Femoral cartilage ultrasound echo-intensity is a valid measure of cartilage composition.

This study aimed to create a conversion equation that accurately predicts cartilage magnetic resonance imaging (MRI) T2 relaxation times using ultrasound echo-intensity and common participant demographics. We recruited 15 participants with a primary anterior cruciate ligament reconstruction between the ages of 18 and 35 years at 1-5 years after surgery. A single investigator completed a transverse suprapatellar scan with the ACLR limb in max knee flexion to image the femoral trochlea cartilage. A single reader manually segmented the femoral cartilage cross-sectional area to assess the echo-intensity (i.e., mean gray-scale pixel value). At a separate visit, a T2 mapping sequence with the MRI beam set to an oblique angle was used to image the femoral trochlea cartilage. A single reader manually segmented the cartilage cross-sectional area on a single MRI slice to assess the T2 relaxation time. A stepwise, multiple linear regression was used to predict T2 relaxation time from cartilage echo-intensity and common demographic variables. We created a conversion equation using the regression betas and then used an ICC and Bland-Altman plot to assess agreement between the estimated and true T2 relaxation time. Cartilage ultrasound echo-intensity and age significantly predicted T2 relaxation time (F = 7.33, p = 0.008, R2 = 0.55). When using the new conversion equation to estimate T2 relaxation time from cartilage echo-intensity and age, there was strong agreement between the estimated and true T2 relaxation time (ICC2,k = 0.84). This study provides promising preliminary data that cartilage echo-intensity combined with age can be used as a clinically accessible tool for evaluating cartilage composition.

Striatal dopamine in anhedonia: A simultaneous [11C]raclopride positron emission tomography and functional magnetic resonance imaging investigation.

BACKGROUND: Anhedonia is hypothesized to be associated with blunted mesocorticolimbic dopamine (DA) functioning in samples with major depressive disorder. The purpose of this study was to examine linkages between striatal DA, reward circuitry functioning, anhedonia, and, in an exploratory fashion, self-reported stress, in a transdiagnostic anhedonic sample. METHODS: Participants with (n = 25) and without (n = 12) clinically impairing anhedonia completed a reward-processing task during simultaneous positron emission tomography and magnetic resonance (PET-MR) imaging with [11C]raclopride, a DA D2/D3 receptor antagonist that selectively binds to striatal DA receptors. RESULTS: Relative to controls, the anhedonia group exhibited decreased task-related DA release in the left putamen, caudate, and nucleus accumbens and right putamen and pallidum. There were no group differences in task-related brain activation (fMRI) during reward processing after correcting for multiple comparisons. General functional connectivity (GFC) findings revealed blunted fMRI connectivity between PET-derived striatal seeds and target regions in the anhedonia group. Associations were identified between anhedonia severity and the magnitude of task-related DA release to rewards in the left putamen, but not mesocorticolimbic GFC. CONCLUSIONS: Results provide evidence for reduced striatal DA functioning during reward processing and blunted mesocorticolimbic network functional connectivity in a transdiagnostic sample with clinically significant anhedonia.

Delayed cartilage oligomeric matrix protein response to loading is associated with femoral cartilage composition post-ACLR.

PURPOSE: To determine associations between immediate and delayed response of serum cartilage oligomeric matrix protein (sCOMP) to loading (i.e., 3000 walking steps) and femoral cartilage interlimb T1ρ relaxation times in individual's post-anterior cruciate ligament reconstruction (ACLR). METHODS: This cross-sectional study included 20 individuals 6-12 months following primary ACLR (65% female, 20.5 ± 4.0 years old, 24.9 ± 3.0 kg/m2, 7.3 ± 1.5 months post-ACLR). Serum samples were collected prior to, immediately following, and 3.5 h following walking 3000 steps on a treadmill at habitual walking speed. sCOMP concentrations were processed using enzyme-linked immunosorbent assays. Immediate and delayed absolute sCOMP responses to loading were evaluated immediately and 3.5 h post-walking, respectively. Participants underwent bilateral magnetic resonance imaging with T1ρ sequences to calculate resting femoral cartilage interlimb T1ρ relaxation time ratios between limbs (i.e., ACLR/Uninjured limb). Linear regression models were fitted to determine associations between sCOMP response to loading and femoral cartilage T1ρ outcomes controlling for pre-loading sCOMP concentrations. RESULTS: Greater increases in delayed sCOMP response to loading were associated with greater lateral (∆R2 = 0.29, p = 0.02) but not medial (∆R2 < 0.01, p = 0.99) femoral cartilage interlimb T1ρ ratios. Associations between immediate sCOMP response to loading with femoral cartilage interlimb T1ρ ratios were weak and non-significant (∆R2 range = 0.02-0.09, p range = 0.21-0.58). CONCLUSION: Greater delayed sCOMP response to loading, a biomarker of cartilage breakdown, is associated with worse lateral femoral cartilage composition in the ACLR limb compared to the uninjured limb. Delayed sCOMP response to loading may be a more indicative metabolic indicator linked to deleterious changes in composition than immediate sCOMP response.

