30, 40, 50, 60, 70, 80 and 90% SUVmax thresholds) and in the subsequent local recurrence (LR, 40 and 90% SUVmax thresholds) were pasted on the initial CT scan and compared[Dice, Jaccard, overlap fraction (OF), common volume/baseline volume, common volume/recurrent volume]. RESULTS: Thirty-five patients had LR. The initial metabolic tumour volume was significantly higher in LR tumours than in the locally controlled tumours (mean 25.4 vs 14.2 cc; p = 0.002). The subvolumes delineated on initial PET/CT with a 30-60% SUVmax threshold were in good agreement with the recurrent volume at 40% SUVmax (OF = 0.60-0.80). The subvolumes delineated on initial PET/CT with a 30-60% SUVmax threshold were in good to excellent agreement with the core volume (90% SUVmax) of the relapse (common volume/recurrent volume and OF indices 0.61-0.89). CONCLUSION: High FDG uptake on pretreatment PET/CT identifies tumour subvolumes that are at greater risk of recurrence after CRT in patients with LAOC. We propose a 60% SUVmax threshold to delineate high FDG uptake areas on initial PET/CT as reduced target volumes for RT dose escalation.

FDG-PET/CT during concomitant chemo radiotherapy for esophageal cancer: Reducing target volumes to deliver higher radiotherapy doses.

BACKGROUND: A planning study investigated whether reduced target volumes defined on FDG-PET/CT during radiotherapy allow total dose escalation without compromising normal tissue tolerance in patients with esophageal cancer. MATERIAL AND METHODS: Ten patients with esophageal squamous cell carcinoma (SCC), candidate to curative-intent concomitant chemo-radiotherapy (CRT), had FDG-PET/CT performed in treatment position, before and during (Day 21) radiotherapy (RT). Four planning scenarios were investigated: 1) 50 Gy total dose with target volumes defined on pre-RT FDG-PET/CT; 2) 50 Gy with boost target volume defined on FDG-PET/CT during RT; 3) 66 Gy with target volumes from pre-RT FDG-PET/CT; and 4) 66 Gy with boost target volume from during-RT FDG-PET/CT. RESULTS: The median metabolic target volume decreased from 12.9 cm3 (minimum 3.7-maximum 44.8) to 5.0 cm3 (1.7-13.5) (p=0.01) between pre- and during-RCT FDG-PET/CT. The median PTV66 was smaller on during-RT than on baseline FDG-PET/CT [108 cm3 (62.5-194) vs. 156 cm3 (68.8-251), p=0.02]. When total dose was set to 50 Gy, planning on during-RT FDG-PET/CT was associated with a marginal reduction in normal tissues irradiation. When total dose was increased to 66 Gy, planning on during-RT PET yielded significantly lower doses to the spinal cord [Dmax=44.1Gy (40.8-44.9) vs. 44.7Gy (41.5-45.0), p=0.007] and reduced lung exposure [V20Gy=23.2% (17.3-27) vs. 26.8% (19.7-30.2), p=0.006]. CONCLUSION: This planning study suggests that adaptive RT based on target volume reduction assessed on FDG-PET/CT during treatment could facilitate dose escalation up to 66 Gy in patients with esophageal SCC.

Advances in PET and MRI imaging of tumor hypoxia.

Tumor hypoxia is a complex and evolving phenomenon both in time and space. Molecular imaging allows to approach these variations, but the tracers used have their own limitations. PET imaging has the disadvantage of low resolution and must take into account molecular biodistribution, but has the advantage of high targeting accuracy. The relationship between the signal in MRI imaging and oxygen is complex but hopefully it would lead to the detection of truly oxygen-depleted tissue. Different ways of imaging hypoxia are discussed in this review, with nuclear medicine tracers such as [18F]-FMISO, [18F]-FAZA, or [64Cu]-ATSM but also with MRI techniques such as perfusion imaging, diffusion MRI or oxygen-enhanced MRI. Hypoxia is a pejorative factor regarding aggressiveness, tumor dissemination and resistance to treatments. Therefore, having accurate tools is particularly important.

