Artificial neural network-statistical approach for PET volume analysis and classification

Copyright © 2012 The Authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.This article has been made available through the Brunel Open Access Publishing Fund.The increasing number of imaging studies and the prevailing application of positron emission tomography (PET) in clinical oncology have led to a real need for efficient PET volume handling and the development of new volume analysis approaches to aid the clinicians in the clinical diagnosis, planning of treatment, and assessment of response to therapy. A novel automated system for oncological PET volume analysis is proposed in this work. The proposed intelligent system deploys two types of artificial neural networks (ANNs) for classifying PET volumes. The first methodology is a competitive neural network (CNN), whereas the second one is based on learning vector quantisation neural network (LVQNN). Furthermore, Bayesian information criterion (BIC) is used in this system to assess the optimal number of classes for each PET data set and assist the ANN blocks to achieve accurate analysis by providing the best number of classes. The system evaluation was carried out using experimental phantom studies (NEMA IEC image quality body phantom), simulated PET studies using the Zubal phantom, and clinical studies representative of nonsmall cell lung cancer and pharyngolaryngeal squamous cell carcinoma. The proposed analysis methodology of clinical oncological PET data has shown promising results and can successfully classify and quantify malignant lesions.This study was supported by the Swiss National Science Foundation under Grant SNSF 31003A-125246, Geneva Cancer League, and the Indo Swiss Joint Research Programme ISJRP 138866. This article is made available through the Brunel Open Access Publishing Fund

Multimodal Partial-Volume Correction: Application to 18F-Fluoride PET/CT Bone Metastases Studies

18F-fluoride PET/CT offers the opportunity for accurate skeletal metastasis staging, compared with conventional imaging methods. 18F-fluoride is a bone-specific tracer whose uptake depends on osteoblastic activity. Because of the resulting increase in bone mineralization and sclerosis, the osteoblastic process can also be detected morphologically in CT images. Although CT is characterized by high resolution, the potential of PET is limited by its lower spatial resolution and the resulting partial-volume effect. In this context, the synergy between PET and CT presents an opportunity to resolve this limitation using a novel multimodal approach called synergistic functional–structural resolution recovery (SFS-RR). Its performance is benchmarked against current resolution recovery technology using the point-spread function (PSF) of the scanner in the reconstruction procedure. Methods: The SFS-RR technique takes advantage of the multiresolution property of the wavelet transform applied to both functional and structural images to create a high-resolution PET image that exploits the structural information of CT. Although the method was originally conceived for PET/MR imaging of brain data, an ad hoc version for whole-body PET/CT is proposed here. Three phantom experiments and 2 datasets of metastatic bone 18F-fluoride PET/CT images from primary prostate and breast cancer were used to test the algorithm performances. The SFS-RR images were compared with the manufacturer’s PSF-based reconstruction using the standardized uptake value (SUV) and the metabolic volume as metrics for quantification. Results: When compared with standard PET images, the phantom experiments showed a bias reduction of 14% in activity and 1.3 cm3 in volume estimates for PSF images and up to 20% and 2.5 cm3 for the SFS-RR images. The SFS-RR images were characterized by a higher recovery coefficient (up to 60%) whereas noise levels remained comparable to those of standard PET. The clinical data showed an increase in the SUV estimates for SFS-RR images up to 34% for peak SUV and 50% for maximum SUV and mean SUV. Images were also characterized by sharper lesion contours and better lesion detectability. Conclusion: The proposed methodology generates PET images with improved quantitative and qualitative properties. Compared with standard methods, SFS-RR provides superior lesion segmentation and quantification, which may result in more accurate tumor characterization

A multifractal approach to space-filling recovery for PET quantification.

Purpose: A new image-based methodology is developed for estimating the apparent space-filling properties of an object of interest in PET imaging without need for a robust segmentation step and used to recover accurate estimates of total lesion activity (TLA). Methods: A multifractal approach and the fractal dimension are proposed to recover the apparent space-filling index of a lesion (tumor volume, TV) embedded in nonzero background. A practical implementation is proposed, and the index is subsequently used with mean standardized uptake value (SUVmean) to correct TLA estimates obtained from approximate lesion contours. The methodology is illustrated on fractal and synthetic objects contaminated by partial volume effects (PVEs), validated on realistic 18F-fluorodeoxyglucose PET simulations and tested for its robustness using a clinical 18F-fluorothymidine PET test-retest dataset. Results: TLA estimates were stable for a range of resolutions typical in PET oncology (4-6 mm). By contrast, the space-filling index and intensity estimates were resolution dependent. TLA was generally recovered within 15% of ground truth on postfiltered PET images affected by PVEs. Volumes were recovered within 15% variability in the repeatability study. Results indicated that TLA is a more robust index than other traditional metrics such as SUVmean or TV measurements across imaging protocols. Conclusions: The fractal procedure reported here is proposed as a simple and effective computational alternative to existing methodologies which require the incorporation of image preprocessing steps (i.e., partial volume correction and automatic segmentation) prior to quantification