Perimenopausal Effects of Estradiol on Anhedonia and Psychosis Study (PEEPs): study protocol for a neural and molecular mechanistic clinical trial.

BACKGROUND: The perimenopausal transition is accompanied by psychiatric symptoms in over 10% of women. Symptoms commonly include depressed mood and anhedonia and less commonly include psychosis. Psychiatric symptoms have been linked to the depletion and/or variability of circulating estradiol, and estradiol treatment reduces perimenopausal anhedonia and psychosis in some women. Estrogen fluctuations may disrupt function in the mesolimbic reward system in some women, leading to psychiatric symptoms like anhedonia or psychosis. The Perimenopausal Effects of Estradiol on Anhedonia and Psychosis Study (PEEPs) is a mechanistic clinical trial that aims to (1) identify relationships between perimenopausal-onset anhedonia and psychosis and neuromolecular markers of mesolimbic reward responses and (2) determine the extent to which estradiol treatment-induced changes in mesolimbic reward responses are associated with alleviation of perimenopausal onset anhedonia or psychosis. METHODS: This study will recruit 100 unmedicated women ages 44-55 in the late-stage perimenopausal transition, sampling across the range of mild-to-high anhedonia and absent-to-moderate psychosis symptoms. Patients will be randomized to receive either estradiol or placebo treatment for 3 weeks. Clinical outcome measures will include symptoms of anhedonia (measured with Snaith-Hamilton Pleasure Scale; SHAPS) and psychosis (measured with Brief Psychiatric Rating Scale; BPRS psychosis subscale) as well as neural markers of mesolimbic reward system functioning, including reward-related fMRI activation and PET-derived measure of striatal dopamine binding. Pre-treatment associations between (1) SHAPS/BPRS scores and (2) reward-related striatal dopamine binding/BOLD activation will be examined. Furthermore, longitudinal mixed models will be used to estimate (1) symptom and neuromolecular trajectories as a function of estradiol vs. placebo treatment and (2) how changes in reward-related striatal dopamine binding and BOLD activation predict variability in symptom trajectories in response to estradiol treatment. DISCUSSION: This clinical trial will be the first to characterize neural and molecular mechanisms by which estradiol treatment ameliorates anhedonia and psychosis symptoms during the perimenopausal transition, thus laying the groundwork for future biomarker research to predict susceptibility and prognosis and develop targeted treatments for perimenopausal psychiatric symptoms. Furthermore, in alignment with the National Institute for Mental Health Research Domain Criteria initiative, this trial will improve our understanding of a range of disorders characterized by anhedonia, psychosis, and reward system dysfunction. TRIAL REGISTRATION: ClinicalTrials.gov NCT05282277.

Worse Tibiofemoral Cartilage Composition Is Associated with Insufficient Gait Kinetics After ACL Reconstruction.

