4DCT Research Articles

Interobserver agreement on definition of the target volume in stereotactic radiotherapy for pancreatic adenocarcinoma using different imaging modalities

PurposeThe aim of this study was to evaluate interobserver agreement (IOA) on target volume definition for pancreatic cancer (PACA) within the Radiosurgery and Stereotactic Radiotherapy Working Group of the German Society of Radiation Oncology (DEGRO) and to identify the influence of imaging modalities on the definition of the target volumes.MethodsTwo cases of locally advanced PACA and one local recurrence were selected from a large SBRT database. Delineation was based on either a planning 4D CT with or without (w/wo) IV contrast, w/wo PET/CT, and w/wo diagnostic MRI. Novel compared to other studies, a combination of four metrics was used to integrate several aspects of target volume segmentation: the Dice coefficient (DSC), the Hausdorff distance (HD), the probabilistic distance (PBD), and the volumetric similarity (VS).ResultsFor all three GTVs, the median DSC was 0.75 (range 0.17–0.95), the median HD 15 (range 3.22–67.11) mm, the median PBD 0.33 (range 0.06–4.86), and the median VS was 0.88 (range 0.31–1). For ITVs and PTVs the results were similar. When comparing the imaging modalities for delineation, the best agreement for the GTV was achieved using PET/CT, and for the ITV and PTV using 4D PET/CT, in treatment position with abdominal compression.ConclusionOverall, there was good GTV agreement (DSC). Combined metrics appeared to allow a more valid detection of interobserver variation. For SBRT, either 4D PET/CT or 3D PET/CT in treatment position with abdominal compression leads to better agreement and should be considered as a very useful imaging modality for the definition of treatment volumes in pancreatic SBRT. Contouring does not appear to be the weakest link in the treatment planning chain of SBRT for PACA.

Assessing carpal kinematics following scapholunate interosseous ligament injury ex vivo using four-dimensional dynamic computed tomography

BackgroundScapholunate interosseous ligament injuries are prevalent and often challenging to diagnose radiographically. Four-dimensional CT allows visualization of carpal bones during motion. We present a cadaveric model of sequential ligamentous sectionings (“injuries”) to quantify their effects on interosseous proximities at the radioscaphoid joint and scapholunate interval. We hypothesized that injury, wrist position, and their interaction affect carpal arthrokinematics. MethodsEight cadaveric wrists were moved through flexion-extension and radioulnar deviation after injuries. Dynamic CT images of each motion were acquired in each injury condition using a second-generation dual-source CT scanner. Carpal osteokinematics were used to calculate arthrokinematic interosseous proximity distributions during motion. Median interosseous proximities were normalized and categorized by wrist position. Linear mixed-effects models and marginal means tests were used to compare distributions of median interosseous proximities. FindingsThe effect of wrist position was significant for both flexion-extension and radioulnar deviation at the radioscaphoid joint; the effect of injury was significant for flexion-extension at the scapholunate interval; and the effect of their interaction was significant for radioulnar deviation at the scapholunate interval. Across wrist positions, radioscaphoid median interosseous proximities were less able to distinguish injury conditions versus scapholunate proximities. Median interosseous proximities at the scapholunate interval are majoritively able to detect differences between less (Geissler I-III) versus more (Geissler IV) severe injuries when the wrist is flexed, extended, and ulnarly-deviated. InterpretationDynamic CT enhances our understanding of carpal arthrokinematics in a cadaveric model of SLIL injury. Scapholunate median interosseous proximities in flexion, extension, and ulnar deviation best demonstrate ligamentous integrity.

The effect of intercarpal arthrodeses on wrist kinematics during radial and ulnar deviation: a cadaveric study using four-dimensional CT.

