SPECT and CT misregistration reduction in [99mTc]Tc-MAA SPECT/CT for precision liver radioembolization treatment planning.

PURPOSE: Respiration and body movement induce misregistration between static [99mTc]Tc-MAA SPECT and CT, causing lung shunting fraction (LSF) and tumor-to-normal liver ratio (TNR) errors for 90Y radioembolization planning. We aim to alleviate the misregistration between [99mTc]Tc-MAA SPECT and CT using two registration schemes on simulation and clinical data. METHODS: In the simulation study, 70 XCAT phantoms were modeled. The SIMIND Monte Carlo program and OS-EM algorithm were used for projection generation and reconstruction, respectively. Low-dose CT (LDCT) at end-inspiration was simulated for attenuation correction (AC), lungs and liver segmentation, while contrast-enhanced CT (CECT) was simulated for tumor and perfused liver segmentation. In the clinical study, 16 patient data including [99mTc]Tc-MAA SPECT/LDCT and CECT with observed SPECT and CT mismatch were analyzed. Two liver-based registration schemes were studied: SPECT registered to LDCT/CECT and vice versa. Mean count density (MCD) of different volumes-of-interest (VOIs), normalized mutual information (NMI), LSF, TNR, and maximum injected activity (MIA) based on the partition model before and after registration were compared. Wilcoxon signed-rank test was performed. RESULTS: In the simulation study, compared to before registration, registrations significantly reduced estimation errors of MCD of all VOIs, LSF (Scheme 1: - 100.28%, Scheme 2: - 101.59%), and TNR (Scheme 1: - 7.00%, Scheme 2: - 5.67%), as well as MIA (Scheme 1: - 3.22%, Scheme 2: - 2.40%). In the clinical study, Scheme 1 reduced 33.68% LSF and increased 14.75% TNR, while Scheme 2 reduced 38.88% LSF and increased 6.28% TNR compared to before registration. One patient may change from 90Y radioembolization untreatable to treatable and other patients may change the MIA up to 25% after registration. NMI between SPECT and CT was significantly increased after registrations in both studies. CONCLUSION: Registration between static [99mTc]Tc-MAA SPECT and corresponding CTs is feasible to reduce their spatial mismatch and improve dosimetric estimation. The improvement of LSF is larger than TNR. Our method can potentially improve patient selection and personalized treatment planning for liver radioembolization.

Transfer learning-based attenuation correction for static and dynamic cardiac PET using a generative adversarial network.

PURPOSE: The goal of this work is to demonstrate the feasibility of directly generating attenuation-corrected PET images from non-attenuation-corrected (NAC) PET images for both rest and stress-state static or dynamic [13N]ammonia MP PET based on a generative adversarial network. METHODS: We recruited 60 subjects for rest-only scans and 14 subjects for rest-stress scans, all of whom underwent [13N]ammonia cardiac PET/CT examinations to acquire static and dynamic frames with both 3D NAC and CT-based AC (CTAC) PET images. We developed a 3D pix2pix deep learning AC (DLAC) framework via a U-net + ResNet-based generator and a convolutional neural network-based discriminator. Paired static or dynamic NAC and CTAC PET images from 60 rest-only subjects were used as network inputs and labels for static (S-DLAC) and dynamic (D-DLAC) training, respectively. The pre-trained S-DLAC network was then fine-tuned by paired dynamic NAC and CTAC PET frames of 60 rest-only subjects to derive an improved D-DLAC-FT for dynamic PET images. The 14 rest-stress subjects were used as an internal testing dataset and separately tested on different network models without training. The proposed methods were evaluated using visual quality and quantitative metrics. RESULTS: The proposed S-DLAC, D-DLAC, and D-DLAC-FT methods were consistent with clinical CTAC in terms of various images and quantitative metrics. The S-DLAC (slope = 0.9423, R2 = 0.947) showed a higher correlation with the reference static CTAC as compared to static NAC (slope = 0.0992, R2 = 0.654). D-DLAC-FT yielded lower myocardial blood flow (MBF) errors in the whole left ventricular myocardium than D-DLAC, but with no significant difference, both for the 60 rest-state subjects (6.63 ± 5.05% vs. 7.00 ± 6.84%, p = 0.7593) and the 14 stress-state subjects (1.97 ± 2.28% vs. 3.21 ± 3.89%, p = 0.8595). CONCLUSION: The proposed S-DLAC, D-DLAC, and D-DLAC-FT methods achieve comparable performance with clinical CTAC. Transfer learning shows promising potential for dynamic MP PET.

