A review of partial volume correction techniques for emission tomography and their applications in neurology, cardiology and oncology

Erlandsson, Kjell; Buvat, Irène; Pretorius, P. Hendrik; Thomas, Benjamin A.; Hutton, Brian F.

doi:10.1088/0031-9155/57/21/r119

Cited by 424 publications

(383 citation statements)

References 154 publications

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“…A wide variety of partial volume correction (PVC) methods have been developed in the past, most of them employing highresolution anatomical information from a co-registered CT or MR image as reference [164][165][166][167]. PVC methods can be grouped into two main categories: post-reconstruction and duringreconstruction methods.…”

Section: Partial Volume Correction In Pet and Spectmentioning

confidence: 99%

Hybrid Imaging: Instrumentation and Data Processing

et al. 2018

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State-of-the-art patient management frequently requires the use of non-invasive imaging methods to assess the anatomy, function or molecular-biological conditions of patients or study subjects. Such imaging methods can be singular, providing either anatomical or molecular information, or they can be combined, thus, providing "anato-metabolic" information. Hybrid imaging denotes image acquisitions on systems that physically combine complementary imaging modalities for an improved diagnostic accuracy and confidence as well as for increased patient comfort. The physical combination of formerly independent imaging modalities was driven by leading innovators in the field of clinical research and benefited from technological advances that permitted the operation of PET and MR in close physical proximity, for example. This review covers milestones of the development of various hybrid imaging systems for use in clinical practice and small-animal research. Special attention is given to technological advances that helped the adoption of hybrid imaging, as well as to introducing methodological concepts that benefit from the availability of complementary anatomical and biological information, such as new types of image reconstruction and data correction schemes. The ultimate goal of hybrid imaging is to provide useful, complementary and quantitative information during patient work-up. Hybrid imaging also opens the door to multi-parametric assessment of diseases, which will help us better understand the causes of various diseases that currently contribute to a large fraction of healthcare costs.

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Section: Partial Volume Correction In Pet and Spectmentioning

confidence: 99%

Hybrid Imaging: Instrumentation and Data Processing

et al. 2018

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“…Here, the term partial volume refers to the possible presence of tissue heterogeneity within a single voxel or ROI. This presence makes quantification via mathematical modelling more complex (potentially confounding) [80].…”

Section: Roi Versus Voxel-level Analysis: Pros and Consmentioning

confidence: 99%

Deriving physiological information from PET images: from SUV to compartmental modelling

Bertoldo

Rizzo

Veronese

2014

Clin Transl Imaging

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Positron emission tomography (PET) imaging has made it possible to detect the in vivo concentration of positron-emitting compounds accurately and non-invasively. In order to relate the radioactivity concentration measured using PET to the underlying physiological or biochemical processes, the application of mathematical models to describe tracer kinetics within a particular region of interest is necessary. Image analysis can be performed both by visual interpretation and quantitative assessment and, depending on the ultimate purposes of the analysis, several alternatives are available. In clinical practice, PET quantification is routinely performed using the standard uptake value (SUV), a semiquantitative index in use since the 1980s. Its computation is very simple since it requires only the PET measure at a prefixed sample time and the injected dose normalised to some anthropometric characteristic of the subject (generally body weight or body surface area). An alternative to the SUV is the tissue-to-plasma ratio (ratio). As its name indicates, this index is computed as the ratio between the tracer activity measured in the tissue and in the plasma pool within a prefixed time window. Moving from static to more informative dynamic PET acquisition, three model classes represent the most frequently used approaches: compartmental models, the spectral analysis modelling approach, and graphical methods. These approaches differ in terms of application assumptions (e.g. reversibility of tracer uptake, model structure, etc.) and computational complexity. They also produce different information about the system under study: from a macro-description of tracer uptake to a full quantitative characterisation of the physiological processes in which the tracer is involved. The application of these approaches to clinical routine is restricted by the need for invasive blood sampling. In order to avoid arterial cannulation and blood sample management, different alternative approaches have been developed for quantification of PET kinetics, including reference tissue methods. Although these approaches are appealing, the results obtained with several tracers are questionable. This review provides a complete overview of the semi-quantitative and quantitative methods used in PET analysis. The pros and cons of each method are evaluated and discussed.

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“…It can be noted that the loss of resolution spreads the image counts and decreases the pixel values in the original image, with a consequence of errors in quantification (11) . The goal of blind deconvolution is to correct for this effect and recover the real shapes of the objects.…”

Section: Resultsmentioning

confidence: 99%

“…In parallel, methods have been developed for correcting the PVE (10,11) . These methods are mainly based on enhancing spatial resolution during reconstruction or determining correction factors by using anatomical information (11) .…”

Section: Introductionmentioning

confidence: 99%

Optimising delineation accuracy of tumours in PET for radiotherapy planning using blind deconvolution

Güveniş

Koc

2015

Radiat Prot Dosimetry

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Positron emission tomography (PET) imaging has been proven to be useful in radiotherapy planning for the determination of the metabolically active regions of tumours. Delineation of tumours, however, is a difficult task in part due to high noise levels and the partial volume effects originating mainly from the low camera resolution. The goal of this work is to study the effect of blind deconvolution on tumour volume estimation accuracy for different computer-aided contouring methods. The blind deconvolution estimates the point spread function (PSF) of the imaging system in an iterative manner in a way that the likelihood of the given image being the convolution output is maximised. In this way, the PSF of the imaging system does not need to be known. Data were obtained from a NEMA NU-2 IQ-based phantom with a GE DSTE-16 PET/CT scanner. The artificial tumour diameters were 13, 17, 22, 28 and 37 mm with a target/background ratio of 4:1. The tumours were delineated before and after blind deconvolution. Student's two-tailed paired t-test showed a significant decrease in volume estimation error ( p < 0.001) when blind deconvolution was used in conjunction with computer-aided delineation methods. A manual delineation confirmation demonstrated an improvement from 26 to 16 % for the artificial tumour of size 37 mm while an improvement from 57 to 15 % was noted for the small tumour of 13 mm. Therefore, it can be concluded that blind deconvolution of reconstructed PET images may be used to increase tumour delineation accuracy.

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A review of partial volume correction techniques for emission tomography and their applications in neurology, cardiology and oncology

Cited by 424 publications

References 154 publications

Hybrid Imaging: Instrumentation and Data Processing

Hybrid Imaging: Instrumentation and Data Processing

Deriving physiological information from PET images: from SUV to compartmental modelling

Optimising delineation accuracy of tumours in PET for radiotherapy planning using blind deconvolution

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