In this cadaveric study, we report quantitative dynamic four-dimensional CT evaluation of the effect on wrist kinematics of three intercarpal arthrodeses during radial and ulnar deviation. In five wrists, we successively performed scaphocapitate, four-corner and two-corner fusions. Four-dimensional CT examinations were performed prior to dissection and after each arthrodesis. The lunocapitate gap, posterior lunocapitate angle, radiolunate radial gap, radiolunate ulnar gap and radiolunate angle were assessed. After scaphocapitate arthrodesis, in radial deviation, we noted midcarpal diastasis and dorsal displacement of the capitate. In ulnar deviation, there was correction of that incongruence. After four-corner and two-corner fusions, in radial deviation, we noted radial radiolunate impingement and ulnar radiolunate incongruence. In ulnar deviation, after two-corner fusion, ulnar radiolunate impingement and radial radiolunate incongruence were present contrary to four-corner fusion. Our findings confirm that the constant radiocarpal and midcarpal congruence during radioulnar deviation in normal wrists is no longer possible with intercarpal kinematic modifications after these arthrodeses.

4D display of CT LV endocardial and epicardial models morphed from PET Rb-82 perfusion studies accurately quantifies segmental myocardial thickening.

MPI-derived LV wall thickening assessments for diagnostic purposes has been part of clinical guidelines for two decades. It relies on visual evaluation of tomographic slices or regional quantification displayed in 2D polar maps. 4D displays have not entered clinical usage nor have they been validated on their potential to provide equivalent information. The purpose of this work was to validate a 4D realistic display recently designed to quantitatively represent the thickening information from gated MPI into CT-morphed endocardial and epicardial moving surfaces. Forty patients who underwent 82Rb PET were selected based on LV perfusion quantification. CTA templates of heart anatomy were selected to represent the LV anatomy. Generic CT-derived LV endocardial and epicardial surfaces were modified to represent the end diastolic (ED) phase according to PET-derived ED LV dimensions and wall thickness. These CT myocardial surfaces were then morphed by means of thin plate spline (TPS) techniques, according to the gated PET slices count changes (WThPET) and LV wall motion (WMoPET). A geometric thickening (GeoTh) equivalent to LV WThPET was defined on epicardial and endocardial CT surfaces over the cardiac cycle and the two measures compared. WThPET and GeoTh correlations were performed on a case-by-case basis, by segment and by pooling all 17 segments. Pearson's correlation coefficients (PCC) were calculated to assess the equivalence of the two measures. Two cohorts of patients (normal and abnormal) were identified based on SSS. R coefficients were as follows: for all pooled segments PCCstress and PCCrest were respectively 0.91 and 0.89 (normal), and 0.9 and 0.91 (abnormal); when individual 17 segments were considered mean PCCstress = 0.92 [0.81-0.98] and mean PCCrest = 0.93 [0.83-0.98] for the abnormal perfusion group; mean PCCstress = 0.89 [0.78-0.97] and mean PCCrest = 0.89 [0.77-0.97] for the normal. When individual studies were considered, R was always > .70 with the exception of five abnormal studies. Inter-user analysis was also conducted. Our novel technique for the visualization of LV wall thickening by means of 4D CT endocardial and epicardial surface models accurately replicated 82Rb slice thickening results showing promise for its usage for diagnostic purposes.

Abstract #1412272: A Case Report of an Ectopic Mediastinal Parathyroid Adenoma that was Challenging to Locate

Primary hyperparathyroidism from an ectopic parathyroid adenoma can pose diagnostic and management challenges when several different imaging modalities do not localize the adenoma and when it is not found on surgical exploration. We report a case of a 69-year-old Caucasian female with history of primary hyperparathyroidism complicated by the inability to localize the adenoma despite several imaging modalities. She underwent neck exploration twice by two different surgeons who were unable to locate a parathyroid adenoma. She underwent 2 separate parathyroid nuclear medicine scans with single photon-emission computed tomographic (SPECT) of the neck and chest done several years apart. The initial SPECT did not demonstrate a parathyroid adenoma in the neck or ectopically nor did several nuclear medicine scans of the neck. Ultimately, the 2nd SPECT of the neck and chest was able to detect a contrast-enhanced mass in the mediastinum. She was referred to a thoracic surgeon who was able to remove the ectopic parathyroid adenoma robotically. The histopathology of the mediastinal mass was consistent with a parathyroid adenoma. She was discharged home from the hospital on calcium and calcitriol supplementation. Her calcium normalized and she ultimately was able to come off calcium and calcitriol supplementation. Calcium and PTH have remained normal several months after surgery. The incidence of ectopic parathyroid glands is approximately 25%. In primary hyperparathyroidism approximately 6-16% of adenomas are located ectopically. Surgical intervention is the mainstay of therapy with preoperative imaging for localization of the adenoma being the standard of care. 4D CT has approximately 85% sensitivity and Sestamibi/SPECT CT has 65% sensitivity for location of a single adenoma with specificity of 92% and 98% respectively. Despite this, in our case the localization of parathyroid adenoma took 7 years, 4 different scans and 3 surgeries in order to successively localize and resect the ectopic parathyroid adenoma.

