Minimizing L 1 over L 2 norms on the gradient.

In this paper, we study the L 1 /L 2 minimization on the gradient for imaging applications. Several recent works have demonstrated that L 1 /L 2 is better than the L 1 norm when approximating the L 0 norm to promote sparsity. Consequently, we postulate that applying L 1 /L 2 on the gradient is better than the classic total variation (the L 1 norm on the gradient) to enforce the sparsity of the image gradient. Numerically, we design a specific splitting scheme, under which we can prove subsequential and global convergence for the alternating direction method of multipliers (ADMM) under certain conditions. Experimentally, we demonstrate visible improvements of L 1 /L 2 over L 1 and other nonconvex regularizations for image recovery from low-frequency measurements and two medical applications of MRI and CT reconstruction. Finally, we reveal some empirical evidence on the superiority of L 1 /L 2 over L 1 when recovering piecewise constant signals from low-frequency measurements to shed light on future works.

A universal aptasensing platform based on cryonase-assisted signal amplification and graphene oxide induced quenching of the fluorescence of labeled nucleic acid probes: application to the detection of theophylline and ATP.

This study describes a universal fluorometric method for sensitive detection of analytes by using aptamers. It is based on the use of graphene oxide (GO) and cryonase-assisted signal amplification. GO is a strong quencher of FAM-labeled nucleic acid probes, while cryonase digests all types of nucleic acid probes. This makes the platform widely applicable to analytes for which the corresponding aptamers are available. Theophylline and ATP were chosen as model analytes. In the absence of targets, dye-labeled aptamers are in a flexible single strand state and adsorb on the GO. As a result, the probes are non-fluorescent due to the efficient quenching of dyes by GO. Upon the addition of a specific target, the aptamer/target complex desorbed from the GO surface and the probe becomes fluorescent. The released complex will immediately become a substrate for cryonase digestion and subsequently releasing the target to bind to another aptamer to initiate the next round of cleavage. This cyclic reaction will repeat again and again until all the related-probes are consumed and all fluorophores light up, resulting in significant fluorescent signal amplification. The detection limits are 47 nM for theophylline and 22.5 nM for ATP. This is much better than that of known methods. The assay requires only mix-and-measure steps that can be accomplished rapidly. In our perception, the detection scheme holds great promise for the design enzyme-aided amplification mechanisms for use in bioanalytical methods. Graphical abstract A cryonase-assisted signal amplification (CASA) method has been developed by using graphene oxide (GO) conjugated with a fluorophore-labeled aptamer for fluorescence signal generation. It has a large scope because it may be applied to numerous analytes.

RNase H meets molecular beacons: an ultrasensitive fluorometric assay for nucleic acids.

An innovative signal amplification strategy assisted by RNase H is described here for the detection of DNA targets in a universal fashion. A tailor-made RNA molecular beacon (RMB) conjugated with a fluorescence resonance energy transfer (FRET) pair (fluorophore and quencher) was designed, characterized, and combined with the employment of RNase H. Its performance is compared to that of other nucleases including Exonuclease III and T7 exonuclease. Fluorometry, performed best at excitation/emission wavelengths of 490/520 nm, gives an amazingly low detection limit of 23 fM for target DNA. The method was verified by the determination of human hemochromatosis (HFE) gene. It is perceived that the method represents a versatile tool for the detection of a wide range of targets. Graphical Abstract An RNase H-assisted signal amplification (RASA) method for the fluorometric assay of nucleic acids has been developed by using a unique RNA molecular beacon (RNA MB) conjugated with a fluorophore (F) and quencher (Q) pair for signal generation.

Antidepressant effects of dexmedetomidine compared with ECT in patients with treatment-resistant depression.