PURPOSE: Greater articular cartilage T1ρ magnetic resonance imaging relaxation times indicate less proteoglycan density and are linked to posttraumatic osteoarthritis development after anterior cruciate ligament reconstruction (ACLR). Although changes in T1ρ relaxation times are associated with gait biomechanics, it is unclear if excessive or insufficient knee joint loading is linked to greater T1ρ relaxation times 12 months post-ACLR. The purpose of this study was to compare external knee adduction (KAM) and flexion (KFM) moments in individuals after ACLR with high versus low tibiofemoral T1ρ relaxation profiles and uninjured controls. METHODS: Gait biomechanics were collected in 26 uninjured controls (50% females; age, 22 ± 4 yr; body mass index, 23.9 ± 2.8 kg·m -2 ) and 26 individuals after ACLR (50% females; age, 22 ± 4 yr; body mass index, 24.2 ± 3.5 kg·m -2 ) at 6 and 12 months post-ACLR. ACLR-T1ρ High ( n = 9) and ACLR-T1ρ Low ( n = 17) groups were created based on 12-month post-ACLR T1ρ relaxation times using a k-means cluster analysis. Functional analyses of variance were used to compare KAM and KFM. RESULTS: ACLR-T1ρ High exhibited lesser KAM than ACLR-T1ρ Low and uninjured controls 6 months post-ACLR. ACLR-T1ρ Low exhibited greater KAM than uninjured controls 6 and 12 months post-ACLR. KAM increased in ACLR-T1ρ High and decreased in ACLR-T1ρ Low between 6 and 12 months, both groups becoming more similar to uninjured controls. There were scant differences in KFM between ACLR-T1ρ High and ACLR-T1ρ Low 6 or 12 months post-ACLR, but both groups demonstrated lesser KFM compared with uninjured controls. CONCLUSIONS: Associations between worse T1ρ profiles and increases in KAM may be driven by the normalization of KAM in individuals who initially exhibit insufficient KAM 6 months post-ACLR.

Loading during Midstance of Gait Is Associated with Magnetic Resonance Imaging of Cartilage Composition Following Anterior Cruciate Ligament Reconstruction.

OBJECTIVE: A complex association exists between aberrant gait biomechanics and posttraumatic knee osteoarthritis (PTOA) development. Previous research has primarily focused on the link between peak loading during the loading phase of stance and joint tissue changes following anterior cruciate ligament reconstruction (ACLR). However, the associations between loading and cartilage composition at other portions of stance, including midstance and late stance, is unclear. The objective of this study was to explore associations between vertical ground reaction force (vGRF) at each 1% increment of stance phase and tibiofemoral articular cartilage magnetic resonance imaging (MRI) T1ρ relaxation times following ACLR. DESIGN: Twenty-three individuals (47.82% female, 22.1 ±4.1 years old) with unilateral ACLR participated in a gait assessment and T1ρ MRI collection at 12.25 ± 0.61 months post-ACLR. T1ρ relaxation times were calculated for the articular cartilage of the weightbearing medial and lateral femoral (MFC, LFC) and tibial (MTC, LTC) condyles. Separate bivariate, Pearson product moment correlation coefficients (r) were used to estimate strength of associations between T1ρ MRI relaxation times in the medial and lateral tibiofemoral articular cartilage with vGRF across the entire stance phase. RESULTS: Greater vGRF during midstance (46%-56% of stance phase) was associated with greater T1ρ MRI relaxation times in the MFC (r ranging between 0.43 and 0.46). CONCLUSIONS: Biomechanical gait profiles that include greater vGRF during midstance are associated with MRI estimates of lesser proteoglycan density in the MFC. Inability to unload the ACLR limb during midstance may be linked to joint tissue changes associated with PTOA development.

In Vivo Compositional Changes in the Articular Cartilage of the Patellofemoral Joint Following Anterior Cruciate Ligament Reconstruction.

OBJECTIVE: To compare T1ρ relaxation times of the medial and lateral regions of the patella and femoral trochlea at 6 and 12 months following anterior cruciate ligament reconstruction (ACLR) on the ACLR and contralateral extremity. Greater T1ρ relaxation times are associated with a lower proteoglycan density of articular cartilage. METHODS: This study involved 20 individuals (11 males, 9 females; mean ± SD age 22 ± 3.9 years, weight 76.11 ± 13.48 kg, and height 178.32 ± 12.32 cm) who underwent a previous unilateral ACLR using a patellar tendon autograft. Magnetic resonance images from both extremities were acquired at 6 and 12 months post-ACLR. Voxel by voxel T1ρ relaxation times were calculated using a 5-image sequence. The medial and lateral regions of the femoral trochlea and patellar articular cartilage were manually segmented on both extremities. Separate extremity (ACLR and contralateral extremity) by time (6 months and 12 months) analysis of variance tests were performed for each region (P < 0.05). RESULTS: For the medial patella and lateral trochlea, T1ρ relaxation times increased in both extremities between 6 and 12 months post-ACLR (medial patella P = 0.012; lateral trochlea P = 0.043). For the lateral patella, T1ρ relaxation times were significantly greater on the contralateral extremity compared to the ACLR extremity (P = 0.001). The T1ρ relaxation times of the medial trochlea on the ACLR extremity were significantly greater at 6 (P = 0.005) and 12 months (P < 0.001) compared to the contralateral extremity. T1ρ relaxation times of the medial trochlea significantly increased from 6 to 12 months on the ACLR extremity (P = 0.003). CONCLUSION: Changes in T1ρ relaxation times occur within the first 12 months following ACLR in specific regions of the patellofemoral joint on the ACLR and contralateral extremity.

