CT Fusion Research Articles

Robotic Needle Insertion With 2D Ultrasound–3D CT Fusion Guidance

Robotic Needle Insertion With 2D Ultrasound–3D CT Fusion Guidance

HBANet: A hybrid boundary-aware attention network for infrared and visible image fusion

Infrared and visible image fusion is an extensively investigated problem in infrared image processing, aiming to extract useful information from source images. However, the automatic fusion of these images presents a significant challenge due to the large domain difference and ambiguous boundaries. In this article, we propose a novel image fusion approach based on hybrid boundary-aware attention, termed HBANet, which models global dependencies across the image and leverages boundary-wise prior knowledge to supplement local details. Specifically, we design a novel mixed boundary-aware attention module that is capable of leveraging spatial information to the fullest extent and integrating long dependencies across different domains. To preserve the integrity of texture and structural information, we introduced a sophisticated loss function that comprises structure, intensity, and variation losses. Our method has been demonstrated to outperform state-of-the-art methods in terms of both visual and quantitative metrics, in our experiments on public datasets. Furthermore, our approach also exhibits great generalization capability, achieving satisfactory results in CT and MRI image fusion tasks.

CT and MRI Image Fusion via Coupled Feature-Learning GAN

The fusion of multimodal medical images, particularly CT and MRI, is driven by the need to enhance the diagnostic process by providing clinicians with a single, comprehensive image that encapsulates all necessary details. Existing fusion methods often exhibit a bias towards features from one of the source images, making it challenging to simultaneously preserve both structural information and textural details. Designing an effective fusion method that can preserve more discriminative information is therefore crucial. In this work, we propose a Coupled Feature-Learning GAN (CFGAN) to fuse the multimodal medical images into a single informative image. The proposed method establishes an adversarial game between the discriminators and a couple of generators. First, the coupled generators are trained to generate two real-like fused images, which are then used to deceive the two coupled discriminators. Subsequently, the two discriminators are devised to minimize the structural distance to ensure the abundant information in the original source images is well-maintained in the fused image. We further empower the generators to be robust under various scales by constructing a discriminative feature extraction (DFE) block with different dilation rates. Moreover, we introduce a cross-dimension interaction attention (CIA) block to refine the feature representations. The qualitative and quantitative experiments on common benchmarks demonstrate the competitive performance of the CFGAN compared to other state-of-the-art methods.

Catamaran SI Joint Fusion System(R) MAINSAILTM Study: a prospective, single-arm, multi-center, post-market study of six-month clinical outcomes and twelve-month radiographic findings

ABSTRACT Background Minimally invasive surgical techniques for sacroiliac joint (SIJ) fixation have the potential to reduce risk and improve patient outcomes, but evidence remains limited. This interim analysis presents initial findings from an ongoing prospective study evaluating the safety and efficacy of the Catamaran System. Methods The primary endpoint of success at 6 months was defined as a ≥20 mm improvement in SIJ pain (Visual Analog Scale, VAS), no neurologic worsening, absence of device-related serious adverse events (SAEs), and no surgical reintervention. Secondary endpoints included 6 month evaluation of the Oswestry Disability Index (ODI), patient satisfaction, and 12 month radiographic CT fusion, performed by an indpendent radiologist. Results Thirty-three consecutive patients (mean age: 58.9 years; %-females: 76%; Body Mass Index: 30.5) were treated across six U.S. clinical sites. At the primary endpoint of 6 months, 80% of patients met the criteria for success, with no device-related SAEs and no surgical reintervention reported. VASSIJ-Pain significantly decreased from preoperative levels (mean: 80.9 mm) to 6 months postoperatively (31.1 mm; p < 0.001). Mean ODI scores also showed a significant improvement from preoperative values (51.9%) to 6 months postoperatively (29.6%, p < 0.01). Patients reported high satisfaction rates throughout all follow-ups, with 93.3% of patients being satisfied at 6 months. Conclusion In patients diagnosed with chronic SIJ pain, minimally invasive inferior-posterior delivery of the Catamaran implant was safe and effective in relieving pain and reducing disability.