OBJECTIVE: This pilot study was designed to investigate the antidepressant effects of dexmedetomidine (DEX), a selective α2-adrenergic receptor agonist, in patients with treatment-resistant depression (TRD). The antidepressant effects of dexmedetomidine was compared with ECT, which is widely used in clinical practice for treatment of patients with TRD. METHODS: Seventy six patients with TRD were randomly assigned to receive 10 sessions of DEX infusions or electroconvulsive therapy (ECT) treatment. The primary outcome was the changes of depression severity determined by the improvement of 24-item Hamilton Depression Rating Scale (HDRS-24). The second outcomes were the rates of therapeutic response (reduction in HDRS-24 ≥ 50 %) and remission (HDRS-24 ≤ 10 and reduction in HDRS-24 ≥ 60 %) at posttreatment and after 3 months of follow-up visits. RESULTS: We found that 10 sessions of DEX infusions or ECT treatments significantly improved HDRS-24 scores at posttreatment and after 3 months of follow-up visits compared with the baseline. In addition, there was no significant difference between DEX infusions and ECT treatments regarding HDRS-24 at these evaluating points. Furthermore, the depression severity dropped to mild after 2 sessions of DEX infusion. In contrast, at least 6 sessions of ECT treatment were needed to achieve a same level. Finally, the rates of therapeutic response and remission were comparable between the two groups. No serious adverse events were observed. CONCLUSIONS: Based on current published evidence, we conclude that DEX exhibits rapid and durable antidepressant properties similar to ECT but with fewer side effects.

Four-dimensional cone beam CT reconstruction and enhancement using a temporal nonlocal means method.

PURPOSE: Four-dimensional cone beam computed tomography (4D-CBCT) has been developed to provide respiratory phase-resolved volumetric imaging in image guided radiation therapy. Conventionally, it is reconstructed by first sorting the x-ray projections into multiple respiratory phase bins according to a breathing signal extracted either from the projection images or some external surrogates, and then reconstructing a 3D CBCT image in each phase bin independently using FDK algorithm. This method requires adequate number of projections for each phase, which can be achieved using a low gantry rotation or multiple gantry rotations. Inadequate number of projections in each phase bin results in low quality 4D-CBCT images with obvious streaking artifacts. 4D-CBCT images at different breathing phases share a lot of redundant information, because they represent the same anatomy captured at slightly different temporal points. Taking this redundancy along the temporal dimension into account can in principle facilitate the reconstruction in the situation of inadequate number of projection images. In this work, the authors propose two novel 4D-CBCT algorithms: an iterative reconstruction algorithm and an enhancement algorithm, utilizing a temporal nonlocal means (TNLM) method. METHODS: The authors define a TNLM energy term for a given set of 4D-CBCT images. Minimization of this term favors those 4D-CBCT images such that any anatomical features at one spatial point at one phase can be found in a nearby spatial point at neighboring phases. 4D-CBCT reconstruction is achieved by minimizing a total energy containing a data fidelity term and the TNLM energy term. As for the image enhancement, 4D-CBCT images generated by the FDK algorithm are enhanced by minimizing the TNLM function while keeping the enhanced images close to the FDK results. A forward-backward splitting algorithm and a Gauss-Jacobi iteration method are employed to solve the problems. The algorithms implementation on GPU is designed to avoid redundant and uncoalesced memory access, in order to ensure a high computational efficiency. Our algorithms have been tested on a digital NURBS-based cardiac-torso phantom and a clinical patient case. RESULTS: The reconstruction algorithm and the enhancement algorithm generate visually similar 4D-CBCT images, both better than the FDK results. Quantitative evaluations indicate that, compared with the FDK results, our reconstruction method improves contrast-to-noise-ratio (CNR) by a factor of 2.56-3.13 and our enhancement method increases the CNR by 2.75-3.33 times. The enhancement method also removes over 80% of the streak artifacts from the FDK results. The total computation time is 509-683 s for the reconstruction algorithm and 524-540 s for the enhancement algorithm on an NVIDIA Tesla C1060 GPU card. CONCLUSIONS: By innovatively taking the temporal redundancy among 4D-CBCT images into consideration, the proposed algorithms can produce high quality 4D-CBCT images with much less streak artifacts than the FDK results, in the situation of inadequate number of projections.

Low-dose 4DCT reconstruction via temporal nonlocal means.