Association of Jump-Landing Biomechanics With Tibiofemoral Articular Cartilage Composition 12 Months After ACL Reconstruction.

BACKGROUND: Excessively high joint loading during dynamic movements may negatively influence articular cartilage health and contribute to the development of posttraumatic osteoarthritis after anterior cruciate ligament reconstruction (ACLR). Little is known regarding the link between aberrant jump-landing biomechanics and articular cartilage health after ACLR. PURPOSE/HYPOTHESIS: The purpose of this study was to determine the associations between jump-landing biomechanics and tibiofemoral articular cartilage composition measured using T1ρ magnetic resonance imaging (MRI) relaxation times 12 months postoperatively. We hypothesized that individuals who demonstrate alterations in jump-landing biomechanics, commonly observed after ACLR, would have longer T1ρ MRI relaxation times (longer T1ρ relaxation times associated with less proteoglycan density). STUDY DESIGN: Cross-sectional study; Level of evidence, 3. METHODS: A total of 27 individuals with unilateral ACLR participated in this cross-sectional study. Jump-landing biomechanics (peak vertical ground-reaction force [vGRF], peak internal knee extension moment [KEM], peak internal knee adduction moment [KAM]) and T1ρ MRI were collected 12 months postoperatively. Mean T1ρ relaxation times for the entire weightbearing medial femoral condyle, lateral femoral condyle (global LFC), medial tibial condyle, and lateral tibial condyle (global LTC) were calculated bilaterally. Global regions of interest were further subsectioned into posterior, central, and anterior regions of interest. All T1ρ relaxation times in the ACLR limb were normalized to the uninjured contralateral limb. Linear regressions were used to determine associations between T1ρ relaxation times and biomechanics after accounting for meniscal/chondral injury. RESULTS: Lower ACLR limb KEM was associated with longer T1ρ relaxation times for the global LTC (ΔR 2 = 0.24; P = .02), posterior LTC (ΔR 2 = 0.21; P = .03), and anterior LTC (ΔR 2 = 0.18; P = .04). Greater ACLR limb peak vGRF was associated with longer T1ρ relaxation times for the global LFC (ΔR 2 = 0.20; P = .02) and central LFC (ΔR 2 = 0.15; P = .05). Peak KAM was not associated with T1ρ outcomes. CONCLUSION: At 12 months postoperatively, lower peak KEM and greater peak vGRF during jump landing were related to longer T1ρ relaxation times, suggesting worse articular cartilage composition.

A simultaneous [11C]raclopride positron emission tomography and functional magnetic resonance imaging investigation of striatal dopamine binding in autism.

The social motivation hypothesis of autism posits that autism spectrum disorder (ASD) is characterized by impaired motivation to seek out social experience early in life that interferes with the development of social functioning. This framework suggests that impaired mesolimbic dopamine function underlies compromised responses to social rewards in ASD. Although this hypothesis is supported by functional magnetic resonance imaging (fMRI) studies, no molecular imaging study has evaluated striatal dopamine functioning in response to rewards in ASD. Here, we examined striatal functioning during monetary incentive processing in ASD and controls using simultaneous positron emission tomography (PET) and fMRI. Using a bolus + infusion protocol with the D2/D3 dopamine receptor antagonist [11C]raclopride, voxel-wise binding potential (BPND) was compared between groups (controls = 12, ASD = 10) in the striatum. Striatal clusters showing significant between-group BPND differences were used as seeds in whole-brain fMRI general functional connectivity analyses. Relative to controls, the ASD group demonstrated decreased phasic dopamine release to incentives in the bilateral putamen and left caudate, as well as increased functional connectivity between a PET-derived right putamen seed and the precuneus and insula. Within the ASD group, decreased phasic dopamine release in the putamen was related to poorer theory-of-mind skills. Our findings that ASD is characterized by impaired striatal phasic dopamine release to incentives provide support for the social motivation hypothesis of autism. PET-fMRI may be a suitable tool to evaluate novel ASD therapeutics targeting the striatal dopamine system.