3D-visualization of segmented contacts of directional deep brain stimulation electrodes via registration and fusion of CT and FDCT

Objectives3D-visualization of the segmented contacts of directional deep brain stimulation (DBS) electrodes is desirable since knowledge about the position of every segmented contact could shorten the timespan for electrode programming. CT cannot yield images fitting that purpose whereas highly resolved flat detector computed tomography (FDCT) can accurately image the inner structure of the electrode. This study aims to demonstrate the applicability of image fusion of highly resolved FDCT and CT to produce highly resolved images that preserve anatomical context for subsequent fusion to preoperative MRI for eventually displaying segmented contactswithin anatomical context in future studies.Material and methodsRetrospectively collected datasets from 15 patients who underwent bilateral directional DBS electrode implantation were used. Subsequently, after image analysis, a semi-automated 3D-registration of CT and highly resolved FDCT followed by image fusion was performed. The registration accuracy was assessed by computing the target registration error.ResultsOur work demonstrated the feasibility of highly resolved FDCT to visualize segmented electrode contacts in 3D. Semiautomatic image registration to CT was successfully implemented in all cases. Qualitative evaluation by two experts revealed good alignment regarding intracranial osseous structures. Additionally, the average for the mean of the target registration error over all patients, based on the assessments of two raters, was computed to be 4.16 mm.ConclusionOur work demonstrated the applicability of image fusion of highly resolved FDCT to CT for a potential workflow regarding subsequent fusion to MRI in the future to put the electrodes in an anatomical context.

Impact of clinically relevant posthepatectomy liver failure predicted by preoperative evaluation of functional remnant hepatic reserve

Abstract Background: Few studies have investigated the remnant hepatic functional reserve before hepatectomy by calculating the functional remnant liver rate (RLR) using threedimensional computed tomography (3D-CT)/technetium-99m-diethylenetriaminepentaacetic acid galactosyl human serum albumin (99mTc-GSA) single-photon emission CT (SPECT) fusion imaging. We aimed to preoperatively evaluate the predictive value of functional remnant liver rate (RLR) and indocyanine green (ICG) disappearance rate (KICG) in determining the occurrence of posthepatectomy liver failure (PHLF). Summary of Background Data: The conventional method of volumetric rem-KICG calculated from remnant liver volume and the KICG is difficult to accurately reflect heterogenous remnant liver function. Methods: In total, 106 patients who underwent major hepatectomy were retrospectively analysed. Of these, 24 (22.6%) developed clinically relevant PHLF grades B/C. We examined the ICG retention rate at 15 min (ICGR15) and KICG and constructed a 3DCT/ 99mTc-GSA SPECT fusion image to calculate the volumetric RLR, functional RLR, volumetric rem-KICG, and functional rem-KICG. Results: The multivariate analysis showed functional rem-KICG as the strongest independent risk factor for PHLF grade B/C. The functional-to-volumetric RLR ratios in the patients with portal vein obstruction and/or tumor volume of ≥500 mL was significantly higher. The volumetric rem-KICG determined that hepatectomy was unsafe in 7 patients, whereas the functional rem-KICG determined that it was unsafe in 3 patients. Conclusions: Functional rem-KICG was more reliable than volumetric rem-KICG in predicting clinically relevant PHLF grade B/C, as the resected side’s hepatic status highly influenced the function of the remnant liver. This finding could lead to a wider application of this technique.

Evaluation of 3D C-Arm Fluoroscopy versus Diagnostic CT for Deep Brain Stimulation Stereotactic Registration and Post-Operative Lead Localization