Deep learning-based dynamic PET parametric Ki image generation from lung static PET.

OBJECTIVES: PET/CT is a first-line tool for the diagnosis of lung cancer. The accuracy of quantification may suffer from various factors throughout the acquisition process. The dynamic PET parametric Ki provides better quantification and improve specificity for cancer detection. However, parametric imaging is difficult to implement clinically due to the long acquisition time (~ 1 h). We propose a dynamic parametric imaging method based on conventional static PET using deep learning. METHODS: Based on the imaging data of 203 participants, an improved cycle generative adversarial network incorporated with squeeze-and-excitation attention block was introduced to learn the potential mapping relationship between static PET and Ki parametric images. The image quality of the synthesized images was qualitatively and quantitatively evaluated by using several physical and clinical metrics. Statistical analysis of correlation and consistency was also performed on the synthetic images. RESULTS: Compared with those of other networks, the images synthesized by our proposed network exhibited superior performance in both qualitative and quantitative evaluation, statistical analysis, and clinical scoring. Our synthesized Ki images had significant correlation (Pearson correlation coefficient, 0.93), consistency, and excellent quantitative evaluation results with the Ki images obtained in standard dynamic PET practice. CONCLUSIONS: Our proposed deep learning method can be used to synthesize highly correlated and consistent dynamic parametric images obtained from static lung PET. KEY POINTS: • Compared with conventional static PET, dynamic PET parametric Ki imaging has been shown to provide better quantification and improved specificity for cancer detection. • The purpose of this work was to develop a dynamic parametric imaging method based on static PET images using deep learning. • Our proposed network can synthesize highly correlated and consistent dynamic parametric images, providing an additional quantitative diagnostic reference for clinicians.

Deep-learning-based estimation of attenuation map improves attenuation correction performance over direct attenuation estimation for myocardial perfusion SPECT.

BACKGROUND: Deep learning (DL)-based attenuation correction (AC) is promising to improve myocardial perfusion (MP) SPECT. We aimed to optimize and compare the DL-based direct and indirect AC methods, with and without SPECT and CT mismatch. METHODS: One hundred patients with different 99mTc-sestamibi activity distributions and anatomical variations were simulated by a population of XCAT phantoms. Additionally, 34 patients 99mTc-sestamibi stress/rest SPECT/CT scans were retrospectively recruited. Projections were reconstructed by OS-EM method with or without AC. Mismatch between SPECT and CT images was modeled. A 3D conditional generative adversarial network (cGAN) was optimized for two DL-based AC methods: (i) indirect approach, i.e., non-attenuation corrected (NAC) SPECT paired with the corresponding attenuation map for training. The projections were reconstructed with the DL-generated attenuation map for AC; (ii) direct approach, i.e., NAC SPECT paired with the corresponding AC SPECT for training to perform direct AC. RESULTS: Mismatch between SPECT and CT degraded DL-based AC performance. The indirect approach is superior to direct approach for various physical and clinical indices, even with mismatch modeled. CONCLUSION: DL-based estimation of attenuation map for AC is superior and more robust to direct generation of AC SPECT.

Deep learning-based denoising in projection-domain and reconstruction-domain for low-dose myocardial perfusion SPECT.

BACKGROUND: Low-dose (LD) myocardial perfusion (MP) SPECT suffers from high noise level, leading to compromised diagnostic accuracy. Here we investigated the denoising performance for MP-SPECT using a conditional generative adversarial network (cGAN) in projection-domain (cGAN-prj) and reconstruction-domain (cGAN-recon). METHODS: Sixty-four noisy SPECT projections were simulated for a population of 100 XCAT phantoms with different anatomical variations and 99mTc-sestamibi distributions. Series of LD projections were obtained by scaling the full dose (FD) count rate to be 1/20 to 1/2 of the original. Twenty patients with 99mTc-sestamibi stress SPECT/CT scans were retrospectively analyzed. For each patient, LD SPECT images (7/10 to 1/10 of FD) were generated from the FD list mode data. All projections were reconstructed by the quantitative OS-EM method. A 3D cGAN was implemented to predict FD images from their corresponding LD images in the projection- and reconstruction-domain. The denoised projections were reconstructed for analysis in various quantitative indices along with cGAN-recon, Gaussian, and Butterworth-filtered images. RESULTS: cGAN denoising improves image quality as compared to LD and conventional post-reconstruction filtering. cGAN-prj can further reduce the dose level as compared to cGAN-recon without compromising the image quality. CONCLUSIONS: Denoising based on cGAN-prj is superior to cGAN-recon for MP-SPECT.