Four-Dimensional Cone Beam CT Imaging Using a Single Routine Scan via Deep Learning.

A novel method is proposed to obtain four-dimensional (4D) cone-beam computed tomography (CBCT) images from a routine scan in patients with upper abdominal cancer. The projections are sorted according to the location of the lung diaphragm before being reconstructed to phase-sorted data. A multiscale-discriminator generative adversarial network (MSD-GAN) is proposed to alleviate the severe streaking artifacts in the original images. The MSD-GAN is trained using simulated CBCT datasets from patient planning CT images. The enhanced images are further used to estimate the deformable vector field (DVF) among breathing phases using a deformable image registration method. The estimated DVF is then applied in the motion-compensated ordered-subset simultaneous algebraic reconstruction approach to generate 4D CBCT images. The proposed MSD-GAN is compared with U-Net on the performance of image enhancement. Results show that the proposed method significantly outperforms the total variation regularization-based iterative reconstruction approach and the method using only MSD-GAN to enhance original phase-sorted images in simulation and patient studies on 4D reconstruction quality. The MSD-GAN also shows higher accuracy than the U-Net. The proposed method enables a practical way for 4D-CBCT imaging from a single routine scan in upper abdominal cancer treatment including liver and pancreatic tumors.

A super-voxel-based method for generating surrogate lung ventilation images from CT.

Purpose: This study aimed to develop and evaluate , a super-voxel-based method for surrogate computed tomography ventilation imaging (CTVI). Methods and Materials: The study used four-dimensional CT (4DCT) and single-photon emission computed tomography (SPECT) images and corresponding lung masks from 21 patients with lung cancer obtained from the Ventilation And Medical Pulmonary Image Registration Evaluation dataset. The lung volume of the exhale CT for each patient was segmented into hundreds of super-voxels using the Simple Linear Iterative Clustering (SLIC) method. These super-voxel segments were applied to the CT and SPECT images to calculate the mean density values (D mean) and mean ventilation values (Vent mean), respectively. The final CT-derived ventilation images were generated by interpolation from the D mean values to yield . For the performance evaluation, the voxel- and region-wise differences between and SPECT were compared using Spearman's correlation and the Dice similarity coefficient index. Additionally, images were generated using two deformable image registration (DIR)-based methods, and , and compared with the SPECT images. Results: The correlation between the D mean and Vent mean of the super-voxel was 0.59 ± 0.09, representing a moderate-to-high correlation at the super-voxel level. In the voxel-wise evaluation, the method achieved a stronger average correlation (0.62 ± 0.10) with SPECT, which was significantly better than the correlations achieved with the (0.33 ± 0.14, p < 0.05) and (0.23 ± 0.11, p < 0.05) methods. For the region-wise evaluation, the Dice similarity coefficient of the high functional region for (0.63 ± 0.07) was significantly higher than the corresponding values for the (0.43 ± 0.08, p < 0.05) and (0.42 ± 0.05, p < 0.05) methods. Conclusion: The strong correlation between and SPECT demonstrates the potential usefulness of this novel method of ventilation estimation for surrogate ventilation imaging.

Intraarterial Four-dimensional CT Angiography with Soft Tissue Perfusion Evaluation in Diabetic Feet.

Intraarterial Four-dimensional CT Angiography with Soft Tissue Perfusion Evaluation in Diabetic Feet.

Mitigation of motion-induced artifacts in cone beam computed tomography using deep convolutional neural networks.