Synthetic MRI for Radiotherapy Planning for Brain and Prostate Cancers: Phantom Validation and Patient Evaluation.

Purpose: We aimed to evaluate the accuracy of T 1 and T 2 mappings derived from a multispectral pulse sequence (magnetic resonance image compilation, MAGiC®) on 1.5-T MRI and with conventional sequences [gradient echo with variable flip angle (GRE-VFA) and multi-echo spin echo (ME-SE)] compared to the reference values for the purpose of radiotherapy treatment planning. Methods: The accuracy of T 1 and T 2 measurements was evaluated with 2 coils [head and neck unit (HNU) and BODY coils] on phantoms using descriptive statistics and Bland-Altman analysis. The reproducibility and repeatability of T 1 and T 2 measurements were performed on 15 sessions with the HNU coil. The T 1 and T 2 synthetic sequences obtained by both methods were evaluated according to quality assurance (QA) requirements for radiotherapy. T 1 and T 2 in vivo measurements of the brain or prostate tissues of two groups of five subjects were also compared. Results: The phantom results showed good agreement (mean bias, 8.4%) between the two measurement methods for T 1 values between 490 and 2,385 ms and T 2 values between 25 and 400 ms. MAGiC® gave discordant results for T 1 values below 220 ms (bias with the reference values, from 38% to 1,620%). T 2 measurements were accurately estimated below 400 ms (mean bias, 8.5%) by both methods. The QA assessments are in agreement with the recommendations of imaging for contouring purposes for radiotherapy planning. On patient data of the brain and prostate, the measurements of T 1 and T 2 by the two quantitative MRI (qMRI) methods were comparable (max difference, <7%). Conclusion: This study shows that the accuracy, reproducibility, and repeatability of the multispectral pulse sequence (MAGiC®) were compatible with its use for radiotherapy treatment planning in a range of values corresponding to soft tissues. Even validated for brain imaging, MAGiC® could potentially be used for prostate qMRI.

Correlations between baseline 18F-FDG PET tumour parameters and circulating DNA in diffuse large B cell lymphoma and Hodgkin lymphoma.

BACKGROUND: 18F-FDG PET/CT is a standard for many B cell malignancies, while blood DNA measurements are emerging tools. Our objective was to evaluate the correlations between baseline PET parameters and circulating DNA in diffuse large B cell lymphoma (DLBCL) and classical Hodgkin lymphoma (cHL). METHODS: Twenty-seven DLBCL and forty-eight cHL were prospectively included. Twelve PET parameters were analysed. Spearman's correlations were used to compare PET parameters each other and to circulating cell-free DNA ([cfDNA]) and circulating tumour DNA ([ctDNA]). p values were controlled by Benjamini-Hochberg correction. RESULTS: Among the PET parameters, three different clusters for tumour burden, fragmentation/massiveness and dispersion parameters were observed. Some PET parameters were significantly correlated with blood DNA parameters, including the total metabolic tumour surface (TMTS) describing the tumour-host interface (e.g. ρ = 0.81 p < 0.001 for [ctDNA] of DLBLC), the tumour median distance between the periphery and the centroid (medPCD) describing the tumour's massiveness (e.g. ρ = 0.81 p < 0.001 for [ctDNA] of DLBLC) and the volume of the bounding box including tumours (TumBB) describing the disease's dispersion (e.g. ρ = 0.83 p < 0.001 for [ctDNA] of DLBLC). CONCLUSIONS: Some PET parameters describing tumour burden, fragmentation/massiveness and dispersion are significantly correlated with circulating DNA parameters of DLBCL and cHL patients. These results could help to understand the pathophysiology of B cell malignancies.

Sub-cutaneous Fat Mass measured on multislice computed tomography of pretreatment PET/CT is a prognostic factor of stage IV non-small cell lung cancer treated by nivolumab.