Evaluation of different respiratory gating schemes for cardiac SPECT.

BACKGROUND: Respiratory gating reduces motion blurring in cardiac SPECT. Here we aim to evaluate the performance of three respiratory gating strategies using a population of digital phantoms with known truth and clinical data. METHODS: We analytically simulated 60 projections for 10 XCAT phantoms with 99mTc-sestamibi distributions using three gating schemes: equal amplitude gating (AG), equal count gating (CG), and equal time gating (TG). Clinical list-mode data for 10 patients who underwent 99mTc-sestamibi scans were also processed using the 3 gating schemes. Reconstructed images in each gate were registered to a reference gate, averaged and reoriented to generate the polar plots. For simulations, image noise, relative difference (RD) of averaged count for each of the 17 segment, and relative defect size difference (RSD) were analyzed. For clinical data, image intensity profile and FWHM were measured across the left ventricle wall. RESULTS: For simulations, AG and CG methods showed significantly lower RD and RSD compared to TG, while noise variation was more non-uniform through different gates for AG. In the clinical study, AG and CG had smaller FWHM than TG. CONCLUSIONS: AG and CG methods show better performance for motion reduction and are recommended for clinical respiratory gating SPECT implementation.

Interpolated average CT for cardiac PET/CT attenuation correction.

BACKGROUND: Previously, we proposed interpolated averaged CT (IACT) for improved attenuation correction (AC) in thoracic PET/CT. This study aims to evaluate its feasibility and effectiveness on cardiac PET/CT. METHODS: We simulated (18)F-FDG distribution using the XCAT phantom with normal and abnormal cardiac uptake. Average activity and attenuation maps represented static PET and respiration average CT (ACT), respectively, while the attenuation maps of end-inspiration/expiration represented 2 helical CTs (HCT). IACT was obtained by averaging the 2 extreme phases and the interpolated phases generated between them. Later, we recruited 4 patients who were scanned 1 hr post 315-428 MBq (18)F-FDG injection. Simulated and clinical PET sinograms were reconstructed with AC using (1) HCT, (2) IACT, and (3) ACT. Polar plots and the 17-segment plots were analyzed. Two regions-of-interest were drawn on lesion and background area to obtain the intensity ratio (IR). RESULTS: Polar plots of PETIACT-AC were more similar to PETACT-AC in both simulation and clinical data. Artifacts were observed in various segments in PETHCT-AC. IR differences of HCT as compared to the phantom were up to ~20%. CONCLUSIONS: IACT-AC reduced respiratory artifacts and improved PET/CT matching similarly to ACT-AC. It is a promising low-dose alternate of ACT for cardiac PET/CT.

Qualitative and semi-quantitative evaluation of myocardium perfusion with 3 T stress cardiac MRI.

BACKGROUND: 3 T MRI has been adopted by some centers as the primary choice for assessment of myocardial perfusion over conventional 1.5 T MRI. However, there is no data published on the potential additional value of incorporating semi-quantitative data from 3 T MRI. This study sought to determine the performance of qualitative 3 T stress magnetic resonance myocardial perfusion imaging (3 T-MRMPI) and the potential incremental benefit of using a semi-quantitative perfusion technique in patients with suspected coronary artery disease (CAD). METHODS: Fifty eight patients (41 men; mean age: 59 years) referred for elective diagnostic angiography underwent stress 3 T MRMPI with a 32-channel cardiac receiver coil. The MR protocol included gadolinium-enhanced stress first-pass perfusion (0.56 mg/kg, dipyridamole), rest perfusion, and delayed enhancement (DE). Visual analysis was performed in two steps. Ischemia was defined as a territory with perfusion defect at stress study but no DE or a territory with DE but additional peri-infarcted perfusion defect at stress study. Semi-quantitative analysis was calculated by using the upslope of the signal intensity-time curve during the first pass of contrast medium during dipyridamole stress and at rest. ROC analysis was used to determine the MPRI threshold that maximized sensitivity. Quantitative coronary angiography served as the reference standard with significant stenosis defined as >70 % diameter stenosis. Diagnostic performance was determined on a per-patient and per-vessel basis. RESULTS: Qualitative assessment had an overall sensitivity and specificity for detecting significant stenoses of 77 % and 80 %, respectively. By adding MPRI analysis, in cases with negative qualitative assessment, the overall sensitivity increased to 83 %. The impact of MPRI differed depending on the territory; with the sensitivity for detection of left circumflex (LCx) stenosis improving the most after semi-quantification analysis, (66 % versus 83 %). CONCLUSIONS: Pure qualitative assessment of 3 T MRI had acceptable performance in detecting severe CAD. There is no overall benefit of incorporating semi-quantitative data; however a higher sensitivity can be obtained by adding MPRI, especially in the detection of LCx lesions.

