Patent Application
20230342999
The invention relates to attenuation correction for single photon emission computed tomography (SPECT) through the application of deep learning networks incorporating images from scatter window(s) and individual subject's information such as BMI (body mass index) and gender.
Single photon emission computed tomography (SPECT) is a non-invasive imaging procedure that provides radiotracer distribution images of the patient body by detecting gamma-ray photons. SPECT plays an important role in the clinical diagnosis of cardiovascular, oncological, and neurological disease. In order to perform qualitative, quantitative, or semi-quantitative analysis for SPECT, accurate attenuation correction is essential.
SPECT continues to play a critical role in the diagnosis and management of coronary artery disease (CAD). While conventional SPECT scanners using parallel-hole collimators are still the foundation of cardiac SPECT, dedicated cardiac SPECT scanners have also been developed. Such dedicated scanners, such as the GE Alcyone 530/570c systems and the D-SPECT™ systems (that is, a medical imaging apparatus featuring nuclear imaging, namely, SPECT imaging as manufactured by Spectrum Dynamics Medical Limited Company), both with CZT (Cadmium Zinc Telluride) detectors, typically have multiple detectors collecting photons emitted from the heart simultaneously. This leads to dramatically improved sensitivity (for example, 2 to 5 times). In addition, the GE Alcyone 530/570c systems use pinhole collimators and can achieve much higher resolution.
These dedicated scanners have opened doors to new applications with significant clinical impact, including, but not limited to, ultra-low-dose imaging, absolute quantification of myocardial blood flow (MBF) and coronary flow reserve (CFR), high resolution molecular imaging, multi-isotope imaging, and motion correction. Most of these new applications are uniquely achievable only using dedicated scanners.
However, an artifact-free reconstruction of radiotracer distribution and absolute activity for SPECT can only be obtained with the assistance of accurate correction of photon attenuation using an individualized attenuation map [Quantitative analysis in nuclear medicine imaging. Springer (2006)]. Therefore, in current clinical practice, computed tomography (CT) is utilized to generate the attenuation map [Pan, T. S., King, M. A., Luo, D. S., Dahlberg, S. T., Villegas, B. J.: Estimation of attenuation maps from scatter and photopeak window single photon-emission computed tomographic images of technetium 99m-labeled sestamibi. Journal of Nuclear Cardiology 4(1) (1997) 42-51; Zaidi, H., Hasegawa, B.: Determination of the attenuation map in emission tomography. Journal of Nuclear Medicine 44(2) (2003) 291-315; Pan, T. S., King, M. A., De Vries, D. J., Ljungberg, M.: Segmentation of the body and lungs from compton scatter and photopeak window data in spect: a monte-carlo investigation. IEEE transactions on medical imaging 15(1) (1996) 13-24]. However, notably, about 80% of SPECT scanners are stand-alone scanners and images are reconstructed without CT transmission scanning.
There are two additional limitations even when the CT is available. First, the use of adjunctive CT scanning for SPECT attenuation introduces additional radiation to patients along with increasing imaging system cost. Secondly, the misalignment between CT and SPECT due to motion can cause attenuation correction artifacts leading to inaccurate assessment of regional myocardial activity [Schäfers, K. P., Stegger, L.: Combined imaging of molecular function and morphology with pet/ct and spect/ct: image fusion and motion correction. Basic research in cardiology 103(2) (2008) 191-199; McQuaid, S. J., Hutton, B. F.: Sources of attenuation-correction artefacts in cardiac pet/ct and spect/ct. European journal of nuclear medicine and molecular imaging 35(6) (2008) 1117-1123]. Previous works have attempted to estimate the attenuation map directly from SPECT emission data [Jha, A. K., Zhu, Y., Clarkson, E., Kupinski, M. A., Frey, E. C.: Fisher information analysis of list-mode spect emission data for joint estimation of activity and attenuation distribution. arXiv preprint arXiv:1807.01767 (2018); Cade, S. C., Arridge, S., Evans, M. J., Hutton, B. F.: Use of measured scatter data for the attenuation correction of single photon emission tomography without trans-mission scanning. Medical physics 40(8) (2013) 082506]. Unfortunately, attenuation map estimation from these methods involving iterative optimization is time-consuming, and often contain high noise level when image activity is relatively low, which can result in SPECT reconstruction errors.