Cone beam computed tomography (CBCT) is often employed on radiation therapy treatment devices (linear accelerators) used in image-guided radiation therapy (IGRT). For each treatment session, it is necessary to obtain the image of the day in order to accurately position the patient and to enable adaptive treatment capabilities including auto-segmentation and dose calculation. Reconstructed CBCT images often suffer from artifacts, in particular those induced by patient motion. Deep-learning based approaches promise ways to mitigate such artifacts. We propose a novel deep-learning based approach with the goal to reduce motion induced artifacts in CBCT images and improve image quality. It is based on supervised learning and includes neural network architectures employed as pre- and/or post-processing steps during CBCT reconstruction. Our approach is based on deep convolutional neural networks which complement the standard CBCT reconstruction, which is performed either with the analytical Feldkamp-Davis-Kress (FDK) method, or with an iterative algebraic reconstruction technique (SART-TV). The neural networks, which are based on refined U-net architectures, are trained end-to-end in a supervised learning setup. Labeled training data are obtained by means of a motion simulation, which uses the two extreme phases of 4D CT scans, their deformation vector fields, as well as time-dependent amplitude signals as input. The trained networks are validated against ground truth using quantitative metrics, as well as by using real patient CBCT scans for a qualitative evaluation by clinicalexperts. The presented novel approach is able to generalize to unseen data and yields significant reductions in motion induced artifacts as well as improvements in image quality compared with existing state-of-the-art CBCT reconstruction algorithms (up to +6.3 dB and +0.19 improvements in peak signal-to-noise ratio, PSNR, and structural similarity index measure, SSIM, respectively), as evidenced by validation with an unseen test dataset, and confirmed by a clinical evaluation on real patient scans (up to 74% preference for motion artifact reduction over standard reconstruction). For the first time, it is demonstrated, also by means of clinical evaluation, that inserting deep neural networks as pre- and post-processing plugins in the existing 3D CBCT reconstruction and trained end-to-end yield significant improvements in image quality and reduction of motionartifacts.

Myocardial Regional Shortening from 4D Cardiac CT Angiography for the Detection of Left Ventricular Segmental Wall Motion Abnormality.

To investigate whether endocardial regional shortening computed from four-dimensional (4D) CT angiography (RSCT) can be used as a decision classifier to detect the presence of left ventricular (LV) wall motion abnormalities (WMAs). One hundred electrocardiographically gated cardiac 4D CT studies (mean age, 59 years ± 14 [SD]; 61 male patients) conducted between April 2018 and December 2020 were retrospectively evaluated. Three experts labeled LV wall motion in each of the 16 American Heart Association (AHA) segments as normal or abnormal; they also measured peak RSCT across one heartbeat in each segment. The data set was split evenly into training and validation groups. During training, interchangeability of RSCT thresholding with experts to detect WMA was assessed using the individual equivalence index (γ), and an optimal threshold of the peak RSCT (RSCT*) that achieved maximum agreement was identified. RSCT* was then validated using the validation group, and the effect of AHA segment-specific thresholds was evaluated. Agreement was assessed using κ statistics. The optimal threshold, RSCT* of -0.19, when applied to all AHA segments, led to high agreement (agreement rate = 92.17%, κ = 0.82) and interchangeability with experts (γ = -2.58%). The same RSCT* also achieved high agreement in the validation group (agreement rate = 90.29%, κ = 0.76, γ = -0.38%). The use of AHA segment-specific thresholds (range: 0.16 to -0.23 across AHA segments) slightly improved agreement (1.79% increase). RSCT thresholding was interchangeable with expert visual analysis in detecting segmental WMA from 4D CT and may be used as an objective decision classifier.Keywords: CT, Left Ventricle, Regional Endocardial Shortening, Wall Motion Abnormality Supplemental material is available for this article. © RSNA, 2023.

The study on the impact of AAA wall motion on the hemodynamics based on 4D CT image data.