Introduction: DBS efficacy depends on accuracy. CT-MRI fusion is established for both stereotactic registration and electrode placement verification. The desire to streamline DBS workflows, reduce operative time, and minimize patient transfers has increased interest in portable imaging modalities such as the Medtronic O-arm® and mobile CT. However, these remain expensive and bulky. 3D C-arm fluoroscopy (3DXT) units are a smaller and less costly alternative, albeit incompatible with traditional frame-based localization and without useful soft tissue resolution. We aimed to compare fusion of 3DXT and CT with pre-operative MRI to evaluate if 3DXT-MRI fusion alone is sufficient for accurate registration and reliable targeting verification. We further assess DBS targeting accuracy using a 3DXT workflow and compare radiation dosimetry between modalities. Methods: Patients underwent robot-assisted DBS implantation using a workflow incorporating 3DXT which we describe. Two intra-operative 3DXT spins were performed for registration and accuracy verification followed by conventional CT post-operatively. Post-operative 3DXT and CT images were independently fused to the same pre-operative MRI sequence and co-ordinates generated for comparison. Registration accuracy was compared to 15 consecutive controls who underwent CT-based registration. Radial targeting accuracy was calculated and radiation dosimetry recorded. Results: Data were obtained from 29 leads in 15 consecutive patients. 3DXT registration accuracy was significantly superior to CT with mean error 0.22 ± 0.03 mm (p < 0.0001). Mean Euclidean electrode tip position variation for CT to MRI versus 3DXT to MRI fusion was 0.62 ± 0.40 mm (range 0.0 mm–1.7 mm). In comparison, direct CT to 3DXT fusion showed electrode tip Euclidean variance of 0.23 ± 0.09 mm. Mean radial targeting accuracy assessed on 3DXT was 0.97 ± 0.54 mm versus 1.15 ± 0.55 mm on CT with differences insignificant (p = 0.30). Mean patient radiation doses were around 80% lower with 3DXT versus CT (p < 0.0001). Discussion: Mobile 3D C-arm fluoroscopy can be safely incorporated into DBS workflows for both registration and lead verification. For registration, the limited field of view requires the use of frameless transient fiducials and is highly accurate. For lead position verification based on MRI co-registration, we estimate there is around a 0.4 mm discrepancy between lead position seen on 3DXT versus CT when corrected for brain shift. This is similar to that described in O-arm® or mobile CT series. For units where logistical or financial considerations preclude the acquisition of a cone beam CT or mobile CT scanner, our data support portable 3D C-arm fluoroscopy as an acceptable alternative with significantly lower radiation exposure.

CT and MRI image fusion via multimodal feature interaction network

CT and MRI image fusion via multimodal feature interaction network

Segmentation-Based Fusion of CT and MR Images.

In this paper, a segmentation-based image fusion method is proposed for the fusion of MR and CT images to obtain a high contrast fused image that contains complementary information from both input images. The proposed method uses the fuzzy C-mean method to extract information about the skull from the CT image. This skull information is used to extract soft tissue information from the MR image. Both the skull information and the soft tissue information are then fused using the fusion rule. The efficiency of the proposed method over other state-of-the-art fusion methods is analyzed and compared using qualitative and quantitative analysis methods. Qualitative analysis shows the improvement in the contrast between the bone and the soft tissue using the proposed method over other state-of-the-art methods without introducing any artifacts or distortions. Classical and gradient-based quantitative analysis also show significant improvement in the fused image obtained using the proposed method over the five state-of-the-art methods. The percentage improvement in the standard deviation, average gradient, entropy, spatial frequency, QABF, and LABF of the proposed method over the best value obtained by the five state-of-the-art methods is 27.11%, 12.06%, 23.64%, 11.30%, 5.59%, and 13.70% respectively.

Development and validation of survival prognostic models for head and neck cancer patients using machine learning and dosiomics and CT radiomics features: a multicentric study

BackgroundThis study aimed to investigate the value of clinical, radiomic features extracted from gross tumor volumes (GTVs) delineated on CT images, dose distributions (Dosiomics), and fusion of CT and dose distributions to predict outcomes in head and neck cancer (HNC) patients.MethodsA cohort of 240 HNC patients from five different centers was obtained from The Cancer Imaging Archive. Seven strategies, including four non-fusion (Clinical, CT, Dose, DualCT-Dose), and three fusion algorithms (latent low-rank representation referred (LLRR),Wavelet, weighted least square (WLS)) were applied. The fusion algorithms were used to fuse the pre-treatment CT images and 3-dimensional dose maps. Overall, 215 radiomics and Dosiomics features were extracted from the GTVs, alongside with seven clinical features incorporated. Five feature selection (FS) methods in combination with six machine learning (ML) models were implemented. The performance of the models was quantified using the concordance index (CI) in one-center-leave-out 5-fold cross-validation for overall survival (OS) prediction considering the time-to-event.ResultsThe mean CI and Kaplan-Meier curves were used for further comparisons. The CoxBoost ML model using the Minimal Depth (MD) FS method and the glmnet model using the Variable hunting (VH) FS method showed the best performance with CI = 0.73 ± 0.15 for features extracted from LLRR fused images. In addition, both glmnet-Cindex and Coxph-Cindex classifiers achieved a CI of 0.72 ± 0.14 by employing the dose images (+ incorporated clinical features) only.ConclusionOur results demonstrated that clinical features, Dosiomics and fusion of dose and CT images by specific ML-FS models could predict the overall survival of HNC patients with acceptable accuracy. Besides, the performance of ML methods among the three different strategies was almost comparable.