Recently, a deep-learning-based approach has been developed to generate the attenuation map for SPECT images using the reconstructed images from both primary and scatter (SC) windows [Shi, L., Onofrey, J., Liu, H, Liu, Y. H., Liu, C.: Deep learning-based attenuation map generation for myocardial perfusion SPECT. Eur J Nucl Med Mol Imaging. 2020 Mar. 26]. This approach generates attenuation maps that can be incorporated into image reconstruction for attenuation correction. Another approach for deep-learning-based attenuation correction is to bypass the step of attenuation map generation, by directly producing images with attenuation correction from images without attenuation correction [Yang, J., Shi, L., Wang, R., Miller, E J., Sinusas, A J., Liu, C., Gullberg, G T., Seo, Y.: Direct Attenuation Correction Using Deep Learning for Cardiac SPECT: A Feasibility Study. J Nucl Med. Feb. 2021]. Such approaches are particularly useful for dedicated small-organ (e.g. cardiac) SPECT scanners with a small field-of-view, such as the GE 530 system with pinhole collimators, where the reconstructed SPECT images do not fully cover the entire human body. Therefore, the approach for generating an attenuation map is not easily applicable to such dedicated SPECT systems.
Previously, U-Net [Çiçik Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., Ronneberger, O.: 3d u-net: learning dense volumetric segmentation from sparse annotation. International conference on medical image computing and computer-assisted intervention, Springer (2016) 424-432; Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer (2015) 234-24110] has been extensively utilized in different medical image translation and correction tasks. However, there is limited exploration of incorporating patient-specific physiological information that is potentially useful in these tasks. With additional information concatenated channel-wise as input, efficient strategy for encoding and learning channel-wise pattern is also under-explored.
In recent years, deep-learning-based approaches have been proposed to estimate images of one modality from another [Nie, D., Trullo, R., Lian, J., Wang, L., et al.: Medical image synthesis with deep convolutional adversarial networks, IEEE Transactions on Biomedical Engineering 65, 2720-2730 (2018); Hwang, D., Kang, S. K., Kim, K. Y., Seo, S., et al.: Generation of PET attenuation map for whole-body time-of-flight 18F-FDG PET/MRI using a deep neural network trained with simultaneously reconstructed activity and attenuation maps, Journal of Nuclear Medicine jnumed, 118.219493 (2019); Han, X.: MR—based synthetic CT generation using a deep convolutional neural network method, Medical physics 44, 1408-1419 (2017)]. Particularly, initial success was obtained for the task of generating attenuation maps from nuclear images. In “MR—based synthetic CT generation using a deep convolutional neural network method,” convolutional neural networks were used to convert magnetic resonance imaging (MRI) images to attenuation CT images for PET/MRI systems. In “Generation of PET attenuation map for whole-body time-of-flight 18F-FDG PET/MRI using a deep neural network trained with simultaneously reconstructed activity and attenuation maps,” Hwang et al. proposed to predict the CT-attenuation maps from PET data alone.
In one aspect a system based upon artificial neural networks to directly generate attenuation-corrected SPECT from non-attenuation-corrected SPECT (single photon emission computed tomography) without any intermediate step of attenuation map estimation is provided. The system includes a SPECT scanner for dynamic SPECT imaging. The system also includes a machine learning system including a 3D Dual Squeeze-and-Excitation Residual Dense Network for directly generating attenuation-corrected SPECT from non-attenuation-corrected SPECT without any intermediate step of attenuation map estimation, wherein the machine learning system reconstructs images from photopeak window and one or more scatter windows of the SPECT scanner are fed to the 3D Dual Squeeze-and-Excitation Residual Dense Network to generate attenuation-corrected SPECT.