Purpose: To analyze the effect of the physiological deformation of the vessel wall on the hemodynamics in the abdominal aortic aneurysm (AAA), this paper compared the hemodynamics in AAA based on the moving boundary (MB) simulation and the rigid wall (RW) simulation. Method: Patient-specific models were reconstructed to generate mesh based on four-dimensional computed tomography angiography (4D CT) data. The dynamic mesh technique was used to achieve deformation of the vessel wall, surface mesh and volume mesh of the fluid domain were successively remeshed at each time step. Besides, another rigid wall simulation was performed. Hemodynamics obtained from these two simulations were compared. Results: Flow field and wall shear stress (WSS) distribution are similar. When using the moving boundary method (MBM), mean time-averaged wall shear stress (TAWSS) is lower, mean oscillatory shear index (OSI) and mean relative residence time (RRT) are higher. When using the 10th and 20th percentile values for TAWSS and 80th and 90th percentile values for RRT, the ratios of areas with low TAWSS, high OSI and high RRT to the entire vessel wall are higher than those assuming the vessel as rigid. In addition, one overlapping region of low TAWSS, high OSI and high RRT by using the MBM is consistent with the location of thrombus obtained from the follow-up imaging data. Conclusion: The hemodynamics results by using the MBM reflect a higher blood retention effect. This paper presents a potential tool to assess the risk of intraluminal thrombus (ILT) formation based on the MBM.

Multi-modality parathyroid imaging: A shifting paradigm.

The goal of parathyroid imaging in hyperparathyroidism is not diagnosis, rather it is the localization of the cause of hyperparathyroidism for planning the best therapeutic approach. Hence, the role of imaging to accurately and precisely localize the abnormal parathyroid tissue is more important than ever to facilitate minimally invasive parathyroidectomy over bilateral neck exploration. The common causes include solitary parathyroid adenoma, multiple parathyroid adenomas, parathyroid hyperplasia and parathyroid carcinoma. It is highly imperative for the radiologist to be cautious of the mimics of parathyroid lesions like thyroid nodules and lymph nodes and be able to differentiate them on imaging. The various imaging modalities available include high resolution ultrasound of the neck, nuclear imaging studies, four-dimensional computed tomography (4D CT) and magnetic resonance imaging. Contrast enhanced ultrasound is a novel technique which has been recently added to the armamentarium to differentiate between parathyroid adenomas and its mimics. Through this review article we wish to review the imaging features of parathyroid lesions on various imaging modalities and present an algorithm to guide their radiological differentiation from mimics.

Technical note: Intensity-based quality assurance criteria for deformable image registration in image-guided radiotherapy.

Deformable image registration is increasingly used in radiotherapy to adapt the treatment plan and accumulate the delivered dose. Consequently, clinical workflows using deformable image registration require quick and reliable quality assurance to accept registrations. Additionally, for online adaptive radiotherapy, quality assurance without the need for an operator to delineate contours while the patient is on the treatment table is needed. Established quality assurance criteria such as the Dice similarity coefficient or Hausdorff distance lack these qualities and also display a limited sensitivity to registration errors beyond soft tissueboundaries. The purpose of this study is to investigate the existing intensity-based quality assurance criteria structural similarity and normalized mutual information for their ability to quickly and reliably identify registration errors for (online) adaptive radiotherapy and compare them to contour-based quality assurancecriteria. All criteria were tested using synthetic and simulated biomechanical deformations of 3D MR images as well as manually annotated 4D CT data. The quality assurance criteria were scored for classification performance, for their ability to predict the registration error, and for their spatial information. We found that besides being fast and operator-independent, the intensity-based criteria have the highest area under the receiver operating characteristic curve and provide the best input for models to predict the registration error on all data sets. Structural similarity furthermore provides spatial information with a higher gamma pass rate of the predicted registration error than commonly used spatial quality assurance criteria. Intensity-based quality assurance criteria can provide the required confidence in decisions about using mono-modal registrations in clinical workflows. They thereby enable automated quality assurance for deformable image registration in adaptive radiotherapytreatments.

Deep learning-based fast volumetric imaging using kV and MV projection images for lung cancer radiotherapy: A feasibility study.

