المؤلفون: David Izquierdo-Garcia, Pauline Désogère, Mariane Le Fur, Sergey Shuvaev, Iris Y. Zhou, Ian Ramsay, Michael Lanuti, Onofrio A. Catalano, Ciprian Catana, Peter Caravan, Sydney B. Montesi

المصدر: Journal of Nuclear Medicine. 64:775-781

مصطلحات موضوعية: Radiology, Nuclear Medicine and imaging

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::61d1e7de767c5c2e13d3512ee87b27fcTest
https://doi.org/10.2967/jnumed.122.264530Test

المؤلفون: Norberto Malpica, Sheung Chee Thomas Ng, Ciprian Catana, Onofrio A. Catalano, Javier Vera-Olmos, Ja Reaungamornrat, Ali Kamen, Hasan Sari, Angel Torrado-Carvajal, Manuel A. Morales, David Izquierdo-Garcia

المصدر: J Nucl Med

مصطلحات موضوعية: Scanner, Similarity (geometry), Computer science, business.industry, Deep learning, Attenuation, Pattern recognition, Image processing, Magnetic Resonance Imaging, Convolutional neural network, Pelvis, Deep Learning, Positron-Emission Tomography, Image Processing, Computer-Assisted, Humans, Radiology, Nuclear Medicine and imaging, Segmentation, Clinical Investigation, Artificial intelligence, Tomography, X-Ray Computed, business, Correction for attenuation

الوصف: Attenuation correction remains a challenge in pelvic PET/MRI. In addition to the segmentation/model-based approaches, deep learning methods have shown promise in synthesizing accurate pelvic attenuation maps (μ-maps). However, these methods often misclassify air pockets in the digestive tract, potentially introducing bias in the reconstructed PET images. The aims of this work were to develop deep learning–based methods to automatically segment air pockets and generate pseudo-CT images from CAIPIRINHA-accelerated MR Dixon images. Methods: A convolutional neural network (CNN) was trained to segment air pockets using 3-dimensional CAIPIRINHA-accelerated MR Dixon datasets from 35 subjects and was evaluated against semiautomated segmentations. A separate CNN was trained to synthesize pseudo-CT μ-maps from the Dixon images. Its accuracy was evaluated by comparing the deep learning–, model-, and CT-based μ-maps using data from 30 of the subjects. Finally, the impact of different μ-maps and air pocket segmentation methods on the PET quantification was investigated. Results: Air pockets segmented using the CNN agreed well with semiautomated segmentations, with a mean Dice similarity coefficient of 0.75. The volumetric similarity score between 2 segmentations was 0.85 ± 0.14. The mean absolute relative changes with respect to the CT-based μ-maps were 2.6% and 5.1% in the whole pelvis for the deep learning–based and model-based μ-maps, respectively. The average relative change between PET images reconstructed with deep learning–based and CT-based μ-maps was 2.6%. Conclusion: We developed a deep learning–based method to automatically segment air pockets from CAIPIRINHA-accelerated Dixon images, with accuracy comparable to that of semiautomatic segmentations. The μ-maps synthesized using a deep learning–based method from CAIPIRINHA-accelerated Dixon images were more accurate than those generated with the model-based approach available on integrated PET/MRI scanners.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::359e30fa4e10ea2ebab083a37d8ddabeTest
https://doi.org/10.2967/jnumed.120.261032Test

المؤلفون: Michele Scipioni, Stefano Pedemonte, David Izquierdo-Garcia, Niccolo Fuin, Ciprian Catana, Lisanne P.W. Canjels, Onofrio A. Catalano

المصدر: Journal of Nuclear Medicine. 59:1474-1479

مصطلحات موضوعية: Physics and Instrumentation, Scanner, Time Factors, DCE-MRI, Image quality, Computer science, Image Processing, Movement, media_common.quotation_subject, Contrast Media, Iterative reconstruction, Signal-To-Noise Ratio, 030218 nuclear medicine & medical imaging, Respiratory motion correction, 03 medical and health sciences, Computer-Assisted, 0302 clinical medicine, Compressed sensing, Motion-correction, PET/MRI, Radiology, Nuclear Medicine and Imaging, Nuclear Medicine and Imaging, Abdomen, Image Processing, Computer-Assisted, Humans, Contrast (vision), Radiology, Nuclear Medicine and imaging, media_common, business.industry, Respiration, Magnetic Resonance Imaging, Motion vector, Positron-Emission Tomography, Dynamic contrast-enhanced MRI, Radiology, Nuclear medicine, business, 030217 neurology & neurosurgery