In some embodiments 126 keV-155 keV is used for the photopeak window.
In some embodiments 55-80 keV, 80-105 keV, and 105-130 keV are used for the scatter windows.
In some embodiments the 3D Dual Squeeze-and-Excitation Residual Dense Network includes 3D Dual Residual Dense Blocks.
In some embodiments each 3D Dual Residual Dense Block consists of a Residual Dense Block and a 3D Dual Squeeze-and-Excitation Block.
In some embodiments the 3D Dual Squeeze-and-Excitation Block consists of two squeeze-and-excitation branches.
In some embodiments the squeeze-and-excitation branches include a spatial-squeeze-and-channel-excitation for re-calibrating feature channels and a channel-squeeze-and-spatial-excitation for recalibrating spatial features.
In some embodiments the Residual Dense Block ensures that each convolutional layer in the Residual Dense Block has access to all the subsequent layers and passes on information that needs to be preserved.
In some embodiments the Residual Dense Block includes multiple convolutional layers with Rectified Linear Units and a local feature fusion.
In some embodiments the 3D Dual Squeeze-and-Excitation Residual Dense Network consists of a Residual Dense Block, a Dual Squeeze-and-Excitation block, and a U-Net backbone architecture supported by the Residual Dense Block and the 3D Dual Squeeze-and-Excitation Block.
In another aspect a method based upon artificial neural networks to directly generate attenuation-corrected SPECT from non-attenuation-corrected SPECT (single photon emission computed tomography) without any intermediate step of attenuation map estimation is provided. The method includes generating images from a photopeak window and one or more scatter windows of a SPECT scanner with CZT cameras and applying a machine learning system including a 3D Dual Squeeze-and-Excitation Residual Dense Network for directly generating attenuation-corrected SPECT from non-attenuation-corrected SPECT without any intermediate step of attenuation map estimation. The machine learning system reconstructs the images from the photopeak window and the one or more scatter windows of the SPECT scanner to generate attenuation-corrected SPECT.
In another aspect, the system upon artificial neural networks also estimates truncated or full attenuation maps from SPECT reconstructions in a photopeak window and one or more scatter windows of the SPECT scanners. The estimated truncated or full attenuation maps are then incorporated into iterative reconstruction to generate attenuation-corrected SPECT images.
In another aspect, a method based upon artificial neural networks to estimate truncated or full attenuation maps is provided. The method includes applying a machine learning system including a 3D Dual Squeeze-and-Excitation Residual Dense Network for generating truncated or full attenuation maps from non-attenuation-corrected SPECT of SPECT scanners with CZT cameras. The machine learning system generates truncated or full attenuation maps from the photopeak window and the one or more scatter windows of the SPECT scanner. The estimated truncated or full attenuation maps are then incorporated into the image reconstruction process to generate the attenuation-corrected SPECT images.
The detailed embodiments of the present invention are disclosed herein. It should be understood, however, that the disclosed embodiments are merely exemplary of the invention, which may be embodied in various forms. Therefore, the details disclosed herein are not to be interpreted as limiting, but merely as a basis for teaching one skilled in the art how to make and/or use the invention.
Referring now to the various drawings, a method and system 100 is disclosed that employs a machine learning system based upon artificial neural networks to directly generate attenuation-corrected (AC) SPECT from non-attenuation-corrected (NC) SPECT without any intermediate step of attenuation map estimation, wherein the machine learning system includes a 3D Dual Squeeze-and-Excitation Residual Dense Network (DuRDN) 10.