The long acquisition time of CBCT discourages repeat verification imaging, therefore increasing treatment uncertainty. In this study, we present a fast volumetric imaging method for lung cancer radiation therapy using an orthogonal 2D kV/MV image pair. The proposed model is a combination of 2D and 3D networks. The proposed model consists of five major parts: (1) kV and MV feature extractors are used to extract deep features from the perpendicular kV and MV projections. (2) The feature-matching step is used to re-align the feature maps to their projection angle in a Cartesian coordinate system. By using a residual module, the feature map can focus more on the difference between the estimated and ground truth images. (3) In addition, the feature map is downsized to include more global semantic information for the 3D estimation, which is useful to reduce inhomogeneity. By using convolution-based reweighting, the model is able to further increase the uniformity of image. (4) To reduce the blurry noise of generated 3D volume, the Laplacian latent space loss calculated via the feature map that is extracted via specifically-learned Gaussian kernel is used to supervise the network. (5) Finally, the 3D volume is derived from the trained model. We conducted a proof-of-concept study using 50 patients with lung cancer. An orthogonal kV/MV pair was generated by ray tracing through CT of each phase in a 4D CT scan. Orthogonal kV/MV pairs from nine respiratory phases were used to train this patient-specific model while the kV/MV pair of the remaining phase was held for model testing. The results are based on simulation data and phantom results from a real Linac system. The mean absolute error (MAE) values achieved by our method were 57.5 HU and 77.4 HU within body and tumor region-of-interest (ROI), respectively. The mean achieved peak-signal-to-noise ratios (PSNR) were 27.6dB and 19.2dB within the body and tumor ROI, respectively. The achieved mean normalized cross correlation (NCC) values were 0.97 and 0.94 within the body and tumor ROI, respectively. A phantom study demonstrated that the proposed method can accurately re-position the phantom after shift. It is also shown that the proposed method using both kV and MV is superior to current method using kV or MV only in image quality. These results demonstrate the feasibility and accuracy of our proposed fast volumetric imaging method from an orthogonal kV/MV pair, which provides a potential solution for daily treatment setup and verification of patients receiving radiation therapy for lung cancer.

Accuracy Verification of Four-Dimensional CT Analysis of Knee Joint Movements: A Pilot Study Using a Knee Joint Model and Motion-Capture System.

Objective This study aimed to use the optical motion-capture method to verify the accuracy of four-dimensional computed tomography (4D-CT) analysis of knee joint movement. Methods One static CT and three 4D-CT examinations of the knee joint model were obtained. The knee joint model was passively moved in the CT gantry during 4D-CT acquisitions. 4D-CT and static CT examinations were matched to perform 3D-3D registration. An optical-motion capture system recorded the position-posture of the knee joint model simultaneously with the 4D-CT acquisitions. Reference axes (X, Y, and Z directions) were defined based on static CT and applied to 4D-CT and the optical-motion capture system. Using the position-posture of the motion capture system as a reference standard, the position-posture measurements using 4D-CT were compared to these values, and the accuracy of the 4D-CT analysis of knee joint movements was quantitatively assessed. Results The position-posture measurements obtained from 4D-CT showed a similar tendency to those obtained from the motion-capture system. In the femorotibial joint, the difference in the spatial orientation between the two measurements was 0.7 mm in the X direction, 0.9 mm in the Y direction, and 2.8 mm in the Z direction. The difference in angle was 1.9° in the varus/valgus direction, 1.1° in the internal/external rotation, and 1.8° in extension/flexion. In the patellofemoral joint, the difference between the two measurements was 0.9 mm in the X direction, 1.3 mm in the Y direction, and 1.2 mm in the Z direction. The difference in angle was 0.9° for varus/valgus, 1.1° for internal/external rotation, and 1.3° for extension/flexion. Conclusions 4D-CT with 3D-3D registration could record the position-posture of knee joint movements with an error of less than 3 mm and less than 2° when compared with the highly accurate optical-motion capture system. Knee joint movement analysis using 4D-CT with 3D-3D registration showed excellent accuracy for in vivo applications.

Evaluation of Scapholunate Injury and Repair with Dynamic (4D) CT: A Preliminary Report of Two Cases.

