The identification of bone lesions is crucial in the diagnostic assessment of multiple myeloma (MM). 68Ga-Pentixafor PET/CT can capture the abnormal molecular expression of CXCR-4 in addition to anatomical changes. However, whole-body detection of dozens of lesions on hybrid imaging is tedious and error prone. It is even more difficult to identify lesions with a large heterogeneity. This study employed deep learning methods to automatically combine characteristics of PET and CT for whole-body MM bone lesion detection in a 3D manner. Two convolutional neural networks (CNNs), V-Net and W-Net, were adopted to segment and detect the lesions. The feasibility of deep learning for lesion detection on 68Ga-Pentixafor PET/CT was first verified on digital phantoms generated using realistic PET simulation methods. Then the proposed methods were evaluated on real 68Ga-Pentixafor PET/CT scans of MM patients. The preliminary results showed that deep learning method can leverage multimodal information for spatial feature representation, and W-Net obtained the best result for segmentation and lesion detection. It also outperformed traditional machine learning methods such as random forest classifier (RF), k-Nearest Neighbors (k-NN), and support vector machine (SVM). The proof-of-concept study encourages further development of deep learning approach for MM lesion detection in population study.
This paper presents an efficient parallel Adaptive Inverse Distance Weighting (AIDW) interpolation algorithm on modern Graphics Processing Unit (GPU). The presented algorithm is an improvement of our previous GPU-accelerated AIDW algorithm by adopting fast
k
-nearest neighbors (
k
NN) search. In AIDW, it needs to find several nearest neighboring data points for each interpolated point to adaptively determine the power parameter; and then the desired prediction value of the interpolated point is obtained by weighted interpolating using the power parameter. In this work, we develop a fast
k
NN search approach based on the space-partitioning data structure, even grid, to improve the previous GPU-accelerated AIDW algorithm. The improved algorithm is composed of the stages of
k
NN search and weighted interpolating. To evaluate the performance of the improved algorithm, we perform five groups of experimental tests. The experimental results indicate: (1) the improved algorithm can achieve a speedup of up to 1017 over the corresponding serial algorithm; (2) the improved algorithm is at least two times faster than our previous GPU-accelerated AIDW algorithm; and (3) the utilization of fast
k
NN search can significantly improve the computational efficiency of the entire GPU-accelerated AIDW algorithm.
Electronic supplementary material
The online version of this article (doi:10.1186/s40064-016-3035-2) contains supplementary material, which is available to authorized users.
Abstract-We describe a TF5 prism-pair-formed pulse shaper for programmable pulse chirp compensation. The advantages of this kind of pulse shaper are: 1) very broad bandwidth of transmission; 2) smaller losses; and 3) no requirement for a large-size spatial light modulator (SLM) if the input spectrum is very broad. In our experiment, an ultrabroad spectral (500-1000 nm) pulse is produced by launching 1-kHz, 30-fs, 400-J pulses at 780 nm into an argon-filled glass capillary fiber at the gas pressure of 2.0 bar. The fiber has an inner diameter of 140 m and a length of 60 cm. The chirped pulse is first precompressed by a pair of BK7 prisms with a separation length of 65 cm and then directed into the prism-pair-formed pulse-shaping apparatus with a 128-pixel SLM, which provides quadratic and cubic phase compensation.When the quadratic and cubic phases are 330 fs 2 and +2000 fs 3 , respectively, at the wavelength of 760 nm, an ultrashort optical pulse of 6 fs (FWHM) is generated. This is, to the best of our knowledge, the shortest optical pulse ever compressed using the SLM pulse-shaping technique.
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