Clinical evaluation of 2D versus 3D whole-body PET image quality using a dedicated BGO PET scanner

Visvikis, Dimitris; Griffiths, David; Costa, Durval C.; Bomanji, Jamshed; Ell, PJ

doi:10.1007/s00259-005-1809-9

Cited by 32 publications

(29 citation statements)

References 12 publications

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“…31, but higher SNR CHO in 3D mode for patients with a BMI # 31, whereas no statistical difference was observed between 2D and 3D when all patients were pooled. The latter finding could explain the results reported recently by Visvikis et al (6), who found similar image quality in a qualitative assessment of 2D and 3D scans.…”

Section: Discussionsupporting

confidence: 79%

“…Unlike previous studies (6,7), this study had the advantage of a gold standard, because the lesion-present cases were generated from artificial lesions that were added to the raw sinograms. Our methodology for adding the lesions was validated in cylindric phantom experiment, and the realism was verified by ensuring that likely locations of disease were chosen by an experienced physician for each bed position.…”

Section: Discussionmentioning

confidence: 99%

“…They found, however, that detectability with 3D PET was greater than or similar to that of 2D PET if a 444-MBq injection was simulated for both modes. Visvikis et al (6) reported that human observers, asked to subjectively assess WB scans, rated 2D and 3D images similarly when overall acquisition time was held constant. LSO scanners are generally expected to perform better than BGO scanners in 3D mode due to higher count-rate capability, better timing resolution, and, therefore, better randoms rejection, as well as better energy resolution, and higher energy threshold, and, therefore, better scatter rejection.…”

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confidence: 99%

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Impact of Acquisition Geometry, Image Processing, and Patient Size on Lesion Detection in Whole-Body 18F-FDG PET

Fakhri

Santos

Badawi³

et al. 2007

Journal of Nuclear Medicine

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The aim of this work was to develop a rigorous evaluation methodology to assess performance of different acquisition and processing methods for variable patient sizes in the context of lesion detection in whole-body 18 F-FDG PET. Methods: Fifty-nine bed positions were acquired in 32 patients in 2-dimensional (2D) and 3-dimensional (3D) modes 1-4 h after 18 F-FDG injection (740 MBq) using a BGO PET scanner. Three spheres (1.0-, 1.3-, and 1.6-cm diameter) containing 68 Ge were also imaged separately in air, at locations corresponding to possible lesion sites in 2D and 3D (590 targets per condition). Each bed position was acquired for 7 min in 2D and 6 min in 3D and corrected for randoms using delayed window randoms subtraction (DWS) or randoms variance reduction (RVR). Sphere sinograms were attenuated using the 2D or 3D attenuation map derived from the transmission scan of the patient, after scaling 2D and 3D sinograms with identical factors to ensure marginal detectability. Resulting 2D sinograms were reconstructed with filtered backprojection (FBP) and ordered-subsets expectation maximization (OSEM) without any scatter or attenuation correction (FBP-NATS and OSEM-NATS) or corrected for scatter and attenuation and reconstructed using FBP (FBP-ATT) or attenuation-weighted OSEM (AWOSEM). 3D sinograms were processed identically after Fourier rebinning. Next, reconstructed volumes were compared on the basis of performance of a 3-channel Hotelling observer (CHO-SNR [SNR is signal-to-noise ratio]) in detecting the presence of a sphere of unknown size on an anatomic background while modeling observer noise. The noise equivalent count (NEC) rate was computed in 2D and 3D for 3 different phantoms sizes (40, 60, and 95 kg) and compared with lesion detection SNR. Results: 3D imaging yielded better lesion detectability than 2D (P , 0.025, 2-tailed paired t test) in patients of normal size (body mass index [BMI] # 31). However, 2D imaging yielded better lesion detectability than 3D in large patients (BMI . 31), as 3D performance deteriorated in large patients (P , 0.05). 2D and 3D yielded similar results for different lesion sizes. CHO-SNR were 40% greater for AWOSEM, FBP-ATT, and FBPNAT than for OSEM (P , 0.05), and AWOSEM yielded significantly better lesion detectability than did FBP. In all patients, RVR yielded a systematic improvement in CHO-SNR over DWS in both 2D and 3D. ONEC was characterized by a behavior similar to that of SNR CHO for the 3 different phantom sizes considered in this study. Three-di mensional (3D) ''septumless'' whole-body (WB) 18 F-FDG PET yields increased sensitivity to true coincidences at the expense of increased sensitivity to scattered and random coincidences. Several groups have assessed the relative merits of 3D and 2-dimensional (2D) WB-PET for BGO-based PET scanners using numeric simulations, phantom studies, or patient studies, applying various metrics. Raylman et al. (1), in a single-observer subjective visibility phantom study, reported equal performance for 2D and 3D PET, whereas Moore ...

