diff --git a/src/transmissionReconstruction/reconstruction/solvingEquationSystem/TVAL/TVAL.cpp b/src/transmissionReconstruction/reconstruction/solvingEquationSystem/TVAL/TVAL.cpp
index 70a9859..152b2e5 100644
--- a/src/transmissionReconstruction/reconstruction/solvingEquationSystem/TVAL/TVAL.cpp
+++ b/src/transmissionReconstruction/reconstruction/solvingEquationSystem/TVAL/TVAL.cpp
@@ -159,13 +159,31 @@ namespace Recon
         int * xIdxs = new int[M.getRowVector().getDataSize()];
         std::copy(M.getRowVector().getData(),M.getRowVector().getData()+M.getRowVector().getDataSize(),xIdxs);
         Aurora::Matrix values = M.getValVector();
+        size_t cols = M.getM(), rows = M.getN();
         int nz = std::max(M.getColVector().getDataSize(),std::max(M.getRowVector().getDataSize(),M.getValVector().getDataSize()));
+        sparse_matrix_t A;
+        sparse_matrix_t csrA;
+        mkl_sparse_d_create_coo(&A, sparse_index_base_t::SPARSE_INDEX_BASE_ZERO,  rows, cols,  nz,  yIdxs, xIdxs,values.getData());
+        mkl_sparse_convert_csr(A, sparse_operation_t::SPARSE_OPERATION_NON_TRANSPOSE, &csrA);
+        int n_rows,n_cols;
+        int *rows_start,*rows_end,*col_indx;
+        double * csrValues;
+        sparse_index_base_t index;
+        mkl_sparse_d_export_csr(csrA, &index, &n_rows, &n_cols, &rows_start, &rows_end, &col_indx, &csrValues);
+        int *row_idx = new int[n_rows+1];
+        std::copy(rows_start,rows_start+n_rows,row_idx);
+        row_idx[n_rows] = rows_end[n_rows-1];
         float* bData = new float[b.getDataSize()];
         std::copy(b.getData(),b.getData()+b.getDataSize(),bData);
         size_t bDims[3]={(size_t)b.getDimSize(0),(size_t)b.getDimSize(1),(size_t)b.getDimSize(2)};
         size_t rdims[3] = {(size_t)dims[0], (size_t)dims[1], (size_t)dims[2]};
         bool pagelocked = false;
-        auto result = TVALGPU(xIdxs, yIdxs, values.getData(), M.getM(), M.getN(), nz, bData, bDims, rdims, opt, device, false);
+
+
+        auto result = TVALGPU(row_idx, col_indx,  csrValues, M.getM(), M.getN(), nz, bData, bDims, rdims, opt, device, false);
+        mkl_sparse_destroy(A);
+        mkl_sparse_destroy(csrA);
+        delete [] row_idx;
         delete [] xIdxs;
         delete [] yIdxs;
         delete [] bData;