Parallel-hole collimator concept for stationary SPECT imaging

Pato, Lara da Rocha Vaz; Vandenberghe, Stefaan; Zedda, Tiziana; Holen, Roel Van

doi:10.1088/0031-9155/60/22/8791

Cited by 6 publications

(4 citation statements)

References 18 publications

(19 reference statements)

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“…Note that precise manufacturing of converging collimators is a difficult process which has caused constraints in collimator design, as for example thin septa are very hard to produce. In recent years new technologies like 3D printing have emerged that allow more challenging designs to be manufactured (Van Audenhaege et al, 2015;Pato et al, 2015) that may better exploit the advantages of strongly focusing collimators. Therefore, a new look at the possibilities of converging collimators might be worthwhile and correct sensitivity expressions for such collimators are then desirable.…”

Section: Discussionmentioning

confidence: 99%

Non-diverging analytical expression for the sensitivity of converging SPECT collimators

Roosmalen

Goorden²

2017

Phys. Med. Biol.

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Accurate analytical expressions for collimator resolution and sensitivity are important tools in the optimization of SPECT systems. However, presently known expressions for the sensitivity of converging collimators either diverge near the focal point or focal line(s), or are only valid on the collimator axis. As a result, these expressions are unsuitable to calculate volumetric sensitivity for e.g. short-focal length collimators that focus inside the object to enhance sensitivity. To also enable collimator optimization for these geometries, we here present non-diverging sensitivity formulas for astigmatic, cone beam and fan beam collimators that are applicable over the full collimator's field-of-view. The sensitivity was calculated by integrating previously derived collimator response functions over the full detector surface. Contrary to common approximations, the varying solid angle subtended by different detector pixels was fully taken into account which results in a closed-form non-diverging formula for the sensitivity. We validated these expressions using ray-tracing simulations of a fan beam and an astigmatic cone beam collimator and found close agreement between the simulations and the sensitivity expression. The largest differences with the simulation were found close to the collimator, where sensitivity depends on the exact placement of holes and septa, while our expression represents an average over all possible placements as is common practice for analytical sensitivity expressions. We checked that average differences between the analytical expression and simulations reduced to less than 1% of the maximum sensitivity when we averaged our simulations over different septa locations. Moreover, we found that our new expression reduced to the traditional diverging formula under certain assumptions. Therefore, the newly derived sensitivity expression may enable the optimization of converging collimators for a wide range of applications, in particular when the focus is close to, or in, the object of interest.

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Section: Discussionmentioning

confidence: 99%

Non-diverging analytical expression for the sensitivity of converging SPECT collimators

Roosmalen

Goorden²

2017

Phys. Med. Biol.

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“…For each phantom, the activity map was generated by assigning tracer concentrations to grey matter, white matter and background regions (e.g. skin, skeletal muscle) with a mean ratio of 80:20:5 (Glick and Soares 1997, Stodilka et al 2000, Pato et al 2015 to mimic a realistic blood flow. This ratio was introduced with a random variation (normally distributed) with a standard deviation of 10% for each number to make the activity distribution variable among phantoms.…”

Section: Digital Phantoms For Simulationmentioning

confidence: 99%

Automatic attenuation map estimation from SPECT data only for brain perfusion scans using convolutional neural networks

2021

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In clinical brain SPECT, correction for photon attenuation in the patient is essential to obtain images which provide quantitative information on the regional activity concentration per unit volume (kBq.ml 1 ). This correction generally requires an attenuation map (m map) denoting the attenuation coefficient at each voxel which is often derived from a CT or MRI scan. However, such an additional scan is not always available and the method may suffer from registration errors. Therefore, we propose a SPECT-only-based strategy for m map estimation that we apply to a stationary multi-pinhole clinical SPECT system (G-SPECT-I) for 99m Tc-HMPAO brain perfusion imaging. The method is based on the use of a convolutional neural network (CNN) and was validated with Monte Carlo simulated scans. Data acquired in list mode was used to employ the energy information of both primary and scattered photons to obtain information about the tissue attenuation as much as possible. Multiple SPECT reconstructions were performed from different energy windows over a large energy range. Locally extracted 4D SPECT patches (three spatial plus one energy dimension) were used as input for the CNN which was trained to predict the attenuation coefficient of the corresponding central voxel of the patch. Results show that Attenuation Correction using the Ground Truth m maps (GT-AC) or using the CNN estimated m maps (CNN-AC) achieve comparable accuracy. This was confirmed by a visual assessment as well as a quantitative comparison; the mean deviation from the GT-AC when using the CNN-AC is within 1.8% for the standardized uptake values in all brain regions. Therefore, our results indicate that a CNN-based method can be an automatic and accurate tool for SPECT attenuation correction that is independent of attenuation data from other imaging modalities or human interpretations about head contours.

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“…A digital Zubal phantom (Zubal et al 1994) was used for simulating normal brain perfusion images (figure 2). The activity map was generated by segmenting the Zubal phantom into grey matter, white matter and cerebral spinal fluid (CSF) and assigning activity concentrations to these regions with a ratio of 4:1:0, respectively, as in Glick and Soares (1997), Stodilka et al (2000) and Pato et al (2015). We forced the phantom to be perfectly symmetric by mirroring the phantom left hemisphere to the right.…”

Section: Simulation Set Upmentioning

confidence: 99%

Optimized sampling for high resolution multi-pinhole brain SPECT with stationary detectors

Chen¹,

Goorden²,

Vastenhouw³

et al. 2020

Phys. Med. Biol.

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Brain perfusion SPECT can be used in the diagnosis of various neurologic or psychiatric disorders, e.g. stroke, epilepsy, dementia and posttraumatic stress disorder. As traditional SPECT provides limited resolution and sensitivity, we recently proposed a high resolution focusing multi-pinhole clinical SPECT scanner dubbed G-SPECT-I (Beekman et al 2015). G-SPECT-I achieves data completeness in the scan region of interest by making small translations of the patient bed while using projections from all bed positions together for image reconstruction. A strategy to restrict the number of bed translations is desired to minimize overhead time. Previously we presented optimized bed translation paths for focused partial brain imaging, while here we focus on whole brain imaging which is the common procedure in perfusion studies. Thus, a series of noise-free scans using a reduced number of bed positions were simulated and compared to an oversampled reference scan acquired with 128 bed positions. Noisy simulations were included to validate the utility of the optimized sequences in more realistic situations. Brain Uptake Ratios (BURs) and left-right Asymmetry Indices (AIs) in 51 selected Regions of Interest (ROIs) were calculated for assessment. Results show that images were barely affected by decreasing the number of bed positions from 128 down to 18 (mean deviation from the reference of only 2.2% and 1.5% for the BUR and AI respectively) while slightly larger deviations (2.9% and 2.7% respectively) were obtained when using 12 positions. For both 18-and 12-position sequences these deviations due to sampling were much smaller than those induced by noise (mean deviation of 6.5% and 8.6% respectively). Given an associated total overhead for bed movement of half a minute (18 positions) or 20 seconds (12 positions), G-SPECT-I can be a clinical platform that brings new protocols for fast (dynamic) whole brain SPECT and motion correction into reach.

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Parallel-hole collimator concept for stationary SPECT imaging

Cited by 6 publications

References 18 publications

Non-diverging analytical expression for the sensitivity of converging SPECT collimators

Non-diverging analytical expression for the sensitivity of converging SPECT collimators

Automatic attenuation map estimation from SPECT data only for brain perfusion scans using convolutional neural networks

Optimized sampling for high resolution multi-pinhole brain SPECT with stationary detectors

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