MRI and CT Fusion in Stereotactic Electroencephalography (SEEG).

Epilepsy is a neurological disorder characterized by spontaneous recurrent seizures. While 20% to 30% of epilepsy cases are untreatable with Anti-Epileptic Drugs, some of these cases can be addressed through surgical intervention. The success of such interventions greatly depends on accurately locating the epileptogenic tissue, a task achieved using diagnostic techniques like Stereotactic Electroencephalography (SEEG). SEEG utilizes multi-modal fusion to aid in electrode localization, using pre-surgical resonance and post-surgical computer tomography images as inputs. To ensure the absence of artifacts or misregistrations in the resultant images, a fusion method that accounts for electrode presence is required. We proposed an image fusion method in SEEG that incorporates electrode segmentation from computed tomography as a sampling mask during registration to address the fusion problem in SEEG. The method was validated using eight image pairs from the Retrospective Image Registration Evaluation Project (RIRE). After establishing a reference registration for the MRI and identifying eight points, we assessed the method's efficacy by comparing the Euclidean distances between these reference points and those derived using registration with a sampling mask. The results showed that the proposed method yielded a similar average error to the registration without a sampling mask, but reduced the dispersion of the error, with a standard deviation of 0.86 when a mask was used and 5.25 when no mask was used.

Opt_att-GANC: Fusion of CT and PET images using an optimal attention-based generative adversarial network with classifier for lung cancer classification

Lung cancer incidence and mortality continue to rise rapidly around the world. According to the American Cancer Society, the five-year survivability for individuals in the metastasis phases is significantly lower, highlighting the importance of early lung cancer diagnosis for effective therapy and improved quality of life. To achieve this, it is crucial to combine PET’s sensitivity for recognizing abnormal regions with CT’s anatomical localization for evaluating PET-CT images in computer-assisted detection implementations. Current PET-CT image evaluation methods either run each modality independently or aggregate the data from both, but they often overlook the fact that different visual features encode different types of data from different modalities. For instance, high atypical PET uptake within the lungs is more crucial for identifying tumors compared to physical PET uptake in the heart. To address the challenges of fine-grained issues during feature extraction and fusion, we propose an interpretable deep learning-based solution for lung cancer diagnosis using CT and PET images. This involves building an Optimal Adversarial Network for merging and an Optimal Attention-based Generative Adversarial Network with Classifier (Opt_att-GANC) to augment the classification of the existence and nonexistence of lung cancer based on extracted features. The performance of the Opt_att-GANC is compared with existing methodologies like global-feature encoding U-Net (GEU-Net), 3D Dense-Net, and 3D Convolutional Neural Network Technique (3D-CNN). Results show that the proposed Opt_att-GANC achieves an F1-score of 67.08%, 93.74% accuracy, 92% precision, 92.1% recall, and 93.74% recall. The prospective study aims to enhance the precision degree with reduced duration by incorporating an ensemble neural network paradigm for feature extraction.

Development of a 3D-printed, patient-specific stereotactic system for bihemispheric deep brain stimulation

