# Improving a Rapid Alignment Method of Tomography Projections by a Parallel Approach
## 4 Conclusions  
In this paper, we proposed a modular framework for (nano)tomography alignment. Often, in biological samples, regular, high-contrast features can be scarce, and this represents a problem if nano-beads are also not in view. If no robust trackable features are present, the dataset is useless. Thus, an automatic alignment algorithm, such as the bootstrap method, becomes extremely useful. A well-known and well-performing CT  
Figure 8: Alignment of a real nano-tomography dataset; raw data (panel (**a**)) and their aligned version (panel (**b**)), where a high-contrast feature effectively creates a recognisable sinogram. Note the effect of the shift, which inevitably creates zero-padded areas.  
bootstrap-based algorithm [12] has been analysed and deconstructed to isolate and determine its three essential components. This algorithm provides good results, but it can be slow, as its implementation is sequential and CPU-based. The entire algorithm has then been re-implemented: the modular approach we designed allows us to adapt the parallelisation paradigms both for a multi-thread CPU implementation and a multi-GPU solution. As a result, the entire algorithm is globally accelerated. For each module, the benchmarks reported a performance gain that is close to one order of magnitude, allowing for a rapid correction on high-resolution datasets. The correction can be even more accurate, as a multi-scale approach is now feasible. The software is released as open-source and can be downloaded from [32]. Thanks to its modular structure, each component can eventually be incorporated easily in a custom code. As a by-product, we provide \"SciCompCT\", a ready-to-be-used solution for a parallel beam multi-GPU SIRT algorithm. We believe that the proposed approach could be particularly useful for low-density matrices such as biological samples.  
Conceptualization, F.G., G.K., F.B., A.S. and S.C.; methodology, F.G., G.K. and S.C.; software, F.G.; validation, G.K., A.G., L.P., A.S., S.C. and F.B.; resources, F.B., G.K. and S.C.; data curation, A.G., L.P., F.G. and A.S. All authors participated in writing the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research has been partially developed under the Advanced Integrated Imaging Initiative (AI3), project P2017004, of Elettra Sincrotrone Trieste in agreement with the University of Trieste.  
Not applicable.  
The \(\upmu\)-CT dataset is available at [32].  
The authors are thankful to [PERSON] for the \(\upmu\)-CT dataset and to the System Administrators of the Elettra IT Group, in particular to [PERSON] for his work on the HPC solution. This research includes experiments that were performed at the MISTRAL beamline at ALBA Synchrotron (proposal number 2019023350) in collaboration with ALBA staff.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
API Application Program Interface  
CCD Charge Coupled Device  
CPU Central Processing Unit  
CT Computed Tomography  
DFT Discrete Fourier Transform  
GPU Graphics Processing Unit  
HPC High Performance Computing  
MSE Mean Square Error  
RAM Random Access Memory  
SIRT Simultaneous Iterative Reconstruction Technique  
STN Spatial Transformer Network