As will be appreciated based upon the following disclosure, the present method and system provides improved imaging techniques for use in conjunction with SPECT scanners 20 to further improve the clinical efficacy of such scanning systems in a variety of significant ways. In accordance with a disclosed embodiment, SPECT scanners with CZT cameras 20, uniquely capable for dynamic SPECT imaging, are used in conjunction with the disclosed method and system. Such SPECT scanners are disclosed, for example, in U.S. Pat. No. 7,968,851, entitled “DYNAMIC SPECT CAMERA,” and U.S. Pat. No. 9,625,310, entitled “SYSTEMS AND METHODS FOR SORTING AND SUMMING SIGNALS FROM AN IMAGING DETECTOR,” both of which are incorporated herein by reference. Such scanners commonly do not have integrated CT, and image quantification with attenuation correction (AC) is challenging. Further still, artifacts are routinely encountered in daily clinical practice. The present invention provides a method and system 100 for improving image quality with these scanners.
As those skilled in the art will certainly appreciate, SPECT is a non-invasive imaging procedure that provides radiotracer distribution images of a patient's body by detecting gamma photons. SPECT plays an important role in the clinical diagnosis of cardiovascular, oncological, and neurological disease. In order to perform qualitative, quantitative, or semi-quantitative analysis for SPECT, accurate attenuation correction is essential. As mentioned above, dedicated SPECT scanners have been developed for imaging smaller organs, such as the heart.
The present method and system 100 use a deep learning-based model for directly generating attenuation-corrected (AC) SPECT 22 from non-attenuation-corrected (NC) SPECT 24 without any intermediate step of attenuation map estimation. As demonstrated below, qualitative and quantitative analysis demonstrate that the present method and system 100 are capable of generating accurate attenuation-corrected (AC) SPECT 22 from non-attenuation-corrected (NC) SPECT 24. Evaluations on real human data shows that the present method and system 100 produce attenuation-corrected (AC) SPECT that are consistent with CT-based attenuation-corrected SPECT.
The present method and system 100 apply various deep learning methods and investigative approaches implemented via computer-based image and/or data systems to improve resolution and quantitative accuracy. The present method and system 100 further provide for development and validation methods for dynamic SPECT imaging, particularly involving direct parametric image reconstruction. Further still, the present method and system 100 provide for development and validation methods for dual-isotope SPECT. Monte Carlo simulation and deep-learning-based methods are contemplated for development for tracers with different spatial distributions and fast kinetics.
As will be appreciated based upon the following disclosure, the present method and system 100 addresses the limitations of the prior art by directly generating attenuation-corrected (AC) SPECT 22 from non-attenuation-corrected (NC) SPECT 24 without any intermediate step of attenuation map estimation, namely CT-free attenuation corrected cardiac SPECT reconstruction.
Referring to
Referring to
As will be appreciated based upon the following disclosure, the present method and system 100 incorporates both imaging and non-imaging data, such as patient BMI (body mass index), gender, and reconstructed images of the scatter window, which are all highly relevant to the level of attenuation effect as additional input for the DuRDN 10 employed in accordance with the present method and system 100. Extensive experiments on clinical cardiac SPECT datasets demonstrate that the DuRDN 10 of the present method and system 100 can efficiently encode additional physiological information and generate quality AC SPECT from NC SPECT for a range of patient sizes and different genders.
As briefly mentioned above, the DuRDN 10 consists of three major parts: a Residual Dense Block (RDB) 14, a Dual Squeeze-and-Excitation block (DuSE) 16 (wherein the RDB and DuSE make up the DuRDB), and U-Net backbone architecture 18 [Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer (2015) 234-241] supported by the RDB 14 and DuSE 16.
Given the NC image volume (VNC) reconstructed from the photopeak window projection data, the scattered image volume(s) (VSC) reconstructed from the scatter window projection data, BMI and gender, the input of DuRDN can be formulated as
I={V
NC
,V
SC
,V
BMI
,V
GD}
where { } is the concatenation operation along channels. VBMI is a constant volume with all voxel equals to BMI, and VGD is a binary volume indicating gender with male (1) and female (0). Denoting the DuRDN of the present invention as (⋅|θ), the loss function used for training the network is
=∥
(I|θ)−vAC∥1
where VAC is the ground truth AC image volume generated with CT-based attenuation correction. The RDB and DuSE details are discussed as follows.