PURPOSE: Four-dimensional computed tomography (4DCT) has been widely used in cancer radiotherapy for accurate target delineation and motion measurement for tumors in the thorax and upper abdomen areas. However, its prolonged scanning duration causes a considerable increase of radiation dose compared to conventional CT, which is a major concern in its clinical application. This work is to develop a new algorithm to reconstruct 4DCT images from undersampled projections acquired at low mA s levels in order to reduce the imaging dose. METHODS: Conventionally, each phase of 4DCT is reconstructed independently using the filtered backprojection (FBP) algorithm. The basic idea of the authors' new algorithm is that by utilizing the common information among different phases, the input information required to reconstruct the image of high quality, and thus the imaging dose, can be reduced. The authors proposed a temporal nonlocal means (TNLM) method to explore the interphase similarity. All phases of the 4DCT images are reconstructed simultaneously by minimizing a cost function consisting of a data fidelity term and a TNLM regularization term. The authors utilized a modified forward-backward splitting algorithm and a Gauss-Jacobi iteration method to efficiently solve the minimization problem. The algorithm was also implemented on a graphics processing unit (GPU) to improve the computational speed. The authors' reconstruction algorithm has been tested on a digital NCAT thorax phantom in three low dose scenarios: All projections with low mA s level, undersampled projections with high mA s level, and undersampled projections with low mA s level. RESULTS: In all three low dose scenarios, the new algorithm generates visually much better CT images containing less image noise and streaking artifacts compared to the standard FBP algorithm. Quantitative analysis shows that by comparing the authors' TNLM algorithm to the standard FBP algorithm, the contrast-to-noise ratio has been improved by a factor of 3.9-10.2 and the signal-to-noise ratio has been improved by a factor of 2.1-5.9, depending on the cases. In the situation of undersampled projection data, the majority of the streaks in the images reconstructed by FBP can be suppressed using the authors' algorithm. The total reconstruction time for all ten phases of a slice ranges from 40 to 90 s on an NVIDIA Tesla C1060 GPU card. CONCLUSIONS: The experimental results indicate that the authors' new algorithm outperforms the conventional FBP algorithm in effectively reducing the image artifacts due to undersampling and suppressing the image noise due to the low mA s level.

GPU-based fast low-dose cone beam CT reconstruction via total variation.

X-ray imaging dose from serial Cone-beam CT (CBCT) scans raises a clinical concern in most image guided radiation therapy procedures. The goal of this paper is to develop a fast GPU-based algorithm to reconstruct high quality CBCT images from undersampled and noisy projection data so as to lower the imaging dose. The CBCT is reconstructed by minimizing an energy functional consisting of a data fidelity term and a total variation regularization term. We develop a GPU-friendly version of a forward-backward splitting algorithm to solve this problem. A multi-grid technique is also employed. We test our CBCT reconstruction algorithm on a digital phantom and a head-and-neck patient case. The performance under low mAs is also validated using physical phantoms. It is found that 40 x-ray projections are sufficient to reconstruct CBCT images with satisfactory quality for clinical purposes. Phantom experiments indicate that CBCT images can be successfully reconstructed under 0.1 mAs/projection. Comparing with the widely used head-and-neck scanning protocol of about 360 projections with 0.4 mAs/projection, an overall 36 times dose reduction has been achieved. The reconstruction time is about 130 sec on an NVIDIA Tesla C1060 GPU card, which is estimated ∼ 100 times faster than similar regularized iterative reconstruction approaches.

Probabilistic Structure Learning for EEG/MEG Source Imaging With Hierarchical Graph Priors.

Brain source imaging is an important method for noninvasively characterizing brain activity using Electroencephalogram (EEG) or Magnetoencephalography (MEG) recordings. Traditional EEG/MEG Source Imaging (ESI) methods usually assume the source activities at different time points are unrelated, and do not utilize the temporal structure in the source activation, making the ESI analysis sensitive to noise. Some methods may encourage very similar activation patterns across the entire time course and may be incapable of accounting the variation along the time course. To effectively deal with noise while maintaining flexibility and continuity among brain activation patterns, we propose a novel probabilistic ESI model based on a hierarchical graph prior. Under our method, a spanning tree constraint ensures that activity patterns have spatiotemporal continuity. An efficient algorithm based on an alternating convex search is presented to solve the resulting problem of the proposed model with guaranteed convergence. Comprehensive numerical studies using synthetic data on a realistic brain model are conducted under different levels of signal-to-noise ratio (SNR) from both sensor and source spaces. We also examine the EEG/MEG datasets in two real applications, in which our ESI reconstructions are neurologically plausible. All the results demonstrate significant improvements of the proposed method over benchmark methods in terms of source localization performance, especially at high noise levels.

