Background: Cortical bone porosity is a major determinant of bone strength. Despite the biomechanical importance of cortical bone porosity, the biological drivers of cortical porosity are unknown. The content of cortical pore space can indicate pore expansion mechanisms; both of the primary components of pore space, vessels and adipocytes, have been implicated in pore expansion. Dynamic contrast-enhanced MRI (DCE-MRI) is widely used in vessel detection in cardiovascular studies, but has not been applied to visualize vessels within cortical bone. In this study, we have developed a multimodal DCE-MRI and high resolution peripheral QCT (HR-pQCT) acquisition and image processing pipeline to detect vessel-filled cortical bone pores. Methods: For this in vivo human study, 19 volunteers (10 males and 9 females; mean age =63±5) were recruited. Both distal and ultra-distal regions of the non-dominant tibia were imaged by HR-pQCT (82 µm nominal resolution) for bone structure segmentation and by 3T DCE-MRI (Gadavist; 9 min scan time; temporal resolution =30 sec; voxel size 230×230×500 µm 3) for vessel visualization. The DCE-MRI was registered to the HR-pQCT volume and the voxels within the MRI cortical bone region were extracted. Features of the DCE data were calculated and voxels were categorized by a 2-stage hierarchical kmeans clustering algorithm to determine which voxels represent vessels. Vessel volume fraction (volume ratio of vessels to cortical bone), vessel density (average vessel count per cortical bone volume), and average vessel volume (mean volume of vessels) were calculated to quantify the status of vessel-filled pores in cortical bone. To examine spatial resolution and perform validation, a virtual phantom with 5 channel sizes and an applied pseudo enhancement curve was processed through the proposed image processing pipeline. Overlap volume ratio and Dice coefficient was calculated to measure the similarity between the detected vessel map and ground truth. Results: In the human study, mean vessel volume fraction was 2.2%±1.0%, mean vessel density was 0.68±0.27 vessel/mm 3 , and mean average vessel volume was 0.032±0.012 mm 3 /vessel. Signal intensity for detected vessel voxels increased during the scan, while signal for non-vessel voxels within pores did not enhance. In the validation phantom, channels with diameter 250 µm or greater were detected successfully, with volume ratio equal to 1 and Dice coefficient above 0.6. Both statistics decreased dramatically for channel sizes less than 250 µm. Conclusions: We have a developed a multi-modal image acquisition and processing pipeline that successfully detects vessels within cortical bone pores. The performance of this technique degrades for vessel diameters below the in-plane spatial resolution of the DCE-MRI acquisition. This approach can be applied to investigate the biological systems associated with cortical pore expansion.

Salient region detection is useful for several image-processing applications, such as adaptive compression, object recognition, image retrieval, filter design, and image retargeting. A novel method to determine the salient regions of images is proposed in this paper. The L₀ smoothing filter and principle component analysis (PCA) play important roles in our framework. The L₀ filter is extremely helpful in characterizing fundamental image constituents, i.e., salient edges, and can simultaneously diminish insignificant details, thus producing more accurate boundary information for background merging and boundary scoring. PCA can reduce computational complexity as well as attenuate noise and translation errors. A local-global contrast is then used to calculate the distinction. Finally, image segmentation is used to achieve full-resolution saliency maps. The proposed method is compared with other state-of-the-art saliency detection methods and shown to yield higher precision-recall rates and F-measures.

Purpose Intra-procedural contrast-enhanced computed tomography (CECT) has been proposed to monitor the growth of ablation zone in order to evaluate the efficacy of microwave ablation more accurately than post-ablation image. However, the dilemma of keeping image quality or following dose regulation becomes a huge challenge to apply CECT in real-time monitoring. The purpose of this study is to evaluate the feasibility of applying local highly constrained backprojection reconstruction (HYPR-LR) on periodic CECT image to enhance visualization of ablation zone by statistical and pathological- radiological analysis. Methods Low-dose ( CTDIvol≤1.49 italicmGy), temporal CECT volumes were acquired during microwave ablation on normal porcine liver. HYPR processing was performed on each volume after image registration. Ablation signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were collected to evaluate the degree of enhancement of image quality and ablation zone visualization. Ablation zone was manually segmented on HYPR and non-HYPR images and compared the similarity to gross pathology by measuring Dice coefficient. The dimension of ablation zone was also compared to gross pathology by correlation and measurement difference. Results The SNR and CNR of ablation zone were increased after involving certain slices into HYPR processing. The manually segmented ablation zone was highly similar to gross pathology with DICE coefficient 0.81±0.03, while the low-dose CECT had smaller DICE coefficient with 0.72±0.05. Both HYPR image and low-dose CECT has high correlation coefficient to gross pathology with 0.99 and 0.94, but the variance of measurements were less on HYPR image than those on unprocessed images. The relative difference in area, length of long axis and short axis for HYPR image were 13.1±5.6%, 9.7±4.2% and 15.2±2.8 %, which were less than those for low-dose CECT with 37.5±6.0%, 17.7±2.8% and 28.9±5.4%. Conclusion HYPR processing applied to periodic CECT images can enhance ablation zone visualization. HYPR processing may potentially enable CECT in real-time ablation monitoring under strict regulation of radiation dose.

Intra-procedural contrast-enhanced CT (CECT) has been proposed to evaluate treatment efficacy of thermal ablation. We hypothesized that contrast material delivered concurrently with thermal ablation may become trapped in the ablation zone, and set out to determine whether such an effect would impact ablation visualization. CECT images were acquired during microwave ablation in normal porcine liver with: (A) normal blood perfusion and no iodinated contrast, (B) normal perfusion and iodinated contrast infusion or (C) no blood perfusion and residual iodinated contrast. Changes in CT attenuation were analyzed from before, during and after ablation to evaluate whether contrast was trapped inside of the ablation zone. Visualization was compared between groups using post-ablation contrast-to-noise ratio (CNR). Attenuation gradients were calculated at the ablation boundary and background to quantitate ablation conspicuity. In Group A, attenuation decreased during ablation due to thermal expansion of tissue water and water vaporization. The ablation zone was difficult to visualize (CNR = 1.57±0.73, boundary gradient = 0.7±0.4 HU/mm), leading to ablation diameter underestimation compared to gross pathology. Group B ablations saw attenuation increase, suggesting that iodine was trapped inside the ablation zone. However, because the normally perfused liver increased even more, Group B ablations were more visible than Group A (CNR = 2.04±0.84, boundary gradient = 6.3±1.1 HU/mm) and allowed accurate estimation of the ablation zone dimensions compared to gross pathology. Substantial water vaporization led to substantial attenuation changes in Group C, though the ablation zone boundary was not highly visible (boundary gradient = 3.9±1.1 HU/mm). Our results demonstrate that despite iodinated contrast being trapped in the ablation zone, ablation visibility was highest when contrast is delivered intra-procedurally. Therefore, CECT may be feasible for real-time thermal ablation monitoring.

As the Walsh (Hadamard) transform can be generalized into the Jacket transform, in this paper, we generalize the Haar transform into the Jacket-Haar transform. The entries of the Jacket-Haar transform are 0 and ±2 k . Compared with the original Haar transform, the basis of the Jacket-Haar transform is general and more suitable for signal processing. Furthermore, with the proposed generalization algorithm, it is possible to define the N-point Jacket-Haar transform, where N is not a power of 2. From our simulations, the proposed Jacket Haar transform has better performance in ECG signal analysis.