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Section: Discussionsupporting

confidence: 79%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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Impact of Acquisition Geometry, Image Processing, and Patient Size on Lesion Detection in Whole-Body 18F-FDG PET

Fakhri

Santos

Badawi³

et al. 2007

Journal of Nuclear Medicine

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“…This uncertainty led some (7) to investigate the optimization of the clinical performance of a BGO-based scanner by interleaving 2D (scan duration, 5 min/bed position) acquisitions with 3D (scan duration, 3, 4, and 5 min/bed position) acquisitions in WB clinical acquisitions, with phantom studies used to optimize 2D and 3D imaging parameters.…”

mentioning

confidence: 99%

“…A 2D versus 3D simulated WB clinical study was performed with dynamic acquisitions on a lutetium oxyorthosilicate-based scanner using a single field of view (SFOV) (8) with noise-matched images. Transmission scanning was performed using 68 Ge rod sources, as in the studies by Visvikis et al (7) and Lodge et al (8); each of these publications concluded that 3 dimensions offered clinical image quality comparable to, if not better than, 2 dimensions.…”

mentioning

confidence: 99%

Scan-Time Reduction Using Noise-Matched Images in 2- and 3-Dimensional Bismuth Germanate PET/CT: Clinical Study in Head and Neck Cancer

Sanghera¹,

Lowe²,

Wellstead³

et al. 2009

Journal of Nuclear Medicine Technology

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We quantitatively and qualitatively investigated 2-dimensional (2D) and 3-dimensional (3D) imaging with scan-time reduction in 14 patients with 17 lesions, who had known or suspected head and neck cancer, using a bismuth germanate (BGO) crystal based PET/CT scanner with noise-matched images. Methods: A 2D and 3D acquisition protocol using scan-time reduction on an axial single field of view resulted in a 2D 4-, 3D 4-, 3D 3-, 2D 3-, 2D 2-, and 3D 2-min scan sequence to minimize redistribution and decay bias. Tumor maximum standardized uptake values (SUVmax) and tumor mean standardized uptake values (SUV mean ) were recorded, and two observers in consensus investigated lesion conspicuity between 2D and 3D paired 4-, 3-, and 2-min noisematched images. Results: We found some minor advantages quantitatively in favor of 2D scanning, with higher mean SUVmax, and qualitatively in favor of 3D scanning, with lesion conspicuity preference. In our cohort, no great advantage or disadvantage to using either acquisition mode was observed, and all lesions were seen irrespective of acquisition mode and scan time. Conclusion: In head and neck cancer patients, we can recommend a scan-time reduction from 4 to 3 min/bed position in 2D acquisitions with a BGO-based PET/CT scanner, using our imaging protocol and reconstruction defaults. Bi smuth germanate (BGO) crystal-based PET and more recent combined PET/CT scanners are in widespread clinical use, predominantly to image 18 F-FDG in cancer studies, but there is still controversy over potential advantages or disadvantages of 2-dimensional (2D) versus 3-dimensional (3D) acquisition and scan-time reduction clinically. In 2D scanning, interplane septa reduce image-degrading random and scattered counts at the expense of overall system sensitivity, with the possibility of long scan times to improve count statistics. In 3D scanning, septa are retracted (typically allowing a large increase in such counts), thereby requiring imaging systems with characteristics of low dead time, fast decay crystal or readout electronics, good energy resolution, and scatter correction to optimize the signal-to-noise ratio in images.Such factors raise questions about the clinical advantages and disadvantages of 2D acquisition, compared with 3D acquisition (1), regarding image quality and potential scantime and dose reduction. Additional parameters that may influence this comparison are patient demographics, fasting time before scanning, choice and amount of tracer administered, time to start scanning after injection, data representation and reconstruction algorithms used (2), scanner used (3), and type or site of cancer.Phantom studies have been used to investigate the lesionto-background ratios for 2D and 3D acquisition modes in whole-body (WB) scanning. In 1 phantom study, injected activity was optimized using 2D and 3D noise equivalent counting (NEC) rates, but no advantage was seen (4). Similarly, no improvement was found between scanning modes in a small tumor or lymph node phantom study (5). However, 3D ...