GPU-based fast cone beam CT reconstruction from undersampled and noisy projection data via total variation.

PURPOSE: Cone-beam CT (CBCT) plays an important role in image guided radiation therapy (IGRT). However, the large radiation dose from serial CBCT scans in most IGRT procedures raises a clinical concern, especially for pediatric patients who are essentially excluded from receiving IGRT for this reason. The goal of this work is to develop a fast GPU-based algorithm to reconstruct CBCT from undersampled and noisy projection data so as to lower the imaging dose. METHODS: The CBCT is reconstructed by minimizing an energy functional consisting of a data fidelity term and a total variation regularization term. The authors developed a GPU-friendly version of the forward-backward splitting algorithm to solve this model. A multigrid technique is also employed. RESULTS: It is found that 20-40 x-ray projections are sufficient to reconstruct images with satisfactory quality for IGRT. The reconstruction time ranges from 77 to 130 s on an NVIDIA Tesla C1060 (NVIDIA, Santa Clara, CA) GPU card, depending on the number of projections used, which is estimated about 100 times faster than similar iterative reconstruction approaches. Moreover, phantom studies indicate that the algorithm enables the CBCT to be reconstructed under a scanning protocol with as low as 0.1 mA s/projection. Comparing with currently widely used full-fan head and neck scanning protocol of approximately 360 projections with 0.4 mA s/projection, it is estimated that an overall 36-72 times dose reduction has been achieved in our fast CBCT reconstruction algorithm. CONCLUSIONS: This work indicates that the developed GPU-based CBCT reconstruction algorithm is capable of lowering imaging dose considerably. The high computation efficiency in this algorithm makes the iterative CBCT reconstruction approach applicable in real clinical environments.

Enzyme-free and protein-assisted dual-amplified fluorescence anisotropy for sensitive miRNA detection in tumor cells.

We here report a double amplification strategy to construct a fluorescence anisotropy sensor for microRNA analysis in practical biological samples. In this strategy, one target can trigger cyclic catalyzed hairpin assembly (CHA), with streptavidin incorporated as an amplifier of molar mass to enhance the signal intensity. The proposed strategy has a good linearity in the range of 5 pM - 0.5 nM with a detection limit down to 2.3 pM. More importantly, by using fluorescence anisotropy as the signal output, the strategy can be used directly for detection of miRNA in practical samples without any tedious sample pretreatment, holding the practical value in real biological systems.

Comparison of three undersampling approaches in computed tomography reconstruction.

BACKGROUND: Projection data undersampling is an effective approach to reduce X-ray radiation dose in computed tomography (CT). In modern CT technologies, undersampling is also a favorable method to reduce projection data size to facilitate rapid CT scan and imaging. It is an intriguing question that given an undersampling ratio, what is the optimal undersampling approach that enables the best CT image reconstruction. While this is in general a challenging mathematical question, it is the motivation of this paper to compare three types of undersampling operations, which we hope to shed some light to this question. METHODS: We considered regular view undersampling that acquires X-ray projections at equiangular projection angles, regular ray undersampling that acquires projections at all angles but with X-ray lines blocked within each projection under a periodic pattern, and random ray undersampling that acquires each X-ray line with a certain probability. By representing the undersampling projection operators under the basis of singular vectors of full projection operator, we generated matrix representations of these undersampling operators and numerically perform singular value decomposition (SVD). Singular value spectra and singular vectors were compared. RESULTS: For a given undersampling ratio, the random ray undersampling approach preserves the properties of the full projection operator better than the other two approaches. This translates to advantages of reconstructing a CT image at a lower error, which has also been demonstrated in the numerical experiments. CONCLUSIONS: We compared three undersampling strategies and found that random undersampling preserves the most information and outperforms the other two in terms of reconstruction quality.

Partially coherent ptychography by gradient decomposition of the probe.

