Real-time patch-based medical image modality propagation by GPU computing

Alcaín, Eduardo; Torrado‐Carvajal, Angel; Montemayor, Antonio S.; Malpica, Norberto

doi:10.1007/s11554-016-0568-0

Cited by 7 publications

(6 citation statements)

References 17 publications

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“…On the other hand, the high ZNCC values indicate that our method can accurately approximate the patient-specific CT volume, despite using an atlas composed of diverse anatomical overlapping MR-CT scans. Previously described patch-based pseudo-CT synthesis methods reported an experimental ZNCC of 0.9349 ± 0.0049 for a whole head and neck atlas including 18 MR-CT datasets [11,29], which is very similar to the experimental ZNCC of 0.9220 ± 0.0255 achieved in this work. Likewise, other patch-based methods of the state-of-the-art provide average ZNCC of 0.91 ± 0.03 and mean MAE of 125.46 ± 24.45 HU [34], in contrast with the results presented in this work of average ZNCC and the experimental MAE of 73.9149 ± 9.2101 HU.…”

Section: Discussionsupporting

confidence: 87%

“…In this work, we propose a method based on the idea of modality propagation using MR-CT atlases described in preceding developments [11,29] and a deep learning architecture approach inspired on our previous work [22]. However, our database is composed by head and neck MR images and local portions of CT including the brain, paranasal sinuses, facial orbits, and neck studies.…”

Section: Introductionmentioning

confidence: 99%

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Franken-CT: Head and Neck MR-Based Pseudo-CT Synthesis Using Diverse Anatomical Overlapping MR-CT Scans

et al. 2021

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Typically, pseudo-Computerized Tomography (CT) synthesis schemes proposed in the literature rely on complete atlases acquired with the same field of view (FOV) as the input volume. However, clinical CTs are usually acquired in a reduced FOV to decrease patient ionization. In this work, we present the Franken-CT approach, showing how the use of a non-parametric atlas composed of diverse anatomical overlapping Magnetic Resonance (MR)-CT scans and deep learning methods based on the U-net architecture enable synthesizing extended head and neck pseudo-CTs. Visual inspection of the results shows the high quality of the pseudo-CT and the robustness of the method, which is able to capture the details of the bone contours despite synthesizing the resulting image from knowledge obtained from images acquired with a completely different FOV. The experimental Zero-Normalized Cross-Correlation (ZNCC) reports a 0.9367 ± 0.0138 (mean ± SD) and 95% confidence interval (0.9221, 0.9512); the experimental Mean Absolute Error (MAE) reports 73.9149 ± 9.2101 HU and 95% confidence interval (66.3383, 81.4915); the Structural Similarity Index Measure (SSIM) reports 0.9943 ± 0.0009 and 95% confidence interval (0.9935, 0.9951); and the experimental Dice coefficient for bone tissue reports 0.7051 ± 0.1126 and 95% confidence interval (0.6125, 0.7977). The voxel-by-voxel correlation plot shows an excellent correlation between pseudo-CT and ground-truth CT Hounsfield Units (m = 0.87; adjusted R2 = 0.91; p < 0.001). The Bland–Altman plot shows that the average of the differences is low (−38.6471 ± 199.6100; 95% CI (−429.8827, 352.5884)). This work serves as a proof of concept to demonstrate the great potential of deep learning methods for pseudo-CT synthesis and their great potential using real clinical datasets.

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Section: Discussionsupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Franken-CT: Head and Neck MR-Based Pseudo-CT Synthesis Using Diverse Anatomical Overlapping MR-CT Scans

et al. 2021

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“…58 The segmentation step used in this pipeline was implemented in Python, including a few C portions. Thus, faster compute times could be achieved utilizing a higher performance implementation such as those described in Alcain et al 52 and potentially leveraging DL. 59 Ultimately, this step can likely be sped-up to less than 10 seconds.…”

Section: Discussionmentioning

confidence: 99%

“…The fat and water volumes yielded the fat and soft tissue classes, respectively, while the in‐phase volume was used to estimate the air and bone tissue classes. Specifically, the in‐phase volume was used to generate a pseudo‐CT by using a groupwise patch‐based approach and an MR–CT atlas dictionary built previously for the purpose of attenuation correction in PET/MRI 46,52‐54 . Segmentation of the bone compartment from the pseudo‐CT is easily performed by thresholding the Hounsfield units.…”

Section: Methodsmentioning

confidence: 99%

“…Specifically, the in-phase volume was used to generate a pseudo-CT by using a groupwise patch-based approach and an MR-CT atlas dictionary built previously for the purpose of attenuation correction in PET/ MRI. 46,[52][53][54] Segmentation of the bone compartment from the pseudo-CT is easily performed by thresholding the Hounsfield units. Finally, the body model was extended 100 mm below the last slice in order to more realistically load the body radiofrequency (RF) coil, as was suggested in Wolf et al 24 The properties of the extended portion of the body model were set to the average of tissue properties in the chest region of the Duke model (ε = 19.3, σ = 0.499 S/m, ρ = 943 kg/m 3 ).…”

Section: Fast Generation Of Patientspecific Modelsmentioning

confidence: 99%

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Individualized SAR calculations using computer vision‐based MR segmentation and a fast electromagnetic solver

Milshteyn

Guryev

Torrado‐Carvajal

et al. 2020

Magnetic Resonance in Med

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We propose a fast, patient-specific workflow for on-line specific absorption rate (SAR) supervision. An individualized electromagnetic model is created while the subject is on the table, followed by rapid SAR estimates for that individual. Our goal is an improved correspondence between the patient and model, reducing reliance on general anatomical body models. Methods: A 3D fat-water 3T acquisition (~2 minutes) is automatically segmented using a computer vision algorithm (~1 minute) into what we found to be the most important electromagnetic tissue classes: air, bone, fat, and soft tissues. We then compute the individual's EM field exposure and global and local SAR matrices using a fast electromagnetic integral equation solver. We assess the approach in 10 volunteers and compare to the SAR seen in a standard generic body model (Duke). Results: The on-the-table workflow averaged 7′44″. Simulation of the simplified Duke models confirmed that only air, bone, fat, and soft tissue classes are needed to estimate global and local SAR with an error of 6.7% and 2.7%, respectively, compared to the full model. In contrast, our volunteers showed a 16.0% and 20.3% population variability in global and local SAR, respectively, which was mostly underestimated by the Duke model. Conclusion: Timely construction and deployment of a patient-specific model is computationally feasible. The benefit of resolving the population heterogeneity compared favorably to the modest modeling error incurred. This suggests that individualized SAR estimates can improve electromagnetic safety in MRI and possibly reduce conservative safety margins that account for patient-model mismatch, especially in non-standard patients. 430 | MILSHTEYN ET aL.

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PET/MRI: technical and methodological aspects

Torrado‐Carvajal¹,

Catana²

2023

Clinical PET/MRI

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Real-time patch-based medical image modality propagation by GPU computing

Cited by 7 publications

References 17 publications

Franken-CT: Head and Neck MR-Based Pseudo-CT Synthesis Using Diverse Anatomical Overlapping MR-CT Scans

Franken-CT: Head and Neck MR-Based Pseudo-CT Synthesis Using Diverse Anatomical Overlapping MR-CT Scans

Individualized SAR calculations using computer vision‐based MR segmentation and a fast electromagnetic solver

PET/MRI: technical and methodological aspects

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