Radiomics incorporating deep features for predicting Parkinson's disease in 123I-Ioflupane SPECT.

PURPOSE: 123I-Ioflupane SPECT is an effective tool for the diagnosis and progression assessment of Parkinson's disease (PD). Radiomics and deep learning (DL) can be used to track and analyze the underlying image texture and features to predict the Hoehn-Yahr stages (HYS) of PD. In this study, we aim to predict HYS at year 0 and year 4 after the first diagnosis with combined imaging, radiomics and DL-based features using 123I-Ioflupane SPECT images at year 0. METHODS: In this study, 161 subjects from the Parkinson's Progressive Marker Initiative database underwent baseline 3T MRI and 123I-Ioflupane SPECT, with HYS assessment at years 0 and 4 after first diagnosis. Conventional imaging features (IF) and radiomic features (RaF) for striatum uptakes were extracted from SPECT images using MRI- and SPECT-based (SPECT-V and SPECT-T) segmentations respectively. A 2D DenseNet was used to predict HYS of PD, and simultaneously generate deep features (DF). The random forest algorithm was applied to develop models based on DF, RaF, IF and combined features to predict HYS (stage 0, 1 and 2) at year 0 and (stage 0, 1 and ≥ 2) at year 4, respectively. Model predictive accuracy and receiver operating characteristic (ROC) analysis were assessed for various prediction models. RESULTS: For the diagnostic accuracy at year 0, DL (0.696) outperformed most models, except DF + IF in SPECT-V (0.704), significantly superior based on paired t-test. For year 4, accuracy of DF + RaF model in MRI-based method is the highest (0.835), significantly better than DF + IF, IF + RaF, RaF and IF models. And DL (0.820) surpassed models in both SPECT-based methods. The area under the ROC curve (AUC) highlighted DF + RaF model (0.854) in MRI-based method at year 0 and DF + RaF model (0.869) in SPECT-T method at year 4, outperforming DL models, respectively. And then, there was no significant differences between SPECT-based and MRI-based segmentation methods except for the imaging feature models. CONCLUSION: The combination of radiomic and deep features enhances the prediction accuracy of PD HYS compared to only radiomics or DL. This suggests the potential for further advancements in predictive model performance for PD HYS at year 0 and year 4 after first diagnosis using 123I-Ioflupane SPECT images at year 0, thereby facilitating early diagnosis and treatment for PD patients. No significant difference was observed in radiomics results obtained between MRI- and SPECT-based striatum segmentations for radiomic and deep features.

Targeted therapies of inflammatory diseases with intracellularly gelated macrophages in mice and rats.

Membrane-camouflaged nanomedicines often suffer from reduced efficacy caused by membrane protein disintegration and spatial disorder caused by separation and reassembly of membrane fragments during the coating process. Here we show that intracellularly gelated macrophages (GMs) preserve cell membrane structures, including protein content, integration and fluidity, as well as the membrane lipid order. Consequently, in our testing GMs act as cellular sponges to efficiently neutralize various inflammatory cytokines via receptor-ligand interactions, and serve as immune cell-like carriers to selectively bind inflammatory cells in culture medium, even under a flow condition. In a rat model of collagen-induced arthritis, GMs alleviate the joint injury, and suppress the overall arthritis severity. Upon intravenous injection, GMs efficiently accumulate in the inflammatory lungs of acute pneumonia mice for anti-inflammatory therapy. Conveniently, GMs are amenable to lyophilization and can be stored at ambient temperatures for at least 1 month without loss of integrity and bio-activity. This intracellular gelation technology provides a universal platform for targeted inflammation neutralization treatment.

PET/CT-Based Absorbed Dose Maps in 90Y Selective Internal Radiation Therapy Correlate with Spatial Changes in Liver Function Derived from Dynamic MRI.