Coherent ptychographic imaging experiments often discard the majority of the flux from a light source to define the coherence of the illumination. Even when the coherent flux is sufficient, the stability required during an exposure is another important limiting factor. Partial coherence analysis can considerably reduce these limitations. A partially coherent illumination can often be written as the superposition of a single coherent illumination convolved with a separable translational kernel. This article proposes the gradient decomposition of the probe (GDP), a model that exploits translational kernel separability, coupling the variances of the kernel with the transverse coherence. An efficient first-order splitting algorithm (GDP-ADMM) for solving the proposed nonlinear optimization problem is described. Numerical experiments demonstrate the effectiveness of the proposed method with Gaussian and binary kernel functions in fly-scan measurements. Remarkably, GDP-ADMM using nanoprobes produces satisfactory results even when the ratio between the kernel width and the beam size is more than one, or when the distance between successive acquisitions is twice as large as the beam width.

Multienergy element-resolved cone beam CT (MEER-CBCT) realized on a conventional CBCT platform.

PURPOSE: Cone beam CT (CBCT) has been widely used in radiation therapy. However, its main application is still to acquire anatomical information for patient positioning. This study proposes a multienergy element-resolved (MEER) CBCT framework that employs energy-resolved data acquisition on a conventional CBCT platform and then simultaneously reconstructs images of x-ray attenuation coefficients, electron density relative to water (rED), and elemental composition (EC) to support advanced applications. METHODS: The MEER-CBCT framework is realized on a Varian TrueBeam CBCT platform using a kVp-switching scanning scheme. A simultaneous image reconstruction and elemental decomposition model is formulated as an optimization problem. The objective function uses a least square term to enforce fidelity between x-ray attenuation coefficients and projection measurements. Spatial regularization is introduced via sparsity under a tight wavelet-frame transform. Consistency is imposed among rED, EC, and attenuation coefficients and inherently serves as a regularization term along the energy direction. The EC is further constrained by a sparse combination of ECs in a dictionary containing tissues commonly existing in humans. The optimization problem is solved by a novel alternating-direction minimization scheme. The MEER-CBCT framework was tested in a simulation study using an NCAT phantom and an experimental study using a Gammex phantom. RESULTS: MEER-CBCT framework was successfully realized on a clinical Varian TrueBeam onboard CBCT platform with three energy channels of 80, 100, and 120 kVp. In the simulation study, the attenuation coefficient image achieved a structural similarity index of 0.98, compared to 0.61 for the image reconstructed by the conventional conjugate gradient least square (CGLS) algorithm, primarily because of reduction in artifacts. In the experimental study, the attenuation image obtained a contrast-to-noise ratio ≥60, much higher than that of CGLS results (~16) because of noise reduction. The median errors in rED and EC were 0.5% and 1.4% in the simulation study and 1.4% and 2.3% in the experimental study. CONCLUSION: We proposed a novel MEER-CBCT framework realized on a clinical CBCT platform. Simulation and experimental studies demonstrated its capability to simultaneously reconstruct x-ray attenuation coefficient, rED, and EC images accurately.

Material elemental decomposition in dual and multi-energy CT via a sparsity-dictionary approach for proton stopping power ratio calculation.

PURPOSE: Accurate calculation of proton stopping power ratio (SPR) relative to water is crucial to proton therapy treatment planning, since SPR affects prediction of beam range. Current standard practice derives SPR using a single CT scan. Recent studies showed that dual-energy CT (DECT) offers advantages to accurately determine SPR. One method to further improve accuracy is to incorporate prior knowledge on human tissue composition through a dictionary approach. In addition, it is also suggested that using CT images with multiple (more than two) energy channels, i.e., multi-energy CT (MECT), can further improve accuracy. In this paper, we proposed a sparse dictionary-based method to convert CT numbers of DECT or MECT to elemental composition (EC) and relative electron density (rED) for SPR computation. METHOD: A dictionary was constructed to include materials generated based on human tissues of known compositions. For a voxel with CT numbers of different energy channels, its EC and rED are determined subject to a constraint that the resulting EC is a linear non-negative combination of only a few tissues in the dictionary. We formulated this as a non-convex optimization problem. A novel algorithm was designed to solve the problem. The proposed method has a unified structure to handle both DECT and MECT with different number of channels. We tested our method in both simulation and experimental studies. RESULTS: Average errors of SPR in experimental studies were 0.70% in DECT, 0.53% in MECT with three energy channels, and 0.45% in MECT with four channels. We also studied the impact of parameter values and established appropriate parameter values for our method. CONCLUSION: The proposed method can accurately calculate SPR using DECT and MECT. The results suggest that using more energy channels may improve the SPR estimation accuracy.