الوصف: We present an approach for concurrent reconstruction of respiratory motion–compensated abdominal dynamic contrast-enhanced (DCE)–MRI and PET data in an integrated PET/MR scanner. The MR and PET reconstructions share the same motion vector fields derived from radial MR data; the approach is robust to changes in respiratory pattern and does not increase the total acquisition time. Methods: PET and DCE-MRI data of 12 oncologic patients were simultaneously acquired for 6 min on an integrated PET/MR system after administration of (18)F-FDG and gadoterate meglumine. Golden-angle radial MR data were continuously acquired simultaneously with PET data and sorted into multiple motion phases on the basis of a respiratory signal derived directly from the radial MR data. The resulting multidimensional dataset was reconstructed using a compressed sensing approach that exploits sparsity among respiratory phases. Motion vector fields obtained using the full 6-min (MC(6-min)) and only the last 1 min (MC(1-min)) of data were incorporated into the PET reconstruction to obtain motion-corrected PET images and in an MR iterative reconstruction algorithm to produce a series of motion-corrected DCE-MR images (moco_GRASP). The motion-correction methods (MC(6-min) and MC(1-min)) were evaluated by qualitative analysis of the MR images and quantitative analysis of SUV(max) and SUV(mean), contrast, signal-to-noise ratio (SNR), and lesion volume in the PET images. Results: Motion-corrected MC(6-min) PET images demonstrated 30%, 23%, 34%, and 18% increases in average SUV(max), SUV(mean), contrast, and SNR and an average 40% reduction in lesion volume with respect to the non–motion-corrected PET images. The changes in these figures of merit were smaller but still substantial for the MC(1-min) protocol: 19%, 10%, 15%, and 9% increases in average SUV(max), SUV(mean), contrast, and SNR; and a 28% reduction in lesion volume. Moco_GRASP images were deemed of acceptable or better diagnostic image quality with respect to conventional breath-hold Cartesian volumetric interpolated breath-hold examination acquisitions. Conclusion: We presented a method that allows the simultaneous acquisition of respiratory motion–corrected diagnostic quality DCE-MRI and quantitatively accurate PET data in an integrated PET/MR scanner with negligible prolongation in acquisition time compared with routine PET/DCE-MRI protocols.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::42b2c1537b0a140d694935305de182b8Test
https://doi.org/10.2967/jnumed.117.203943Test

المؤلفون: Farrokh Dehdashti, Onofrio A. Catalano, Susanna I. Lee

المصدر: Journal of Nuclear Medicine. 56:436-443

مصطلحات موضوعية: Thiosemicarbazones, medicine.medical_specialty, Cu-labeled diacetyl-bis (N4-methylthiosemicarbazone) PET, medicine.medical_treatment, Uterine Cervical Neoplasms, Salvage therapy, Multimodal Imaging, Imaging modalities, dynamic contrast enhanced MRI, Coordination Complexes, Fluorodeoxyglucose F18, Gynecologic cancer, diffusion weighted imaging, Organometallic Compounds, medicine, Humans, Radiology, Nuclear Medicine and imaging, Tomography, Ovarian Neoplasms, Salvage Therapy, Modalities, Estradiol, business.industry, 16α-18F-fluoro-17β-estradiol PET, Prognosis, Magnetic Resonance Imaging, Mr imaging, X-Ray Computed, Radiation therapy, Neoplasm Recurrence, Local, Positron-Emission Tomography, Uterine Neoplasms, perfusion MRI, Female, Neoplasm Recurrence, Local, Tomography, X-Ray Computed, Fdg pet ct, Pet mr imaging, Radiology, business

الوصف: MR imaging and (18)F-FDG PET/CT play central and complementary roles in the care of patients with gynecologic cancer. Because treatment often requires combinations of surgery, radiotherapy, and chemotherapy, imaging is central to triage and to determining prognosis. This article reviews the use of the 2 imaging modalities in the initial evaluation of 3 common cancers: uterine cervical, uterine endometrial, and epithelial ovarian. Imaging features that affect management are highlighted, as well as the relative strengths and weaknesses of the 2 modalities. Use of imaging after initial therapy to assess for recurrence and to plan salvage therapy is described. Newer functional and molecular techniques in MR imaging and PET are evaluated. Finally, we describe our initial experience with PET/MR imaging, an emerging technology that may prove to be a mainstay in personalized gynecologic cancer care.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::529d563162f5ad708053d25ea4bbc1d1Test
https://doi.org/10.2967/jnumed.114.145011Test