Lung nodules: size still matters

The incidence of indeterminate pulmonary nodules has risen constantly over the past few years. Determination of lung nodule malignancy is pivotal, because the early diagnosis of lung cancer could lead to a definitive intervention. According to the current international guidelines, size and growth rate represent the main indicators to determine the nature of a pulmonary nodule. However, there are some limitations in evaluating and characterising nodules when only their dimensions are taken into account. There is no single method for measuring nodules, and intrinsic errors, which can determine variations in nodule measurement and in growth assessment, do exist when performing measurements either manually or with automated or semi-automated methods. When considering subsolid nodules the presence and size of a solid component is the major determinant of malignancy and nodule management, as reported in the latest guidelines. Nevertheless, other nodule morphological characteristics have been associated with an increased risk of malignancy. In addition, the clinical context should not be overlooked in determining the probability of malignancy. Predictive models have been proposed as a potential means to overcome the limitations of a sized-based assessment of the malignancy risk for indeterminate pulmonary nodules

Effect of image registration on 3D absorbed dose calculations in 177 Lu-DOTATOC Peptide Receptor Radionuclide Therapy

Peptide receptor radionuclide therapy (PRRT) is an effective MRT (molecular radiotherapy) treatment, which consists of multiple administrations of a radiopharmaceutical labelled with 177Lu or 90Y. Through sequential functional imaging a patient specific 3D dosimetry can be derived. Multiple scans should be previously co-registered to allow accurate absorbed dose calculations. The purpose of this study is to evaluate the impact of image registration algorithms on 3D absorbed dose calculation. A cohort of patients was extracted from the database of a clinical trial in PRRT. They were administered with a single administration of 177Lu-DOTATOC. All patients underwent 5 SPECT/CT sequential scans at 1 h, 4 h, 24 h, 40 h, 70 h post-injection that were subsequently registered using rigid and deformable algorithms. A similarity index was calculated to compare rigid and deformable registration algorithms. 3D absorbed dose calculation was carried out with the Raydose Monte Carlo code. The similarity analysis demonstrated the superiority of the deformable registrations (p < .001). Average absorbed dose to the kidneys calculated using rigid image registration was consistently lower than the average absorbed dose calculated using the deformable algorithm (90% of cases), with percentage differences in the range [−19; +4]%. Absorbed dose to lesions were also consistently lower (90% of cases) when calculated with rigid image registration with absorbed dose differences in the range [−67.2; 100.7]%. Deformable image registration had a significant role in calculating 3D absorbed dose to organs or lesions with volumes smaller than 100 mL. Image based 3D dosimetry for 177Lu-DOTATOC PRRT is significantly affected by the type of algorithm used to register sequential SPECT/CT scans

Multi-observation PET image analysis for patient follow-up quantitation and therapy assessment.: Multi observation PET image fusion for patient follow-up quantitation and therapy response

International audienceIn positron emission tomography (PET) imaging, an early therapeutic response is usually characterized by variations of semi-quantitative parameters restricted to maximum SUV measured in PET scans during the treatment. Such measurements do not reflect overall tumor volume and radiotracer uptake variations. The proposed approach is based on multi-observation image analysis for merging several PET acquisitions to assess tumor metabolic volume and uptake variations. The fusion algorithm is based on iterative estimation using a stochastic expectation maximization (SEM) algorithm. The proposed method was applied to simulated and clinical follow-up PET images. We compared the multi-observation fusion performance to threshold-based methods, proposed for the assessment of the therapeutic response based on functional volumes. On simulated datasets the adaptive threshold applied independently on both images led to higher errors than the ASEM fusion and on clinical datasets it failed to provide coherent measurements for four patients out of seven due to aberrant delineations. The ASEM method demonstrated improved and more robust estimation of the evaluation leading to more pertinent measurements. Future work will consist in extending the methodology and applying it to clinical multi-tracer datasets in order to evaluate its potential impact on the biological tumor volume definition for radiotherapy applications