MULTI-ENERGY CONE-BEAM CT RECONSTRUCTION WITH A SPATIAL SPECTRAL NONLOCAL MEANS ALGORITHM.

Multi-energy computed tomography (CT) is an emerging medical image modality with a number of potential applications in diagnosis and therapy. However, high system cost and technical barriers obstruct its step into routine clinical practice. In this study, we propose a framework to realize multi-energy cone beam CT (ME-CBCT) on the CBCT system that is widely available and has been routinely used for radiotherapy image guidance. In our method, a kVp switching technique is realized, which acquires x-ray projections with kVp levels cycling through a number of values. For this kVp-switching based ME-CBCT acquisition, x-ray projections of each energy channel are only a subset of all the acquired projections. This leads to an undersampling issue, posing challenges to the reconstruction problem. We propose a spatial spectral non-local means (ssNLM) method to reconstruct ME-CBCT, which employs image correlations along both spatial and spectral directions to suppress noisy and streak artifacts. To address the intensity scale difference at different energy channels, a histogram matching method is incorporated. Our method is different from conventionally used NLM methods in that spectral dimension is included, which helps to effectively remove streak artifacts appearing at different directions in images with different energy channels. Convergence analysis of our algorithm is provided. A comprehensive set of simulation and real experimental studies demonstrate feasibility of our ME-CBCT scheme and the capability of achieving superior image quality compared to conventional filtered backprojection-type (FBP) and NLM reconstruction methods.

Joint CT/CBCT deformable registration and CBCT enhancement for cancer radiotherapy.

This paper details an algorithm to simultaneously perform registration of computed tomography (CT) and cone-beam computed (CBCT) images, and image enhancement of CBCT. The algorithm employs a viscous fluid model which naturally incorporates two components: a similarity measure for registration and an intensity correction term for image enhancement. Incorporating an intensity correction term improves the registration results. Furthermore, applying the image enhancement term to CBCT imagery leads to an intensity corrected CBCT with better image quality. To achieve minimal processing time, the algorithm is implemented on a graphic processing unit (GPU) platform. The advantage of the simultaneous optimization strategy is quantitatively validated and discussed using a synthetic example. The effectiveness of the proposed algorithm is then illustrated using six patient datasets, three head-and-neck datasets and three prostate datasets.

Multimodal deformable registration of traumatic brain injury MR volumes via the Bhattacharyya distance.

An important problem of neuroimaging data analysis for traumatic brain injury (TBI) is the task of coregistering MR volumes acquired using distinct sequences in the presence of widely variable pixel movements which are due to the presence and evolution of pathology. We are motivated by this problem to design a numerically stable registration algorithm which handles large deformations. To this end, we propose a new measure of probability distributions based on the Bhattacharyya distance, which is more stable than the widely used mutual information due to better behavior of the square root function than the logarithm at zero. Robustness is illustrated on two TBI patient datasets, each containing 12 MR modalities. We implement our method on graphics processing units (GPU) so as to meet the clinical requirement of time-efficient processing of TBI data. We find that 6 sare required to register a pair of volumes with matrix sizes of 256 × 256 × 60 on the GPU. In addition to exceptional time efficiency via its GPU implementation, this methodology provides a clinically informative method for the mapping and evaluation of anatomical changes in TBI.

GPU-based iterative cone-beam CT reconstruction using tight frame regularization.