Gait Mechanics and T1ρ MRI of Tibiofemoral Cartilage 6 Months after ACL Reconstruction.

PURPOSE: Aberrant walking biomechanics after anterior cruciate ligament reconstruction (ACLR) are hypothesized to be associated with deleterious changes in knee cartilage. T1ρ magnetic resonance imaging (MRI) is sensitive to decreased proteoglycan density of cartilage. Our purpose was to determine associations between T1ρ MRI interlimb ratios (ILR) and walking biomechanics 6 months after ACLR. METHODS: Walking biomechanics (peak vertical ground reaction force (vGRF), vGRF loading rate, knee extension moment, knee abduction moment) were extracted from the first 50% of stance phase in 29 individuals with unilateral ACLR. T1ρ MRI ILR (ACLR limb/uninjured limb) was calculated for regions of interest in both medial and lateral femoral (LFC) and medial and lateral tibial condyles. Separate, stepwise linear regressions were used to determine associations between biomechanical outcomes and T1ρ MRI ILR after accounting for walking speed and meniscal/chondral injury (P ≤ 0.05). RESULTS: Lesser peak vGRF in the ACLR limb was associated with greater T1ρ MRI ILR for the LFC (posterior ΔR = 0.14, P = 0.05; central ΔR = 0.15, P = 0.05) and medial femoral condyle (central ΔR = 0.24, P = 0.01). Lesser peak vGRF loading rate in the ACLR limb (ΔR = 0.21, P = 0.02) and the uninjured limb (ΔR = 0.27, P = 0.01) was associated with greater T1ρ MRI ILR for the anterior LFC. Lesser knee abduction moment for the injured limb was associated with greater T1ρ MRI ILR for the anterior LFC (ΔR = 0.16, P = 0.04) as well as the posterior medial tibial condyle (ΔR = 0.13, P = 0.04). CONCLUSION: Associations between outcomes related to lesser mechanical loading during walking and greater T1ρ MRI ILR were found 6 months after ACLR. Although preliminary, our results suggest that underloading of the ACLR limb at 6 months after ACLR may be associated with lesser proteoglycan density in the ACLR limb compared with the uninjured limb.

3D Auto-Context-Based Locality Adaptive Multi-Modality GANs for PET Synthesis.

Positron emission tomography (PET) has been substantially used recently. To minimize the potential health risk caused by the tracer radiation inherent to PET scans, it is of great interest to synthesize the high-quality PET image from the low-dose one to reduce the radiation exposure. In this paper, we propose a 3D auto-context-based locality adaptive multi-modality generative adversarial networks model (LA-GANs) to synthesize the high-quality FDG PET image from the low-dose one with the accompanying MRI images that provide anatomical information. Our work has four contributions. First, different from the traditional methods that treat each image modality as an input channel and apply the same kernel to convolve the whole image, we argue that the contributions of different modalities could vary at different image locations, and therefore a unified kernel for a whole image is not optimal. To address this issue, we propose a locality adaptive strategy for multi-modality fusion. Second, we utilize 1 ×1 ×1 kernel to learn this locality adaptive fusion so that the number of additional parameters incurred by our method is kept minimum. Third, the proposed locality adaptive fusion mechanism is learned jointly with the PET image synthesis in a 3D conditional GANs model, which generates high-quality PET images by employing large-sized image patches and hierarchical features. Fourth, we apply the auto-context strategy to our scheme and propose an auto-context LA-GANs model to further refine the quality of synthesized images. Experimental results show that our method outperforms the traditional multi-modality fusion methods used in deep networks, as well as the state-of-the-art PET estimation approaches.

Quadriceps weakness associates with greater T1ρ relaxation time in the medial femoral articular cartilage 6 months following anterior cruciate ligament reconstruction.