A New Image Quantitative Method for Diagnosis and Therapeutic Response

abstract: Accurate quantitative information of tumor/lesion volume plays a critical role in diagnosis and treatment assessment. The current clinical practice emphasizes on efficiency, but sacrifices accuracy (bias and precision). In the other hand, many computational algorithms focus on improving the accuracy, but are often time consuming and cumbersome to use. Not to mention that most of them lack validation studies on real clinical data. All of these hinder the translation of these advanced methods from benchside to bedside. In this dissertation, I present a user interactive image application to rapidly extract accurate quantitative information of abnormalities (tumor/lesion) from multi-spectral medical images, such as measuring brain tumor volume from MRI. This is enabled by a GPU level set method, an intelligent algorithm to learn image features from user inputs, and a simple and intuitive graphical user interface with 2D/3D visualization. In addition, a comprehensive workflow is presented to validate image quantitative methods for clinical studies. This application has been evaluated and validated in multiple cases, including quantifying healthy brain white matter volume from MRI and brain lesion volume from CT or MRI. The evaluation studies show that this application has been able to achieve comparable results to the state-of-the-art computer algorithms. More importantly, the retrospective validation study on measuring intracerebral hemorrhage volume from CT scans demonstrates that not only the measurement attributes are superior to the current practice method in terms of bias and precision but also it is achieved without a significant delay in acquisition time. In other words, it could be useful to the clinical trials and clinical practice, especially when intervention and prognostication rely upon accurate baseline lesion volume or upon detecting change in serial lesion volumetric measurements. Obviously, this application is useful to biomedical research areas which desire an accurate quantitative information of anatomies from medical images. In addition, the morphological information is retained also. This is useful to researches which require an accurate delineation of anatomic structures, such as surgery simulation and planning.Dissertation/ThesisDoctoral Dissertation Biomedical Informatics 201

Investigation of accuracy in quantitation of 18F-FDG concentration of PET/CT

The PET/CT scanner has been recognized as a powerful diagnostic imaging modality in oncology and radiation treatment planning. Traditionally, PET has been used for quantitative analysis, and diagnostic interpretations of PET images greatly relied on a nuclear medicine physician’s experience and knowledge. The PET data set represents a positron emitter’s activity concentration as a gray scale in each pixel. The assurance of the quantitative accuracy of the PET data is critical for diagnosis and staging of disease and evaluation of treatment. The standard uptake value (SUV) is a widely employed parameter in clinical settings to distinguish malignant lesions from others. SUV is a rough normalization of radioactive tracer uptake where normal tissue uptake is unity. The PET scanner is a sensitive diagnostic method to detect small lesions such as lymph node metastasis less than 1 cm in diameter, whereas the CT scanner may be limited in detecting these lesions. The accuracy of quantitation of small lesions is critical for predicting prognosis or planning a treatment of the patient. PET/CT uses attenuation correction factors obtained from CT scanner data sets. Non-biological materials such as metals and contrast agents are recognized as a factor that leads to a wrong scaling factor in the PET image. We challenge the accuracy of the quantitative method that physicians routinely use as a parameter to distinguish malignant lesions from others under clinical settings in commercially available CT/PET scanners. First, we verified if we could recover constant activity concentration throughout the field of view for small identical activity concentration sources. Second, we tested how much the CT-based attenuation correction factor could be influenced by contrast agents. Third, we tested how much error in quantitation could be introduced by object size. Our data suggest that the routine normalization process of the PET scanner does not guarantee an accurate quantitation of discrete uniform activity sources in the PET/CT scanner. Also, activity concentrations greatly rely on an object’s dimensions and object size. A recovery correction factor is necessary on these quantitative data for oncological evaluation to assure accurate interpretation of the activity concentration. Development of parameters for quantitation other than SUV may overcome SUV’s inherent limitations reflecting patient-specific physiology and the imaging characteristics of individual scanners

Tumor Extraction for Brain Magnetic Resonance Imaging Using Modified Gaussian Distribution

Magnetic Resonance Imaging (MRI) is extensively used in the study of brain. Segmentation of MR brain images is necessary for a number of clinical investigations of various complexity, change detection, cortical labeling, and visualization in surgical planning. The volume of enhancing lesions, following the administration of paramagnetic contrast agent is an important indicator of pathology in multiple sclerosis (MS). Manual estimation of enhancing lesion volumes introduces significant errors, and operator bias, besides being time consuming and subjective. Therefore, there is a need for automatic identification and estimation of volumes of the present MS lesions specially by dealing with a large number of images that are typically acquired in multi-center clinical trials. In the developed techniques, 150 T1- and T2-weighted spin echo images were taken from the routine scans of Kuala Lumpur General Hospital.Multiple sclerosis lesions visualized by morphological MRI are classified through a feature map technique on T1 weighted MRI tissue. Gray level morphology methods are used to make tissue types in the images more homogenous and minimize difficulties with connections to outside tissue. A method for hzzy connectedness and combinations of the different segmentation techniques were experimented. A gain-based correction method; probability density function model are used to cluster white and gray matters, cerebrospinal fluid, and meninges. Results of segmentation have been validated by a group of neuro-radiologists. 3D visualization has been implemented for the segmented regions as well as brain lesion. The visualization of the segmented structures uses a combination of volume rendering and surface rendering. The mutual information algorithms used in this work has been developed and experimented in the system and has proven to yield more accurate and stable results than other algorithms. Currently testing the validation of the proposed segmentation in a validation study that compares resulting MS lesion as well as gray and white matter tissue structures with Neural Network expert segmentation system. The proposed method versus Neural Network rater validation showed an average validation score of overlap ratio of >85% for gray and white matters tissue segmentation and for MS lesion the rater validation showed an average overlap ratio of > 87%