Background In predynamic or dynamic scapholunate (SL) instability, standard diagnostic imaging may not identify SL interosseous ligament (SLIL) injury, leading to delayed detection and intervention. This study describes the use of four-dimensional computed tomography (4DCT) in identifying early SLIL injury and following injured wrists to 1-year postoperatively. Description of Technique 4DCT acquires a series of three-dimensional volume data with high temporal resolution (66 ms). 4DCT-derived arthrokinematic data can be used as biomarkers of ligament integrity. Patients and Methods This study presents the use of 4DCT in a two-participant case series to assess changes in arthrokinematics following unilateral SLIL injury preoperatively and 1-year postoperatively. Patients were treated with volar ligament repair with volar capsulodesis and arthroscopic dorsal capsulodesis. Arthrokinematics were compared between uninjured, preoperative injured, and postoperative injured (repaired) wrists. Results 4DCT detected changes in interosseous distances during flexion-extension and radioulnar deviation. Generally, radioscaphoid joint distances were greatest in the uninjured wrist during flexion-extension and radioulnar deviation, and SL interval distances were smallest in the uninjured wrist during flexion-extension and radioulnar deviation. Conclusion 4DCT provides insight into carpal arthrokinematics during motion. Distances between the radioscaphoid joint and SL interval can be displayed as proximity maps or as simplified descriptive statistics to facilitate comparisons between wrists and time points. These data offer insight into areas of concern for decreased interosseous distance and increased intercarpal diastasis. This method may allow surgeons to assess whether (1) injury can be visualized during motion, (2) surgery repaired the injury, and (3) surgery restored normal carpal motion. Level of Evidence Level IV, Case series.

RMSim: controlled respiratory motion simulation on static patient scans

Objective. This work aims to generate realistic anatomical deformations from static patient scans. Specifically, we present a method to generate these deformations/augmentations via deep learning driven respiratory motion simulation that provides the ground truth for validating deformable image registration (DIR) algorithms and driving more accurate deep learning based DIR. Approach. We present a novel 3D Seq2Seq deep learning respiratory motion simulator (RMSim) that learns from 4D-CT images and predicts future breathing phases given a static CT image. The predicted respiratory patterns, represented by time-varying displacement vector fields (DVFs) at different breathing phases, are modulated through auxiliary inputs of 1D breathing traces so that a larger amplitude in the trace results in more significant predicted deformation. Stacked 3D-ConvLSTMs are used to capture the spatial-temporal respiration patterns. Training loss includes a smoothness loss in the DVF and mean-squared error between the predicted and ground truth phase images. A spatial transformer deforms the static CT with the predicted DVF to generate the predicted phase image. 10-phase 4D-CTs of 140 internal patients were used to train and test RMSim. The trained RMSim was then used to augment a public DIR challenge dataset for training VoxelMorph to show the effectiveness of RMSim-generated deformation augmentation. Main results. We validated our RMSim output with both private and public benchmark datasets (healthy and cancer patients). The structure similarity index measure (SSIM) for predicted breathing phases and ground truth 4D CT images was 0.92 ± 0.04, demonstrating RMSim’s potential to generate realistic respiratory motion. Moreover, the landmark registration error in a public DIR dataset was improved from 8.12 ± 5.78 mm to 6.58mm ± 6.38 mm using RMSim-augmented training data. Significance. The proposed approach can be used for validating DIR algorithms as well as for patient-specific augmentations to improve deep learning DIR algorithms. The code, pretrained models, and augmented DIR validation datasets will be released at https://github.com/nadeemlab/SeqX2Y.

Clinical utility of combined assessments of 4D volumetric perfusion CT, diffusion-weighted MRI and 18F-FDG PET-CT for the prediction of outcomes of head and neck squamous cell carcinoma treated with chemoradiotherapy