The aim of the project was to develop a patient-specific stereotactic system that allows simultaneous and thus time-saving treatment of both cerebral hemispheres and that contains all spatial axes and can be used as a disposable product. Furthermore, the goal was to reduce the size and weight of the stereotactic system compared to conventional systems to keep the strain on the patient, who is awake during the operation, to a minimum. In addition, the currently mandatory computed tomography should be avoided in order not to expose the patient to harmful X-ray radiation as well as to eliminate errors in the fusion of CT and MRI data.3D printing best meets the requirements in terms of size and weight: on the one hand, the use of plastic has considerable potential for weight reduction. On the other hand, the free choice of the individual components offers the possibility to optimize the size and shape of the stereotactic system and to adapt it to the individual circumstances while maintaining the same precision. The all-in-one stereotactic system was produced by means of the Multi Jet Fusion process. As a result, the components are highly precise, stable in use, lightweight and sterilizable. The number of individual components and interfaces, which in their interaction are potential sources of error, was significantly reduced. In addition, on-site manufacturing leads to faster availability of the system.Within the project, a patient-specific stereotaxy system was developed, printed, and assembled, which enables the execution of deep brain stimulation via only three bone anchors located on the skull. Pre-developed MRI markers, which can be screwed directly onto the bone anchors via the sleeves, eliminate the need for a CT scan completely. The fusion of the data, which is no longer required, suggests an improvement in target accuracy.

CT and MRI image fusion based on variance and pixel significance

In the medical imaging field, images are obtained using various modalities, including the computed tomography procedure and the magnetic resonance imaging procedure. In which every image contains different information from the other image. For better treatment and diagnosis of a patient, a single composite image must be created by fusing all the pertinent data. This process is known as image fusion. We present an innovative and effective image fusion technique utilizing ILWT and DCT for combining brain-related medical images acquired through there are two steps to this strategy. First, the variance is employed as a contrast evaluation in the DCT domain to combine the approximation coefficients generated by the ILWT decomposition. Second, the coefficients representing the fine details are combined by finding the ideal weighted average based on the importance of the pixels in the ILWT domain. Our method is straightforward, making it simple and suitable for deployment in real-time applications. The experimental results demonstrate our method's outstanding performance with regard to both result quality and in contrast to a number of picture fusion techniques.

ADDNS: An asymmetric dual deep network with sharing mechanism for medical image fusion of CT and MR-T2

Medical images with different modalities have different semantic characteristics. Medical image fusion aiming to promotion of the visual quality and practical value has become important in medical diagnostics. However, the previous methods do not fully represent semantic and visual features, and the model generalization ability needs to be improved. Furthermore, the brightness-stacking phenomenon is easy to occur during the fusion process. In this paper, we propose an asymmetric dual deep network with sharing mechanism (ADDNS) for medical image fusion. In our asymmetric model-level dual framework, primal Unet part learns to fuse medical images of different modality into a fusion image, while dual Unet part learns to invert the fusion task for multi-modal image reconstruction. This asymmetry of network settings not only enables the ADDNS to fully extract semantic and visual features, but also reduces the model complexity and accelerates the convergence. Furthermore, the sharing mechanism designed according to task relevance also reduces the model complexity and improves the generalization ability of our model. In the end, we use the intermediate supervision method to minimize the difference between fusion image and source images so as to prevent the brightness-stacking problem. Experimental results show that our algorithm achieves better results on both quantitative and qualitative experiments than several state-of-the-art methods.