Functional liver parenchyma can be damaged from treatment of liver malignancies with 90Y selective internal radiation therapy (SIRT). Evaluating functional parenchymal changes and developing an absorbed dose (AD)-toxicity model can assist the clinical management of patients receiving SIRT. We aimed to determine whether there is a correlation between 90Y PET AD voxel maps and spatial changes in the nontumoral liver (NTL) function derived from dynamic gadoxetic acid-enhanced MRI before and after SIRT. Methods: Dynamic gadoxetic acid-enhanced MRI scans were acquired before and after treatment for 11 patients undergoing 90Y SIRT. Gadoxetic acid uptake rate (k1) maps that directly quantify spatial liver parenchymal function were generated from MRI data. Voxel-based AD maps, derived from the 90Y PET/CT scans, were binned according to AD. Pre- and post-SIRT k1 maps were coregistered to the AD map. Absolute and percentage k1 loss in each bin was calculated as a measure of loss of liver function, and Spearman correlation coefficients between k1 loss and AD were evaluated for each patient. Average k1 loss over the patients was fit to a 3-parameter logistic function based on AD. Patients were further stratified into subgroups based on lesion type, baseline albumin-bilirubin scores and alanine transaminase levels, dose-volume effect, and number of SIRT treatments. Results: Significant positive correlations (ρ = 0.53-0.99, P < 0.001) between both absolute and percentage k1 loss and AD were observed in most patients (8/11). The average k1 loss over 9 patients also exhibited a significant strong correlation with AD (ρ ≥ 0.92, P < 0.001). The average percentage k1 loss of patients across AD bins was 28%, with a logistic function model demonstrating about a 25% k1 loss at about 100 Gy. Analysis between patient subgroups demonstrated that k1 loss was greater among patients with hepatocellular carcinoma, higher alanine transaminase levels, larger fractional volumes of NTL receiving an AD of 70 Gy or more, and sequential SIRT treatments. Conclusion: Novel application of multimodality imaging demonstrated a correlation between 90Y SIRT AD and spatial functional liver parenchymal degradation, indicating that a higher AD is associated with a larger loss of local hepatocyte function. With the developed response models, PET-derived AD maps can potentially be used prospectively to identify localized damage in liver and to enhance treatment strategies.

T1rho and T2 relaxation times for lumbar disc degeneration: an in vivo comparative study at 3.0-Tesla MRI.

OBJECTIVE: To determine the relative performance of T1rho and T2 relaxation times in disc degeneration assessment. METHODS: Lumbar sagittal MRI was performed at 3 T in 52 subjects. With a spin-lock frequency of 500 Hz, T1rho was measured using a rotary echo spin-lock pulse embedded in a three-dimensional (3D) balanced fast field echo sequence. A multi-echo TSE sequence was used for T2 mapping. Regions of interest (ROIs) were drawn over the T1rho and T2 maps, including nucleus pulposus (NP) and annulus fibrosus (AF). Eight- and five-level disc degeneration semi-quantitative grading was performed. RESULTS: For NP, T1rho and T2 decreased quadratically with disc degeneration grades and had no significant trend difference (P = 0.40). For AF, T1rho decreased linearly as the disc degenerated and had a slope of -3.02 and -4.56 for eight- and five-level gradings respectively; while the slopes for T2 values were -1.43 and -1.84 respectively, being significantly flatter than those of T1rho (P < 0.001). There was no significant difference in T1rho and T2 values for both NP and AF among discs of grade 5/8 to 8/8 degeneration. CONCLUSION: T1rho is better suited for evaluating AF in degenerated disc than T2. In NP, T1rho and T2 decrease in a similar pattern following disc degeneration.

Multi-pinhole collimator design in different numbers of projections for brain SPECT.

Purpose: High resolution and sensitivity brain SPECT is promising for the accurate diagnosis of Alzheimer's disease (AD) and Parkinson's disease (PD). Multi-pinhole (MPH) collimators with a good performance in imaging small field-of-view (FOV) could be better used for brain SPECT. In this study, we aim to evaluate the impact of varying the number of pinholes and the number of projections on the performance of MPH brain SPECT. Methods: The system design was based on a commercial clinical dual-head SPECT/CT scanner, with target spatial resolutions of 12 mm and 8 mm for AD and PD SPECT, respectively. In total, 1-25 pinholes were modeled for 64, 32, 16, 8, 4, and 2 projections. The 3D NURBS-based HUman Brain (NHUB) phantom was used in the analytical simulation to model 99mTc-HMPAO and 99mTc-TRODAT distributions. The 2D Derenzo hot-rod phantom and star phantom were used in Monte Carlo simulations to evaluate the spatial resolution and angular sampling performance of MPH. The influence of different detector positions was also evaluated for 2, 4, and 6 angular views. The projections were reconstructed using the 3D MPH ML-EM method. Normalized mean square error, coefficient of variation, and image profiles were evaluated. Results: Along with the decrease in the number of projections, more pinholes are required to achieve the optimum performance. For 32 projections, 9- and 7-pinhole collimators provide the best normalized mean square error (NMSE) to the coefficient of variation (COV) trade-off for 99mTc-HMPAO and 99mTc-TRODAT, respectively. Detector positions substantially affect the image quality for MPH SPECT for 2 and 4 angular views. The smallest rod size for the Derenzo hot-rod phantom, which could be resolved, is 7.9 mm for the MPH general purpose collimator (MPGP) with more than 16 projections and 6.4 mm for MPH high-resolution collimator (MPHR) with more than 8 projections. Conclusion: The number of pinholes affects the performance of the MPH collimator, especially when the projection views become fewer. More pinholes are required for fewer projections to provide better angular sampling in MPH for complex activity distributions. Detector positions affect the image quality of MPH SPECT for 2 and 4 angular views, where L-mode acquisition is slightly superior to H-mode. MPH collimators exhibited improved spatial resolution and angular sampling compared with both LEHR and single pinhole collimators.