PURPOSE: Quadriceps weakness following anterior cruciate ligament reconstruction (ACLR) is linked to decreased patient-reported function, altered lower extremity biomechanics and tibiofemoral joint space narrowing. It remains unknown if quadriceps weakness is associated with early deleterious changes to femoral cartilage composition that are suggestive of posttraumatic osteoarthritis development. The purpose of the cross-sectional study was to determine if quadriceps strength was associated with T1ρ relaxation times, a marker of proteoglycan density, of the articular cartilage in the medial and lateral femoral condyles 6 months following ACLR. It is hypothesized that individuals with weaker quadriceps would demonstrate lesser proteoglycan density. METHODS: Twenty-seven individuals (15 females, 12 males) with a patellar tendon autograft ACLR underwent isometric quadriceps strength assessments in 90°of knee flexion during a 6-month follow-up exam. Magnetic resonance images (MRI) were collected bilaterally and voxel by voxel T1ρ relaxation times were calculated using a five-image sequence and a monoexponential equation. Following image registration, the articular cartilage for the weight-bearing surfaces of the medial and lateral femoral condyles (MFC and LFC) were manually segmented and further sub-sectioned into posterior, central and anterior regions of interest (ROI) based on the corresponding meniscal anatomy viewed in the sagittal plane. Univariate linear regression models were used to determine the association between quadriceps strength and T1ρ relaxation times in the entire weight-bearing MFC and LFC, as well as the ROI in each respective limb. RESULTS: Lesser quadriceps strength was significantly associated with greater T1ρ relaxation times in the entire weight-bearing MFC (R2 = 0.14, P = 0.05) and the anterior-MFC ROI (R2 = 0.22, P = 0.02) of the ACLR limb. A post hoc analysis found lesser strength and greater T1ρ relaxation times were significantly associated in a subsection of participants (n = 18) without a concomitant medial tibiofemoral compartment meniscal or chondral injury in the entire weight-bearing MFC, as well as anterior-MFC and central-MFC ROI of the ACLR and uninjured limb. CONCLUSIONS: The association between weaker quadriceps and greater T1ρ relaxation times in the MFC suggests deficits in lower extremity muscle strength may be related to cartilage composition as early as 6 months following ACLR. Maximizing quadriceps strength in the first 6 months following ACLR may be critical for promoting cartilage health early following ACLR. LEVEL OF EVIDENCE: Prognostic level 1.

3D conditional generative adversarial networks for high-quality PET image estimation at low dose.

Positron emission tomography (PET) is a widely used imaging modality, providing insight into both the biochemical and physiological processes of human body. Usually, a full dose radioactive tracer is required to obtain high-quality PET images for clinical needs. This inevitably raises concerns about potential health hazards. On the other hand, dose reduction may cause the increased noise in the reconstructed PET images, which impacts the image quality to a certain extent. In this paper, in order to reduce the radiation exposure while maintaining the high quality of PET images, we propose a novel method based on 3D conditional generative adversarial networks (3D c-GANs) to estimate the high-quality full-dose PET images from low-dose ones. Generative adversarial networks (GANs) include a generator network and a discriminator network which are trained simultaneously with the goal of one beating the other. Similar to GANs, in the proposed 3D c-GANs, we condition the model on an input low-dose PET image and generate a corresponding output full-dose PET image. Specifically, to render the same underlying information between the low-dose and full-dose PET images, a 3D U-net-like deep architecture which can combine hierarchical features by using skip connection is designed as the generator network to synthesize the full-dose image. In order to guarantee the synthesized PET image to be close to the real one, we take into account of the estimation error loss in addition to the discriminator feedback to train the generator network. Furthermore, a concatenated 3D c-GANs based progressive refinement scheme is also proposed to further improve the quality of estimated images. Validation was done on a real human brain dataset including both the normal subjects and the subjects diagnosed as mild cognitive impairment (MCI). Experimental results show that our proposed 3D c-GANs method outperforms the benchmark methods and achieves much better performance than the state-of-the-art methods in both qualitative and quantitative measures.

Locality Adaptive Multi-modality GANs for High-Quality PET Image Synthesis.