The Residual Dense Block contains t densely connected convolution layers, local feature fusion, and local residual learning with details illustrated in the gray box of
F
t=i{Fin,F1, . . . ,Ft-1},
Where , denotes the t-th convolution followed by ReLU and { } means concatenation along featured channel. Then the method and system 100 of the present invention apply a local feature fusion (LFF), concatenation layer and a 1×1×1 convolution layer, to fuse the output from the input and all convolution layers. Thus, the LFF output can be expressed as:
F
LF=LFF({Fin,F1,F2, . . . ,Ft})
where LFF denotes the LFF operation. Finally, the system and method apply the local residual learning to LFF output by adding the residual connection from the RDB input, generating the RDB output:
F
out
=F
LF
+F
in
In a disclosed embodiment, the number of convolutions is set as t≤4 in the RDB.
The Dual Squeeze-and-Excitation Block (DuSE) contains two 3D Squeeze-and-Excitation branches for spatial-Squeeze-channel-Excitation (scSE) and channel-Squeeze-spatial-Excitation (csSE) [Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. (2018) 7132-7141; Roy, A. G., Navab, N., Wachinger, C.: Recalibrating fully convolutional networks with spatial and channel squeeze and excitation” blocks. IEEE transactions on medical imaging 38(2) (2018) 540-549], respectively. The framework is illustrated in the box labeled “DuSE” of
For scSE, the input feature map is spatial-wise squeeze using global average pooling, where the feature map is formulated as F=[f1,f2, . . . ,fC] here with fn∈H×W×D denoting the individual feature channel. The global average pooling output is flattened, generating v∈
C with its z-th element:
where vector v embeds the spatial-wise global information. Then, v is fed into two fully connected layers with weights of ω1∈
and ω2∈
producing the channel-wise calibration vector:
{circumflex over (v)}=σ(ω2η(ω1v)
where η and σ are the ReLU and Sigmoid activation function, respectively. The calibration vector is applied to the input feature map using channel-wise multiplication, namely channel-Excitation:
{circumflex over (F)}
ac
=[f
1
{circumflex over (v)}
1
,f
2
{circumflex over (v)}
2
, . . . ,f
C
{circumflex over (v)}
C]
where {circumflex over (v)}i indicates the importance of the i-th feature channel and lies in [0,1]. With scSE embedded into the network, the calibration vector adaptively learns to emphasize the important feature channels while down playing the others.
In csSE, the feature map is formulated as F=[f1,1,1, . . . fi,j,k, . . . , fH,W,D], where fi,j,k∈C indicates the feature at spatial location (i,j,k) with i∈{1, . . . ,H}, j∈{1, . . . ,W} and k∈{1, . . . , D}. The input feature map is channel-wise squeezed using a convolutional kernel with weights of ω3∈
1×1×1×C×1, generating a volume tensor m=ω3
F with m∈
H×W×D. Each fi,j,k is a linear combination of all feature channel at spatial location (i,j,k). Then, the spatial-wise calibration volume that lies in [0, 1] can be written as:
{circumflex over (m)}=σ(m)=σ(ω3F)
where σ is the Sigmoid activation function. Applying the calibration volume to the input feature map, we have:
{circumflex over (m)}=σ(m)=σ(w3F)
where calibration parameter of {circumflex over (m)}i,j,k provides the relative importance of a spatial information of a given feature map. Similarly, with csSE embedded into the network, the calibration volume learns to stress the most important spatial locations while ignoring the irrelevant ones.