Introduction: Our aim was to explore the prognostic value of anthropometric parameters in patients treated with nivolumab for stage IV non-small cell lung cancer (NSCLC). Methods: We retrospectively included 55 patients with NSCLC treated by nivolumab with a pretreatment 18FDG positron emission tomography coupled with computed tomography (PET/CT). Anthropometric parameters were measured on the CT of PET/CT by in-house software (Anthropometer3D) allowing an automatic multi-slice measurement of Lean Body Mass (LBM), Fat Body Mass (FBM), Muscle Body Mass (MBM), Visceral Fat Mass (VFM) and Sub-cutaneous Fat Mass (SCFM). Clinical and tumor parameters were also retrieved. Receiver operator characteristics (ROC) analysis was performed and overall survival at 1 year was studied using Kaplan-Meier and Cox analysis. Results: FBM and SCFM were highly correlated (ρ = 0.99). In ROC analysis, only FBM, SCFM, VFM, body mass index (BMI) and metabolic tumor volume (MTV) had an area under the curve (AUC) significantly higher than 0.5. In Kaplan-Meier analysis using medians as cut-offs, prognosis was worse for patients with low SCFM (<5.69 kg/m2; p = 0.04, survivors 41% vs 75%). In Cox univariate analysis using continuous values, BMI (HR = 0.84, p= 0.007), SCFM (HR = 0.75, p = 0.003) and FBM (HR = 0.80, p= 0.004) were significant prognostic factors. In multivariate analysis using clinical parameters (age, gender, WHO performance status, number prior regimens) and SCFM, only SCFM was significantly associated with poor survival (HR = 0.75, p = 0.006). Conclusions: SCFM is a significant prognosis factor of stage IV NSCLC treated by nivolumab.

In vitro assessment of a 3D segmentation algorithm based on the belief functions theory in calculating renal volumes by MRI.

OBJECTIVE: Renal volumetry is an essential part of split renal function assessment in MR urography. The aim of this study was to assess the accuracy and repeatability of a 3D segmentation algorithm based on the belief functions theory for calculating renal volumes from MR images. MATERIALS AND METHODS: The true volumes of 20 animal kidneys of various sizes were obtained by fluid displacement. Each kidney was examined using two different MR units. Three-dimensional proton density-weighted acquisitions with an incremental slice thickness were performed. The MR volume was then measured with a segmentation algorithm based on the belief functions theory. Two independent observers performed all segmentations twice. Accuracy, intraobserver variability, and interobserver variability were evaluated by the Bland-Altman method. The number and type of manual corrections were recorded as well as the entire processing time. RESULTS: The mean renal volume estimated by fluid displacement was 114 mL (range, 38-224 mL). With regard to the renal volumes obtained from assessments of adjacent axial MR images, the maximal SDs of the difference were 2.2 mL (accuracy), 0.6 mL (intraobserver variability), and 1.8 mL (interobserver variability). Segmentation of axial slices provided better accuracy and reproducibility than coronal slices. Overlapped coronal slices yielded poor results because of the partial volume effect. The mean processing time including optional manual modifications was less than 75 seconds. CONCLUSION: The belief functions theory can be considered an accurate and reproducible mathematic method to assess renal volume from MR adjacent images.

Comparison between 2D and 3D dosimetry protocols in 90Y-ibritumomab tiuxetan radioimmunotherapy of patients with non-Hodgkin's lymphoma.