Positron emission topography (PET) has been substantially used in recent years. To minimize the potential health risks caused by the tracer radiation inherent to PET scans, it is of great interest to synthesize the high-quality full-dose PET image from the low-dose one to reduce the radiation exposure while maintaining the image quality. In this paper, we propose a locality adaptive multi-modality generative adversarial networks model (LA-GANs) to synthesize the full-dose PET image from both the low-dose one and the accompanying T1-weighted MRI to incorporate anatomical information for better PET image synthesis. This paper has the following contributions. First, we propose a new mechanism to fuse multi-modality information in deep neural networks. Different from the traditional methods that treat each image modality as an input channel and apply the same kernel to convolute the whole image, we argue that the contributions of different modalities could vary at different image locations, and therefore a unified kernel for a whole image is not appropriate. To address this issue, we propose a method that is locality adaptive for multimodality fusion. Second, to learn this locality adaptive fusion, we utilize 1 × 1 × 1 kernel so that the number of additional parameters incurred by our method is kept minimum. This also naturally produces a fused image which acts as a pseudo input for the subsequent learning stages. Third, the proposed locality adaptive fusion mechanism is learned jointly with the PET image synthesis in an end-to-end trained 3D conditional GANs model developed by us. Our 3D GANs model generates high quality PET images by employing large-sized image patches and hierarchical features. Experimental results show that our method outperforms the traditional multi-modality fusion methods used in deep networks, as well as the state-of-the-art PET estimation approaches.

Magnetic Resonance-Derived Improvements in PET Imaging.

Simultaneous PET-MR imaging improves deficiencies in PET images. The primary areas in which magnetic resonance (MR) has been applied to guide PET results are in correction for patient motion and in improving the effects of PET resolution and partial volume averaging. MR-guided motion correction of PET has been applied to respiratory, cardiac, and gross body movements and shown to improve lesion detectability and contrast. Partial volume correction or resolution improvement of PET governed by MR imaging anatomic information improves visualization of structures and quantitative accuracy. Evaluation in clinical applications is needed to determine their true impacts.

Semisupervised Tripled Dictionary Learning for Standard-Dose PET Image Prediction Using Low-Dose PET and Multimodal MRI.

OBJECTIVE: To obtain high-quality positron emission tomography (PET) image with low-dose tracer injection, this study attempts to predict the standard-dose PET (S-PET) image from both its low-dose PET (L-PET) counterpart and corresponding magnetic resonance imaging (MRI). METHODS: It was achieved by patch-based sparse representation (SR), using the training samples with a complete set of MRI, L-PET and S-PET modalities for dictionary construction. However, the number of training samples with complete modalities is often limited. In practice, many samples generally have incomplete modalities (i.e., with one or two missing modalities) that thus cannot be used in the prediction process. In light of this, we develop a semisupervised tripled dictionary learning (SSTDL) method for S-PET image prediction, which can utilize not only the samples with complete modalities (called complete samples) but also the samples with incomplete modalities (called incomplete samples), to take advantage of the large number of available training samples and thus further improve the prediction performance. RESULTS: Validation was done on a real human brain dataset consisting of 18 subjects, and the results show that our method is superior to the SR and other baseline methods. CONCLUSION: This paper proposed a new S-PET prediction method, which can significantly improve the PET image quality with low-dose injection. SIGNIFICANCE: The proposed method is favorable in clinical application since it can decrease the potential radiation risk for patients.

Multi-Level Canonical Correlation Analysis for Standard-Dose PET Image Estimation.

Positron emission tomography (PET) images are widely used in many clinical applications, such as tumor detection and brain disorder diagnosis. To obtain PET images of diagnostic quality, a sufficient amount of radioactive tracer has to be injected into a living body, which will inevitably increase the risk of radiation exposure. On the other hand, if the tracer dose is considerably reduced, the quality of the resulting images would be significantly degraded. It is of great interest to estimate a standard-dose PET (S-PET) image from a low-dose one in order to reduce the risk of radiation exposure and preserve image quality. This may be achieved through mapping both S-PET and low-dose PET data into a common space and then performing patch-based sparse representation. However, a one-size-fits-all common space built from all training patches is unlikely to be optimal for each target S-PET patch, which limits the estimation accuracy. In this paper, we propose a data-driven multi-level canonical correlation analysis scheme to solve this problem. In particular, a subset of training data that is most useful in estimating a target S-PET patch is identified in each level, and then used in the next level to update common space and improve estimation. In addition, we also use multi-modal magnetic resonance images to help improve the estimation with complementary information. Validations on phantom and real human brain data sets show that our method effectively estimates S-PET images and well preserves critical clinical quantification measures, such as standard uptake value.