Finally, channel-wise calibration and spatial-wise calibration are combined via element-wise addition operation FDusE={circumflex over (F)}sc+{circumflex over (F)}cs. With the two SE branch fusion, feature at (i,j,k,c) possess high activation only when it receives high activation from both scSE and csSE. The present DuSE encourages the networks to recalibrate the feature map such that a more accurate and relevant feature map can be learned.
Evaluation with Human Dataset
The dataset for testing consisted of 176 anonymous clinical hybrid SPECT/CT myocardial perfusion studies scanned from Jan. 8, 2020 to Feb. 28, 2020, with pairs of AC, NC, SC stress-state images along with gender and BMI information. Each hybrid myocardial perfusion SPECT scan was acquired following the injection of 99mTc-tetrofosmin on a GE Discovery NM/CT 570c SPECT/64-slice hybrid scanner. AC and NC image volumes were reconstructed from projection data using the photopeak window (133-148 keV), then corrected with and without CT attenuation map respectively. SC image volumes were respectively reconstructed from 3 scatter windows (55-80 keV, 80-105 keV, 105-130 keV) projection data without attenuation correction. All data were reconstructed into 70×70×50 volumes, and the central 32×32×32 ROI (Region of Interest) containing the left ventricle was cropped to reduce the effect from surrounding artifacts.
The dataset was divided into the training set including 108 patient studies and the testing set including 68 patient studies. Table 1 outlines the corresponding characteristics of the patients, including gender, BMI, height, and weight for the training set and the testing set, respectively. For quantitative evaluation, the results were evaluated using normalized mean square error (NMSE), normalized mean absolute error (NMAE), and peak signal-to-noise-ratio (PSNR) by comparing the synthetic AC volume with the ground truth AC volume. The method of the present invention was compared with a conventional existing method, 3D UNet, with various additional input combinations including scatter windows, BMI, and gender. For all models, the networks were trained for 1000 epochs with a batch size of 2. The Adam solver is used to optimize all the models with a momentum of 0.99 and a learning rate of 5×e−4. The network was implemented using PyTorch, an open source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing. The training took about 5 hours on an NVIDIA GTX 1080 Ti GPU.
Table 2 lists the quantitative results of the testing data. Results of different networks using different input settings are reported. Comparing the performance within each network, it was observed that using additional information into conventional 3D UNet as the input does not always improve the performance due to the non-ideal channel-wise encoding ability. In contrast, the present method and system 100 using the disclosed DuRDN 10 demonstrates superior performance from two aspects. First, the DuRDN baseline (only NC input) achieves better performance as compared to 3D UNet, demonstrating the efficient design of DuRDN for the attenuation correction task. Second, a progressive performance improvement was found as more information was added as the input, demonstrating that DuRDN can better encode the additional information provided in the input channels. Comparing the DuRDN+BMI&Gender and DuRDN+Scattering in Table 2, it was observed that the scatter window information is potentially more useful than BMI & gender information for the attenuation correction task, given that scattering provides voxel-wise scattering property that provides both spatial and attenuation-related information into the network. The qualitative comparison results of a sample subject are shown in
Table 3 outlines the performance of the model of the present invention evaluated on various patient groups. In the application of SPECT attenuation correction, the larger size patients with greater radiotracer signal attenuation caused by soft-tissue could lead to more attenuation artifacts, and therefore be more difficult to correct. As observed from Table 3, the best attenuation correction result was obtained for patients with BMI <30, while analysis of female patients with BMI >30 led to the worst results, which could be due to the effect of breast attenuation. The DuRDN of the present invention with BMI, gender, and scattering information can efficiently reduce the attenuation correction error for female patients with BMI >30, while maintaining stable performance for other patients.