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Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211

et al. 2017

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Purpose The purpose of this educational report is to provide an overview of the present state-of-the-art PET auto-segmentation (PET-AS) algorithms and their respective validation, with an emphasis on providing the user with help in understanding the challenges and pitfalls associated with selecting and implementing a PET-AS algorithm for a particular application. Approach A brief description of the different types of PET-AS algorithms is provided using a classification based on method complexity and type. The advantages and the limitations of the current PET-AS algorithms are highlighted based on current publications and existing comparison studies. A review of the available image datasets and contour evaluation metrics in terms of their applicability for establishing a standardized evaluation of PET-AS algorithms is provided. The performance requirements for the algorithms and their dependence on the application, the radiotracer used and the evaluation criteria are described and discussed. Finally, a procedure for algorithm acceptance and implementation, as well as the complementary role of manual and auto-segmentation are addressed. Findings A large number of PET-AS algorithms have been developed within the last 20 years. Many of the proposed algorithms are based on either fixed or adaptively selected thresholds. More recently, numerous papers have proposed the use of more advanced image analysis paradigms to perform semi-automated delineation of the PET images. However, the level of algorithm validation is variable and for most published algorithms is either insufficient or inconsistent which prevents recommending a single algorithm. This is compounded by the fact that realistic image configurations with low signal-to-noise ratios (SNR) and heterogeneous tracer distributions have rarely been used. Large variations in the evaluation methods used in the literature point to the need for a standardized evaluation protocol. Conclusions Available comparison studies suggest that PET-AS algorithms relying on advanced image analysis paradigms provide generally more accurate segmentation than approaches based on PET activity thresholds, particularly for realistic configurations. However, this may not be the case for simple shape lesions in situations with a narrower range of parameters, where simpler methods may also perform well. Recent algorithms which employ some type of consensus or automatic selection between several PET-AS methods have potential to overcome the limitations of the individual methods when appropriately trained. In either case, accuracy evaluation is required for each different PET scanner and scanning and image reconstruction protocol. For the simpler, less robust approaches, adaptation to scanning conditions, tumor type, and tumor location by optimization of parameters is necessary. The results from the method evaluation stage can be used to estimate the contouring uncertainty. All PET-AS contours should be critically verified by a physician. A standard test, i.e., a benchmark dedicated to ...

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Clinical evaluation of 2D versus 3D whole-body PET image quality using a dedicated BGO PET scanner

Abstract: 3D WB imaging using a dedicated BGO-based PET scanner offers similar image quality to that obtained in 2D considering similar overall times of acquisitions.

Cited by 32 publications

References 12 publications

Impact of Acquisition Geometry, Image Processing, and Patient Size on Lesion Detection in Whole-Body 18F-FDG PET

Impact of Acquisition Geometry, Image Processing, and Patient Size on Lesion Detection in Whole-Body 18F-FDG PET

Scan-Time Reduction Using Noise-Matched Images in 2- and 3-Dimensional Bismuth Germanate PET/CT: Clinical Study in Head and Neck Cancer

Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211

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