Prospective Evaluation of Rate of Fusion with Augment Injectable Bone Graft

Category: Hindfoot; Basic Sciences/Biologics Introduction/Purpose: Augment® Bone Graft is a mixture of rhPDGF-BB, beta-TCP and bovine collagen which has received FDA approval for use as a substitute for autografts during hindfoot and/or ankle arthrodesis. Although a recent meta analysis demonstrated that the rhPDGF-BB and beta-TCP mixture has comparable CT fusion rates and radiographic union rates to those of autografts, these studies excluded patients with prior joint site arthrodesis. In our study, we specifically use Augment® Injectable Bone Graft (AIBG) which has only very recently received labeling change approval. The goals of our study are to assess fusions rates in patients who receive AIBG during hindfoot and/or ankle arthrodesis, and then subsequently compare fusion rates between primary vs. revision hindfoot and/or ankle arthrodesis as primary outcomes. Methods: We prospectively enrolled patients aged 18-85 who underwent subtalar, calcaneocuboid, talonavicular, and/or ankle arthrodesis from one of two academic surgeons including both primary and revisions fusions. The patients received AIBG and were excluded if any other graft material was used. Demographic data and patient reported outcomes were collected preoperatively and at follow-up visits including Visual Analog Pain Scale (VAS), Foot Function Index (FFI), Foot and Ankle Ability Measure (FAAM) Sports Subscale, and Veterans Rand 12 (VR-12) physical and mental subscores. Patients were followed at 2 weeks, 6 weeks, 3 months, 6 months, 1 year, and 2 years postoperatively. The primary outcome measure was fusion rate via CT scan at 4 months with secondary outcomes consisting of the patient reported outcome data and rate of revisions. Results: 62 patients met inclusion criteria, 50 patients had a 4-month postoperative CT scan to assess for fusion with 78% of the CT scans (39 out of 50) demonstrating fusion at that time. There were only 3 revision fusion patients, with 1 of the 3 failing to fuse. 54 of the patients were followed through their 6 month visits, 44 patients through 1 year, and 24 patients were seen for their 2-year follow-up visit. All patient reported outcomes demonstrated improvement from preoperatively to final follow-up with VAS scores improving from 5.52 to 3.21, FFI improving from 57.87 to 35.64, FAAM-sports improving from 18.65 to 29.30, VR-12 Physical improving from 28.67 to 35.18, and VR-12 Mental improving from 44.27 to 47.74. Conclusion: This prospective study demonstrates that Augment® Injectable Bone Graft is an acceptable alternative to allograft for either primary or revision hindfoot arthrodesis procedures with 78% of patients achieving fusion on a 4-month postoperative CT scan and with steady noted improvement in multiple validated patient-reported outcome measures.

Successful Limb Salvage Reconstruction Using Bulk Femoral Head Allograft

Category: Ankle Introduction/Purpose: The use of femoral head allografts in salvage surgeries for lower extremity reconstruction help address surgical problems of large bone deficits. There are very few publications and mixed results in the literature as to the outcomes of this salvage procedure. These limb salvage procedures involve significant surgical complexity with the end goal of retaining a stable, brace-able lower extremity. The purpose of this retrospective review is to report the results and utility of bulk femoral head allograft for limb salvage including time to weight bearing, CT confirmed fusion time, reoperation rates, and ultimate limb salvage success. Additionally, this article categorizes femoral head surgical preparation, adjunct biologics use, fixation constructs, and dissection techniques used. Methods: Patients who underwent reconstructive limb salvage surgery using femoral head allografts over a 5-year period (2015- 2020) were identified. The surgeries were among 5 different fellowship-trained surgeons at a single institution. Indications included post-traumatic AVN, Charcot ankle, revisional surgery, and failed total ankle replacement. Femoral heads were used in either ankle fusions alone or in tibiotalocalcaneal fusion. Time to weight bearing, CT fusion time, reoperations, and ultimate limb salvage were recorded. Reoperations were reported minor if involved partial HWR, superficial I&amp;Ds, or exostosis/HO removal. Major reoperations were defined as full revision surgery/failure of femoral head construct, placement of antibiotic rods for deep infection, or BKA. Limb salvage was defined as being successful if the patient was walking with or without bracing at the last follow-up. Results: Twenty-three cases were identified with 13 performed for failed total ankle replacements, 7 post-traumatic avascular necrosis of the talus, and 3 neuropathic Charcot ankles. The average radiographic follow-up time was 665 days with an average time to weight bearing of 86.48 days. 18/23 patients had CT confirmation of femoral graft union with an average time to union of 461.92 days. 9 patients were found to have less than 50% graft incorporation at the last follow-up. There were 9 reoperations recorded, 8 minor and 1 major. All patients were chart documented stable with independent weight bearing either with or without a brace on the last clinic follow-up. Conclusion: Limb salvage in cases of large bone defects can be very challenging. We report a high success rate of limb salvage using bulk femoral head allograft. Complete fusion of bulk femoral head allograft although desirable, was not necessary to maintain limb salvage in this retrospective study. Patients were able to weight bear and maintain the integrity of the bulk allograft despite the lengthy time to achieve fusion in cases where fusion was achieved. This study highlights that bulk femoral head allograft can offer excellent salvage options with a high degree of success in complex limb salvage reconstruction.

Real-Time Ultrasound-Computed Tomography Fusion with Volume Navigation to Assess Pancreatic Cystic Lesions.