Lipid bilayer-based biological nanoplatforms for sonodynamic cancer therapy.

Sonodynamic therapy (SDT) has been developed as a promising alternative therapeutic modality for cancer treatment, involving the synergetic application of sonosensitizers and low-intensity ultrasound. However, the antitumor efficacy of SDT is significantly limited due to the poor performance of conventional sonosensitizers in vivo and the constrained tumor microenvironment (TME). Recent breakthroughs in lipid bilayer-based nanovesicles (LBBNs), including multifunctional liposomes, exosomes, and isolated cellular membranes, have brought new insights into the advancement of SDT. Despite their distinct sources and preparation methods, the lipid bilayer structure in common allows them to be functionalized in many comparable ways to serve as ideal nanocarriers against challenges arising from the tumor-specific sonosensitizer delivery and the complicated TME. In this review, we provide a comprehensive summary of the recent advances in LBBN-based SDT, with particular attention on how LBBNs can be engineered to improve the delivery efficiency of sonosensitizers and overcome physical, biological, and immune barriers within the TME for enhanced sonodynamic cancer therapy. We anticipate that this review will offer valuable guidance in the construction of LBBN-based nanosonosensitizers and contribute to the development of advanced strategies for next-generation sonodynamic cancer therapy.

Voxel-S-value methods adapted to heterogeneous media for quantitative Y-90 microsphere radioembolization dosimetry.

PURPOSE: The absorbed dose estimation from Voxel-S-Value (VSV) method in heterogeneous media is suboptimal as VSVs are calculated in homogeneous media. The aim of this study is to develop and evaluate new VSV methods in order to enhance the accuracy of Y-90 microspheres absorbed dose estimation in liver, lungs, tumors and lung-liver interface regions. METHODS: Ten patients with Y-90 microspheres SPECT/CT and PET/CT data, six of whom had additional Tc-99m-macroaggregated albumin SPECT/CT data, were analyzed from the Deep Blue Data Repository. Seven existing VSV methods along with three newly proposed VSV methods were evaluated: liver and lung kernel with center voxel scaling (LiLuCK), liver kernel with density correction and lung kernel with center voxel scaling (LiKDLuCK), liver kernel with center voxel scaling and lung kernel with density correction (LiCKLuKD). Monte Carlo (MC) results were regarded as the gold standard. Absolute absorbed dose errors (%AADE) of these methods for the liver, lungs, tumors, upper liver, and lower lungs were assessed. RESULTS: Liver and tumor's median %AADE of all methods were <3% for three types of imaging data. In the lungs, however, three recently proposed VSV methods provided median %AADEs of less than 7%, whereas the differences exceeded 20% for existing methods that did not use a lung kernel. LiCKLuKD could achieve median %AADE <2% in the liver, upper liver and tumors, and median %AADE <7% in the lungs and lower lungs in three types of data. CONCLUSION: All methods are consistent with MC in the liver and tumors. Methods with tissue-specific kernel and effective correction achieve smaller errors in lungs. LiCKLuKD has comparable results with MC in absorbed dose estimation of Y-90 radioembolization for all target regions.

The effects of mismatch between SPECT and CT images on quantitative activity estimation - A simulation study.