UNLABELLED: We compared the radiation-absorbed dose obtained from a two dimensional (2D) protocol, based on planar whole-body (WB) scans and fixed reference organ masses with dose estimates, using a 3D single-photon emission computed tomography (SPECT) imaging protocol and patient-specific organ masses. METHODS: Six (6) patients with follicular non-Hodgkin's lymphoma underwent a computed tomography (CT) scan, 5 2D planar WB, and 5 SPECT scans between days 0 and 6 after the injection of (111)In-ibritumomab tiuxetan. The activity values in the liver, spleen, and kidneys were calculated from the 2D WB scans, and also from the 3D SPECT images reconstructed, using quantitative image processing. Absorbed doses after the administration of (90)Y-ibritumomab tiuxetan were calculated from the (111)In WB activity values combined with reference organ masses and also from the SPECT activity values and organ masses as estimated from the patient CT scan. To assess the quantitative accuracy of the WB and SPECT scans, an abdominal phantom was also studied. RESULTS: The differences between organ masses estimated from the patient CT and from the reference MIRD models were between -10% and +98%. Using the phantom, errors in organ and tumor activity estimates were between -86% and 10% for the WB protocol and between -43% and -6% for the SPECT protocol. Patient liver, spleen, and kidney activity values determined from SPECT were systematically less than those from the WB scans. Radiation-absorbed doses calculated with the 3D protocol were systematically lower than those calculated from the WB protocol (29%+/-26%, 73%+/-26%, and 33%+/-53% differences in the liver, spleen, and kidney, respectively), except in the kidneys of 2 patients and in the liver of 1 patient. CONCLUSIONS: Accounting for patient-specific organ mass and using SPECT activity quantification have both a great impact on estimated absorbed doses.

Development and validation of the random walk algorithm: application to the classification of diffuse heterogeneity in brain SPECT perfusion images.

UNLABELLED: Heterogeneity analysis has been studied for radiological imaging, but few methods have been developed for functional images. Diffuse heterogeneous perfusion frequently appears in brain single photon emission computed tomography (SPECT) images, but objective quantification is lacking. An automatic method, based on random walk (RW) theory, has been developed to quantify perfusion heterogeneity. We assess the robustness of our algorithm in differentiating levels of diffuse heterogeneity even when focal defects are present. METHODS: Heterogeneity is quantified by counting R (percentage), the mean rate of visited pixels in a fixed number of steps of the stochastic RW process. The algorithm has been tested on the numerical anthropomorphic Zubal head phantom. Seven diffuse cortical heterogeneity levels were simulated with an adjustable Gaussian function and 6 temporoparietal focal defects simulating Alzheimer Disease, leading to 42 phantoms. Data were projected and smoothed (full width at half maximum, 5.5 mm), and Poisson noise was added to the 64 projections. The SPECT data were reconstructed using filtered backprojection (Hamming filter, 0.5 c/p). R values for different levels of perfusion defect and diffuse heterogeneity were evaluated on 3 parameters: the number of slices studied (20 vs 40), the use of Talairach normalization versus original space, and the use of a cortical mask within the Talairach space. For each parameter, regression lines for heterogeneity and temporoparietal defect quantification were analyzed by covariance statistics. R values were also evaluated on SPECT images performed on 25 subjects with suspected focal dementia and on 15 normal controls. Scans were blindly ranked by 2 experienced nuclear physicians according to the degree of diffuse heterogeneity. RESULTS: Variability of R was smaller than 0.17% for repeated measurements. R was more particularly influenced by diffuse heterogeneity compared with focal perfusion defect. The Talairach normalization had a significant influence on the heterogeneity quantification. The number of slices visited by the RW and the cortical masking have a weak influence on the heterogeneity quantification but only for very low heterogeneity levels. The Spearman coefficient between physicians' consensus and RW automatic ranking is 0.85, in the same order of magnitude as the Spearman coefficient between the rankings of the 2 senior physicians (0.86). CONCLUSIONS: Random walk is an original and objective method and is able to quantify heterogeneous brain perfusion, even in presence of cortical defects. This method is repeatable, robust, and mainly influenced by spatial normalization.

Semi-automatic lymphoma detection and segmentation using fully conditional random fields.

