Fusion biopsy reduces false negative rates in prostatic cancer detection compare to systemic biopsy. However, accuracy in biopsy sampling depends upon quality of alignment between pre-operative 3D MR and intra-operative 2D US. During live biopsy, the US-MR alignment may be disturbed due to prostate or patient rigid motion. Further, prostate gland deform due to probe pressure, which add error in biopsy sampling. In this paper, we describe a method for real-time 2D-3D multimodal registration, utilizing deep learning, to correct for rigid and deformable errors. Our method do not require an intermediate 3D US and works in real-time with an average runtime of 112 ms for both rigid and deformable corrections. On 12 patient data, our method reduces mean trans-registration error (TRE) from 8.890±5.106 mm to 2.988±1.513 mm, comparable to other state of the arts in accuracy.
A fast and automatic method, using machine learning and min-cuts on a sparse graph, for segmenting Liver from CT Contrast enhanced (CTCE) datasets is proposed. The method first localizes the liver by estimating its centroid using a machine learnt model with features that capture global contextual information. Individual ‘N’ rapid segmentations are carried out by running a min-cut on a sparse 3D rectilinear graph placed at the estimated liver centroid with fractional offsets. Edges of the graph are assigned a cost that is a function of a conditional probability, predicted using a second machine learnt model, which encodes relative location along with a local context. The costs represent the likelihood of the edge crossing the liver boundary. Finally, 3D ensembles of ‘N’ such low resolution, high variance sparse segmentations gives a final high resolution, low variance semantic segmentation. The proposed method is tested on three publically available challenge databases (SLIVER07, 3Dircadb1 and Anatomy3) with M-fold cross validation. On the most popular database: SLIVER07 alone, consisting of 20 datasets we obtained a mean dice score of 0.961 with 4-fold cross validation and an average run-time of 6.22s on a commodity hardware (Intel 3.6GHz dual core, with no GPU). On a combined database of 60 datasets from all three, we obtained a mean dice score of 0.934 with 6-fold cross validation.
CT and MR perfusion weighted imaging (PWI) enable quantification of perfusion parameters in stroke studies. These parameters are calculated from the residual impulse response function (IRF) based on a physiological model for tissue perfusion. The standard approach for estimating the IRF is deconvolution using oscillatory-limited singular value decomposition (oSVD) or Frequency Domain Deconvolution (FDD). FDD is widely recognized as the fastest approach currently available for deconvolution of CT Perfusion/MR PWI. In this work, three faster methods are proposed. The first is a direct (model based) crude approximation to the final perfusion quantities (Blood flow, Blood volume, Mean Transit Time and Delay) using the Welch-Satterthwaite approximation for gamma fitted concentration time curves (CTC). The second method is a fast accurate deconvolution method, we call Analytical Fourier Filtering (AFF). The third is another fast accurate deconvolution technique using Showalter’s method, we call Analytical Showalter’s Spectral Filtering (ASSF). Through systematic evaluation on phantom and clinical data, the proposed methods are shown to be computationally more than twice as fast as FDD. The two deconvolution based methods, AFF and ASSF, are also shown to be quantitatively accurate compared to FDD and oSVD.