The present CT-free attenuation correction method that is applicable to both general SPECT and dedicated cardiac SPECT systems with a limited field-of-view. Specifically, the information that is highly relevant to attenuation was incorporated to facilitate the learning of attenuation correction. Given the additional information as input, a 3D dual squeeze-and-excitation residual dense network was customized that can efficiently encode this channel-wise input information to improve the attenuation correction performance. A comprehensive evaluation of present approach using clinical hybrid SPECT/CT myocardial perfusion studies demonstrate that the present method and system can outperform traditional network designs by better incorporating input information of scatter window information, BMI and gender, thereby generating high-quality attenuation-corrected SPECT reconstructions without a CT attenuation scan.
It is appreciated that due to the small field-of-view (FOV) of dedicated cardiac SPECT, generating full FOV attenuation maps from small FOV SPECT images is challenging. The DuRDN embodiment described above with reference to
As those skilled in the art will appreciate, and as discussed above, attenuation correction using CT transmission scanning enables accurate quantitative assessment of cardiac SPECT. While deep-learning-based indirect approaches are used to predict attenuation maps from emission data for rotational SPECT-only scanners with parallel-hole collimators and NaI crystals, it is appreciated direct methods, such as disclosed above, to generate AC images from NAC images might be easier to implement without the intermediate step of AC map generation, particularly useful for the small field-of-view of dedicated cardiac SPECT scanners with CZT detectors.
For example, a recent indirect approach has been developed to predict the μ-maps from emission images for parallel-hole SPECT [See, for example, Applicant's prior application PCT Application No. PCT/US2020/028672, which published as PCT Publication No. WO 2020/214911, entitled “METHOD AND SYSTEM FOR GENERATING ATTENUATION MAP FROM SPECT EMISSION DATA BASED UPON DEEP LEARNING AND ASSOCIATED ATTENUATION CORRECTION METHODS FOR SPECT IMAGING,” ('911 Publication) and L. Shi, et al. “Deep learning-based attenuation map generation for myocardial perfusion SPECT,” EJNMMI, pp. 1-13, 2020, both of which are incorporated herein by reference]. However, dedicated cardiac SPECT scanners can provide accurate reconstructions only in the central limited field-of-view (FOV) of about 19 cm in diameter, with inaccurate artifacts in the regions outside the FOV. It is challenging to generate full-FOV μ-maps required for the attenuation correction from limited-FOV SPECT images. Thus, the present direct approach has been explored to address this limitation [See, J. Yang, et al. “Direct Attenuation Correction Using Deep Learning for Cardiac SPECT: A Feasibility Study,” JNM, 2021, which is incorporated herein by reference], which might over-corrected true myocardial perfusion defects in certain clinical cases.
With this in mind, the following presents a mechanism wherein the previously described DuRDN embodiment is used in the generation of truncated or full FOV attenuation maps from small FOV SPECT images using various reconstruction system matrix sizes.
1. Generation of Truncated Attenuation Map
Referring to
2. Generation of Full Attenuation Map
Referring to
Since the dedicated SPECT scanner has a small field-of-view (˜19 cm in diameter), the truncated size reconstruction of emission data is expected to be accurate, while the regions outside of 19-cm field-of-view (FOV) is not accurately reconstructable. That being said, the deep learning model, that is, the DuRDN 10 described above, was robust enough to generate accurate full FOV attenuation maps from SPECT emission data acquired from a small FOV scanner.
20.04 ± 5.05
17.78 ± 1.76
17.91 ± 5.66
30.45 ± 6.85
19.62 ± 1.70
1.20 ± 0.72
0.948 ± 0.019
38.46 ± 2.34
While the preferred embodiments have been shown and described, it will be understood that there is no intent to limit the invention by such disclosure, but rather, is intended to cover all modifications and alternate constructions falling within the spirit and scope of the invention.
This invention was made with government support under R01HL123949 and R01HL154345 awarded by National Institutes of Health. The government has certain rights in the invention.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US2021/041601 | 7/14/2021 | WO |
| Number | Date | Country | |
|---|---|---|---|
| 63051685 | Jul 2020 | US |
