The rest period for coronary arteries in the cardiac cycle varies substantially from patient to patient, which may cause quality to be inconsistent in current coronary MR angiography. A cardiac motion image prior to coronary data acquisition (preimage) may be used to estimate the optimal duration and timing in the cardiac cycle for coronary MR angiography.
Preliminary data support the combining of 2D MR digital subtraction angiography with 3D bolus chase MR angiography to extend the utility of 3D MR angiography in treatment planning to include patients being evaluated for limb salvage, as well as those being evaluated for claudication.
The therapeutic management of the patient with transformed low-grade lymphoma is quite different from those with pure indolent disease. In cases of transformation FDG uptake characteristics may help identify an area of transformation in indolent disease and thereby impact management. Our objective was to evaluate the potential of FDG-PET imaging in the determination of transformation Methods: A total of 60 patients with low-grade lymphoma (48 patients) and those with known transformation to DLCL (12 patients) underwent FDG-PET imaging prior to therapy. In the indolent group, 27 patients were diagnosed with a follicular histology and 11 with chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL) and 10 were with marginal zone lymphoma. A total of 370 lesions were evaluated and SUV’s were obtained for all visualized lesions. Mean values were calculated on a patient and total site basis. Additionally, mean SUV values were calculated for lesions with the highest SUV’s, in groups with indolent and transformation histology. Results: Results are summarized in the Table. There was a significant difference in the mean SUV’s between the group with transformation and that with indolent lymphoma (7.6 ± 4.6 vs. 4.5 ±2.5). More importantly, the mean values for SUV in the lesions with the highest FDG uptake were significantly different between the two groups (13.8 ±5.3 vs 5.8±2.9). Among the patients who had a biopsy taken from the lesions with the highest SUV’s the range was 8 to 27 and the mean value was 14.9±8.0. Conclusions: In patients with indolent lymphoma, areas of significant increase in FDG (particularly SUV’s >12) should be considered targets for biopsy. Differences in SUV’s Between Transformed and Indolent Lesions Patient Number Mean SUV Mean SUV in Hottest Lesions SUV Range in Hottest Lesions SUV: standardized uptake values Patients with Transformation 12 7.6+/−4.6 13.8+/−5.3 8–27 Patients with Indolent Lymphoma 48 4.5+/−2.5 5.8+/−2.9 2–12
Lymphomas may often present with more than one pathologic type in the same individual. This study assesses the variability of FDG uptake values as determined by SUV’s in various lymphoma subtypes to serve as a platform for differentiating and confirming the pathologic diagnosis in all disease sites. Methods: We retrospectively evaluated the PET-CT studies of 184 lymphoma patients at initial staging or at relapse prior to therapy. Lymphoma subtypes were classified according to the WHO classification. SUV’s were obtained in a total of 1120 nodal or extranodal disease sites in 11 lymphoma subtypes. Mean SUV’s were calculated for each lymphoma subtype by dividing the total maximum SUV’s by the number of disease sites in all patients. Consequently, mean SUV +/− SD and SUV range were determined and correlated with histologic diagnosis as shown in the Table. Results: Results are summarized in Table 1. The highest mean SUV’s were obtained in aggressive non-Hodgkin’s lymphomas (NHL) followed by Hodgkin’s disease (HD) and indolent NHL. Starting from the subtype with the highest mean SUV of the means and in a rank of decreasing order, the subtypes of lymphoma are as follows; Burkitts, DLCL, T cell rich B cell, natural killer T cell, HD, Anaplastic T-cell, mantle cell, marginal zone, follicular, T cell peripheral and chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL). Conclusions: While SUV’s may be quite variable, a general pattern does exist for each lymphoma subtype. A major discrepancy between the SUV’s and histologic diagnosis should be assessed by repeat biopsy. SUV Distribution Among Subtypes of Lymphoma* Type of Lymphoma Patient Number Mean+/−SD Range *Table does not cover all lymphoma subtypes studied in this study due to limited space, HD: Hodgkins Disease NHL: Non Hodgkins lymphoma, DLCL: Diffuse large cell lymphoma, CLL/SLL: Chronic lymphocytic leukemia/small lymphocytic lymphoma HD 45 7.5+/−3.7 2–22 Mantle cell 8 6.8+/−4.9 2.1–21 DLCL 58 11.3+/−7.3 2.2–58 T cell Rich B cell 5 9.1+/−5.5 4–25 Burkitts 9 11.8+/−10.5 2–44 Follicular 30 5.1+/−2.7 1.6–11 CLL/SLL 11 3.0+/−1.5 1.1–8.8 Marginal Zone 10 5.5+/−3.8 2.1–21
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.