Segmentation-Based MR Attenuation Correction Including Bones Also Affects Quantitation in Brain Studies: An Initial Result of <sup>18</sup>F-FP-CIT PET/MR for Patients with Parkinsonism

Choi, Hongyoon; Cheon, Gi Jeong; Kim, Han Joon; Choi, Seung Hong; Lee, Jae Sung; Kim, Yong Il; Kang, Keon Wook; Chung, June Key; Kim, E. Edmund; Lee, Dong Hoon

doi:10.2967/jnumed.114.138636

Cited by 23 publications

(21 citation statements)

References 29 publications

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“…The incorrect hard structures displayed by ZTE in the third ventricle were likely induced by a combination of magnetization saturation and the thin, slitlike configuration of this cerebrospinal fluid space, with 2 brain/fluid interfaces being close, as opposed to the fourth ventricle, for which pseudocalcifications were found less commonly. Classification issues in the ventricles have also been reported for UTE-based methods (28). Anatomy-based postprocessing may need to be included in the segmentation to mitigate this issue.…”

Section: Discussionmentioning

confidence: 99%

Clinical Evaluation of Zero-Echo-Time MR Imaging for the Segmentation of the Skull

Delso¹,

et al. 2015

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MR-based attenuation correction is instrumental for integrated PET/ MR imaging. It is generally achieved by segmenting MR images into a set of tissue classes with known attenuation properties (e.g., air, lung, bone, fat, soft tissue). Bone identification with MR imaging is, however, quite challenging, because of the low proton density and fast decay time of bone tissue. The clinical evaluation of a novel, recently published method for zero-echo-time (ZTE)-based MR bone depiction and segmentation in the head is presented here. Methods: A new paradigm for MR imaging bone segmentation, based on proton density-weighted ZTE imaging, was disclosed earlier in 2014. In this study, we reviewed the bone maps obtained with this method on 15 clinical datasets acquired with a PET/CT/MR trimodality setup. The CT scans acquired for PET attenuation-correction purposes were used as reference for the evaluation. Quantitative measurements based on the Jaccard distance between ZTE and CT bone masks and qualitative scoring of anatomic accuracy by an experienced radiologist and nuclear medicine physician were performed. Results: The average Jaccard distance between ZTE and CT bone masks evaluated over the entire head was 52% ± 6% (range, 38%-63%). When only the cranium was considered, the distance was 39% ± 4% (range, 32%-49%). These results surpass previously reported attempts with dualecho ultrashort echo time, for which the Jaccard distance was in the 47%-79% range (parietal and nasal regions, respectively). Anatomically, the calvaria is consistently well segmented, with frequent but isolated voxel misclassifications. Air cavity walls and bone/fluid interfaces with high anatomic detail, such as the inner ear, remain a challenge. Conclusion: This is the first, to our knowledge, clinical evaluation of skull bone identification based on a ZTE sequence. The results suggest that proton density-weighted ZTE imaging is an efficient means of obtaining high-resolution maps of bone tissue with sufficient anatomic accuracy for, for example, PET attenuation correction.

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Section: Discussionmentioning

confidence: 99%

Clinical Evaluation of Zero-Echo-Time MR Imaging for the Segmentation of the Skull

Delso¹,

et al. 2015

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“…For example, fast striatal volume measurement could be useful in a clinical setting as a diagnostic feature of Huntington disease (Rosas et al, 2001). Quantification of dopamine transporter imaging commonly used in diagnosis of movement disorders requires fast and accurate striatal segmentation (Choi et al, 2014;Nobili et al, 2013). Functional connectivity study especially focusing on the corticostriatal connectivity which affected cognitive functions needs accurate striatal segmentation (Ystad et al, 2011).…”

Section: Discussionmentioning

confidence: 99%

Fast and robust segmentation of the striatum using deep convolutional neural networks

Choi¹,

Jin

2016

Journal of Neuroscience Methods

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h i g h l i g h t s• We describe a new method for stratium segmentation.• We employ two serial deep convolutional neural networks (CNN).• Segmentation accuracy of deep CNN is comparable with that of previous methods.• The processing time of the new method is much faster than previous methods. a r t i c l e i n f o a b s t r a c tBackground: Automated segmentation of brain structures is an important task in structural and functional image analysis. We developed a fast and accurate method for the striatum segmentation using deep convolutional neural networks (CNN). New method: T1 magnetic resonance (MR) images were used for our CNN-based segmentation, which require neither image feature extraction nor nonlinear transformation. We employed two serial CNN, Global and Local CNN: The Global CNN determined approximate locations of the striatum. It performed a regression of input MR images fitted to smoothed segmentation maps of the striatum. From the output volume of Global CNN, cropped MR volumes which included the striatum were extracted. The cropped MR volumes and the output volumes of Global CNN were used for inputs of Local CNN. Local CNN predicted the accurate label of all voxels. Segmentation results were compared with a widely used segmentation method, FreeSurfer. Results: Our method showed higher Dice Similarity Coefficient (DSC) (0.893 ± 0.017 vs. 0.786 ± 0.015) and precision score (0.905 ± 0.018 vs. 0.690 ± 0.022) than FreeSurfer-based striatum segmentation (p = 0.06). Our approach was also tested using another independent dataset, which showed high DSC (0.826 ± 0.038) comparable with that of FreeSurfer.Comparison with existing method Segmentation performance of our proposed method was comparable with that of FreeSurfer. The running time of our approach was approximately three seconds. Conclusion: We suggested a fast and accurate deep CNN-based segmentation for small brain structures which can be widely applied to brain image analysis.