BackgroundMultiparametric imaging has been seen as a route to improved prediction of chemoradiotherapy treatment outcomes. Four-dimensional volumetric perfusion CT (4D PCT) is useful for whole-organ perfusion measurement, as it reflects the heterogeneity of the tumor and its perfusion parameters. However, there has been no study using multiparametric imaging including 4D PCT for the prognostic prediction of chemoradiotherapy. The purpose of this study was to determine whether combining assessments of 4D PCT with diffusion-weighted MRI (DWI) and 18F-fluorodeoxyglucose PET-CT could enhance prognostic accuracy in head and neck squamous cell carcinoma (HNSCC) patients treated with chemoradiotherapy.MethodsWe examined 53 patients with HNSCC who underwent 4D PCT, DWI and PET-CT before chemoradiotherapy. The imaging and clinical parameters were assessed the relations to locoregional control (LRC) and progression-free survival (PFS) by logistic regression analyses. A receiver operating characteristic (ROC) analysis was performed to assess the accuracy of the significant parameters identified by the multivariate analysis for the prediction of LRC and PFS. We additionally assessed using the scoring system whether these independent parameters could have a complementary role for the prognostic prediction.ResultsThe median follow-up was 30 months. In multivariate analysis, blood flow (BF; p = 0.02) and blood volume (BV; p = 0.04) were significant prognostic factors for LRC, and BF (p = 0.03) and skewness of the ADC histogram (p = 0.02) were significant prognostic factors for PFS. A significant positive correlation was found between BF and BV (ρ = 0.6, p < 0.001) and between BF and skewness (ρ = 0.46, p < 0.01). The ROC analysis showed that prognostic accuracy for LRC of BF, BV, and combination of BF and BV were 77.8%, 70%, and 92.9%, and that for PFS of BF, skewness, and combination of BF and skewness were 55.6%, 63.2%, and 77.5%, respectively. The scoring system demonstrated that the combination of higher BF and higher BV was significantly associated with better LRC (p = 0.04), and the combination of lower BF and lower skewness was significantly associated with worse PFS (p = 0.004).ConclusionA combination of parameters derived from 4DPCT and ADC histograms may enhance prognostic accuracy in HNSCC patients treated with chemoradiotherapy.

Case of the Month from the Peter MacCallum Cancer Centre, Melbourne, Australia: combination of stereotactic ablative body radiotherapy and radical nephrectomy for synchronous bilateral primary renal cell carcinoma.

Case of the Month from the Peter MacCallum Cancer Centre, Melbourne, Australia: combination of stereotactic ablative body radiotherapy and radical nephrectomy for synchronous bilateral primary renal cell carcinoma.

4D lung MRI with high-isotropic-resolution using half-spoke (UTE) and full-spoke 3D radial acquisition and temporal compressed sensing reconstruction

Objective. To develop a respiratory motion-resolved four-dimensional (4D) magnetic resonance imaging (MRI) technique with high-isotropic-resolution (1.1 mm) using 3D radial sampling, camera-based respiratory motion sensing, and temporal compressed sensing reconstruction for lung cancer imaging. Approach. Free-breathing half- and full-spoke 3D golden-angle radial acquisitions were performed on eight healthy volunteers and eight patients with lung tumors of varying size. A back-and-forth k-space ordering between consecutive interleaves of the 3D radial acquisition was performed to minimize eddy current-related artifacts. Data were sorted into respiratory motion states using camera-based motion navigation and 4D images were reconstructed using temporal compressed sensing to reduce scan time. Normalized sharpness indices of the diaphragm, apparent signal-to-noise ratio (aSNR) and contrast-to-noise ratio (CNR) of the lung tumor (patients only), liver, and aortic arch were compared between half- and full-spoke 4D MRI images to evaluate the impact of respiratory motion and image contrast on 4D MRI image quality. Respiration-induced changes in lung volumes and center of mass shifts were compared between half- and full-spoke 4D MRI measurements. In addition, the motion measurements from 4D MRI and the same-day 4D CT were presented in one of the lung tumor patients. Main results. Half-spoke 4D MRI provides better visualization of the lung parenchyma, while full-spoke 4D MRI presents sharper diaphragm images and higher aSNR and CNR in the lung tumor, liver, and aortic arch. Lung volume changes and center of mass shifts measured by half- and full-spoke 4D MRI were not statistically different. For the patient with 4D MRI and same-day 4D CT, lung volume changes and center of mass shifts were generally comparable. Significance. This work demonstrates the feasibility of a motion-resolved 4D MRI technique with high-isotropic-resolution using 3D radial acquisition, camera-based respiratory motion sensing, and temporal compressed sensing reconstruction for treatment planning and motion monitoring in radiotherapy of lung cancer.