The x-ray imaging dose from serial cone-beam computed tomography (CBCT) scans raises a clinical concern in most image-guided radiation therapy procedures. It is the goal of this paper to develop a fast graphic processing unit (GPU)-based algorithm to reconstruct high-quality CBCT images from undersampled and noisy projection data so as to lower the imaging dose. For this purpose, we have developed an iterative tight-frame (TF)-based CBCT reconstruction algorithm. A condition that a real CBCT image has a sparse representation under a TF basis is imposed in the iteration process as regularization to the solution. To speed up the computation, a multi-grid method is employed. Our GPU implementation has achieved high computational efficiency and a CBCT image of resolution 512 × 512 × 70 can be reconstructed in ∼5 min. We have tested our algorithm on a digital NCAT phantom and a physical Catphan phantom. It is found that our TF-based algorithm is able to reconstruct CBCT in the context of undersampling and low mAs levels. We have also quantitatively analyzed the reconstructed CBCT image quality in terms of the modulation-transfer function and contrast-to-noise ratio under various scanning conditions. The results confirm the high CBCT image quality obtained from our TF algorithm. Moreover, our algorithm has also been validated in a real clinical context using a head-and-neck patient case. Comparisons of the developed TF algorithm and the current state-of-the-art TV algorithm have also been made in various cases studied in terms of reconstructed image quality and computation efficiency.

Beam orientation optimization for intensity modulated radiation therapy using adaptive l(2,1)-minimization.

Beam orientation optimization (BOO) is a key component in the process of intensity modulated radiation therapy treatment planning. It determines to what degree one can achieve a good treatment plan in the subsequent plan optimization process. In this paper, we have developed a BOO algorithm via adaptive l(2, 1)-minimization. Specifically, we introduce a sparsity objective function term into our model which contains weighting factors for each beam angle adaptively adjusted during the optimization process. Such an objective function favors a small number of beam angles. By optimizing a total objective function consisting of a dosimetric term and the sparsity term, we are able to identify unimportant beam angles and gradually remove them without largely sacrificing the dosimetric objective. In one typical prostate case, the convergence property of our algorithm, as well as how beam angles are selected during the optimization process, is demonstrated. Fluence map optimization (FMO) is then performed based on the optimized beam angles. The resulting plan quality is presented and is found to be better than that of equiangular beam orientations. We have further systematically validated our algorithm in the contexts of 5-9 coplanar beams for five prostate cases and one head and neck case. For each case, the final FMO objective function value is used to compare the optimized beam orientations with the equiangular ones. It is found that, in the majority of cases tested, our BOO algorithm leads to beam configurations which attain lower FMO objective function values than those of corresponding equiangular cases, indicating the effectiveness of our BOO algorithm. Superior plan qualities are also demonstrated by comparing DVH curves between BOO plans and equiangular plans.

Mitigation of motion artifacts in CBCT of lung tumors based on tracked tumor motion during CBCT acquisition.

An algorithm capable of mitigating respiratory motion blurring artifacts in cone-beam computed tomography (CBCT) lung tumor images based on the motion of the tumor during the CBCT scan is developed. The tumor motion trajectory and probability density function (PDF) are reconstructed from the acquired CBCT projection images using a recently developed algorithm Lewis et al (2010 Phys. Med. Biol. 55 2505-22). Assuming that the effects of motion blurring can be represented by convolution of the static lung (or tumor) anatomy with the motion PDF, a cost function is defined, consisting of a data fidelity term and a total variation regularization term. Deconvolution is performed through iterative minimization of this cost function. The algorithm was tested on digital respiratory phantom, physical respiratory phantom and patient data. A clear qualitative improvement is evident in the deblurred images as compared to the motion-blurred images for all cases. Line profiles show that the tumor boundaries are more accurately and clearly represented in the deblurred images. The normalized root-mean-squared error between the images used as ground truth and the motion-blurred images are 0.29, 0.12 and 0.30 in the digital phantom, physical phantom and patient data, respectively. Deblurring reduces the corresponding values to 0.13, 0.07 and 0.19. Application of a -700 HU threshold to the digital phantom results in tumor dimension measurements along the superior-inferior axis of 2.8, 1.8 and 1.9 cm in the motion-blurred, ground truth and deblurred images, respectively. Corresponding values for the physical phantom are 3.4, 2.7 and 2.7 cm. A threshold of -500 HU applied to the patient case gives measurements of 3.1, 1.6 and 1.7 cm along the SI axis in the CBCT, 4DCT and deblurred images, respectively. This technique could provide more accurate information about a lung tumor's size and shape on the day of treatment.