Transabdominal ultrasound is a promising imaging modality for pancreatic cystic lesions. This study aims to determine if transabdominal ultrasonography with CT fusion (TAUS-f) using volume navigation can be used to measure pancreatic cystic lesions (PCLs) compared to CT alone. We evaluated 33 patients prospectively with known PCLs. The readers evaluated each PCL's size and imaging characteristics on TAUS-f and CT alone. These were compared to endoscopic ultrasonography reports. A total of 43 PCLs from 32 patients were evaluated. The detection rate by TAUS-f was 93%. Two of the three undetected PCLs were in the tail of the pancreas. Inter-reader variabilities for TAUS-f and CT were 0.005 cm and 0.03 cm, respectively. Subgroup analysis by size and location demonstrated that inter-modality variability between TAUS-f and CT was smallest for lesions < 1.5 cm with a size difference of -0.13 cm for each reader and smallest in the pancreatic head with a size difference of -0.16 cm and -0.17 cm for readers 1 and 2. We found that TAUS-f effectively evaluates PCLs compared to CT alone, thus suggesting that it should be considered part of the surveillance algorithm for a subset of patients.

The value of bone SPECT/CT in evaluation of foot and ankle arthrodesis and adjacent joint secondary osteoarthritis.

To evaluate the value of SPECT/CT (single photon emission computed tomography/computed tomography) in foot and ankle arthrodesis and development of secondary osteoarthritis in the adjacent joints. SPECT/CT of 140 joints in the foot and ankle (34 upper ankle (UA), 28 lower ankle (LA), 27 talonavicular (TN), 12 calcaneo-cuboidal (CC), and 39 other smaller joints after arthrodesis in 72 patients were evaluated retrospectively regarding fusion grade in CT (0 = no fusion, 1 = < 50% fusion, 2 = > 50% fusion, 3 = complete fusion) and radiotracer uptake (0 = no uptake, 1 = mild uptake, 2 = moderate uptake, 3 = high uptake) on SPECT/CT. Severity of osteoarthritis (1 = mild, 2 = moderate, 3 = severe) and radiotracer uptake grade in adjacent joints was also assessed. In 54 patients, clinical information about interventions in the follow-up was available. According to the SPECT/CT, arthrodesis was successful (grade 2 or 3 CT fusion and grade 0 or 1 uptake) in 73% (25/34) of UA joints, 71% (20/28) of LA joints, 67% (18/27) TN, 100% (12/12) CC joints, and 62% (24/39) of other smaller joints. In 12 joints, there were discrepant findings in SPECT/CT (fusion grade 2 and uptake grade 2 or 3 (n = 9); or, fusion grade 0 or 1 and uptake grade 1 (n = 3)). The fusion rate 6-12months after arthrodesis was 42% (14/33), 59% (20/34) after 13-24months, and 89% (65/73) after more than 24months, respectively. Average radiotracer uptake in arthrodesis decreased with age: 6-12months: 1.60, 12-24months: 1.32, > 24months: 0.38. There was a significant negative correlation between radiotracer uptake grade and CT fusion grade. Osteoarthritis was observed in 131 adjacent joints. During the post scan follow-up, additional arthrodeses were performed in 33 joints, of which 11 joints were re-arthrodesis and 22 were new arthrodeses in osteoarthritic adjacent joints. All these 11 joints with failed arthrodesis had grade 0 of CT fusion and grade 2 or 3 of radiotracer uptake. All 22 adjacent joints with osteoarthritis, which subsequently underwent arthrodesis, had grade 2 or 3 radiotracer uptake, and the primary arthrodesis joints were healed and fused in all these cases. Bone SPECT/CT is a valuable hybrid imaging tool in the evaluation of foot and ankle arthrodesis and gives additional useful information about the development of secondary osteoarthritis in the adjacent joints with higher value for the assessment of secondary osteoarthritis. A practical four-type classification ('Lucerne Criteria') combining metabolic and morphologic SPECT/CT information for evaluation of arthrodesis joints has been proposed.

PO108: Influence of Intraoperative CT and Immediate Postoperative CT Fusion on Dosimetric Parameters and Efficacy of 125i Particles in the Treatment of Non-Small Cell Lung Cancer

PO108: Influence of Intraoperative CT and Immediate Postoperative CT Fusion on Dosimetric Parameters and Efficacy of 125i Particles in the Treatment of Non-Small Cell Lung Cancer