Data on biodistribution and radiation absorbed dose profile of a novel (64)Cu-labeled high affinity cell-specific peptide for positron emission tomography imaging of tumor vasculature.

New peptide-based diagnostic and therapeutic approaches hold promise for highly selective targeting of cancer leading to more precise and effective diagnostic and therapeutic modalities. An important feature of these approaches is to reach the tumor tissue while limiting or minimizing the dose to normal organs. In this context, efforts to design and engineer materials with optimal in vivo targeting and clearance properties are important. This Data In Brief article reports on biodistribution and radiation absorbed dose profile of a novel high affinity radiopeptide specific for bone marrow-derived tumor vasculature. Background information on the design, preparation, and in vivo characterization of this peptide-based targeted radiodiagnostic is described in the article "Synthesis and comparative evaluation of novel 64Cu-labeled high affinity cell-specific peptides for positron emission tomography of tumor vasculature" (Merrill et al., 2016) [1]. Here we report biodistribution measurements in mice and calculate the radiation absorbed doses to normal organs using a modified Medical Internal Radiation Dosimetry (MIRD) methodology that accounts for physical and geometric factors and cross-organ beta doses.

Synthesis and comparative evaluation of novel (64)Cu-labeled high affinity cell-specific peptides for positron emission tomography imaging of tumor vasculature.

Tumor angiogenesis, the formation of new tumor blood supply, has been recognized as a hallmark of cancer and represents an important target for clinical management of various angiogenesis-dependent solid tumors. Previously, by screening a bacteriophage peptide library we have discovered the FHT-peptide sequence that binds specifically to bone marrow-derived tumor vasculature with high affinity. Here in an effort to determine the potential of the FHT-peptide for in vivo positron emission tomography (PET) imaging of aggressive tumor vasculature we studied four FHT-derivatives: NOTA-FHT, NOTA-(FHT)2, NOTA-PEG-FHT, and NOTA-PEG-(FHT)2. These peptide analogs were synthesized, labeled with the PET radionuclide (64)Cu, and characterized side-by-side with small animal PET and computed tomography imaging (microPET/CT) at 1 h, 4 h, and 24 h post injection in a subcutaneous Lewis lung carcinoma (LLC) tumor model. Because of its excellent in vivo kinetic properties and high tumor-to-background ratio, the (64)Cu-NOTA-FHT radiopeptide was selected for more detailed evaluation. Blocking studies with excess of unlabeled peptide showed specific and peptide mediated (64)Cu-NOTA-FHT tumor uptake. Biodistribution experiments in the same tumor model confirmed microPET/CT imaging results. Human radiation absorbed dose extrapolated from rodent biodistribution of (64)Cu-NOTA-FHT revealed favorable dosimetry profile. The findings from this investigation warrant further development of (64)Cu-NOTA-FHT as a potential targeted diagnostic radiopharmaceutical for PET imaging of aggressive tumor vasculature.

Predicting standard-dose PET image from low-dose PET and multimodal MR images using mapping-based sparse representation.

Positron emission tomography (PET) has been widely used in clinical diagnosis for diseases and disorders. To obtain high-quality PET images requires a standard-dose radionuclide (tracer) injection into the human body, which inevitably increases risk of radiation exposure. One possible solution to this problem is to predict the standard-dose PET image from its low-dose counterpart and its corresponding multimodal magnetic resonance (MR) images. Inspired by the success of patch-based sparse representation (SR) in super-resolution image reconstruction, we propose a mapping-based SR (m-SR) framework for standard-dose PET image prediction. Compared with the conventional patch-based SR, our method uses a mapping strategy to ensure that the sparse coefficients, estimated from the multimodal MR images and low-dose PET image, can be applied directly to the prediction of standard-dose PET image. As the mapping between multimodal MR images (or low-dose PET image) and standard-dose PET images can be particularly complex, one step of mapping is often insufficient. To this end, an incremental refinement framework is therefore proposed. Specifically, the predicted standard-dose PET image is further mapped to the target standard-dose PET image, and then the SR is performed again to predict a new standard-dose PET image. This procedure can be repeated for prediction refinement of the iterations. Also, a patch selection based dictionary construction method is further used to speed up the prediction process. The proposed method is validated on a human brain dataset. The experimental results show that our method can outperform benchmark methods in both qualitative and quantitative measures.