BACKGROUND: Quantitative activity estimation is essential in nuclear medicine imaging. Mismatch between SPECT and CT images at the same imaging time point due to patient movement degrades accuracy in both diagnostic studies and target radionuclide therapy dosimetry. This work aims to study the mismatch effects between CT and SPECT data on attenuation correction (AC), volume-of-interest (VOI) delineation, and registration for activity estimation. METHODS: Nine 4D XCAT phantoms were generated at 1, 24, and 144 h post In-111 Zevalin injection, varying in activity distributions, body sizes, and organ sizes. Realistic noisy SPECT projections were generated by an analytical projector and reconstructed with a quantitative OS-EM method. CT images were shifted, corresponding to SPECT images at each imaging time point, from -5 to 5 voxels and also according to a clinical reference. The effect of mismatched AC maps was evaluated using mismatched CT images for AC in SPECT reconstruction while VOIs were mapped out from matched CTs. The effect of mismatched VOI drawings was evaluated using mismatched CTs to map out target organs while using matched CTs for AC. The effect of mismatched CT images for registration was evaluated by registering sequential mismatched CTs to align corresponding SPECT images, with no AC and VOI mismatch. Bi-exponential curve fitting was performed to obtain time-integrated activity (TIA). Organ activity errors (%OAE) and TIA errors (%TIAE) were calculated. RESULTS: According to the clinical reference, %OAE was larger for organs near ribs for AC effect. For VOI effect, %OAE was larger for small and low uptake organs. For registration effect, %TIAE were larger when mismatch existed in more numbers of SPECT/CT images, while no substantial difference was observed when using mismatched CT at different imaging time points as registration reference. %TIAE was highest for VOI, followed by registration and AC, e.g., 20.62%±8.61%, 9.33%±4.66% and 1.13%±0.90% respectively for kidneys. CONCLUSIONS: The mismatch between CT and SPECT images poses a significant impact on the accuracy of quantitative activity estimation, attributed particularly from VOI delineation errors. It is recommended to perform registration between emission and transmission images at the same time point to ensure diagnostic and dosimetric accuracy.

Voxel-S-Value based 3D treatment planning methods for Y-90 microspheres radioembolization based on Tc-99m-macroaggregated albumin SPECT/CT.

Partition model (PM) for Y-90 microsphere radioembolization is limited in providing 3D dosimetrics. Voxel-S-Values (VSV) method has good agreement with Monte Carlo (MC) simulations for 3D absorbed dose conversion. We propose a new VSV method and compare its performance along with PM, MC and other VSV methods for Y-90 RE treatment planning based on Tc-99m MAA SPECT/CT. Twenty Tc-99m-MAA SPECT/CT patient data are retrospectively analyzed. Seven VSV methods are implemented: (1) local energy deposition; (2) liver kernel; (3) liver kernel and lung kernel; (4) liver kernel with density correction (LiKD); (5) liver kernel with center voxel scaling (LiCK); (6) liver kernel and lung kernel with density correction (LiLuKD); (7) proposed liver kernel with center voxel scaling and lung kernel with density correction (LiCKLuKD). Mean absorbed dose and maximum injected activity (MIA) obtained by PM and VSV are evaluated against MC results, and 3D dosimetrics generated by VSV are compared with MC. LiKD, LiCK, LiLuKD and LiCKLuKD have the smallest deviation in normal liver and tumors. LiLuKD and LiCKLuKD have the best performance in lungs. MIAs are similar by all methods. LiCKLuKD could provide MIA consistent with PM, and precise 3D dosimetrics for Y-90 RE treatment planning.

Fast myocardial perfusion SPECT denoising using an attention-guided generative adversarial network.

Purpose: Deep learning-based denoising is promising for myocardial perfusion (MP) SPECT. However, conventional convolutional neural network (CNN)-based methods use fixed-sized convolutional kernels to convolute one region within the receptive field at a time, which would be ineffective for learning the feature dependencies across large regions. The attention mechanism (Att) is able to learn the relationships between the local receptive field and other voxels in the image. In this study, we propose a 3D attention-guided generative adversarial network (AttGAN) for denoising fast MP-SPECT images. Methods: Fifty patients who underwent 1184 MBq 99mTc-sestamibi stress SPECT/CT scan were retrospectively recruited. Sixty projections were acquired over 180° and the acquisition time was 10 s/view for the full time (FT) mode. Fast MP-SPECT projection images (1 s to 7 s) were generated from the FT list mode data. We further incorporated binary patient defect information (0 = without defect, 1 = with defect) into AttGAN (AttGAN-def). AttGAN, AttGAN-def, cGAN, and Unet were implemented using Tensorflow with the Adam optimizer running up to 400 epochs. FT and fast MP-SPECT projection pairs of 35 patients were used for training the networks for each acquisition time, while 5 and 10 patients were applied for validation and testing. Five-fold cross-validation was performed and data for all 50 patients were tested. Voxel-based error indices, joint histogram, linear regression, and perfusion defect size (PDS) were analyzed. Results: All quantitative indices of AttGAN-based networks are superior to cGAN and Unet on all acquisition time images. AttGAN-def further improves AttGAN performance. The mean absolute error of PDS by AttcGAN-def was 1.60 on acquisition time of 1 s/prj, as compared to 2.36, 2.76, and 3.02 by AttGAN, cGAN, and Unet. Conclusion: Denoising based on AttGAN is superior to conventional CNN-based networks for MP-SPECT.