The detection and delineation of the lymphoma volume are a critical step for its treatment and its outcome prediction. Positron Emission Tomography (PET) is widely used for lymphoma detection. Two common types of approaches can be distinguished for lymphoma detection and segmentation in PET. The first one is ROI dependent which needs a ROI defined by physicians. The second one is based on machine learning methods which need a large learning database. However, such a large standard database is quite rare in medical field. Considering these problems, we propose a new approach that combines PET (metabolic information) with CT (anatomical information). Our approach is semi-automatic, it consists of three steps. First, an anatomical multi-atlas segmentation is applied on CT to locate and remove the organs having physiologic hypermetabolism in PET. Then, CRFs (Conditional Random Fields) detect and segment a set of possible lymphoma volumes in PET. The conditional probabilities used in CRFs are usually estimated by a learning step. In this work, we propose to estimate them in an unsupervised way. The final step is to visualize the detected lymphoma volumes and select the real ones by simply clicking on them. The false detection is low thanks to the first step. Our method is tested on 11 patients. The rate of good detection of lymphoma is 100%. The average of Dice indexes for measuring the lymphoma segmentation performance is 84.4% compared to the manual lymphoma segmentation. Comparing with other methods in terms of Dice index shows the best performance of our method.

A new methodology to derive 3D kinetic parametric FDG PET images based on a mathematical approach integrating an error model of measurement.

BACKGROUND: In FDG-PET, SUV images are hampered by large potential biases. Our aim was to develop an alternative method (ParaPET) to generate 3D kinetic parametric FDG-PET images easy to perform in clinical oncology. METHODS: The key points of our method are the use of a new error model of PET measurement extracted from a late dynamic PET acquisition of 15 min, centered over the lesion and an image-derived input function (IDIF). The 15-min acquisition is reconstructed to obtain five images of FDG mean activity concentration and images of its variance to model errors of PET measurement. Our approach is carried out on each voxel to derive 3D kinetic parameter images. ParaPET was evaluated and compared to Patlak analysis as a reference. Hunter and Barbolosi methods (Barbolosi-Bl: with blood samples or Barbolosi-Im: with IDIF) were also investigated and compared to Patlak. Our evaluation was carried on Ki index, the net influx rate and its maximum value in the lesion (Ki,max). RESULTS: This parameter was obtained from 41 non-small cell lung cancer lesions associated with 4 to 5 blood samples per patient, required for the Patlak analysis. Compare to Patlak, the median relative difference and associated range (median; [min;max]) in Ki,max estimates were not statistically significant (Wilcoxon test) for ParaPET (- 3.0%; [- 31.9%; 47.3%]; p = 0.08) but statistically significant for Barbolosi-Bl (- 8.0%; [- 30.8%; 53.7%]; p = 0.001), Barbolosi-Im (- 7.9%; [- 38.4%; 30.6%]; p = 0.007) or Hunter (32.8%; [- 14.6%; 132.2%]; p < 10- 5). In the Bland-Altman plots, the ratios between the four methods and Patlak are not dependent of the Ki magnitude, except for Hunter. The 95% limits of agreement are comparable for ParaPET (34.7%), Barbolosi-Bl (30.1%) and Barbolosi-Im (30.8%), lower to Hunter (81.1%). In the 25 lesions imaged before and during the radio-chemotherapy, the decrease in the FDG uptake (ΔSUVmax or ΔKi,max) is statistically more important (p < 0.02, Wilcoxon one-tailed test) when estimated from the Ki images than from the SUV images (additional median variation of - 2.3% [- 52.6%; + 19.1%] for ΔKi,max compared to ΔSUVmax). CONCLUSION: None of the four methodologies is yet ready to replace the Patlak approach, and further improvements are still required. Nevertheless, ParaPET remains a promising approach, offering a non-invasive alternative to methods based on multiple blood samples and only requiring a late PET acquisition. It allows deriving Ki values, highly correlated and presenting the lowest relative bias with Patlak estimates, in comparison to the other methods we evaluated. Moreover, ParaPET gives access to quantitative information at the pixel level, which needs to be evaluated in the perspective of radiomic and tumour response. TRIAL REGISTRATION: NCT 02821936 ; May 2016.