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“…One of these was the 18 F-FP-CIT PET/CT and PET/MRI data acquired in our previous study for evaluating the accuracy of existing MR-AC methods in patients with Parkinson disease (35). Four more PET studies have been added since the publication of the previous study; thus, a total of 20 patients (11 men, 9 women; mean age 6 SD, 59.6 6 9.1 y; age range, 54-71 y) were enrolled in this study.…”

Section: Patient Populationmentioning

confidence: 99%

“…Thus, the most widely used clinical PET/MRI system (Biograph mMR; Siemens Healthcare) offers the UTE-based MR-AC along with the Dixon-based method for brain PET/MRI studies. However, the initial versions of the UTE sequence (i.e., mMR software version VB18P) yielded frequent segmentation errors at the boundary between soft tissue, bone, and air, and misclassification of the ventricle as air (33)(34)(35). Although a recent upgrade of the software from VB18P to VB20P offers more reliable attenuation maps than before, significant segmentation errors in the regions around the inferior part of the brain (i.e., sinus and lower skull structures) still exist.…”

mentioning

confidence: 99%

“…Although a recent upgrade of the software from VB18P to VB20P offers more reliable attenuation maps than before, significant segmentation errors in the regions around the inferior part of the brain (i.e., sinus and lower skull structures) still exist. Moreover, considerable quantification errors because of the inaccurate UTE MR-AC have been reported in several articles (33)(34)(35).…”

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MRI-Based Attenuation Correction for PET/MRI Using Multiphase Level-Set Method

Seo

Kang

et al. 2015

J Nucl Med

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Inaccuracy in MR image-based attenuation correction (MR-AC) leads to errors in quantification and the misinterpretation of lesions in brain PET/MRI studies. To resolve this problem, we proposed an improved ultrashort echo time MR-AC method that was based on a multiphase level-set algorithm with main magnetic field (B 0 ) inhomogeneity correction. We also assessed the feasibility of this levelset-based MR-AC method (MR-AC level ), compared with CT-AC and MR-AC provided by the manufacturer of the PET/MRI scanner (MR-AC mMR ). Methods: Ten healthy volunteers and 20 Parkinson disease patients underwent 18 F-FDG and 18 F-fluorinated-N-3-fluoropropyl-2-β-carboxymethoxy-3-β-(4-iodophenyl)nortropane ( 18 F-FP-CIT) PET scans, respectively, using both PET/MRI and PET/CT scanners. The level-set-based segmentation algorithm automatically delimited air, bone, and soft tissue from the ultrashort echo time MR images. For the comparison, MR-AC maps were coregistered to reference CT. PET sinogram data obtained from PET/CT studies were then reconstructed using the CT-AC, MR-AC mMR , and MR-AC level maps. The accuracies of SUV, SUVr (SUV and its ratio to the cerebellum), and specific-to-nonspecific binding ratios obtained using MR-AC level and MR-AC mMR were compared with CT-AC using region-ofinterest-and voxel-based analyses. Results: There was remarkable improvement in the segmentation of air cavities and bones and the quantitative accuracy of PET measurement using the level set. Although the striatal and cerebellar activities in 18 F-FP-CIT PET and frontal activity in 18 F-FDG PET were significantly underestimated by the MR-AC mMR , the MR-AC level provided PET images almost equivalent to the CT-AC images. PET quantification error was reduced by a factor of 3 using MR-AC level ( The development of tomographic imaging technologies has made dramatic progress in recent decades. Among the modern medical imaging systems, PET and MRI have greatly contributed to understanding normal and abnormal brain functions and evaluating various neurologic disorders (1-4). Although PET is the most sensitive medical imaging device, providing both functional and biochemical information, it has limited spatial resolution, signalto-noise ratio, and anatomic information. Conversely, MRI offers detailed anatomic information about the brain along with excellent soft-tissue contrast and various types of hemodynamic information (i.e., perfusion and diffusion). Accordingly, the combination of PET and MRI can provide a 1-stop shop for clinical examination and new methodology for exploring the brain with multiparametric and complementary imaging information (5,6).In addition, fully integrated PET/MRI scanners based on semiconductor photosensors, such as avalanche photodiodes and silicon photomultipliers, allow the simultaneous acquisition of both image datasets, which possess several distinct advantages over the sequential scan in conventional PET/CT examinations (7-11). Accurate spatiotemporal correlation of PET/MRI signals permits the studies to demonstrat...

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Segmentation-Based MR Attenuation Correction Including Bones Also Affects Quantitation in Brain Studies: An Initial Result of ¹⁸F-FP-CIT PET/MR for Patients with Parkinsonism

Cited by 23 publications

References 29 publications

Clinical Evaluation of Zero-Echo-Time MR Imaging for the Segmentation of the Skull

Clinical Evaluation of Zero-Echo-Time MR Imaging for the Segmentation of the Skull

Fast and robust segmentation of the striatum using deep convolutional neural networks

MRI-Based Attenuation Correction for PET/MRI Using Multiphase Level-Set Method

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Segmentation-Based MR Attenuation Correction Including Bones Also Affects Quantitation in Brain Studies: An Initial Result of 18F-FP-CIT PET/MR for Patients with Parkinsonism

Cited by 23 publications

References 29 publications

Clinical Evaluation of Zero-Echo-Time MR Imaging for the Segmentation of the Skull

Clinical Evaluation of Zero-Echo-Time MR Imaging for the Segmentation of the Skull

Fast and robust segmentation of the striatum using deep convolutional neural networks

MRI-Based Attenuation Correction for PET/MRI Using Multiphase Level-Set Method

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Segmentation-Based MR Attenuation Correction Including Bones Also Affects Quantitation in Brain Studies: An Initial Result of ¹⁸F-FP-CIT PET/MR for Patients with Parkinsonism