Platelet-mimicking supramolecular nanomedicine with precisely integrated prodrugs for cascade amplification of synergistic chemotherapy.

Camptothecin (CPT) and cisplatin (Pt) have shown synergistic effects on a variety of cancers during preclinical and clinical studies. However, the ratio of the two drugs often could not be precisely regulated in different delivery systems, which hinders the desired synergistic effect. In addition, the low delivery efficiency of the two drugs to the tumor further impedes the ideal therapeutic outcomes. Herein, we report that a platelet-mimicking supramolecular nanomedicine (SN) could precisely control of the ratio of CPT and Pt with a high tumor accumulation rate for cascade amplification of synergistic chemotherapy. The SN was fabricated via the host-guest interaction between cucurbit[7]uril conjugated hyaluronic acid (HA-CB[7]) and adamantane (ADA) respectively functionalized CPT- and Pt-based prodrugs. The ratio of CPT and Pt in the SN could be facilely regulated by simply controlling the loading ratio, based on the strong binding affinity between CB[7] and ADA, and SN60 with 60% CPT and 40% Pt showed the highest synergistic effects on 4T1 cells. To improve the tumor accumulation efficiency of SN, 5,6-dimethylxanthenone-4-acetic acid (DMXAA, a tumor vasculature-disruptive agent) was loaded into the optimized SN and then coated with platelet membrane to yield platelet-mimicking supramolecular nanomedicine (D@SN-P). D@SN-P could first passively accumulate in tumors owing to the enhanced permeability and retention (EPR) effect after intravenous administration. The initially release of DMXAA from D@SN-P could induce tumor vascular disruption, and the resultant epithelial collagen exposure around the disrupted tumor vasculature provided a target for further recruitment of platelet-mimicking SN, leading to cascade amplification of tumor accumulation with synergistic chemotherapy. Hence, this platelet-mimicking supramolecular nanomedicine presents a universal supramolecular strategy to finely regulate the ratio of loaded pro-drugs, and improve the accumulation efficiency to amplify chemotherapy via platelet-mimics.

Lu-177-PSMA dosimetry for kidneys and tumors based on SPECT images at two imaging time points.

Background: Personalized dosimetry for Lu-177-PSMA treatment requires multiple-time-point SPECT/CT scans to calculate time-integrated activity (TIA). This study evaluates two-time-point (TTP) methods for TIA calculation for kidneys and tumors. Methods: A total of 18 patients treated with 3.7-7.4 GBq Lu-177 PSMA-617 were analyzed retrospectively, including 18 sets of left and right kidneys, as well as 45 tumors. Four quantitative SPECT/CT (4TP) were acquired at 2 h, 20 h, 40 h, 60 h (n = 11), or 200 h (n = 7) after treatment, and they were fit bi-exponentially as reference. The TTP method was fitted by a mono-exponential washout function using two selected imaging time points for kidneys. For tumors, one uptake and one washout phase were modeled, assuming linear (type I) and same (type II) uptake phase between 0 h to the first time point and mono-exponential washout thereafter. Two single-time-point (STP) methods were also implemented for comparison. TIA calculated by TTP and STP methods were compared with reference to the 4TP TIA. Results: For the kidneys, the TTP methods using 20 h-60 h and 40 h-200 h had smaller mean absolute errors of 8.05 ± 6.05% and 4.95 ± 3.98%, respectively, as compared to other combinations of time points and STP methods. For tumors, the type I and type II TTP methods using 20h-60 h and 40-200 h had smaller mean absolute errors of 6.14 ± 5.19% and 12.22 ± 4.44%, and 8.31 ± 7.16% and 4.48 ± 7.10%, respectively, as compared to other TTP and STP methods. Conclusion: The TTP methods based on later imaging time demonstrated fewer errors than the STP methods in kidney and tumor TIA. Imaging at 20 h-60 h and 40 h-200 h could simplify the dosimetry procedures with fewer TIA estimation errors.